0960483 September 15, 2009 Etiology and Impact of Digital Natives on Cultures, Commerce and Societies Digital Natives is a term used to describe individuals who were born after 1980 and who have lived their entire life in the presence of easy access to large scale data networks via the Internet, easy access to computing through personal computers, and wide spread communications via cellular phones. These Digital Natives are entering college and rapidly joining the US workforce. Understanding how they view their life goals and objectives will have a profound impact on the way we educate them and how they can be integrated effectively into society. This award provides travel support travel for US participants to a research workshop on Digital Natives. The workshop will take place at KAIST (Korean Advanced Institute for Science and Technology) in Daejeon, South Korea, 4-6 November 2009. The workshop will characterize our current understanding of Digital Natives and their potential impacts on culture, commerce and society at local and global levels. Interactions in the workshop will serve to frame a research agenda that could serve as a basis for future proposals that study the phenomena around Digital Natives in depth. The workshop will foster opportunities to exchange and share knowledge among invited thought leaders and facilitate identification of leading researchable questions to help satisfy knowledge gaps that will be identified through the workshop. The workshop will expose participants to an internationally diverse set of perspectives on intellectual topics around Digital Natives. This diversity will broaden and strengthen the intellectual capabilities of US researchers who attend the workshop. IIS CSE HPCC Standard Grant 9215 GA Tech Research Corporation - GA Institute of Technology GA HUMAN-CENTERED COMPUTING 7367 15000 7367 David W. McDonald Boff, Ken 0960391 September 1, 2009 EAGER: Adolescent Computer and Electronic Game Use Computer-based technologies have become pervasive in our society. Children, and even more so adolescents, have grown to think of the Internet, along with the computers used to surf it and the many platforms meant for game playing, as toys. Supporters of the new technologies point to their potential to educate and to impart skills that will be useful later on in life. Others view the overuse of the Internet and gaming as a harmful addiction. The truth most likely falls in between these two viewpoints. Yet with the exception of investigations into the negative impact of violence (whether on television or in video games), there has been scant research into the positive and negative effects of technology and computers on our nation's youth. In this exploratory project, the PI will seek to determine which factors are responsible for making technology helpful to adolescents and which are detrimental. To this end, adolescents will be asked to participate in a couple of anonymous Internet surveys. One of these surveys will seek to establish the relationship between frequency of use and content of use (gaming, e-mail, educational games, etc.) with a variety of factors such as interest in science, grades, individual psychological characteristics (risk-taking, social influences from friends, psychological well-being, self-control, etc.), engagement in sports or other physical activity, injuries related to computer use, parental monitoring of and limits on computer use, regular bed time and how much sleep per night, cigarette use, alcohol use, height, weight, age, and gender. In a second survey the PI will examine the positive aspects of social networking websites (e.g., Facebook and MySpace) for adolescents with disabilities, who are chronically ill, and who are recovering from major surgery or disabling injury; among other things, participants will be asked what improvements would better serve their needs on social networking sites. The adolescent surveys will be supplemented with parental versions for parents of adolescents. Broader Impacts: This research will provide much needed insight into how computers are affecting our nation's youth. Project outcomes will enable scientists to advise parents and others how to best use technology with adolescents, and will also inform designers how to create better and more inclusive systems in the future. IIS CSE NY Ephraim P. Glinert HPCC Standard Grant 9215 0116000 Human Subjects networking HUMAN-CENTERED COMPUTING 7367 7916 Epstein, Jennifer Joan and Sanford I. Weill Medical College of Cornell University 98112 0960014 September 15, 2009 III:EAGER:Collaborative Research: A Collaborative Scientific Workflow Composition Tool Supporting Scientific Collaboration The goal of this research aims to produce a general-purpose but domain-customizable collaborative scientific workflow tool for accelerating scientific discovery, particularly facilitating large-scale and cross-disciplinary research projects that are collaborative in nature and require intensive user interaction from multiple distributed domain scientists. As a natural extension to the existing single user-oriented scientific workflow management tools by providing direct system support for scientific collaboration, this project seeks to pave a way toward a next-generation tool supporting scientific collaboration over the Internet. The expected tool can be easily expanded to support data-centric collaborative information management in any intelligence community. To achieve this broader impact, this research seeks to establish a set of foundational models and techniques supporting collaborative scientific workflow composition and management; and based on them, construct an Internet-based collaborative scientific workflow tool framed with an open-source initiative. The initial focus of the Early Concept Grants Exploratory Research (EAGER) project is on rapidly creating a prototype tool as a proof of concept, equipped with basic dataflow-oriented scientific workflow models and collaboration patterns integrated in an agile service-oriented architecture. To avoid reinventing the wheel, the efforts will be concentrated on transforming and extending Taverna, a known open-source scientific workflow management tool, into a collaborative version. Evaluations and validations will be conducted through partnerships with multiple collaborative scientific communities. For further information, see the project website at http://www.CollaborativeScientificWorkflows.org/ IIS CSE HPCC 9216 architecture Standard Grant MI INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 Wayne State University 7916 Lu, Shiyong 58692 0959215 September 15, 2009 III:EAGER:Collaborative Research:A Collaborative Scientific Workflow Composition Tool Supporting Scientific Collaboration The goal of this research aims to produce a general-purpose but domain-customizable collaborative scientific workflow tool for accelerating scientific discovery, particularly facilitating large-scale and cross-disciplinary research projects that are collaborative in nature and require intensive user interaction from multiple distributed domain scientists. As a natural extension to the existing single user-oriented scientific workflow management tools by providing direct system support for scientific collaboration, this project seeks to pave a way toward a next-generation tool supporting scientific collaboration over the Internet. The expected tool can be easily expanded to support data-centric collaborative information management in any intelligence community. To achieve this broader impact, this research seeks to establish a set of foundational models and techniques supporting collaborative scientific workflow composition and management; and based on them, construct an Internet-based collaborative scientific workflow tool framed with an open-source initiative. The initial focus of the Early Concept Grants Exploratory Research (EAGER) project is on rapidly creating a prototype tool as a proof of concept, equipped with basic dataflow-oriented scientific workflow models and collaboration patterns integrated in an agile service-oriented architecture. To avoid reinventing the wheel, the efforts will be concentrated on transforming and extending Taverna, a known open-source scientific workflow management tool, into a collaborative version. Evaluations and validations will be conducted through partnerships with multiple collaborative scientific communities. For further information, see the project website at http://www.CollaborativeScientificWorkflows.org/ IIS CSE HPCC 9216 architecture IL Standard Grant INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 7916 Zhang, Jia Northern Illinois University 43335 0959096 September 15, 2009 EAGER: Secure Peer-to-peer Data Management Peer-to-peer (P2P) networks have increased in popularity partly because they can be implemented atop a diverse collection of hardware and software, making them relatively inexpensive to deploy and maintain. The network infrastructure also tends to be highly fault-tolerant, and bandwidth and other computational resources tend to be well balanced across peers, making the network highly robust. In practice, many P2P networks remain vulnerable to denial of service attacks because the homogeneity of the network results in greater interdependence among hosts. Many existing P2P networks also suffer from serious data integrity vulnerabilities because it is easy for peers in the network to lie to other peers about the data they serve. The confidentiality desired by P2P users typically comes in two forms: Data confidentiality policies prohibit the leaking of high confidentiality, shared objects to low-privileged peers, while user anonymity policies prohibit the divulging of a user?s private information. Trusted data management is a key layer in providing environments for trusted collaboration. The team will use their secure P2P infrastructure with low level security enforcement mechanisms as a basis for developing a reputation-based trust management system to enforce discretionary access control. They will provide a comprehensive solution for the design and development of secure data management hosted on a peer to peer network, focused on query processing in such an environment with considerations for confidentiality, integrity, and trust. IIS CSE HPCC Standard Grant 9218 network Sylvia J. Spengler security TX University of Texas at Dallas 7916 TRUSTWORTHY COMPUTING 7795 Hamlen, Kevin Bhavani Thuraisingham 79999 0958132 September 15, 2009 Student Travel for PerMIS 2009 This is funding to support the participation of approximately 15 students, both undergraduate and graduate, in the 2009 Performance Metrics for Intelligent Systems (PerMIS) Workshop, which will be held in Gaithersburg, Maryland, on September 21-23, 2009. The PerMIS workshop brings together researchers from colleges, universities, research laboratories, and government laboratories to discuss how to design and evaluate intelligent systems. This workshop is the only one of its kind dedicated to defining measures and methodologies of evaluating performance of intelligent systems. Started in 2000, the PerMIS series focuses on applications of performance measures to practical problems in commercial, industrial, homeland security, and military applications. It has proved to be an excellent forum for discussions and partnerships, dissemination of ideas, and future collaborations between researchers, graduate students, and practitioners from industry, academia, and government agencies. As its main theme, this year's workshop seeks to address the question: Does performance measurement accelerate the pace of advancement for intelligent systems? In addition to the benefits students derive from attending a workshop where they can learn about current research, present their own work, and get feedback from professionals in their field, PerMIS organizers will provide structured opportunities for mentoring and networking. Students will meet at the start of the workshop to be introduced to one another and some of the workshop organizers. They will also have a luncheon with select senior researchers, to encourage interaction. And they will be given guidance about how to listen to research presentations and think critically about them. The organizers will take proactive steps to encourage diversity in the applicant pool by announcing the availability of funding to mailing lists for women in computer science, the Tapia Workshop mailing list, and through NSF's Broadening Participation in Computing Alliances. Broader Impacts: Participation in workshops and conferences during one's student years can contribute to a successful research career. Student funding for travel to the PerMIS workshop will serve to introduce students to other researchers in the field, increase their knowledge of the research done in the field, and encourage them to continue their pursuit of research. Each student will be asked to write a report on his or her experiences at PerMIS 2009, which will be due with the travel reimbursement paperwork. In their reports, the students will be asked to write brief summaries of the plenary talks and their reactions to them. They will also be asked to discuss how each of the plenary talks relates to at least two other talks at the workshop. These reports will be used to evaluate the success of the funded student participation in the event, and will be submitted to NSF with the final project report. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 MA security networking University of Massachusetts Lowell HUMAN-CENTERED COMPUTING 7367 7367 Yanco, Holly 16609 0957394 September 1, 2009 The IntelliDrive Database Management System IntelliDrive(SM) is a major U.S. Department of Transportation (U.S. DOT) initiative of Intelligent Transportation Systems (ITS), aimed at building a fully connected surface transportation environment via advanced communication and information technologies. The grand vision is "to provide transformational safety, mobility, and environmental improvements in surface transportation". The IntelliDrive initiative envisions a networked environment among vehicles (V2V), between vehicles and infrastructure components (V2I) or among vehicles, infrastructure, and wireless hand-held Devices (e.g., cell phones and PDAs) (V2D) that enables numerous safety and mobility improvement applications. For example, in a V2V environment, if the vehicle in front has a malfunctioning brake light, the following drivers or travelers will be notified in real-time and take precautionary actions (e.g., slow down or change lanes); or if roadside sensors detect a patch of ice on the road at milepost 305, drivers will be notified immediately through V2I or V2D about the situation in terms what, where, and the degree of severity, etc. With respect to improving mobility, IntelliDrive provides real-time traveler information concerning traffic conditions, accidents, transit vehicle on-time performance, parking availability, ride share opportunities, etc. Thus travelers will be able to make informed decisions about their travel so to improve the individual and consequently the overall system efficiency and mobility. The main objective of this proposal is to develop the framework of a Database Management System (DBMS) in support of IntelliDrive applications. This means, among others, a set of services that will answer the questions: What data is relevant to answer the query? In what nodes does this data reside? How to access the data on these nodes? For further information please see the project web site: www.cs.uic.edu/~wolfson/html/IntelliDrive IIS CSE HPCC 9216 IL Standard Grant University of Illinois at Chicago database vision wireless 100000 Wolfson, Ouri INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 7916 Jie Lin 0956571 September 1, 2009 Proposal for Two NSF Workshops: Technology-Mediated Social Participation Technology mediated social participation is now a way of life for millions of people as they text family and friends via cell phones, track leaders and celebrities on Twitter, update their Facebook page to tell others about their activities, and interact with numerous web portals. Use of these technologies spans a broad range of users, from kids under ten years old to retirees. This proposal will develop an intellectual framework and research agenda for studying and facilitating social participation for national priorities, such as health care, education, energy, disaster response, community safety, and environmental protection. Two workshops will cover six topics that will spur research challenges: (a) theoretical integration (b) social capital, social intelligence, and effective action, (c) sharable infrastructure, ethics, and protections, (d) design to motivate, (e) graduate training and (f) the unique challenges for government use of social media. The primary output of the workshops will be a report that specifies challenges and outcomes for each of the topic areas. IIS CSE HPCC Standard Grant 9215 education MD University of Maryland College Park HUMAN-CENTERED COMPUTING 7367 Ben Shneiderman 7367 David W. McDonald Preece, Jennifer Peter Pirolli 86744 0952731 September 15, 2009 Travel Scholarships for the Semantic Robot Vision Challenge The Semantic Robot Challenge is a photo treasure hunt for robots. Competing mobile robot platforms equipped with multiple cameras and laser range scanners are given a text list of objects dispersed on furniture or on the floor in a conference room. They can learn about what the objects are supposed to look like by connecting to the Internet and finding images of them. Then they have to autonomously search the environment and take snapshots of as many objects as they can, adding bounding boxes around the objects in the pictures and labeling them. The winner is the robot that takes pictures of the largest number of objects, with correct bounding boxes and labels (evaluated by human judges according to well-defined rules). This competition challenges the teams to successfully implement a large number of strategies that humans would use to accomplish the same task. Successful robots are able to perform navigation, exploration, obstacle avoidance and mapping, peripheral vision, foveal vision, observation from multiple viewpoints, bottom-up feature detection and top-down object model verification. The PI is with the organizing committee of the Third Semantic Robot Vision Challenge (SRVC), and this NSF award supports travel scholarships for all participating teams. This challenge is scheduled to take place as a Special Track of the Fifth International Symposium on Visual Computing (ISVC09) in Las Vegas on December 1, 2009. The first SRVC event took place at the AAAI Conference in Vancouver, British Columbia, Canada in 2007. The second SRVC event was organized at CVPR in Anchorage, Alaska in 2008. The practice of providing travel support to participating teams has been shown to provide a great encouragement for teams to attend, and it was implemented for the past two SRVC events. The web site for the Semantic Robot Vision Challenge is http://www.semantic-robot-vision-challenge.org/ IIS CSE HPCC Standard Grant 9215 MD verification Johns Hopkins University vision ROBUST INTELLIGENCE Jie Yang 7495 7495 DeMenthon, Daniel 23691 0949911 September 1, 2009 EAGER: Combining Sketching and Computer Vision Techniques in Cultural Heritage Applications Computer graphics has been successfully used in a wide range of cultural heritage applications. It has been proven both in the context of study by subject specialists and in the preparation of exhibits for education of the general public. Each year hundreds more computer graphics based cultural heritage projects are initiated around he world. However, the use of digital material remains demanding and requires technologists as well as subject specialists. This research project will impact the development of exhibitions used to communicate historical and cultural information to the general public. Preparing such exhibits will be easier for subject specialists, and it will be possible to create more engaging interactive displays of information The goal of this research is to change the methods for both authoring and using digital objects in communication. The research addresses the problem of generating compelling 3D displays of heritage sites, where large archival photographic documentation is available. Recent innovations in sketch-based modeling, in particular the ?Mental Canvas? framework that relies only on 3D strokes rather than full solids, will be exploited along with computer vision techniques. The Mental Canvas framework will allow user annotation in order to facilitate the application of recent improvements in image feature detection and bundle estimation to compute 3D points from archival images. Annotation is required since current methods often fail when all camera characteristics are unknown and photographs were not taken from views designed to facilitate 3D reconstruction. The output of the proposed system will be a 3D model that can be easily navigated by a casual user. The system will then be able to naturally navigate through space and browse solid models, archival imagery and textual data. An exhibit of artifacts and imagery from the Dura Europos site that is being prepared by the Yale Art Gallery is serving as the testbed for the research. IIS CSE HPCC Standard Grant 9215 education vision computer graphics graphics computer vision 100000 CT Yale University GRAPHICS & VISUALIZATION 7453 7453 Lawrence Rosenblum 7916 Rushmeier, Holly 0949891 September 1, 2009 EAGER: Analysis and Intelligent Search for Cypriot Works of Art and Secretariat Corpus This NSF EAGER project is to develop novel prototypes for (1) iconic image analysis and recognition for retrieval, classification annotation, and analysis of iconic digital imagery of Cypriot cultural heritage materials, and (2) searching and exploring the Ancient Cypriot Secretariat Corpus. (The corpus contains works from antiquity to early Christian era written in period text and describing scientific, philosophical, social commentary, etc.) The project will involve computer scientists, archeologists and art historians from Penn State and The Cyprus Institute. The systems developed will allow users to search for the iconic images in representative of Cypriot culture at various times of history based on such characteristics as image content, shape sketches, and metadata. From the Secretariat Corpus, end-users will be able to search for items belonging to different categories such as daily life at different periods, mythology, religion, politics, language, landscape, events and other categories. Strong support for this project has been pledged by the Cyprus Institute. The NSF Office of International Science and Engineering will co-sponsor the award. IIS CSE HPCC 9216 Standard Grant PA Stephen Griffin Pennsylvania State Univ University Park 7484 COLLABORATIVE RESEARCH 7298 5976 INFO INTEGRATION & INFORMATICS 7364 Wang, James 7916 Dean Snow Loukas Kalisperis C. Giles Prasenjit Mitra 249915 7515 0949467 October 1, 2009 EAGER: Exploring Spline Theory for Shapes of Arbitrary Topology Title: EAGER: Exploring Spline Theory for Shapes of Arbitrary Topology PI: Hong Qin, SUNY at Stony Brook (Stony Brook University) Project Abstract: The goal of this research is to clearly articulate a novel spline-driven computational method (splines are piecewise polynomials that satisfy certain continuity requirements) for the geometric modeling of shapes that are of arbitrary topological type. In order to achieve this objective, the research is exploring a new mathematical theory of splines over shapes of arbitrarily complicated topology and geometry. The flexible and effective construction of splines defined over an arbitrary manifold has immediate impact for computer-aided geometric design, visual information processing, and computer graphics. Moreover, this theory-centered research will enable a more accurate, more efficient, and easier-to-use software system for processing geometric and scientific datasets. The research is demonstrating that splines are a powerful data modeling, analysis, and simulation tool that not only continue to be applicable to geometric and shape modeling, but also have important extensions to a general data modeling and analysis framework. Traditionally, splines have their mathematical root in approximation theory. Continuous representations such as splines can enable compact representation, analysis (especially quantitative analysis), simulation, and digital prototyping. In order to bridge the large gap between conventional spline formulations and the strong demand to accurately and efficiently model acquired datasets towards quantitative analysis and finite element simulation, our research effort centers on the unexplored mathematical theory of manifold splines that will extend popular spline schemes to effectively represent objects over arbitrary topology. In particular, we are conducting a comprehensive study of new theoretical foundations that can transform the spline-centric representations to the accurate and effective modeling of surfaces of arbitrary topology. IIS CSE NY HPCC Standard Grant 9215 simulation SUNY at Stony Brook computer graphics graphics Qin, Hong GRAPHICS & VISUALIZATION 7453 7453 Lawrence Rosenblum 7916 69973 0948946 September 15, 2009 Support for Student Participation in the International Conference on Multimodal Interfaces / Machine Learning for Multimodal Interfaces '09 This is funding to support attendance by approximately 12 graduate students in a Doctoral Spotlight (workshop) to be held in conjunction with the 2009 International Conference on Multimodal Interfaces/Machine Learning for Multimodal Interfaces (ICMI-MLMI 2009), which will take place November 2-6 in Cambridge, Mass., and is organized by the Association for Computing Machinery (ACM) with co-sponsorship from the Institute of Electrical and Electronics Engineers (IEEE). This year the Eleventh International Conference on Multimodal Interfaces (ICMI 2009) will merge with the Workshop on Machine Learning for Multimodal Interfaces; the combined ICMI-MLMI will be the premier event representing the growing interest in next-generation perceptive, adaptive and multimodal user interfaces. Such interfaces represent an emerging interdisciplinary research direction, involving spoken and natural language understanding, image processing, computer vision, pattern recognition, experimental psychology, etc. They aim to promote efficient and natural interaction and communication between computers and human users, and represent a radical departure from previous computing that should ultimately enable users to interact with computers using everyday skills. The main goals of ICMI-MLMI 2009 are to further scientific research within the broad field of multimodal interaction and systems, to focus on major trends and challenges, and to help identify a roadmap for future research and commercial success. Topics of interest this year include: multimodal and multimedia processing; multimodal input and output interfaces; multimodal applications; human interaction analysis and modeling; and multimodal data, evaluation, and standards. The 3-day main event on the MIT campus, to be followed by 5 affiliated workshops, will bring together researchers from academia and industry from around the world to present and discuss the latest multi-disciplinary work in the field. The invited talks, panels, and single-track oral and poster presentations will facilitate interaction and discussion among researchers; the conference promises to be an international venue for brainstorming and coming up with creative directions for future research in multimodal interfaces. Participants in the Doctoral Spotlight will get to showcase their ongoing thesis work, either orally or via posters, in a special "spotlight session" during which they will receive feedback from an invited committee composed of senior personnel, and including the Advisory Committee chair and the General and Program chairs. As a further incentive for high-quality student participation, ICMI-MLMI will be awarding outstanding paper awards, with a special category just for student papers. More information about ICMI-MLMI is available online at http://icmi2009.acm.org. Broader Impacts: The Doctoral Spotlight will give student participants exposure to their new research community, both by presenting their own work and by observing and interacting with established professionals in the field. It will encourage students at this critical time in their careers to begin building a social support network of peers and mentors. Student participants will be selected by the PI with oversight from the Advisory Committee chair and the Conference General chair, with the goal of increasing breadth of participation; to this end, priority will be given to students whose advisors or departments have insufficient funds to otherwise support their participation. Students funded under this award will predominantly be U.S. residents enrolled at U.S. institutions of higher education. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 network MA machine learning education vision computer vision multimedia Massachusetts Institute of Technology HUMAN-CENTERED COMPUTING 7367 7367 el Kaliouby, Rana 17400 0948893 September 1, 2009 III/EAGER: Temporal Relationships Among Clusters in Data Streams (TRACDS) State-of-the-art data stream clustering algorithms developed by the data mining community do not utilize the temporal order of events and therefore in the resulting clustering all temporal information is lost. This is quite strange as one of the salient features of data streams is temporal ordering of events. In this project we develop a technique to efficiently incorporate temporal ordering into the clustering process and prove its usefulness on large, high-throughput data streams. Temporal ordering is introduced into the data stream clustering process by dynamically constructing an evolving Markov Chain where the states represent clusters. Our approach is based on the previously developed Extensible Markov Model (EMM). The results of this project will provide a framework upon which important stream mining applications such as anomaly detection and prediction of future events are easily implemented. By showing that state-of-the-art data steam clustering algorithms can incorporate temporal order information efficiently, this project will have a broad impact on many areas where temporal order is essential. As examples, NOAA Hurricane Data and NASA satellite data will be used throughout this project. Results, including open source software will be distributed via the project Web site (http://lyle.smu.edu/ida/tracds). IIS CSE HPCC 9216 Standard Grant Maria Zemankova algorithms data mining TX Southern Methodist University INFO INTEGRATION & INFORMATICS 7364 7364 Dunham, Margaret 180000 7916 Michael Hahsler 0948820 September 15, 2009 EAGER: Machines that Learn and Teach Seamlessly This proposed work seeks to develop a computational approach that can be used to learn a skill from humans and, through the same medium, turn around and teach other less proficient humans what it learned. The learning and teaching will be done through agents that contain the knowledge relevant to the desired skills. Such an agent can be referred to as a learning and teaching agent (LATA). The work plans to accomplish this through two sequential approaches: 1) observational learning (by the agent only), and 2) force feedback learning and teaching. Observational learning will be used to build a minimally proficient LATA agent by observing a human perform the desired task or display the desired skill on a simulator. This agent will be called the baseline agent. This baseline agent will then be enhanced through force feedback learning, where a human will coach the system by providing corrective counter force in real time when the LATA agent errs in its performance of the task. The skills to be learned/taught will require the use of a haptic device such as a joystick or steering wheel as the primary interface. It will learn the actions that the trainer employs to execute the task competently, and use the same haptic device to coach and/or evaluate a less proficient human trainee in learning the same skill. The planned approach centers on using neuroevolutionary techniques. Neuroevolution has been successfully used to address highly complex problems such as pole balancing, abnormal behavior in drivers, and to evolve bots in video games that gradually improve their performance. The proposed work will modify the basic concept of neuroevolutionary techniques as necessary, and apply the resulting system to observational learning as well as force feedback refinement. The testbed domain will be a crane that off-loads boxes or containers from a ship and places them in some other conveyance such as a railroad car or truck. A computer simulation will be used for teaching the LATA agents how to do this. Instructors are increasingly difficult to find, especially for specialty areas that require special skills. From a practical standpoint, this research would give organizations involved in training new tools to teach their constituents. Specific beneficiaries of this technology would include organizations that train students to perform tasks requiring complex motor skills such as driving a car, flying an airplane or operating a crane. The resulting agents could also be used to train operators of tele-operated robots, cranes, unmanned aerial vehicles and other such devices. Surgery training is another potential application of this approach given the appropriate haptic devices. Another interesting application could be for training disabled people basic motor skills. IIS CSE HPCC Standard Grant 9215 simulation 0116000 Human Subjects University of Central Florida FL 100000 HUMAN-CENTERED COMPUTING 7367 7367 Amy Baylor 7916 Gonzalez, Avelino 0948639 September 1, 2009 EAGER: Cultural Issues in Sharing Expertise: Characterizing and Evaluating Online Question-Answer Communities This is an exploratory study of question and answer (Q&A) communities, which constitute one of the most interesting and pervasive forms of informal knowledge production on the Web. The wide-scale adoption of the Web and of networking in general has led to knowledge production on a scale never seen before. However, the computer and information science research community lacks an understanding of many of the characteristics of Internet-based knowledge production. It has not yet been established which characteristics are important for knowledge production, especially how expertise is arranged within these communities in order to provide adequate information. It is also important to discover which structural characteristics -- such as reward structures, participation rates, and network of interactions -- influence knowledge production or expertise provision. Researchers especially do not understand how cultural differences affect these issues -- from participation, to motivation, to types of interaction, to the likelihood users will remain on the site or change their participation role. A large number of Q&A forums exist, creating ample opportunities for cultural comparisons within similar topics. The present work is necessarily explorative, as even the measures by which to compare sites are unclear in the research literature, and relatively few cross-cultural studies of online communities exist. While the ultimate goal of this work is to design better systems and online spaces to support people in sharing knowledge and expertise across cultural divides, a better understanding of current use is first required. This project therefore consists of two phases. The first phase investigates a number of Q&A communities empirically to understand their current use and to create metrics for cross-site comparison. The second phase examines key issues believed to be influenced by culture, including evolution of key users, points and incentives, and consistency of participation, and cross-cultural differences. This work could add substantially to what is understood about Internet-scale communities. It will examine how culture affects the distribution of expertise, knowledge provision, and expertise networks. The network metrics to be developed will help systematize the study of online communities and should find applications in other areas of network science. As such, this exploratory work will lead to the development of a deeper scientific understanding of knowledge production, especially for informal knowledge, across cultures. It could also substantially improve citizens' everyday lives, since Q&A sites are vital for providing immediate, everyday help and information to citizens, and help foster a more productive knowledge economy. IIS CSE HPCC Standard Grant 9215 network William Bainbridge University of Michigan Ann Arbor MI 0116000 Human Subjects networking Mark Ackerman HUMAN-CENTERED COMPUTING 7367 7367 Adamic, Lada 7916 237752 0948601 September 1, 2009 EAGER: Spatial Awareness Through Sapient Interfaces Spatial awareness is the key to successful orientation and wayfinding. While navigation through familiar terrain allows us the luxury of reaching our destination without guidance, unfamiliar or only partially familiar environments require the use of external spatial information, which is most often presented verbally or in the form of maps. If we were to ask a knowledgeable local person for directions the information imparted to us might point out some landmarks along the way or inform us about a structuring present in the street names (e.g., that they are alphabetically ordered). People have long exploited the capability to recognize salient objects, and to use those objects to understand a space and guide their actions. But mobile GPS-based navigation systems typically overlook our abilities in spatial knowledge construction and application. The growing popularity of such devices has resulted in an increasing dependence on individual location-action pairs (e.g., turn left here) to guide us step by step; this has the drawback of not affording an overall understanding of the spatial environment, which in emergency situations, for example, may prove harmful. With spatial information now available in a much wider array of formats, with better currency and increased fidelity, it is time to rethink how to provide spatial information for orientation and wayfinding from the perspective of creating spatial awareness, via what the PI refers to as sapient interfaces. In this project the PI will explore first steps toward this goal, by drawing on theories from different fields that contribute to our understanding of the cognition of spaces. The central research question to be addressed is: How can spatial awareness be supported by a mapping system that focuses on the cognitively ergonomic organization and presentation of spatial information? The PI will seek a systematic understanding of the factors contributing to the development of spatial awareness (e.g., object saliency), and to identify principles and derive design guidelines for mobile navigation mapping systems that focus on the cognitively ergonomic organization and presentation of spatial information. A critical part of the research will be the cross-validation of formal approaches to spatial analysis; project outcomes will include a theoretical foundation of spatial awareness that is grounded in formal spatial analysis measures. Broader Impacts: The growing dependence on GPS-based navigation systems has negative impacts on our ability to think spatially, because current devices provide information in a manner that fails to build an understanding of spatial relations. This project will rethink the way in which spatial information is provided by such systems, so as to foster rather than impede spatial thinking and to thereby avoid spatial illiteracy in coming generations. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 PA 0116000 Human Subjects Pennsylvania State Univ University Park HUMAN-CENTERED COMPUTING 7367 7367 7916 Klippel, Alexander Xiaolong (Luke) Zhang 149705 0948521 September 1, 2009 Workshop: User Interface Software and Technology (UIST) 2009 Doctoral Symposium This is funding to support a doctoral research symposium (workshop) of approximately 8 promising doctoral students from the United States and abroad, along with distinguished research faculty. The event will take place in conjunction with and immediately preceding the 22nd ACM Symposium on User Interface Software and Technology (UIST 2009), to be held October 4-7 in Victoria, British Columbia, Canada, and sponsored by the Association for Computing Machinery's Special Interest Group on Human Computer Interaction (SIGCHI). The UIST conference is the premier international forum for presenting innovations in the software and technology of human computer interaction. It brings together researchers and practitioners from diverse areas that include traditional graphical and web user interfaces, tangible and ubiquitous computing, virtual and augmented reality, multimedia, new input and output devices, and computer-supported cooperative work. Although UIST is a long-standing annual conference, this workshop will be just the 7th doctoral research symposium associated with the conference (NSF has supported these events from their inception). The three goals of this full-day event are to increase the exposure and visibility of the participants' work within the community, to help establish a sense of community among this next generation of researchers, and to help foster their research efforts by providing substantive feedback and guidance from a group of senior researchers in a supportive and interactive environment. Student participants will make formal presentations of their work during the workshop, and will receive feedback from a faculty panel. The feedback is geared to helping students understand and articulate how their work is positioned relative to other research, whether their topics are adequately focused for thesis research projects, whether their methods are correctly chosen and applied, and whether their results are appropriately analyzed and presented. Student position papers will be published in the UIST Conference Companion, and the students will also present posters relating to their work at a special session the first night of the conference. Broader Impacts: The doctoral symposium will help expand the participation of young researchers pursuing graduate studies in the field of user interface software and technology, by providing them an opportunity to gain wider exposure in the community for their innovative work and to obtain feedback and guidance from senior members of the research community. It will further help foster a sense of community among these young researchers, by allowing them to create a social network both among themselves and with senior researchers at a critical stage in their professional development. Because the students and faculty constitute a diverse group across a variety of dimensions, the students' horizons are broadened to the future benefit of the field. The organizers of the event will make special efforts to attract students from institutions not historically heavily represented at UIST and to foster diversity across under-represented groups. IIS CSE NY Ephraim P. Glinert HPCC Standard Grant 9215 network human computer interaction 20000 ubiquitous augmented reality multimedia Feiner, Steven Columbia University HUMAN-CENTERED COMPUTING 7367 7367 0948429 September 1, 2009 III/EAGER: Towards Workflows as First-Class Citizens in Cyberinfrastructure: Designing Shared Repositories Scientific computing has entered a new era of scale and sharing with the arrival of cyberinfrastructure for computational experimentation. A key emerging concept is scientific workflows, which provide a declarative representation of scientific applications as complex compositions of software components and the dataflow among them. Workflow systems manage their execution in distributed resources, track provenance of analysis products, and enable rapid reproducibility of results. In current cyberinfrastructure, there are well-understood mechanisms for sharing data, instruments, and computing resources. This is not the case for sharing workflows, though there is an emerging movement for sharing analysis processes in the scientific community. This project explores computational mechanisms for sharing workflows as a key missing element of cyberinfrastructure for scientific research, with three major research foci: (1) Elicitation of new requirements that workflow sharing poses over current techniques to share software tools and libraries; (2) Understanding how shared workflow catalogs should be designed, as the existing data catalogs are a successful model, and software components require different representations and access functions; and (3) Studying what sharing paradigms might be appropriate for scientific communities. Expected results from this work include: use cases for workflow sharing and reuse that motivate this research area, a comparison between software reuse and workflow reuse requirements, a specification of a workflow catalog defining expected functions and services, and an investigation of social issues that arise in building this new kind of shared resource in scientific communities. Results are available at the project Web site (http://workflow-sharing.isi.edu). IIS CSE HPCC 9216 Standard Grant CA Maria Zemankova University of Southern California scientific computing INFO INTEGRATION & INFORMATICS 7364 200000 Gil, Yolanda 7916 0948260 September 1, 2009 EAGER: Designing Touch-Sound Interfaces to Geospatial Information for Visually-Impaired Users The increasing use of geospatial information (that is to say, data connected to a location on Earth) in applications such as Google Maps and Google Earth underscores its growing importance to mainstream computing. However, nearly all such geospatial applications use a highly visual interface and are therefore largely inaccessible to visually-impaired users. Although today's most widely used solutions for touch interfaces (point haptic devices) are rudimentary when compared to the richness and complexity of human touch, ongoing developments suggest that it will eventually become technically possible to design and implement a system that uses touch and sound to convey many different forms of geospatial data. The PI's ultimate goal is to create such a system that will allow visually-impaired users to effectively access and interact with geospatial information. In this exploratory project, he will conduct research on how to design such touch/sound based systems. The work will cut across disciplines and actively involve visually-impaired persons as users and designers, in order to leverage the unique perspective on the use and design of non-visual interfaces that visually-impaired users have; a significant part of the research will be carried out by a visually-impaired graduate research assistant. The expected outcomes of the PI's four step research plan will include an analysis of the goals and needs of visually-impaired users and their support personnel regarding geospatial information, a list of potential tasks for this context, a catalog of how different touch/sound methods could be used for these tasks, and an evaluation of some of these methods with visually-impaired users. Broader Impacts: The interdisciplinary human-computer interaction program at the PI's institution has recently admitted its first visually-impaired graduate student, who will contribute his technical skills and his connections to the visually-impaired community to this project. The results of this work will impact the quality of touch/sound user interfaces by providing designers with a broader overview, by helping them consider multiple alternatives, and by enabling them to make more informed and inclusive design decisions. While these new user interfaces will initially benefit primarily persons with visual impairments in accessing and interacting with geospatial data, it is likely that some of the tasks and methods explored in this research will have relevance for enhancing future interfaces to geospatial data for sighted users as well. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 Iowa State University IA human-computer interaction 0116000 Human Subjects HUMAN-CENTERED COMPUTING 7367 7367 Harding, Chris 7916 117951 0948123 September 1, 2009 EAGER: Understanding Social Behavior in Real-Time Strategy Games This is a study of how people make decisions in dynamic, social environments, achieving a methodological innovation by using online real-time strategy (RTS) games as laboratories for studying human behavior. Investigation into social interactions has been discussed in the context of virtual worlds and online role-playing games, but not in RTS games, nor have researchers yet developed the data-collection techniques or theoretical principles for doing research in this important sector of human-centered computing. In addition to their entertainment value, RTS games have emerged to become virtual platforms that simulate real-world, real-time physics, scenarios, characters, and strategies. Particularly, multi-player online RTS games are providing a new model of human interaction that is in line with decision theory, game theory, planning, learning, and other concepts from research fields such as Computer Science, Artificial Intelligence, Economics, and Behavioral Sciences. This research will study users' social strategies in RTS games in three major ways: developing a gaming environment, a user study with experiments, and an automated learning approach. The game will present users with a series of missions to be accomplished, and users will receive points for successful completion. In early stages these missions can be accomplished alone and with little effort, but as the player progresses they will require alliances with others. The first phase of user studies will involve a qualitative analysis of user behaviors, identifying specific points in the game when users must make decisions where the social relationships will be important factors. This qualitative analysis will be followed by a quantitative analysis of the players' performance, developing techniques for measuring the payoffs from each action. Once players begin developing strong alliances, a set of experiments will analyze their reasoning when making strategic decisions. This will include measurements of social tie strength, structural social network features, the past history of interactions, and evolutionary simulations of strategies. The final phase of research will involve controlled experiments with users, presenting them with situations where they have to make a decision that requires consideration of the social structure. This project will make software, test suites, documentation, and teaching materials freely available on the Internet. Decision making is an important problem in artificial intelligence, and effective decision-making is important in all kinds of organizations and real-world applications. This research could provide the theoretical and experimental basis for developing practical algorithms and applications for social decision making, to make it easier for the organizations and the users of such applications in their decision-making process. IIS CSE HPCC Standard Grant 9215 network algorithms William Bainbridge artificial intelligence MD 0116000 Human Subjects University of Maryland College Park HUMAN-CENTERED COMPUTING 7367 7367 7916 Golbeck, Jennifer Ugur Kuter 196199 0948101 September 1, 2009 Workshop Proposal: Content of Linguistic Annotation: Standards and Practices (CLASP) At a September, 2009 NSF-sponsored meeting in New York City, the NLP community is discussing the standardization and harmonization of the content of manual/automatic linguistic annotation. The meeting is building on the results of the previous Computing Research Infrastructure (CRI) award "Towards a Comprehensive Linguistic Annotation of Language" by establishing standards that researchers and developers are likely to follow. These standards govern tokenization, part of speech, head selection and other basic components of linguistic content that higher level annotation schema assume in common. Once standards are set, violations should be conscious (not accidental) and researchers should justify any violations. The meeting also aims to set up incentives, in the form of grants for small (e.g., student) projects, because several initial standard-compliant annotation projects could plant the seeds needed for the standards to take root. Intellectual merit: Establishing a common base for linguistic annotation will: (1) make it easier to use, merge and compare different types of annotation (from different transducers, different manual sets of annotation, etc.); (2) make a more rigorous set of annoation standards possible; and (3) facilitate the use of sophisticated natural language informed applications that can draw on annotation created by several different projects simultaneously. Broader impact: This standardization process will bring about greater cooperation among annotation researchers and, as a result, greatly improve the efficiency of such research. This could significantly improve the state of the art of all linguistic processing, and thus, all applications (automatic search, translation, etc.) that rely on the automatic linguistic analysis of text. IIS CSE NY HPCC Standard Grant 9215 22500 New York University Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 7495 Meyers, Adam 0947841 August 15, 2009 EAGER: CISE/IIS/RI/Program Element 7495: Crowdsourcing for NLP: Exploring Two Approaches "Crowdsourcing" is the idea of using the "wisdom of crowds", that is, combining large numbers of judgments by non-experts, to produce reliable answers to complex problems. In the field of natural language processing (NLP), annotating sentences to show what events they express (and which parts of the sentence express which participants) is such a complex task. For example, the sentence "Maria rides the bus from home to her office" should be recognized as a Ride_vehicle event, with "Maria" as Mover, "the bus" as the Vehicle, "from home" as the Source and "to her office" as the Goal; NLP systems should also be able to recognize the same event with the same participants in the sentence "Maria's bus ride from home to her office takes 40 minutes", but most current systems cannot. FrameNet (http://framenet.icsi.berkeley.edu) is building a lexical database of hundreds of event types (called "semantic frames") and examples of each in annotated sentences, which can be used to train NLP systems. But expert annotation of sentences is slow and expensive; this project is testing whether crowdsourcing can speed up the creation of such databases, specifically by exploring two crowdsourcing techniques to see which works better for these tasks: (1) online games, where players compete to see who can annotate rapidly and accurately (similar to the "Verbosity" game) and (2) a system in which people are paid small amounts of money to complete such tasks, using Amazon's "Mechanical Turk" (www.mturk.com). If successful, these techniques could be used to build better databases for new NLP systems that really understand "who did what to whom", thus improving question answering and web searching. IIS CSE HPCC Standard Grant 9215 CA database Tatiana D. Korelsky International Computer Science Institute INFO INTEGRATION & INFORMATICS 7364 ROBUST INTELLIGENCE 7495 200000 7495 Baker, Collin 0947483 September 1, 2009 EAGER: Intelligent Interfaces for Managing User Experience in Hypermedia-Enabled Virtual Workspaces This project will combine design principles and technology advancements to enable the deployment and evaluation of new interface paradigms for managing continuities between hyperlinked 3D virtual workspaces and for managing user orientation with respect to 2D and 3D information resources within virtual workspaces. Having built the open source 3D collaboration system (Open Cobalt) as a means of enabling the deployment of hyperlinked virtual workspaces, the research group will leverage the modularity and flexibility of Open Cobalt's graphical user interface (GUI) framework to explore optimal user interface approaches for: (1) defining optimal affordances for virtual workspace authors and end-users to manage the configuration and functionality of 3D portals (links) within hypermedia-enabled virtual workspaces, and (2) managing the perceptual relationships among 2D and 3D components displayed within virtual workspaces, which include multiple interacting avatars and aggregated 2D and 3D content. The research will assess the effectiveness of multiple GUI approaches in each of these areas in the context of two exemplar implementations involving scientific collaboration and education. This work will provide key insights into optimal GUI approaches for managing content and connectivity within virtual workspaces. It will also: (1) further advance usability within a software infrastructure designed to support the cost-effective, large-scale deployment of interconnected and deeply capable virtual workspaces, (2) advance a common and extensible software framework supporting synchronized communication and collaboration among large numbers of people across distributed and hyperlinked virtual workspace contexts, and (3) provide open source software products that can serve as the basis for future research experimentation. This project has great potential in advancing an effective 3D GUI for managing virtual place and space so that people are better able to manage access to social and informational resources within hyperlinked virtual workspaces. This will have direct impact on the efficacy of using virtual workspaces in support of education, research, business, social interaction, and entertainment. The resulting functionality of Open Cobalt will; (1) provide a highly capable open source platform in support of further research on virtual worlds; (2) further advance a powerful medium for enabling scientific outreach and education; and (3) serve as a powerful 'force multiplier' that by supporting an interlinked network of multi-user virtual world spaces, can leverage individual and recombined intellectual communities across the nation and beyond. IIS CSE HPCC Standard Grant 9215 network William Bainbridge education NC Duke University HUMAN-CENTERED COMPUTING 7367 299998 7367 7916 Lombardi, Julian 0946665 August 1, 2009 International Travel: Working Group on (Re)Defining Computing The foundations of computing science need rethinking. This group will convene a working group at the Annual Conference on Innovation and Technology in Computer Science (ITiCSE) in 2009 in Paris, France to articulate the fundamental properties of computing and computational thinking, and to explain what computing curricula built around this way of thinking might look like. Although much of the previous work in this area has focused on undergraduate majors, this project is equally important to pre-college, post-baccalaureate, and non-majors programs. As such, the program will directly address those creating high school sequences, new PhD programs, concentrations for non-majors who want or need to continue beyond CS 101 and those who are trying to reach students traditionally underrepresented in computing. Prior to the meeting, a group using electronic collaboration tools will draft a rough document describing the motivations for curricular change and the idea at its heart. We will use electronic collaboration tools for this phase. Each workshop member will circulate a description of at least one curriculum and the ways in which it supports or is supported by the model. At the meeting itself, members will discuss and refine the draft document. During part of the working group meeting, structured brainstorming techniques and facilitated discussions will aid in envisioning alternative curricula at all levels that might build on this idea. These visions, together with existing case studies will be part of the report. The working group is made of international scholars and members of industry as well as academics from the United States from a variety of institutions. IIS CSE HPCC 9216 Standard Grant Sylvia J. Spengler GA Tech Research Corporation - GA Institute of Technology GA 7484 INFO INTEGRATION & INFORMATICS 7364 25000 Isbell, Charles 0946632 September 1, 2009 Travel Support for 2009 IJCAI Robotics Workshop and Exhibition In 2005, the NSF hosted ?Robots: An Exhibition of U.S. Automatons from the Leading Edge of Research?, which was the result of a 2-year comprehensive study assessing robotics programs in the U.S., Asia, and Europe. The subsequent report concluded that America is in danger of losing its leading position in robotics. Arguably, the U.S. remains the world leader in artificial intelligence (AI) and machine learning research. Such research is critical to advancing the cognition that robots need to interact with people and manipulate objects in unstructured environments. This need for machine cognition thus provides American roboticists with opportunities to fill critical gaps and both vertically advance and lead the field. The annual IJCAI event (International Joint Conference on Artificial Intelligence) is a venue that can bring American and International roboticists and AI experts together. For over a decade-and-a-half, the IJCAI has held the Robotics Workshop and Exhibition as part of the overall conference. Such a venue provides a unique opportunity for the community to formulate roadmaps that leverage and apply America?s strengths in artificial intelligence to robotics. Towards this, the 2009 IJCAI Robotics Exhibition and Workshop (Pasadena CA July 13-17, 2009) will emphasize the use of robotics outside of academia by creating a joint forum with the commercial and amateur sectors. Intellectual Merit: The workshop ?Beyond Academia: Exploring the Lessons and Best Practices in Commercial and Amateur Robotics? focuses on sharing the needs and expectations of robotics from all communities. Speakers from Willow Garage, iRobot and the amateur robotics publication Make Magazine will offer unique perspectives and approaches to common challenges from the perspective of repeatability, measurement and situated deployment. The cross-pollination of these ideas and experiences is crucial to the successful integration of AI into robotics. The ultimate goal of the workshop is to generate a white paper describing joint research opportunities. This has intellectual merit because both the AI and robotics communities are at a crossroads regarding how to best move robotics forward toward algorithms and experimental approaches that produce repeatable results in unstructured environments. The exhibition consists of four themes that relate to subsequent robotics challenges: 1) multirobot teaming, 2) learning by demonstration, 3) manipulation, and 4) undergraduate robotics challenges. Each of these areas has been identified as focus areas for the integration of AI and robotics by previous NSF supported workshops. Broader Impact: Complementing the workshop discussions and panel will be a significant number of hands-on exhibits. Here, research teams will showcase working demonstrations that support the challenge themes of learning, teaming and manipulation. Exhibits will be on display for 2 full days during the general IJCAI Conference, providing an excellent opportunity to engage a broad technical audience. The exhibits will also be open to the general public to raise awareness of the state-of-the-art. Students from local schools and summer camps will be invited to visit. In previous years, the exhibition attracted local school groups that attended via a day field trip. Access to the exhibits provides an opportunity for students and leaders to learn how robotics and AI play important roles in society. IIS CSE Standard Grant CA algorithms artificial intelligence machine learning 20000 OTHR 0000 robotics Paul Yu Oh ROBUST INTELLIGENCE 7495 American Association for Artificial Intelligence 7495 Howard, Ayanna 0946598 September 1, 2009 EAGER: Comparative Visualization Visualization tools are an essential part of how many users work with complex data. However, many of the tasks in a broad range of domains require comparing complex objects (graphs, volumes, time series, molecular motions, etc.), and while there exist a wealth of tools for visualizing individual objects there is little support at present for comparing two or more of them. The PI argues that the visual comparison of complex objects is best handled with visualization tools and techniques specifically designed for that purpose, but the design of such tools is challenging as it adds new issues to the more general visualization problems, and the challenges increase as the visualization tasks scale to larger or more complex objects, or to comparisons among larger numbers of objects. While there are examples of comparative visualization tools, existing solutions are highly specialized; they provide only limited help in developing new tools, or in understanding the more general problem of comparative visualization. In this exploratory project, the PI will take the first steps toward development of a science of comparative visualization. He will develop comparative visualization systems as case studies, providing insights into the more general problem as well as testbeds for new techniques and evaluation. He will develop a concept framework of visual comparison to codify ideas and principles, and he will develop new techniques that address issues common in comparative visualizations. Specific techniques the PI intends to develop include cartographic principles for informative display in 3D, mechanisms for the static depiction of complex motions, generalized applications of registration, interactive and automated view controls for juxtaposed displays, and generalized methods for data abstraction. These techniques are all motivated by general needs in comparative visualizations, and build upon prior technical developments and visual principles. Broader Impacts: By providing a general analysis of the issues involved, along with principles and guidelines for the design of visualization tools, a taxonomy and catalog of known solutions, and new techniques to address common problems, a science of comparative visualization will allow developers to create better, and possibly more general, tools, which will impact a wide range of disciplines in science, engineering, and medicine. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 visualization WI University of Wisconsin-Madison Gleicher, Michael GRAPHICS & VISUALIZATION 7453 7453 7916 178311 0946400 August 1, 2009 EAGER: Link Free Graph Visualization for Exploring Large Complex Graphs This project develops proof-of-concept examples for a novel graph visualization technique named Link Free Graph Visualization (LFGV). The research addresses problems in existing graph visualization techniques: (1) becoming cluttered when visualizing large graphs; (2) having limited applicability to complex graphs, such as graphs with high dimensional node attributes and time evolving graphs; (3) misleading users due to the information loss caused by 2D or 3D node layouts and efforts toward increasing scalability. The research leads to new graph visualization techniques with better scalability, better applicability to complex graphs, and less misleading insights than NLDs. LFGV first projects a graph to a multidimensional space while preserving major graph topology information. LFGV then uses extended multidimensional visualization techniques to visualize the projection for exploring graph topology and other information carried by the graph, such as multidimensional node attributes. This project develops a working LFGV prototype for plain/multivariate graphs and conducts evaluations to prove that LFGV is viable. It also develops a weblog visual analysis approach to provide a test bed and an application example for LFGV. Graph visualization is widely used in lots of applications, such as social network analysis, bioinformatics, and web information exploration. LFGV impacts the application fields where large complex graphs need to be analyzed. The weblog visual analysis approach has a direct impact on online information browsing, retrieval, and analysis. The project publishes software for research and educational purposes. IIS CSE HPCC Standard Grant 9215 network visualization NC 0116000 Human Subjects bioinformatics Jie Yang University of North Carolina at Charlotte GRAPHICS & VISUALIZATION 7453 7495 Yang, Jing Jianping Fan 144444 0946385 August 15, 2009 Workshop Proposal to Define Future Research Areas in Computer Graphics Greenberg (0946385) This Workshop is to define the future research directions for computer graphics and visualization. Over the past two decades, computing environments have radically changed in terms of processing power, parallelization, bandwidth, storage, and computer architectures. More recently, with the introduction of touch panel displays and the availability of large area screens, new user interfaces are now possible. Yet there does not appear to be any long-term research strategy which is compatible for future computing environments. According the workshop is bringing together computer graphics leaders and visionaries, as well as innovators from closely related fields, to define broader, more fundamental, longer term research areas. The workshop will establish an appropriate set of research challenges to help guide the National Science Foundation in their mission. A broad range of topics and unsolved problems will be identified, each with a set of specific goals. The workshop is being organized by: ? Professor James Foley ? Stephen Fleming Chair in Telecommunications, Georgia Institute of Technology ? Professor Donald P. Greenberg ? Jacob Schurman Professor of Computer Graphics, Director, Program of Computer Graphics, Cornell University ? Professor Pat Hanrahan ? Canon USA Professor, Computer Graphics Laboratory, CSEE Dept., School of Engineering, Stanford University ? Professor Jessica Hodgins ? Computer Science and Robotics, Associate Director for the Faculty in the Robotics Institute Carnegie Mellon University, part-time Director of the new Disney Research, Pittsburgh Laboratory During the two-day workshop, each participant will be assigned the responsibility of making a brief presentation during the first day. For each sub-area one participant will be selected and will have the responsibility for the documentation of the summary findings. At the conclusion of the workshop, the four primary investigators will edit and compile the results. IIS CSE NY HPCC Standard Grant 9215 visualization computer graphics graphics Cornell University robotics GRAPHICS & VISUALIZATION 7453 Lawrence Rosenblum Greenberg, Donald 28300 0944249 October 1, 2009 VisWeek 2009 Doctoral Colloquium This is funding to support a Doctoral Colloquium (workshop) of promising doctoral students and distinguished research faculty, to be held in conjunction with this year's IEEE VisWeek Conference, which will take place October 11-16, 2009, in Atlantic City, NJ. Visualization, or the use of interactive graphics to support data analysis and understanding, has become an integral part and critical component of many application areas. VisWeek consists of three main events: IEEE Visualization (Vis), IEEE Information Visualization (InfoVis), and the IEEE Visual Analytics Science and Technology Symposium (VAST). IEEE Vis is the oldest of the three venues of VisWeek, and will celebrate its 20th anniversary this year. Its traditional focus has been on a wide range of topics in scientific and medical visualization. InfoVis centers around helping people explore or explain abstract data through interactive software that exploits the capabilities of the human perceptual system, focusing on cognitively useful spatial mappings of abstract datasets that are not inherently spatial, and accompanying the mappings with interaction techniques that allow people to intuitively explore the data. VAST, the youngest event, was founded in 2006 to address the growing interest in the science of analytical reasoning supported by highly interactive visual interfaces; its focus is on visual analytics tools and techniques to synthesize information into knowledge, derive insight from massive, dynamic, and often conflicting data; detect the expected and discover the unexpected, provide timely, defensible, and understandable assessments, and communicate assessments effectively for action. IEEE VisWeek is the premier forum for visualization advances in science and engineering for academia, government, and industry, bringing together about 800 researchers and practitioners from around the world with a shared interest in techniques, tools, and technology. The papers published in the special conference issue of IEEE Transactions of Visualization and Computer Graphics are rigorously refereed and widely cited. The Doctoral Colloquium at IEEE VisWeek 2009 will bring together approximately 12 dissertation stage doctoral students in the field of visualization, from the United States and abroad, who will come together on Saturday, October 10, for a day of discussions and interaction with 6 faculty researchers, with follow-up events that will take place during the conference?s technical program. A primary goal of the colloquium is to allow students to discuss their research directions in a supportive atmosphere with a panel of distinguished leaders and with their peers, who will provide helpful feedback and fresh perspectives. The colloquium also supports community building, by connecting beginning and advanced researchers so as to build a cohort group of new researchers who will then have a network of colleagues across the world. Student research will be disseminated via posters during the VisWeek 2009 technical program, and via extended abstracts published in the VisWeek 2009 Extended Abstracts. Feedback about the Doctoral Colloquium will be provided to future conference committees. The PI has affirmed that in managing this event he and his colleagues will try explicitly to identify and include the broadest possible group of highly qualified participants, and that they will ensure that NSF funds are used chiefly to support participation by students enrolled in graduate programs in the United States. Broader Impacts: The VisWeek Doctoral Colloquium has taken place annually at the Visualization conference since 2006, and has helped launch the careers of a number of outstanding young Vis researchers. It brings together the best of the next generation of visualization researchers, and allows them to create a social network both among themselves and with senior researchers, which plays a major role in their enculturation into the profession. Since the students and faculty are a diverse group on several dimensions (nationality, scientific discipline, research specialization), the students' horizons are broadened at a critical stage in their professional development. IIS CSE NY Ephraim P. Glinert HPCC Standard Grant 9215 network visualization SUNY at Stony Brook computer graphics graphics information visualization data analysis GRAPHICS & VISUALIZATION 7453 visual analytics 7453 Mueller, Klaus 19968 0944126 September 15, 2009 Student Travel Fellowships to Attend the 2nd Workshop on Resource Discovery The second workshop on Resource Discovery (RED) is co-located with the 35th International Conference on Very Large Data Bases (VLDB) on August 28, 2009, in Lyon, France. This project provides travel fellowships to students enrolled in US universities in order to attend the workshop. The workshop is concerned with the design and development of models and prototypes that support the representation of resources, the discovery of resources on the Web, the identification, localisation, and composition of resources. A resource corresponds to an information source such as a data repository or database management system (e.g., a query form or a textual search engine), a link between resources (an index or hyperlink), or a service such as an application or tool. Resources are characterized by core information including a name, a description of its input and its output (parameters or format), its address, and various additional properties expressed as metadata. Resource discovery is the process of identifying and locating existing resources that have a particular property. Machine-based resource discovery relies on crawling, clustering, and classifying resources discovered on the Web automatically. Resources are organized with respect to metadata that characterize their content (for data sources), their semantics (in terms of ontological classes and relationships), their characteristics (syntactical properties), their performance (with metrics and benchmarks), their quality (curation, reliability, trust), etc. Resource discovery systems allow the expression of queries to identify and locate resources that implement specific tasks. For further information see the project web page at: http://bioinformatics.eas.asu.edu/RED/red2009.html IIS CSE HPCC 9216 Standard Grant database Arizona State University AZ 7484 bioinformatics INFO INTEGRATION & INFORMATICS 7364 Frank Olken Lacroix, Zoe 12500 0943753 September 1, 2009 Knowledge Transfer in Computational Neuroscience This award provides partial support for the Computational Neuroscience meetings, CNS*2009, 2010, and 2011, to be held in Berlin, Germany; San Antonio, Texas; and Stockholm, Sweden. The conferences will bring together a large body of international scientists working across scales (molecular to systems) and disciplinary perspectives (e.g., biology, computer science, engineering, mathematics) on various aspects of computational and theoretical understanding of the nervous system. This award will contribute to an expanded tutorial program, a new Frontiers in Computational Neuroscience Lecture, and travel support for women and minorities. IIS CSE HPCC Standard Grant 9215 Arizona State University AZ Kenneth C. Whang ROBUST INTELLIGENCE 7495 9102 CRCNS 7327 7327 49997 Jung, Ranu 0943412 August 15, 2009 Human-Environment Mobile-Based Interactions Increasing numbers of scientific projects leverage mobile devices to monitor and at times interact with elements of the natural environment. This important emerging trend however is not paired with an adequate forum for publication and exchange of information among researchers as that of other disciplines. This workshop gathers a representative sample of researchers and practitioners with expertise and interest in human-environment, mobile-based interactions. It provides a forum to present current technology trends in areas such as networking, communication, sensing, data mining, data visualization and robotics as well as discussions on synergistic applications. In addition to the presentations, the workshop participants are meeting to identify and plan sessions for presentations at future conferences. Impacts of this workshop are expected in mobile and sensing technologies as well as in application areas including biology, agronomy, environmental science, public health and citizen science. The workshop invitees are drawn from diverse areas and social-ethic backgrounds. The presentation abstracts of the workshop will be disseminated through the workshop website, http://hembi.media.mit.edu. IIS CSE HPCC 9216 Standard Grant 50000 MA data mining visualization networking robotics Massachusetts Institute of Technology 7484 INFO INTEGRATION & INFORMATICS 7364 James C. French ROBUST INTELLIGENCE 7495 Joachim, Dale 0943304 September 1, 2009 The Artemis Project This award supports the Artemis project, a 5-week Summer program administered by Brown University's Department of Computer Science designed to encourage girls from local public schools to pursue careers in Computer Science, and more broadly in science and engineering. Participants are rising ninth graders and are exposed to the breadth of applications of Computer Science, and are introduced to a variety of the technologies underlying computing. An equally important goal of Artemis is to develop the leadership and entrepreneurial skills of undergraduate women who serve as coordinators each year. These coordinators serve the local community in their capacity as social entrepreneurs. IIS CSE HPCC Standard Grant 9215 9150 Brown University RI INFO INTEGRATION & INFORMATICS 7364 Douglas H. Fisher ROBUST INTELLIGENCE 7495 Greenwald, Amy 9102 BROADENING PARTIC IN COMPUTING 7482 7495 23830 0942997 August 1, 2009 Virtual Civility, Trust, and Avatars: Ethnology in Second Life This is a sociological study of the ways in which people voluntarily develop "virtual civility" and trustworthy identities in 3-dimensional virtual communities such as Second Life. This exploratory project will develop a comparative ethnographical study of several carefully chosen virtual sites in which what might be called "spirituality" or "self-help" plays an important role. These virtual sites are intriguing because when people share their inner experiences - which are usually considered highly private - this would normally indicate a deep trust of others. But in 3D virtual worlds, avatar-actors' real identities are often hidden. The project is intended to clarify how these virtual sites manage to create virtual civility and trust among geographically-distanced "strangers," and what specific cultural mechanisms prompt and enable these avatars to develop trustworthy identities. Studies to date of virtual worlds tend to be dominated by approaches that emphasize technological and regulatory aspects of ensuring trustworthiness. In contrast, this research is sociological because it locates the civility and trust that emerge within the interactional context of collaborative knowledge projects in particular cultural settings. For example, the ways that communities accomplish the actual building of a virtual culture of civility might in part depend on shared images of transcendence associated with 'spiritual approaches.' This observation is entirely in line with the classical but still-influential theories of sociologists like Max Weber and Emile Durkheim, yet its implications have not yet been explored in virtual, online environments. The project will study relational processes in which the development of such site-specific cultures and the emergence of individual avatars' civilized behavior, as well as the developments of their trustworthy identities, are intimately connected. This project will contribute to the knowledge required to make virtual worlds with user-created contents positive grounds for socially meaningful collaborative knowledge productions. The study's potential import may go beyond virtual community dynamics, because it is possible that the new social forms shaped by the unique characteristics of virtual worlds may then in turn migrate back out into real life, becoming the new standard forms of trust and civility in human interactions generally. IIS CSE NY HPCC Standard Grant 9215 William Bainbridge 0116000 Human Subjects New School University HUMAN-CENTERED COMPUTING 7367 7367 7916 Ikegami, Eiko 149419 0942646 July 15, 2009 EAGER: An Empirical Exploration of Contemplative Multitasking While personal computers, the Internet, cell phones and other information services and technologies are unquestionably powerful resources for communication and for information access, there is growing concern that the widespread use of these tools may be negatively affecting individuals, groups, and even society as a whole. The press is filled with articles on information overload, multitasking, fragmented attention, and the accelerating pace of life. And there is a growing body of scientific literature that bears on these issues, demonstrating in particular that multitasking degrades human performance. New technologies are sometimes suggested as a way to alleviate the problem. This study explores a different but complementary approach: training users through meditation to work more effectively and less stressfully with existing information technologies. The PI will offer people training in meditation and then test their ability to perform a set of time-limited information-intensive tasks. While prior studies of the efficacy of meditation have provided experimental evidence that such training helps people maintain focus and reduce interference resulting from distraction, no one has yet attempted to demonstrate such effects specifically in relation to the use of information technology. The PI's goal in this study is to do just this. Human resources personnel in San Francisco and Seattle will be recruited to attend eight weeks of training in either meditation or relaxation. Participants will be given a test of their multitasking abilities (involving the use of e-mail, instant messaging, phones, and face-to-face conversation in an office-like setting) both before and after the training, and their performance will be evaluated along four dimensions: accuracy; time to completion; satisfaction and well-being; and memory for the task. Project outcomes will thus make a contribution both to the growing body of scientific literature demonstrating that multitasking degrades human performance, as well as to the largely separate literature demonstrating that meditation training can help people maintain focus and reduce interference resulting from distraction, and will serve as a bridge between them. Broader Impacts: There is growing evidence that rampant multitasking and accelerated modes of working can result in health problems and diminished effectiveness. If it can be demonstrated that meditation training diminishes some of these negative effects, then the door will be open to creating ameliorative training for both students and adult workers. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 University of Washington WA HUMAN-CENTERED COMPUTING 7367 7367 Levy, David Jacob Wobbrock 7916 127872 0942320 September 1, 2009 Travel Support for U.S.-Based Students to Attend 8th International Semantic Web Conference 2009 The 8th International Semantic Web Conference (ISWC), held October 25-29, 2009 in Northern Virginia is the major international forum where the latest research results and technical innovations on all aspects of the Semantic Web are presented. This student travel support enables students who want to become part of the Semantic Web research community to participate in ISWC 2009. In particular, the ISWC Doctoral Consortium creates an opportunity for doctoral students to test their research ideas, present their current progress and future plans, and to receive constructive criticism and insights related to their future work and career perspectives. This is expected to be a significant event in the graduate students' careers. In selecting applications for the ISWC 2009 Travel Support, preference is given to students selected to participate in the Doctoral Consortium, followed by students who are first author on a paper accepted at the conference, followed by students who have other authorship on a conference or related workshop paper, with an additional aim to broaden participation in computer science among the underrepresented students. ISWC details, including the Doctoral Symposium and Travel Support, can be found at the conference website (http://iswc2009.semanticweb.org). IIS CSE HPCC 9216 Standard Grant Maria Zemankova 20000 MD INFO INTEGRATION & INFORMATICS 7364 University of Maryland Baltimore County 7364 Timothy Finin Sachs, Joel 0941727 September 1, 2009 EAGER: InfoCalc, a Spreadsheet Interface to Web Archive Analysis It has long been recognized that while the World Wide Web (WWW) has a wealth of information, there is a severe lack of tools to access and analyze its contents. The tool proposed here, if the effort is fully successful has the potential to radically extend use of WWW contentsThis project will construct a new set of tools that will make World Wide Web archives and present web data available to a wide group of researchers whose efforts depend upon examination of large amounts of data captured in different forms over extensive periods of time. This would include such researchers from the social sciences, health information management research, political science, an environmental sciences, to name a few. The project will comprise two components: a user interface to analysis tools that operate and an architecture for supporting the tools. The analysis tools will provide fundamental data management operations such as formula creation, and data summarization. IIS CSE HPCC 9216 architecture Standard Grant Stephen Griffin CA Stanford University Garcia-Molina, Hector INFO INTEGRATION & INFORMATICS 7364 225000 7916 0941581 July 1, 2009 EAGER: Preliminary Investigation of Virtual Tactual Stereognosis Imagine reaching into your pocket, locating and grasping your car;'s key fob amongst a clutter of coins, bills and keys, then finding the unlock button (not the lock button, not the trunk release, especially not the panic button) and pressing it. This is an example of Tactual Stereognosis (TS), the ability of people to identify familiar items using touch alone. It is commonplace and uneventful. Yet no programmable haptic interface has ever been developed that would allow people to identify virtual objects using what might be termed Virtual Tactual Stereognosis (VTS). Creating such an interface is a long-range goal of the PIs. VTS has long been out of reach because it depends on active touch, multi-finger interaction, and bare fingertips, which are all difficult to achieve with existing display technology. The PIs' recent research has led to a new class of prototype devices (xPaDs), which address each of these limitations. These devices use friction modulation to control forces between the fingertip and a flat plate. The basic TPaD (Tactile Pattern Display) can create effects such as virtual textures, virtual bumps and holes, and more. More advanced versions such as the ShiverPad and SwirlPad synchronize in-plane vibrations to friction levels in order to generate active pushing forces on the fingertip. It is possible to generate many additional effects with these devices, including virtual edges that can be traced with the fingertip. The xPaD devices thus appear to be well suited to VTS. They are active touch devices that work with bare fingertips (in other words, the xPaD is fixed and the finger slides over it). Moreover, they are very compact, potentially enabling multiple panels to be arrayed over the surface of an object in order to support a multi-finger interface. In this study the PIs will begin exploration of a multi-xPaD interface, by performing experiments on an interface consisting of two opposing ShiverPads with subjects employing a pinch grip (that is, index finger on one ShiverPaD, and thumb on the other), in order to demonstrate "binding," the perceptual fusion of the finger and thumb percepts into an integrated object representation. Broader Impacts: Virtual Tactual Stereognosis is an important goal for many reasons. Graphical displays have become an important form of interface in venues ranging from the living room, to the office, to the car, to any place that a person may be. Yet as graphical displays grow more prevalent, natural touch interactions seemingly grow more obsolete. VTS aims to achieve the opposite, to empower future interfaces with sophisticated tactual capabilities that engage perceptual as well as sensory mechanisms in the hand and brain. Imagine a doctor able to simultaneously look at and palpate tissue within the body, a pregnant mother able to caress the ultrasound image of her unborn child, an autistic child able to cooperate with an animated character in a construction task, or a driver able to reach out, find and operate touch screen controls without taking his/her eyes off the road. VTS would be an enabler for new types of electronic displays for the blind. And it would be a powerful new tool to extend our basic knowledge of haptic perception as it occurs naturally. IIS CSE Ephraim P. Glinert HPCC Northwestern University IL Standard Grant 9215 Michael Peshkin 150000 Colgate, J. Edward HUMAN-CENTERED COMPUTING 7367 7367 7916 0941421 September 1, 2009 CDI-Type II: IC-CRIME: Interdisciplinary Cyber-Enabled Crime Reconstruction through Innovative Methodology and Engagement Crime scene investigation (CSI) is a highly visual and quantitative analysis characterized by a time-sensitive need to gather, organize, analyze, model, and visualize large, multi-scale, heterogeneous and context-rich data. CSI is also characterized by a fundamental need for rapid coordination and data translation across disciplines, agencies and levels of expertise as crime scenes are processed, reconstructed, solved and ultimately prosecuted over time, often critically in front of laypeople comprising a jury. The core intellectual contributions of this research include a shift to 3D virtual crime scene reconstruction and collaboration protocols for virtual CSI work. Embedded within the virtual scene will be all of the conventional evidence data, but will also include advanced data resulting from the development of non-invasive nano-scale methodologies and databases for advanced surface and cross-sectional materials chemistry analysis, especially fibers. The new data will lead to development of revolutionary high statistical significance when comparing similar materials commonly encountered in CSI. Further, collaboration protocols for dynamic virtual team assembly and interaction will be developed, assessed and optimized for contextual knowledge transfer. The broader impacts of this research and education agenda include critical benefits to research and education infrastructure, public health and safety, and the justice system. The resulting system will be used as a novel research tool to increase the level of scientific rigor and advance the knowledge of forensic analysis. It provides a platform for integrating research with engaging science and engineering education opportunities for K-12, undergraduate and graduate students, including those from underrepresented groups and communities. IIS CSE HPCC Standard Grant 9215 education NC 0116000 Human Subjects North Carolina State University 1400000 David W. McDonald Robert Young 7721 CDI TYPE II 7751 7751 Montoya-Weiss, Mitzi David Hinks 0940840 July 1, 2009 Collaborative Research: 1st Sino-USA Summer School in Vision, Learning, Pattern Recognition, VLPR 2009 NATIONAL SCIENCE FOUNDATION Proposal Abstract Proposal Title: Collaborative Research: 1st Sino-USA Summer School in Vision, Learning, Pattern Recognition VLPR 2009 Institution: Princeton University Abstract Date: 05/21/09 The 1st Sino-USA Summer School in Vision, Learning and Pattern Recognition (VLPR2009) is the first NSF-sponsored summer school in China that brings together leading American and Chinese researchers and students in the field of computer vision and machine learning for a week of educational and cultural exchange program. The summer school will be held on the campus of Peking University in Beijing, China. This award will provide travel support for American researchers and students to attend the summer school. The summer school provides a forum for not only technical interactions but also culture exchanges among researchers and students from two countries. IIS CSE HPCC Standard Grant 9215 PA machine learning vision computer vision University of Pennsylvania ROBUST INTELLIGENCE 7495 7495 Shi, Jianbo Qiang Ji 24460 0940743 September 1, 2009 IEEE Intelligence and Security Informatics Conference Intelligence and Security Informatics (ISI) has been established as an interdisciplinary subject that focuses on the development and use of advanced information technologies, including methodologies, models and algorithms, infrastructure, systems, and tools, for local, national/international, and global security related applications through an integrated technological, organizational, behavioral, and policy based approach. The international conference was first held in 2003; ISI 2009 will be held in Richardson Texas. This award will support student scholarships to attend the meeting, encouraging participation from a wide range of students. IIS CSE HPCC 9216 Standard Grant algorithms 10000 Sylvia J. Spengler security TX 7484 University of Texas at Dallas Thuraisingham, Bhavani Latifur Khan TRUSTWORTHY COMPUTING 7795 Murat Kantarcioglu 0940723 July 1, 2009 EAGER: Drummer Game: A Massive-Interactive Socially-Enabled Strategy Game The goal of this project is to foster new research collaborations across different disciplines by developing a novel massive interaction game that represents a unique genre wherein teams of users strategically direct armies of virtual 3D "terra cotta" soldiers by employing physical drum beats as control signals to determine the warriors' behavior. It is intended that the game will engage and enthrall both user teams and a live audience with the special demands of cooperative and competitive game play in a compelling and creative scenario. Successful completion of the research will require intensive collaboration across multiple fields of computational science, art, and music. The culmination of the project will be a social event centrally located on the Virginia Tech campus, in which the finished game is publically introduced to the community at large. To achieve these goals, the PI team will engage interactive game design approaches to draft and enrich the overall game experience. They will adopt commoditized Graphics Processing Units (GPUs) to meet the computational challenges of real-time simulation, animation, and rendering of massive number of 3D characters. Novel numerical methods will be developed to enhance the efficiency of global path finding for massive crowd simulation. The individual combat behavior of the virtual soldiers will be automatically regulated by an artificially intelligent component, which is supported by GPU accelerated agent-based simulation. In order to provide visualization capacity for a massive crowd in an enormous environment, the investigators will develop technologies to enable level-of-detail rendering and animation. For massive interaction support, signal processing and computer vision approaches will be used to recognize the drumbeats and to interpret the audience participation behavior (which will be tracked with the aid of microphones embedded among the members of audience and cameras aimed at them). Synthetic music generation approaches will be utilized to produce real-time accompaniment to the drums. Broader Impacts: The outcome outlined above will be the result of broad and intensive collaboration between engineering and the arts. Computer engineers will design and develop the overall game engine architecture, graphics rendering technologies, numerical solutions for character path planning, and approaches to recognize drum beats. Graphics artists will design 3D geometry models and animations for "terra cotta" soldiers, and will work closely with engineers to build a smooth "pipeline" to import digital content into the game software system. Researchers in modern music will produce accompaniment to the cadence of the drums. The team-based approach adopted in this project will expose students to cross-disciplinary research, so that they come away with an understanding and appreciation of the need to function cooperatively to achieve rewarding and exciting results. Teaming of undergraduate students with graduate students will, it is hoped, lead some of the undergraduates to choose research related career paths while also encouraging the graduate students to learn how to better mentor others. The PI team hopes that the mass media event at the culmination of the project will imbue the broader public (and especially K-12 students) with the excitement of computing and the arts. IIS CSE Ephraim P. Glinert HPCC architecture Standard Grant 9215 simulation visualization VA Virginia Polytechnic Institute and State University vision graphics animation computer vision agent-based HUMAN-CENTERED COMPUTING 7367 7367 computational science Francis Quek 7916 Cao, Yong Ivica Bukvic Dane Webster 149648 0940687 July 1, 2009 Collaborative Research: 1st Sino-USA Summer School in Vision, Learning, Pattern Recognition VLPR 2009 The 1st Sino-USA Summer School in Vision, Learning and Pattern Recognition (VLPR2009) is the first NSF-sponsored summer school in China that brings together leading American and Chinese researchers and students in the field of computer vision and machine learning for a week of educational and cultural exchange program. The summer school will be held on the campus of Peking University in Beijing, China. This award will provide travel support for American researchers and students to attend the summer school. The summer school provides a forum for not only technical interactions but also culture exchanges among researchers and students from two countries. IIS CSE HPCC Standard Grant 9215 machine learning Princeton University NJ vision computer vision ROBUST INTELLIGENCE 7495 9102 7495 Qiang Ji Li, Fei-Fei 24460 0940553 July 15, 2009 WORKSHOP:WSC2009 Web Services Composition Systems Inter-organization collaboration resulting in enterprise integration is experiencing a promising advancement considering the recent inception and potential acceptance of network-accessible services, or web services. Commercial, academic, and government organizations alike are beginning to share their capabilities via the exposition of their underlying software services. The notion of millions or even billions of universally accessible service-based capabilities is not only promising to the individual looking for a specific consumer-based service but also to organizations hoping to enhance their own capabilities by incorporating the services of external entities. Yet the ability to automate the discovery and composition of such services into higher-level capabilities remains elusive, due to open issues of both a theoretical and applied nature (including, but not limited to, potential semantic and syntactic mismatch among services, performance constraints on service discovery, and fault tolerance). This is funding to support the 5th Web Services Challenge, whose goal is to find applied solutions to unresolved issues in web service integration. Building on the success of the first four competitions (all but the first of which were funded in part by NSF), the 2009 competition will be held in conjunction with the IEEE Conferences on Electronic Commerce and on Enterprise Computing, E-Commerce and E-Services (CEC/EEE'09), which will take place in Vienna, Austria, on July 20-23, 2009. NSF funds will support participation in the competition of approximately 5 teams from the United States consisting of about 2 students apiece. Small amounts are earmarked for travel of the organizers to the competition and for their conference registration fees. The IEEE International Conferences on Electronic Commerce and on Enterprise Computing, E-Commerce and E-Services bring together researchers and developers from diverse areas of computing. The conferences provide a venue for developers and practitioners to explore and address challenging research issues surrounding e-technology, in order to develop a common research agenda and vision for e-commerce and e-business. The focus is two-fold: to investigate enabling technologies to facilitate next generation e-transformation; and to disseminate application and deployment experience in e-themes such as e-business, e-learning, e-government, e-finance, etc. The CEC/EEE conferences are particularly timely and relevant because they focus on the application of electronic services as they cut across several ?e-domains? whereas other conferences tend to either focus on a specific technology or on a single domain. As a means of voluntary cost sharing this year, the conference will be providing a special reduced registration rate for the organizers of and all participants in WSC2009. Broader Impacts: The workshop and resulting artifacts will serve as a centralized repository of algorithms, software, and techniques in a timely emerging area. The workshop will provide participants with an opportunity to gain exposure in the community for their innovative work, and to obtain feedback and guidance from senior members of the research community. It will further help foster a sense of community among these young researchers, by allowing them to create a social network both among themselves and with senior researchers at a critical stage in their professional development. The workshop experience will integrate well with the goals of a software engineering education, as participants are evaluated on their design in addition to the performance of their approaches. To engage a broader audience in this year?s event, the PI will expand his previously successful efforts to solicit teams from under-represented universities. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 IN network algorithms education vision software engineering HUMAN-CENTERED COMPUTING 7367 7367 University of Notre Dame Blake, Malworsth 32686 0940129 July 1, 2009 Pioneers Mentoring Program Diary Note (Conference Support): Kasic, ACM Siggraph Pioneers, IIS - 0940129 This proposal supports student involvement and attendance at the 2009 ACM Siggraph Conference. This is the premier computer graphics conference in the world, attracting over 15,000 attendees and filling over 100,000 feet of exhibition space. The annual film show is a venue for viewing the latest advances in animation and visualization. Although many attendees come for the exhibition, the Siggraph Conference has long been the premier place to publish technical papers in computer graphics. The Siggraph Pioneers (20+ years of work in the field) started a mentoring program in 2003 to add more students to the technology pipeline. While small in number, the impact has been high as the students do not merely attend the conference, but rather are mentored by longstanding members of the community. Because each Pioneer is assigned no more than two students, the Pioneer guides the students to talks, courses, etc. that were not immediately on the student?s radar. The intellectual merit and broader impact of the proposed activity lies in the educational opportunities provided the student by the mentoring process. The program also focuses on underrepresented groups (e.g., women, Hispanics, and African-American) who would have no opportunity to hear about Siggraph, let alone attend, without the mentoring program. The students are exposed to the latest research and hardware covering the use of computer graphics for numerous topics that benefit society ranging from engineering design to entertainment. Sample student reactions can be found at http://pioneers.siggraph.org/student_letters.html IIS CSE NY HPCC Standard Grant 9215 visualization computer graphics graphics animation David Ebert GRAPHICS & VISUALIZATION 7453 7453 Lawrence Rosenblum Kasik, David Association Computing Machinery 5100 0940080 January 1, 2010 WORKSHOP: Computer Supported Cooperative Work Doctoral Research Colloquium 2010 This award supports a Doctoral Colloquium (DC) at the 2010 ACM Conference on Computer Supported Cooperative Work (CSCW) to be held February 6-10, 2010 in Savannah, GA. The CSCW conference brings together researchers from the fields of organizational behavior, information systems, social informatics, information sciences, and human-computer interaction (HCI). As such the conference is a critical link between the research communities supported by IIS and the broader social, behavioral, and management sciences. The primary intellectual contributions of this project lies in bringing together a diverse set of advanced PhD students from across the range of research represented in CSCW. This workshop enables both close mentoring and the development of a group of future intellectual leaders within the CSCW community. Furthermore, bringing together these students in close contact with key CSCW researchers, both during the workshop itself and during conference poster presentations, will support the development of interdisciplinary dialogs, creating an environment for exchange and conversation that will further enable progress on the projects in the DC. There are short and long-term benefits from this work. In the short term, we will provide significant feedback to the specific students participating in the DC and more general feedback that can spread amongst other student attendees of the conference. In the long-term, we expect that students who have participated in the DC will then give back to the community by engaging as mentors to undergraduate students, and in the future to doctoral students. Furthermore, we expect these exceptional students to be the future leaders of the CSCW community. IIS CSE HPCC Standard Grant 9215 CA human-computer interaction University of California-Irvine cscw HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald Hayes, Gillian 25018 0940002 August 1, 2009 Workshop: Organizational Communication and Information Systems (OCIS) Doctoral Research Consortium This award supports an Organizational Communication and Information Systems (OCIS) Doctoral Colloquium at the 2009 Academy of Management (AOM) Annual Meeting to be held August 7-12, 2009 in Chicago, IL. The OCIS division within the AOM conference brings together researchers in the areas communications and information systems and issues related to the contemporary information-based society. The focus of the OCIS Doctoral Consortium is the intellectual content of the students' doctoral dissertations. These represent cutting edge research in the field of organizational communication and information systems. The OCIS doctoral consortium brings together highly talented students and several senior researchers, facilitating the development of a social network that will play a major role in the career success of new researchers. Since both faculty and students are diverse on several dimensions (research topics, methodological approaches, national and cultural background), the students' horizons are broadened at a critical stage in their professional development. IIS CSE HPCC Standard Grant 9215 network University of Louisville Research Foundation Inc KY HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald 19945 Ahuja, Manju 0939966 August 1, 2009 Workshop on Speech Summarization Automatic summarization of spoken documents (e.g., everyday meetings, broadcast news, lectures, political speeches) provides an efficient way for information access in these data sources. Different summarization approaches have been developed and evaluated for speech data in different domains, such as meetings, broadcast news, and lectures. However, compared to text summarization, speech summarization is not as mature. There are no benchmark tests, the evaluation metrics are not clear, and there are not many annotated data sets available. Because of the recent increasing interest in speech summarization in the community, there is a need to gather researchers in the field to discuss various issues in this task and future research directions. This workshop brings together researchers dedicated to speech summarization in various aspects, helps build a research community for this task, and fosters discussions about open problems, future directions, data collection and sharing, algorithms and results. The topics discussed include the definition of speech summarization, data collection and annotation, data sharing, evaluation metrics, possible shared tasks, domain effect, summarization approaches, similarity and differences between text and speech summarization, and modality beyond speech in human communication. The discussions from this meeting will be disseminated using a dedicated website. This meeting will greatly help move forward the research in speech summarization. Students participating in this meeting will benefit from the discussions and be able to use them in their future research. IIS CSE HPCC Standard Grant 9215 algorithms TX Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 9102 University of Texas at Dallas 7495 Liu, Yang 17980 0939824 July 1, 2009 Travel Grant for 2009 Chicago Summer School/Workshop on Computational Learning This award provides support to students and other young researchers for travel and accomodation to the 2009 Chicago Learning School/Workshop on Theory and Practice of Computational Machine Learning to be held in Chicago, June 1-11, 2009. Tutorial presentations of the Summer School are being recorded and are being made available over the Web. The workshop and summer school provide a forum on geometry and high-dimensional inference in computational learning, and will be effective in bringing these subjects to the attention of a broad scientific community interested in problems of machine learning and inference. IIS CSE HPCC Standard Grant 9215 OH machine learning 20000 Ohio State University Research Foundation Douglas H. Fisher ROBUST INTELLIGENCE 7495 7495 Belkin, Mikhail 0939505 June 1, 2009 Student and Junior Researcher Participation in CompSust09: 1st International Conference on Computational Sustainability Computational sustainability is an emerging field, developing and applying computational methods, notably of articial intelligence, to difficult problems of long-term environmental and societal sustainability. This award supports the travel and subsistence of graduate students, postdoctoral fellows, and other junior researchers for participation at the 1st International Conference on Computational Sustainability (June 8-11, 2009, Ithaca, NY). IIS CSE NY HPCC Standard Grant 9215 Cornell University Douglas H. Fisher ROBUST INTELLIGENCE 7495 15000 9102 7495 Gomes, Carla 0939253 August 1, 2009 Workshop on Integrating Digital Library Content with Computational Tools and Services This proposal requests funding to support an exploratory workshop On Integrating Digital Library Content with Computational Tools and Services to bring together a cross-disciplinary group of researchers and stakeholders to examine the promise of combining cutting-edge high performance computing analytic tools and services with digital library content. It will bring together a cross-disciplinary group from several distinct communities including o content scholars who wish to more fully exploit the content of digital libraries and similar repositories of digital materials o content providers who wish to provide extended mechanisms for users in order to manipulate and analyze digital materials o software developers and administrators o researchers engaged in new tool development The workshop has been accepted as an official full-day workshop of the 2009 ACM/IEEE Joint Conference on Digital Libraries (JCDL 2009) IIS CSE HPCC 9216 IL Standard Grant Stephen Griffin University of Illinois at Urbana-Champaign INFO INTEGRATION & INFORMATICS 7364 Downie, John 24735 0938504 July 1, 2009 EAGER: Subsequent Similar Cases to Unexpected, Exceptional Cases Given that change is ubiquitous, it is necessary to understand how systems can learn in response to unexpected, exceptional, and provocative events. This project addresses reasoning about strikingly novel, perhaps disruptive cases, so that subsequent cases similar to the harbinger case do not take an intelligent agent by surprise; rather, the agent can guard against harm and/or lay groundwork to reap reward. The project explores similarity assessment among novel, possibly high-impact cases and uses hypothetical reasoning to explore the space of possible future cases and the implications of these cases on an agent's environment. The extent in time and quality of this exploration (e.g., to include representation change) are conditioned on the costs and benefits associated with anticipating (or not) new events like the original disruptive event. These issues are being explored in two application domains: (1) law where there are numerous areas with apt historical episodes of surprising exceptional cases provoking dramatic change (e.g., warrantless search), and (2) multi-agent distributed resource planning that allow controlled experimentation with models, model-revision policies, and hypothetical problem scenarios (e.g., the Producer-Consumer-Transporter domain). The research is a fundamental step in understanding and representing the actionability facet of concepts, particularly in the face of problematic or atypical instances. It contributes to efforts to promote critical thinking by showing how critical cases can provoke change. It furthers our knowledge of how insightful hypotheticals, so called 'what if' situations, can be used to shed light on the ramifications of actions, classifications, and policy decisions. IIS CSE HPCC Standard Grant 9215 MA University of Massachusetts Amherst ubiquitous Douglas H. Fisher ROBUST INTELLIGENCE 7495 intelligent agent 9102 7495 Rissland, Edwina 162149 0938382 April 1, 2009 WORKSHOP: Web Service Discovery and Composition Competition (WSC2008) Although service-oriented environments and technologies are receiving a great deal of attention in both academic and corporate arenas, the ability to automate the discovery and composition of such services into higher-level capabilities remains elusive. This is funding to support the 4th international web service discovery and composition competition (the WS-Challenge), whose objective is to identify, evaluate and baseline approaches to solving that problem. Building on the success of the first three competitions, which were held in 2005 in Hong Kong, in 2006 in San Francisco, and in 2007 in Tokyo (the latter two with NSF funding), the PI will organize the planning and enactment of the 2008 competition to be held in conjunction with the IEEE International Conferences on Electronic Commerce (CEC 08) and on Enterprise Computing, E-Commerce and E-Services (EEE 08), which will take place in Washington DC in July. The first competition was limited to syntactical matching using the Web Services Description Language (WSDL); participants were required to identify and compose WSDL-specified services based on their input and output messages as specified in a directory of WSDL documents. In 2006 the competition focused additionally on the semantic linking of web services using XML-based semantic notations. The 2007 competition supported the implementation of an initial Web service interface package that served as a platform upon which participants built composition engines, which were then exposed as a Web service; teams were evaluated in terms of architecture and design as well as performance. The 2008 competition will focus on issues relating to dissemination, such as code generalization and creation of APIs that can be used to help new teams access the competition. NSF funding will support participation in the competition of approximately 4 teams from the United States consisting of about 2 students apiece. A small amount is earmarked for travel for the organizers to the competition and the conference registration of the PIs and research assistants. The IEEE International Conferences on Electronic Commerce and on Enterprise Computing, E-Commerce and E-Services bring together researchers and developers from diverse areas of computing. The conferences provide a venue for developers and practitioners to explore and address challenging research issues surrounding e-technology, in order to develop a common research agenda and vision for e-commerce and e-business. The focus is two-fold: to investigate enabling technologies to facilitate next generation e-transformation; and to disseminate application and deployment experience in e-themes such as e-business, e-learning, e-government, e-finance, etc. The CEC/EEE conferences are particularly timely and relevant because they focus on the application of electronic services as they cut across several "e-domains", whereas other conferences tend to either focus on a specific technology or on a single domain. Broader Impacts: The workshop and resulting artifacts will serve as a centralized repository of algorithms, software, and techniques in a timely emerging area. The workshop will provide participants with an opportunity to gain exposure in the community for their innovative work, and to obtain feedback and guidance from senior members of the research community. It will further help foster a sense of community among these young researchers, by allowing them to create a social network both among themselves and with senior researchers at a critical stage in their professional development. The workshop experience will integrate well with the goals of a software engineering education, as participants are evaluated on their design in addition to the performance of their approaches. To engage a broader audience in this year's event, the PI will expand his previously successful efforts to solicit teams from under-represented universities. IIS CSE Ephraim P. Glinert HPCC architecture Standard Grant 9215 IN network algorithms education vision software engineering HUMAN-CENTERED COMPUTING 7367 7367 University of Notre Dame Blake, M. Brian 6384 0938239 June 1, 2009 The 2nd Workshop on Question Generation This award supports a student session at the 2nd Workshop on Question Generation. The aim of the workshop is to strengthen the Question Generation (QG) research community and create consensus with respect to QG as a shared task. The 2nd Workshop on Question Generation is a 1-day workshop organized into two sets of sessions. In the morning sessions, regular paper presentations on general topics related to QG are scheduled. The afternoon sessions are dedicated to discussions and presentations related to QG in Intelligent Tutoring Systems, one category of shared tasks identified at the previous Workshop on The Question Generation Shared Task and Evaluation Challenge. As part of the afternoon sessions we have a student session. Attending the workshop will greatly impact the scientific awareness and skills of students. It will allow students to become real contributors and leaders of the QG research community by engaging in discussions at the workshop about important research issues in this area of research. The workshop is a great venue for student to be exposed to interdisciplinary research which will definitely strengthen their skills. The broad implications of QG research will increase students? motivation to do research in this area. The students attending the workshop will be among future leading scientists who could boost our national STEM education, a mission of particular interest to the National Science Foundation, due to the importance of QG in learning technologies. We will encourage participation of groups underrepresented in science and engineering, including minorities, women, and persons with disabilities. IIS CSE HPCC Standard Grant 9215 education 9150 Tatiana D. Korelsky University of Memphis TN ROBUST INTELLIGENCE 7495 7495 Arthur Graesser 16000 Rus, Vasile 0937612 April 1, 2009 CAREER: Modeling Time Invariances in Human Motor Coordination for Robot-Assisted Rehabilitation Robot-assisted rehabilitation can enhance and speed motor control recovery following brain injury such as stroke. Most existing rehabilitation approaches and modeling work supporting them focus primarily on the forces required to generate controlled movements, in effect deemphasizing planning that occurs at a kinematic level. A thorough understanding of the role of kinematics in coordination is needed to help diagnose the level at which deficiencies lie, and then focus treatment at that level. In this project, the PI will formulate and experimentally validate unified, coherent models of human motor coordination based on a foundational time-invariant, kinematic mapping between the output space and the control space. Inspired by curvature theory, which is traditionally applied to mechanism synthesis, such a mapping provides an elegant description of motion. With this novel approach, the PI will seek to demonstrate that decoupling the kinematic geometry from the time-based trajectory tracking of arm motion leads to a compact internal model of path planning consistent with experimental data. The work will further demonstrate that a similarly compact internal dynamic model provides an additional layer for an efficient formulation of motor control. The internal kinematic and dynamic models to be developed will enable investigation of optimization mechanisms at the kinematic level, the dynamic level, and through mutual interaction of the two. In order to experimentally validate this model, the PI will develop an actuated, but back-drivable, planar x-y table that has uniform apparent endpoint inertia throughout its workspace. This device will both enable the model validation experiments and serve as a first prototype for a new robot-assisted rehabilitation device that can be used to clinically leverage the knowledge gained from the modeling effort. The PI will collaborate with colleagues in his institution's Physical Therapy Division and Physical Medicine and Rehabilitation Department to develop and evaluate new diagnostic and rehabilitation techniques that implement the new device based on the findings. Broader Impacts: Robot-assisted rehabilitation is likely to have an increasingly significant impact on society as health care costs rise and the number of strokes increases with population aging. Grounded in understanding human motor coordination, this research will impact the fields of locomotion, neurally controlled prosthetics, digital human modeling, and robot control. The investigator will integrate this research into his educational activities with two foci: increasing student understanding of the significance of kinematics and dynamics in human motor coordination; and enhancing student ability to internally visualize physical movement in mechanical systems. These activities will involve modifications to a required undergraduate kinematics course, an elective undergraduate product design course, and two graduate kinematics courses to incorporate the results of the research. Additionally, a seminar course for students from all academic units will be developed to more broadly disseminate the work. In conjunction with four other faculty in the Ohio State College of Engineering, the PI will organize a weeklong engineering summer camp for women and minority high school students. The PI will create two modules for the camp: one related to human motor coordination in which the students will conduct experiments using the x-y table developed in the project; and one related to visualization of motion in mechanical systems. IIS CSE Ephraim P. Glinert Continuing grant HPCC 9218 IN visualization 1187 1045 HUMAN-CENTERED COMPUTING 7367 University of Notre Dame Schmiedeler, James 127337 0937593 June 1, 2009 Support for Participation in the 2009 International Summer School on Planning and Scheduling This award gives travel, housing, and registration-cost support to selected students and other young researchers from U.S. universities for their participation in the International Summer School on Planning and Scheduling, which is affiliated with the 19th International Conference on Automated Planning and Scheduling (ICAPS-09) held September 19-23 in Thessaloniki, Greece. Artificial Intelligence planning and scheduling is relevant to a wide variety of applications such as software engineering, manufacturing, transportation, and robotics. The ICAPS-09 Summer School includes a poster session, where students present their research ideas for commentary by more senior researchers, as well as intensive study of foundational material and the latest research in automated planning and scheduling. The Summer School realizes an integration of research and education in its preparation of emerging scientists. IIS CSE HPCC Standard Grant 9215 MA artificial intelligence University of Massachusetts Amherst education robotics software engineering Douglas H. Fisher ROBUST INTELLIGENCE 7495 Zilberstein, Shlomo 7495 25900 0937586 September 1, 2009 EAGER: Geometric Mapping and Diffusion for 3D Imaging Informatics Established software approaches in mapping and matching of a large collection of multimodality cross-subject data is becoming a bottleneck for the overall work stream and hindering progress in understanding and utilization of large-scale data for scientiÞc discovery. In many scenarios, intrinsic geometric structures embedded in 3D imaging of real-world objects are very effective in mapping individual objects for interpretation of their similarity and disparity. But recent computational techniques are still focused on the extraction and measurement of geometric and physical properties in a single dataset. Global modeling of geometric data and assessment of patterns and relationships of related information within and across large individual datasets are under-explored. A rigorous computational framework that tightly couples geometric mapping and matching is of great importance to accomplish integrative analysis of a variety of underlying relationships in features of contained in large-scale image datasets and advance 3D imaging informatics substantially. This EAGER proposal focuses on potentially transformative research ideas and approaches in Riemannian geometry mapping and geometric diffusion, which are tightly coupled together to establish the accurate mapping and matching across a large number of subjects IIS CSE HPCC 9216 Standard Grant Stephen Griffin MI INFO INTEGRATION & INFORMATICS 7364 7364 Wayne State University Hua, Jing 7916 90727 0937540 July 1, 2009 Travel Support for Graduate Students to Attend ICAPS09 Doctoral Consortium This award gives travel, housing, and registration-cost support to selected doctoral students from U.S. universities for their participation in the Doctoral Consortium of the 19th International Conference on Automated Planning and Scheduling (ICAPS-09) held September 19-23 in Thessaloniki, Greece. ICAPS is the premier conference for research in artificial intelligence planning and scheduling, with relevance to a wide variety of applications such as software engineering, manufacturing, transportation, and robotics. The ICAPS-09 Doctoral Consortium includes a poster session, where students present their research, and a mentoring program that pairs senior scientists with doctoral students. IIS CSE HPCC Standard Grant 9215 CA artificial intelligence robotics software engineering Douglas H. Fisher ROBUST INTELLIGENCE 7495 15000 Howe, Adele 9102 American Association for Artificial Intelligence 7495 0936687 July 1, 2009 EAGER: First Order Decision Diagrams for Relational Markov Decision Processes Decision making under uncertainty in dynamic, structured, and complex environments is a major area of research in AI. Research under this award aims to make novel contributions in this area by developing and expanding a new compact representation known as First Order Decision Diagrams (FODDs)and new learning and planning algorithms based on abstract Markov Decision Processes that have relational structure. The development of such structures, semantics and algorithms is a crucial step towards optimization and planning in complex real-world domains, such as emergency response, product delivery, and other service domains. IIS CSE HPCC Standard Grant 9215 algorithms MA Douglas H. Fisher Khardon, Roni Tufts University ROBUST INTELLIGENCE 7495 7495 77808 0936487 July 1, 2009 Support for Instinctive Computing Workshop Instinctive computing is a computational simulation of biological and cognitive instincts. Instincts profoundly influence how we see, feel, appear, think, and act. If as society we want a computer to be genuinely secure, intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, and even to have primitive instincts. In this crosscut workshop, the PI will explore transformational developments in this area, including the building blocks for instinctive computing systems and potential applications such as security, privacy, human-computer interaction, next generation networks, and product design. The funding will cover the costs for invited outstanding students to present their prototypes and to interact with leading researchers in this area. This workshop will have broader impacts on many disciplines including philosophy, robotics, computer science, cognition science, computer networks, security, information privacy, cybernetics and electrical and computer engineering. It transforms isolated applications into an academic field. It serves as an incubator for young students to explore new approaches from diverse fields. The Program Committee will encourage outstanding undergraduates, especially women and minority students, to participate in the workshop. IIS CSE Standard Grant Carnegie-Mellon University PA simulation human-computer interaction Sylvia J. Spengler security OTHR 0000 robotics privacy INFO INTEGRATION & INFORMATICS 7364 Cai, Yang 12750 7795 0936204 September 1, 2009 Workshop Proposal: Scholarly Evaluation Metrics: Opportunities and Challenges This workshop aims to bring together leading scientists and practitioners in the domains of bibliometrics, informetrics, network and web science, digital libraries, academic policies, and open repositories for a public discussion on the quantitative evaluation of scholarly impact and value of publications. on scientific research. Quantitative evaluation of scholarly impact and value has historically been conducted on the basis of citation data. This approach is not always appropriate or accurate in the fast-paced, open, and interdisciplinary nature of scholarship which is, to a large degree, dependent upon digital data and sources. The workshop is meant to provide guidance and research agendas for further development of metrics for scientific impact and value. The workshop's intellectual merit is found in defining the scientific criteria and objectives that would lead to a more general community-acceptance of various impact metrics. The impact of science as traditionally measured is at the point of fundamental change as the digital research environment grows and attracts more participants. Novel impact metrics, that gain community acceptance, may lead to a more diverse and balanced scientific landscape in which the contributions of a larger and more diverse community are more equally recognized and valued. IIS CSE HPCC 9216 Standard Grant Stephen Griffin IN network Indiana University INFO INTEGRATION & INFORMATICS 7364 Herbert Van de Sompel Johan Bollen Ding, Ying 20756 0936105 January 1, 2009 HCC-Small: Collaborative Research: Design and Evaluation of the Next Generation of E-book Readers. This research will evaluate the potential of a new generation of electronic document readers that present information across multiple displays - a design that anticipates the future availability of fast, bi-stable, display technology. Despite the fact that e-book readers have been available to the general public for several years, paper remains far more popular as a medium for reading and annotating documents. Although electronic devices for reading can provide unique affordances such as a large storage capacity, keyword search, indexing, and some interactivity, they remain unpopular probably because they fail to offer several core affordances of paper such as efficient page-to-page navigation, quick access to multiple documents, and efficient handling of annotations. Starting from an existing proof of concept, this project will design a fully functional prototype that addresses a large spectrum of reading activities that include: reading a book or magazine, lateral reading, and active reading. A set of deployable prototypes will be used to evaluate the potential of the design through a series of longitudinal studies. In producing prototypes of a next generation electronic document reader, this project will systematically study the design parameters that might enhance the reading experience on such devices in a wide variety of scenarios encompassing a diversity of reading activities. It is possible that digital displays will become the predominant technology for consuming text information. However, digital reading devices will be used only if they combine physical design, software infrastructure, and interface features that support a wide variety of reading patterns. Increasing amounts of reading material (both classic and modern) are available through digital distribution. By making it convenient and enjoyable to access this wealth of digital content, this project will spur new interest in reading both for work and pleasure. IIS CSE NY HPCC Standard Grant 9215 William Bainbridge Cornell University HUMAN-CENTERED COMPUTING 7367 7367 Guimbretiere, Francois 348685 0935952 June 1, 2009 Doctoral Consortium and Mentoring of Local Students at the International Conference on Autonomous Agents and Multi-Agent Systems This project supports US student researchers to travel to the Doctoral Consortium and the premier international conference on autonomous agents and multiagent systems. The International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009) will be held in Budapest, Hungary between May 10 and May 15, 2009. The conference provides these students with a platform to present their research results and allows them to participate in valuable discussions that will likely shape the future of this critically important field. The technical program is complemented with an array of workshops, tutorials and other events, as it has in previous years. The wide variety and significance of the topics typically presented at AAMAS, in both the technical program and the workshop program, provides opportunities for students to share, exchange and learn from each other. A Doctoral Mentoring Program is available for Ph.D. students in advanced stages of their research. This program will provide an opportunity for students to interact closely with established researchers, to receive feedback on their work and to get advice on managing their careers. IIS CSE HPCC Standard Grant 9215 Gini, Maria University of Minnesota-Twin Cities MN multi-agent systems Douglas H. Fisher ROBUST INTELLIGENCE 7495 9102 40000 7495 0935360 July 1, 2009 EAGER: Corpus-Based Narrative Semantics This Early-concept Grant for Exploratory Research (EAGER) explores approaches for computational analysis of narrative. Despite the ubiquitous nature of narrative, computational linguists have shied away from research on narrative since the 1970's, viewing analysis of stories and literature as too difficult. The goal of this EAGER project is to show that analysis of narrative is now possible and that its study can also be relevant to the development of practical, web-based systems. The project features the development of a declarative, symbolic representation of narrative, a method for manually analyzing the content units of narrative using this representation, and a computational approach for automatically processing a corpus of narratives to derive structural and content-oriented patterns. For example, a learning model may be developed to identify and describe dilemmas that a character faces or to identify thematic similarity between stories. In the first 12 months of the project, researchers are focusing on the development of the annotation methodology, a collection project for annotations of short fables and parables, and the development of learning algorithms. In the following six months, the researchers plan to apply the work to a larger domain in order to show larger impact -- namely, the processing of news text for tasks such as summarization. The project features a collaboration between computer scientists and an expert in literary theory in order to incorporate modes of analysis that are well-grounded from the perspective of narratology. The researchers will provide a range of resources for further work in the narratology and computational linguistics communities, including the annotated corpus and annotation methodology (called DramaBank) as well as software for annotation and automatic analysis; these will enable both communities to continue a new line of research on literature and other forms of narrative occurring on the web. IIS CSE NY HPCC Standard Grant 9215 algorithms ubiquitous Columbia University Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 9102 McKeown, Kathleen 7495 253888 0935331 May 1, 2009 Video and Audio Streaming of the RESNA 2009 Student Design Competition This is funding to support the Student Design Competition (SDC), which will be part of the 2009 annual RESNA conference to be held June 23-27 in New Orleans. Today, between 40 million and 56 million people in the United States report some type of disability, and this number will likely grow in the coming years as the baby boom generation enters late life. RESNA is the one organization with an international focus that is devoted solely to technology for individuals with disabilities. Comprised of over 1,000 individual and institutional members (researchers, clinicians, manufacturers, suppliers, professionals and end-users of technology devices and equipment), the organization has as its purpose to improve the health and participation of people with disabilities in mainstream society. To this end, RESNA supports individuals engaged in research, development, education, advocacy and the provision of technology through a number of programs and activities, which in addition to the annual conference include a credentialing program for assistive technology practitioners, suppliers and rehabilitation engineering technologists, as well as sponsored projects. RESNA's Technical Standards Board is the U.S. Technical Advisory Group to ANSI, the official U.S. representative to the International Organization for Standardization (ISO), for the development of ISO standards pertaining to assistive technology and other products for persons with disabilities. The RESNA Student Design Competition fosters innovation and creativity with the ultimate goal of producing technology that can contribute to the independence of individuals with disabilities. The first SDC was held in 1980 as part of the inaugural RESNA conference. Since then, over 200 designs have been identified as winning entries, chosen from more than 600 submissions by students from over 115 different universities. SDC entries are required to represent the work of students ONLY, including the design documentation; both undergraduates and graduates are eligible to take part. Many past participants in the event are now leaders in service, research, and educational areas related to technology for people with disabilities. Some past student designs have been patented and are now available commercially. NSF has been a supporter since 2005. This year's funding will enable the SDC to be further expanded and enhanced, so as to include more entries and support for more design teams, especially from minorities, women, and individuals with disabilities. A call for participation has been posted on the conference website, and also distributed electronically to a large number of colleges and universities with engineering and design schools. A team of 5 judges will pre-select entries from up to 10 teams, from which two members each will be invited to attend the conference supported with travel and hotel funds as well as complimentary registration. During a half-day session in which the teams will make presentations before the judges and public audience at the conference, 5 teams will ultimately be selected as the final winners. Judges will have an opportunity to ask questions and make suggestions and recommendations to the design teams. A platform session will be held this year for the second time as part of the SDC, in which the 5 finalists will make presentations to the general conference attendees. All SDC teams invited to the conference will have an opportunity to present their projects in a poster session during the general conference time. More information is available online at http://www.resna.org/conference/index.php. Broader Impacts: The annual RESNA Conference and the Student Design Competition combine to create a forum for interaction between working and experienced rehabilitation engineers and students who are about to enter the field. Unique in its primary focus on undergraduates, the event will provide participants with experience and skills that assist them to be successful in their engineering and design careers, and will further encourage, support and mentor students in various disciplines to become involved in the assistive technology and rehabilitation engineering fields. In an effort to help communicate and disseminate information about this important opportunity to impact the lives of individuals with disabilities, and to increase participant diversity even further both with respect to individuals and universities, NSF funding will be used in part this year for the second time to provide video and audio streaming of the SDC activities that can then be made into podcasts or videos for the RESNA website. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 education VA assistive technology HUMAN-CENTERED COMPUTING 7367 7367 Bailey, Nell Rehabilitation Engineering and Assistive Tech Society of NA 27893 0935087 June 1, 2009 Student Poster Program and Travel Scholarships for International Conference on Machine Learning (ICML) 2009 The project supports graduate student participation in the 26th International Conference on Machine Learning (ICML 2009). Specifically, the project supports travel to the conference for those who might not otherwise be able to attend for financial reasons and organizes a student poster-presentation program that will facilitate one-on-one discussions and other mentoring with the world's leading researchers in machine learning. Students will be exposed to state-of-the-art work by other researchers and will have the opportunity to attend tutorials on material that is not taught at their home institutions. Participating students will receive feedback from senior researchers beyond their institutional and national boundaries. Furthermore, participation in the poster session and conference will help integrate these students into the research community and represents a natural integration of research and education. IIS CSE HPCC Standard Grant 9215 machine learning education MD University of Maryland College Park 25000 ROBUST INTELLIGENCE 7495 9102 Getoor, Lise 7495 Qiang Ji 0934672 September 1, 2009 Narrative-Centered Computing for Childhood Environmental Awareness This project seeks to create a framework for narrative-centered computing (NCC) that will help children reason more effectively about (a) the distributed chains of causation that mediate environmental change, and (b) how they can intervene effectively within those chains. The NCC framework begins with a new mechanism for computational storytelling called spatiotemporal anchoring. Spatiotemporally anchored stories consist not of a linear "filmstrip," but instead of a network of story nodes. Each node depicts a small element of the overall plot, and is anchored to a specific location in space and time. To advance the story, users explore a rich geographical representation of the relevant spatiotemporal locale, discovering story nodes and the interconnections between them. Because nodes can be anchored at variable levels of spatiotemporal resolution and interlinked in non-linear ways, exploring these narratives will help children to develop more nuanced abilities for reasoning about distributed causation and variable scale. These abilities, in turn, will translate into more effective engagement with environmental issues. While focusing on a particular topic in education, this project seeks to develop a new information technology method for enhancing human cognitive abilities in general. A striking feature of our global environmental predicament is the disparity between the breadth of the problem and the limited nature of humanity's current response. This disconnection may reflect underlying limitations on our intuitive cognitive and emotional processes. As organisms that are adapted to "humansized" scales of complexity and causation, we lack effective means for reasoning about the kinds of temporally, spatially, and socially distributed interactions that drive environmental phenomena. This innovative research seeks to address this problem and to overcome our difficulties in reasoning about distributed data by focusing instead upon our substantial ability to connect to stories. The NCC framework also includes novel interaction mechanisms through which users can influence unfolding events in a story world via targeted behaviors in the real world. These mechanisms will allow children to see how their own environmentally relevant patterns of behavior, mediated by intuitively understandable causal mappings, could cause positive or negative changes in a story ecosystem. This feedback between user's actions and the unfolding story will have powerful implications for children's developing sense of environmental responsibility. The data that drives these interaction mechanisms will also provide a natural means of evaluating the effectiveness of this research. As a way of testing and refining the NCC framework, the research will include the creation of a testbed interactive narrative, to be deployed online and as a temporary science museum exhibit. This narrative will use spatiotemporal anchoring along with video and traditional cinematographic techniques to dramatize the interactions that take place within a representative California ecosystem, for example, a marine environment in which sea otters, kelp forests, and sea urchins all interact. The behavioral impact and educational effectiveness of this narrative will be evaluated both via the aforementioned data collection mechanisms as well as through interviews with users. This research will make a significant contribution to Human-Centered Computing by developing a novel approach to embedding complex distributed phenomena in an interactive narrative format. The spatiotemporal anchoring technique developed here will be useful not just in the context of the present environmental system, but in a variety of other domains such as formal pedagogy (e.g., interactive narratives for understanding other STEM topics), social networking, games (e.g., massively multiplayer worlds in which players create story nodes to drive the plot), and personal information architecture (e.g., a geographically-anchored personal life history device). By focusing on children as the target audience, this research will also make substantial contributions to the emerging domain of child-centered computing. In particular, this work will lay the foundation for further investigation into how story and narrative can be used to create computational systems that even very young children can readily utilize. The environmental themes of the system will also conform to both national and California state guidelines for science education, thus giving it the potential to be broadly deployed in formal classroom settings as well as in informal learning contexts such as homes and science museums. IIS CSE HPCC architecture Standard Grant 9215 CA network William Bainbridge education 0116000 Human Subjects networking University of California-Irvine HUMAN-CENTERED COMPUTING 7367 7367 Tomlinson, William 7916 280371 0934509 September 1, 2008 HCC: Assessing Cognitive Function from Interactive Agent Behavior This is a project to develop new methods for scientifically studying and assessing human cognitive function. It will employ sophisticated statistical multimodal data analysis techniques that will fuse contextual, behavioral, and neural information simultaneously obtained from human beings in the process of completing complex batteries of cognitive tasks. The tasks will be presented in the form of customized computer games that are designed to exhibit the crucial aspects of established cognitive assessment tests and at the same time provide a motivating and engaging environment for the subject's interactions with the game and computer agents. The tasks will involve exploiting our existing capabilities of monitoring and controlling certain enjoyable and challenging computer games that involve various combinations of cognitive tasks ranging from working memory and attention to executive functions. Multimodal information fusion will be accomplished by utilizing Bayesian inference techniques and information theoretic data analysis and dimensionality reduction methods. The work to be carried out under this grant aims to develop sophisticated pattern analysis techniques for the purpose of analyzing the fine-grain behaviors of elderly when they are engaged in complex cognitive tasks in the form of computer games. Expected significant scientific findings from the proposed research are two-fold: (1) improved statistical signal processing and pattern recognition algorithms for EEG processing, (2) an enhanced understanding of the interplay of multiple cognitive processes and their neural signatures in EEG during the execution of complex tasks. The approach is innovative in terms of three aspects: (1) an advanced adaptive interaction protocol that modifies the task parameters to maintain maximal sensitivity to cognitive state changes will be employed, (2) novel information theoretic techniques will be developed and utilized for the extraction of maximally discriminative features from EEG measurements for cognitive state estimation and neural activity visualization, (3) the developed closed-loop system will be utilized to study the human-agent interaction in complex cognitive tasks resulting in mathematical models of micro-behavior in realistic evolving environments as opposed to traditional stationary repetitive experimental paradigms. The successful completion of the work will open the way to further collaborative activities in brain interface design, closed-loop collaborative augmented cognition human-agent interfaces for improved performance, and early diagnosis of cognitive decline in elderly. An interdisciplinary research environment will engage the participating graduate students in a multidisciplinary educational setting and will help them develop skills to perform collaborative interdisciplinary research. IIS CSE Continuing grant HPCC 9215 algorithms William Bainbridge MA visualization Northeastern University data analysis HUMAN-CENTERED COMPUTING 7367 computer games pattern analysis 7367 Erdogmus, Deniz 383225 0934052 July 1, 2009 Conference Proposal: Statistical Modeling and Data Analysis for Neural Coding Understanding the nature of the neural code is of intrinsic scientific interest since it brings us closer to understanding the computational basis of intelligence. In addition, technical advances have made it possible to monitor the activity of populations of neurons in human subjects. The volume of data generated by multiple-neuron recordings poses significant challenges for data analysis. The development of new statistical techniques is necessary to provide a meaningful basis for testing hypotheses about the nature of neural coding. One concrete example is in the area of neuronal synchrony. The coordination on a short time scale of neuronal populations has been suggested as the basis for multiple neural processes such as attention and feature binding. However, the co-variation in firing rates among neurons, especially on a short time scale, poses significant practical challenges to specifying appropriate data analysis and statistical methods. This award provides support for an international symposium on the topic of Statistical Modeling and Data Analysis for Neural Coding to be held in conjunction with the International Statistics Institute's biennial meeting at Durban, South Africa, August 2009. This symposium will bring together several statistical experts on neural coding to discuss a range of approaches to this problem. These approaches include both the generation of surrogate data sets and new statistical methods. These statistical methods may also have value for other scientific areas where multiple elements show short time scale temporal co-variation. The symposium is part of a large meeting of statistical experts. In addition to bringing together the symposium speakers to address this issue, the symposium will expose the larger statistical community to these questions and to the new statistical approaches. As the meeting will be happening on the African continent, the meeting provides an opportunity for education and outreach to African university and graduate students. IIS CSE HPCC Standard Grant 9215 education 9150 Kenneth C. Whang STATISTICS 1269 Brown University RI data analysis ROBUST INTELLIGENCE 7495 7495 7556 5000 Bienenstock, Lucien 0932712 September 1, 2009 Applying Virtual Worlds to Ethics Education in Science The literature is replete with examples of scientists' inattention to ethical issues. Too often scientists and other academicians think of ethics as something outside the realm of their concerns; required courses in higher education frequently do not discuss ethical issues at all. In this project the focus is on facilitating ethics education for scientists by utilizing the heretofore untapped power of interactive technologies such as Second Life. The PI argues that the hands-on and exciting nature of virtual worlds is uniquely suited to maximizing the effectiveness of ethics education, by increasing the ethical engagement of students and faculty. Second Life, the most popular virtual world in the academic arena, hosts hundreds of colleges and universities. Yet none of those member institutions have developed sites committed to ethics education. The PI and her team will develop interactive ethics modules in Second Life, for use in the content of graduate-level science courses. To this end, they will assemble a dedicated group of science faculty who teach graduate-level courses and graduate students in the sciences, who will participate in summer workshops in order to design and implement the online ethics modules. Four main features of ethics training particularly conducive to the virtual world, as confirmed in the research literature, will be addressed: the training will be context- or discipline-specific to increase its effectiveness; the modules will emphasize the ambiguity of the ethical context, helping to free participants of the inevitable bias that comes with the awareness of being studied; the hands-on nature of virtual worlds correlates well with the most successful conditions for increasing ethical awareness; because those in leadership positions (faculty, lead researchers, laboratory supervisors, etc.) have the largest impact on the development of ethical behavior in subordinates, the virtual world modules developed will be able to encourage simulations of those interactions, increasing the likelihood of the success of such training. Data collected during the piloting of the modules will be utilized for improving them. The PI team has been carefully selected to include members with diverse expertise that includes teaching ethics courses, training in ethics, teaching science courses, developing effective curricula, analyzing qualitative and quantitative data, and creating content in virtual worlds. Broader Impacts: This project reaches far beyond the walls of West Chester University students and faculty. The partnering organization for the project is Cheyney University of Pennsylvania, the oldest historically black post-secondary institution in the United States. In addition to the diverse nature of the partnering organizations, the results of research and educational efforts will be disseminated through publications, conferences and web-based activities. Due to the global and self-perpetuating nature of Second Life, the completed modules will be available world-wide, far beyond the time-frame of the life of the grant. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 PA education 0116000 Human Subjects EESE 7787 7787 Woolfrey, Joan Carolyn Sealfon Seth Kahn Larysa Nadolny Matthew Pierlott West Chester University of Pennsylvania 299383 0932602 September 1, 2009 G&V: Request for Student Travel Support for 2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling This proposal supports student involvement and attendance at the 2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling. This four day event draws between 200?300 attendees across a broad set of interdisciplinary academic areas including mathematics (geometry, topology, numerical methods, optimization), engineering (design, modeling, simulation) and computer science (graphics, computational geometry). The meeting is a unique, multi-disciplinary event. Whereas most scientific meetings in the above areas are ?stove-piped? by the individual disciplines, the SIAM Activity Group on Geometric Design and the ACM Symposium on Solid and Physical Modeling have an over 30 year history of spanning disciplines. The above event is bringing together scientists and engineers, both from academia and industry, around the themes of geometry and topology and their role in product design, manufacturing, simulation and other applications. The intellectual merit of the proposed activity lies in the educational opportunities and the shared research objectives of the community. By providing travel grants for students studying in these areas, the NSF encourages the students to pursue interdisciplinary work and enhance their academic experiences. Secondarily, the academic subjects covered by this meeting are of vital importance in many areas of critical national need. Work presented at this meeting covers diverse topics ranging from biomedical CAD and biomanufacturing to sustainable design and optimization of next generation materials. The broader impact is found in developing the careers of the student supported by these travel grants and their work in these areas of critical national need. Given the central role geometry and topology play in everything from product design to virtual training systems to advanced manufacturing technology, the proper nurturing of these activities?especially those of an interdisciplinary nature?is vital to producing scientists and engineers who can meet the nation?s needs. IIS CSE HPCC Standard Grant 9215 PA simulation graphics computational geometry Drexel University GRAPHICS & VISUALIZATION 7453 Regli, William 7453 Lawrence Rosenblum 7500 0932277 September 1, 2009 CPS: Small: A Real-Time Cognitive Operating System The objective of this research is to develop a real-time operating system for a virtual humanoid avatar that will model human behaviors such as visual tracking and other sensori-motor tasks in natural environments. This approach has become possible to test because of the development of theoretical tools in inverse reinforcement learning (IRL) that allow the acquisition of reward functions from detailed measurements of human behavior, together with technical developments in virtual environments and behavioral monitoring that allow such measurements to be obtained. The central idea is that complex behaviors can be decomposed into sub-tasks that can be considered more or less independently. An embodied agent learns a policy for actions required by each sub-task, given the state information from sensori-motor measurements, in order to maximize total reward. The reward functions implied by human data can be computed and compared to those of an avatar model using the newly-developed IRL technique, constituting an exacting test of the system. The broadest impact of the project would provide a formal template for further investigations of human mental function. Modular RL models of human behavior would allow realistic humanoid avatars to be used in training for emergency situations, conversation, computer games, and classroom tutoring. Monitoring behavior in patients with diseases that exhibit unusual eye movements (e.g., Tourettes, Schizophrenia, ADHD) and unusual body movement patterns (e.g., Parkinsons), should lead to new diagnostic methods. In addition the regular use of the laboratory in undergraduate courses and outreach programs promotes diversity. IIS CSE HPCC 9216 Standard Grant INFORMATION TECHNOLOGY RESEARC 1640 0116000 Human Subjects TX operating system University of Texas at Austin Douglas H. Fisher ROBUST INTELLIGENCE 7495 computer games COMPUTER SYSTEMS 7354 7354 BIO COMPUTING 7946 7923 Ballard, Dana 569806 7918 0932097 July 1, 2009 Next Generation Data Mining Summit: Dealing with the Energy Crisis, Global Warming, and Transportation Challenges This project supports the participation of researchers in data mining and machine learning, as well as domain experts, at the "Next Generation Data Mining Summit: Dealing with the Energy Crisis, Global Warming, and Transportation Challenges" (NGDM 2009) from September 30 - October 2, 2009 in Baltimore, MD. Through invited talks, competitively-assessed papers, panel and audience discussion, the summit encourages the participants and larger community to cooperatively define data mining problems and share resources so as to advance solutions to long-term sustainability challenges. IIS CSE HPCC Standard Grant data mining machine learning MD Kargupta, Hillol Douglas H. Fisher University of Maryland Baltimore County ROBUST INTELLIGENCE 7495 9217 7495 32753 0931531 July 15, 2009 A Symposium on Combinatorial Search The project is the second in an international symposium series on the topic of combinatorial search. Currently, work in this area appears scattered across many conferences in several fields. The intellectual merit of this project stems from the sharing of new results, ideas, and problems across the many areas in AI and robotics where combinatorial search is used. Broad impact comes not only from this intermixing but from having a single locus of activity for efforts in combinatorial search, one that we expect to become known in the wider community as the place to look when one wants a snapshot of the latest developments in the area. The first symposium will be held as a AAAI workshop in 2008. This second meeting in the series is held just before and in the vicinity of the International Joint Conference for Artificial Intelligence in 2009. NSF funding supports authors of oral and poster presentations, as well as invited speakers. IIS CSE HPCC Standard Grant 9215 artificial intelligence 9150 University of New Hampshire NH robotics Douglas H. Fisher ROBUST INTELLIGENCE 7495 7495 Ruml, Wheeler Sven Koenig Rong Zhou 30971 0931278 September 1, 2009 HCC: Small: The CUbiC CAReS Note-Taker: Enabling Students who are Legally Blind to Take Notes in Class Although the benefits of note-taking in the classroom are widely recognized, there has not been enough research focused on alleviating the difficulties encountered by legally blind and low vision students in their attempts to take notes during lecture. The problem is particularly acute in fast-paced STEM courses. Students who are legally blind typically write by placing their head close to the writing surface. They may be able to use a monocular to see what is being written on a board in the front of the classroom. But monoculars with high magnification also have narrow fields of view, which forces the student to "hunt" for the target at the front of the classroom each time s/he looks up from the writing surface. The repeated delay in switching between the writing surface and the board can make it hard for the student to keep up. In this project, the PI will develop and evaluate a portable Note-Taker device that does not require any adaptation of the existing classroom infrastructure, and which allows visually impaired students to shift their attention between the writing surface and the class presentation without inefficient context switching. The device will employ a Tablet PC, a zooming video camera, and an electronic pan/tilt mechanism, which can all be easily carried in a backpack and set up in a few seconds on any classroom desk. On the Tablet PC's display surface the student will be able both to see a zoomed video of the lecturer's presentation at the front of the classroom in real time, and to take notes with digital ink. The student will be able to adjust the camera's aim at any time by simply tapping on the point of interest in the video window on the display surface of the Tablet PC. The PI's goal is to go beyond mere "accessibility" and to create a device that allows legally blind students to take notes as efficiently as fully sighted students. Development and evaluation of the Note-Taker prototype will be done with the full involvement of legally blind and low vision students on the campus of Arizona State University, under the auspices of the Cognitive Ubiquitous Computing Center for Assistive and Rehabilitative Systems (CUbiC CAReS). The PI hypothesizes that his Note-Taking device will improve the learning of students who employ it in their secondary or post-secondary classrooms to take notes during lectures, and that it will also help those students to review their own notes at a later time, in conjunction with cross-referenced audio and video recordings. Broader Impacts: Difficulties in note-taking are not limited to students with low vision. Students with certain learning disabilities, for example, often also have difficulty taking notes at the pace at which material is presented in the classroom. The PI's Note-Taker will reduce irrelevant stimuli, thereby making it easier for such students to successfully absorb, record, and ultimately understand the material presented in the classroom. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 ubiquitous Arizona State University AZ 0116000 Human Subjects vision HUMAN-CENTERED COMPUTING 7367 Black, John 7923 Sethuraman Panchanathan Gaurav Pradhan 434658 0930677 June 1, 2009 ACM Creativity and Cognition Graduate Symposium This is funding to support a Graduate Student Symposium (workshop) for about 12 promising graduate students, along with a panel of distinguished research faculty mentors, which will take place in conjunction with the 2009 ACM Creativity & Cognition Conference (CC 2009), to be held October 27-30, 2009, at the Berkeley Art Museum in Berkeley, California. ACM's Creativity & Cognition Conference series began in 1993, and has evolved into a lively multidisciplinary event combining research and practice. As computers become pervasive in all of society, there is a growing need to encourage interaction between the disciplines of computer and information technology and the disciplines of digital arts, design, and cognition. Many envision that interactions across these disciplinary boundaries are fundamental to research advances. Thanks in part to this conference series, rigorous research is expanding as theoretic foundations are emerging and goals become more well-defined. Successful practice manifests itself in a growing array of creativity support tools for discovery and composition by software and other engineers, diverse scientists, product and graphic designers, architects, new media artists, musicians, educators, students, and many others. Creativity & Cognition 2009 is focused on the theme of everyday creativity: shared languages and collective action. Involving young researchers during their graduate education is a key opportunity to seed and encourage the interactions the conference seeks to nurture. Thus, the primary goal of the Graduate Student Symposium is to expand the participation of young researchers pursuing graduate degrees in areas with an emphasis on creativity and cognition, by giving their work wider exposure in the community, by helping to foster a sense of community among them, and by providing them with an opportunity to obtain feedback and guidance from senior members of the research community in an interactive and supportive environment. Student participants will meet and discuss their ideas with each other and with a panel of experienced researchers and practitioners, the objective being to offer them fresh perspectives on their current work. The student participants will take part in all aspects of the conference; descriptions of their research projects will be published in the conference proceedings and posted on the conference web site. Broader Impacts: This workshop will substantially increase participation in the Creativity & Cognition 2009 conference by young researchers pursuing graduate degrees in fields contributing to understanding creativity and cognition. The event will increase the exposure and visibility of these young researchers' ideas within the community. PhD and MFA students from all disciplines concerned with creativity and cognition will be encouraged to apply. Thus, links will be created across these disciplines, and a sense of community will be established among the next generation of researchers. In selecting the student participants, the PI and the members of the organizing committee will consider factors of institutional balance (e.g., ensuring that there are not too many students from one institution, and that institutions not normally represented are included), as well as diversity across traditionally underrepresented groups. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 education NC HUMAN-CENTERED COMPUTING 7367 University of North Carolina at Charlotte 7367 Latulipe, Celine 22378 0930158 June 1, 2009 Constraint Programming Tutorial Although Constraint Programming (CP) plays an increasingly important role in optimization and modeling, very few U.S. universities offer training in the field. In particular, there is limited awareness in the U.S. operations research (OR) community of the benefits of combining OR and CP, even though several commercial and non-commercial solvers now make it possible to do so. A tutorial is therefore designed for AI and particularly OR people who want to learn what CP is all about. It presupposes no background in CP. Top people in the field will serve as instructors. This grant provides financial support to PhD students who want to attend the tutorial, and provides modest support for instructor participation. The CP tutorial takes place on May 27-28, 2009 and precedes the main conference, CPAIOR on 29-31 May, 2009 at the Tepper School of Business, Carnegie Mellon University (http://web.tepper.cmu.edu/CPAIOR09). The tutorial represents a valuable integration of research and education, and the tutorial and conference are likely to increase the visibility of constraint programming (CP) in the United States. IIS CSE HPCC Standard Grant 9215 Carnegie-Mellon University PA education Douglas H. Fisher ROBUST INTELLIGENCE 7495 15000 7495 Hooker, John 0929989 June 1, 2009 WORKSHOP: VL/HCC'09 Doctoral Consortium: Democratizing Access to Computational Tools This is funding to support a Doctoral Consortium (workshop) for about 10-14 promising graduate students, along with a panel of 4-5 distinguished research faculty mentors, which will take place in conjunction with the 2009 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2009), to be held September 20-24, 2009, in Corvallis, Oregon, and sponsored by the IEEE Computer Society. The long-running VL/HCC series occupies a unique niche among HCI and Programming Language conferences, in that it focuses specifically on how to help humans successfully develop and use software. The diversity and ubiquity of people's goals, interactions, and concerns with information systems is continually increasing. Not only does interactive computer software permeate many individuals' working lives, people commonly rely on computing and information systems for leisure and home activities as well. As a result, end users now expect considerable flexibility and control in their interactions with computer software. For many, it is no longer sufficient to consume the packaged software and scripted tasks developed by the professional software industry; they now must produce their own computational solutions to a wide variety of problems, including spreadsheet models, web sites, educational media and simulations, automated business procedures, and visualizations. To produce such software, even for domain-specific problems, end users must look beyond surface-level interaction with computers and acquire the conceptual models and skills of computational thinking. But current advances toward computational thinking by end users are not evenly distributed across all segments of the population. Thus, this year's VL/HCC Doctoral Consortium, the seventh to be funded by NSF in this series, will focus on expanding the benefits of computational thinking to diverse populations. Ensuring that designers of computational languages and tools consider the needs of populations historically overlooked in information technology will increase the chance that these individuals/groups are able to learn and use the more powerful tools that are fast becoming essential to information literacy. At the same time, such efforts may lead researchers to identify new software construction metaphors and techniques that increase the usability of their languages and environments more generally. The workshop's primary goal is to stimulate graduate students' and other researchers' thinking about how varying communication media, representations, and problem-solving support affect end users' willingness and ability to access, manipulate, and program solutions to their work or everyday problems. What are the special needs of disadvantaged populations? How can computational problem-solving tools and devices be designed to meet these needs? The workshop will bring together and build community among young researchers working on different aspects of these problems from the perspectives of diverse fields including computer science, the social sciences, and education. It will guide the work of these new researchers by providing an opportunity for experts in the research field (as well as their peers) to give them advice, in that student participants will make formal presentations of their work during the workshop and will receive feedback from a faculty panel. The feedback is geared to helping students understand and articulate how their work is positioned relative to other human-computer interaction research, whether their topics are adequately focused for thesis research projects, whether their methods are correctly chosen and applied, and whether the results are appropriately analyzed and presented. As in prior years the VL/HCC 2009 Doctoral Consortium will be part of the regular conference program. A 2-page extended abstract of each participant's work will be published in the conference proceedings. Broader Impacts: The workshop will help shape ongoing and future research projects aimed at alleviating a pressing problem of relevance to a great many people within our society. This event will promote discovery and learning, by encouraging the student researchers to explore a difficult and challenging open problem, through involvement of a panel of well-known researchers whose task is to provide constructive feedback, and through inclusion of other conference participants who will also learn from and provide additional feedback to the students and to each other. The PI and the members of the organizing committee will make special efforts to attract a diverse and interdisciplinary group of student participants, with special attention paid to recruitment of minorities and females. The PI expects that most of the students supported by this award will come from U.S. universities but as in past years, due to the highly international make-up of the research community, a few non-U.S. students may be invited to participate as well. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 University of Washington WA human-computer interaction education HUMAN-CENTERED COMPUTING 7367 7367 14880 Ko, Andrew 0929988 September 1, 2008 COMET: An Efficient and Scalable Trajectory Data Management System The use of location-aware devices, such as cell phones with GPS or objects with RFID (Radio Frequency Identification) tags, is exploding in a number of emerging spatio-temporal applications. Traditional database management systems (DBMS) are not designed to handle such applications, especially if the application requires managing a large number of moving objects. The goal of the COMET (Continuous Management of Evolving Trajectories) project is to design, implement, and build a database management system for managing large repositories of continuously evolving trajectories data sets. Since in these environments location updates are issued continually, the DBMS must support extremely efficient methods for dealing with updates. In addition, to allow querying on previous locations of the moving objects, the DBMS must keep track of past trajectories. As time passes these trajectories continue to increase in length, and with large and often increasing number of moving objects, the database size can increase dramatically. Consequently, the backend DBMS must deploy scalable techniques to deal with increasing data sizes, and increasing number of mobile objects. The key focus of the COMET project is on developing efficient and scalable methods for querying on past, present, and future locations of moving objects, and on scalable trigger mechanism in this environment. The expected results of this project may have a strong impact on emerging notification-based applications, such as emergency response systems, in which critical data needs to be disseminated to a physical mobile user based on the user's current and changing spatial location. The project will also train a number of graduate and undergraduate students. The project Web site (http://www.eecs.umich.edu/~jignesh/comet) will be used for making the COMET software, all developed applications, and real user movement data freely available to a broad research community. IIS CSE HPCC 9216 Standard Grant 9218 INFORMATION & KNOWLEDGE MANAGE 6855 WI database University of Wisconsin-Madison Patel, Jignesh Frank Olken 65895 0929851 September 1, 2009 Space Time Place Workshop Proposal This proposal seeks support for US researchers participation in a workshop/conference to be held in association with the Third International Conference on Remote Sensing Archaeology (http://www.spacetimeplace2009.org). The proposed symposium aims to take stock of the emerging trends in Remote Sensing and also explore promising new research pathways as related to multi-diciplinary interaction in emerging topical areas made possible by large scale geospacial and temporal data stores. The agenda is being designed by the The Electronic Cultural Atlas Initiative (ECAI) (www.ecai.org), a research unit of International and Area Studies at the University of California, Berkeley . In the last twelve years, ECAI and other scholarly groups have pursued the capabilities in remote sensing and digital application in archaeology. Concepts like space, place, landscape, time and "context" plus technological inputs in mapping and management of heritage are becoming conceptual frameworks in recent trends in the field of archaeology and related areas. The study is inherently international in scope and such conferences as this are integral in moving scholarship forward. IIS CSE HPCC 9216 Standard Grant Stephen Griffin CA University of California-Berkeley 5976 INFO INTEGRATION & INFORMATICS 7364 Lancaster, Lewis 5919 24882 0929705 December 15, 2008 HCC-Medium: Collaborative Research: Multimodal Capture of Teamwork in Collocated Collaboration The design and use of information systems to support the collaborative activity of collocated teams in dynamic, high-risk scenarios remains a challenge. This project will develop novel methods to more efficiently capture and communicate this activity in environments that currently rely on human observation, verbal communication, and collective memory. More efficient teamwork capture processes will enable both larger-scale collection (which supports retrospective analysis that is critical for improved training and technology design) and contemporaneous collection (which provides real-time feedback to workers to assist in error detection). To achieve these goals, domain-specific knowledge and probabilistic reasoning will be used to identify patterns of work and communication. The representative domain of trauma resuscitation is ideal for this work since the roles and tasks of players are well-defined and the flow of work follows a general schema regardless of the patient?s injuries. Because of the complexity of this environment, manual tracking of all activities using video recordings requires repeated review and is very time-consuming even for experienced observers. A computer system will be developed that uses video analysis to determine the location of each player, motion analysis to track their movements, and speech recognition targeted at a limited lexicon to identify their communication. Using these inputs, a probabilistic reasoning model will be constructed that correlates data from the environment with a domain-specific model of teamwork. The tagged recording of the resuscitation event will be available in real time during the event as well as post-event for analysis. The scientific importance of this work is in the need to tag these video observations. Many forms of videos are of repetitive behaviors, whether in surveillance applications, work situations, or other uses. In all such cases, applying a grammar to the video, and matching actions and sounds to that grammar, has the possibility of greatly simplifying work analysis, which is the critical phase in the development computer support for complex, high-risk human activities. The proposed approach will develop novel algorithms and methods for: (i) person and resource tracking in crowded collaborative environments; (ii) recognition of human activity based on fusion of unreliable data from multimodal sensors and a model of the process being recorded; and (iii) reasoning about human activities at different time scales based on heterogeneous technologies (Hidden Markov Models, Bayesian Nets, and Petri Nets) that mutually interact for activity and event detection. Moreover, the methods will be developed and evaluated in a clinical environment that currently uses limited information technology. Broader Impacts. This work will also provide the foundation for implementing decision aids in environments such as trauma resuscitation and related medical domains that lack effective methods for instrumented tracking of teamwork. Trauma care is a significant health care crisis and any improvements in resuscitation processes will save lives. IIS CSE HPCC Standard Grant 9215 algorithms DC 150000 HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald Burd, Randall Children's Research Institute 0929125 April 1, 2009 Travel support for the OSS 2009 Doctoral Consortium This award supports travel and attendance by US doctoral students to the Doctoral Consortium at the 5th International Conference on Open Source Systems (OSS 2009) to be held June 3, 2009. OSS 2009 brings together a wide range of researchers from the fields of software engineering, information systems, social science, and information science. This conference is a critical link between researchers who study the formal practices of software engineering and those who study the less formal practices of the Open Source Software movement. The focus of the OSS 2009 Doctoral Consortium is the intellectual content of the students? doctoral dissertations. These dissertations represent state-of-the-art research in the study and development of complex software systems. The Doctoral Consortium will help initiate an intellectual community in an emergent research area. Students and faculty are anticipated to be a diverse group on several dimensions (nationality, scientific discipline, gender, institutional affiliation, under-represented minority status), therefore participation in the consortium will broaden the students? intellectual and social perspectives at a critical stage in their professional development. IIS CSE Crowston, Kevin Syracuse University NY HPCC Standard Grant 9215 software engineering HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald 19994 0926376 July 15, 2009 COLLABORATIVE - RAPID: Information processing under stress: A study of Mumbai police control room first responders during terrorist attacks of 11/26/08 The Mumbai attacks of 11/26/08 were one of the worst terror incidents in India. In the modern world, the security of citizens depend upon the success of anti-terrorist operations by security forces in all major countries around the world, not just India. This proposal focuses on a critical operational unit in successful anti-terror response - the police control room. These attacks provide a unique and time-limited opportunity to examine and understand the information processing performed by information first responders under conditions that are far from equilibrium. Intellectual merit Though critical to emergency response, little is known about the information processing limitations of trained informational first responders and their supporting technology during a severe, novel and unexpected disaster. Early findings suggest that exposure to terrorist attacks impairs memory performance and raises anxiety levels. This study will inform theory about the emergence of information processing limitations in such situations. The simultaneous terrorist operations at multiple sites in this incident provide an opportunity to understand the consequences of multiple streams of information on information processing in these situations. Unlike the 9/11 attacks in the US, the 11/26 attacks were an ongoing, drawn out event with the situation unfolding in real time, creating unique decision-making challenges. Further, this proposal looks at information processing within police control rooms in developing countries, an under-studied area with implications for security for US citizens. The project will be completed in collaboration with police research organizations in India. Structural equation modeling will be used to analyze paths and causal relationships from the data. Analysis of variance and covariance, and t-tests would be used. Broader impact The broader impact of the research will be to improve society's ability to understand the impact of extreme stress and time pressure on the dimensions of performance of informational first responders. This will help in the development of organizational and technical support systems for processing front-line information to improve our ability to withstand terror attacks and other forms of severe, unexpected disasters. IIS CSE HPCC 9216 Lawrence Brandt Standard Grant security FL 7484 INFO INTEGRATION & INFORMATICS 7364 University of South Florida Agrawal, Manish 19894 0926371 July 15, 2009 COLLABORATIVE- RAPID: Information processing under stress: A study of Mumbai police control room first responders during terrorist attacks of 11/26/08 The Mumbai attacks of 11/26/08 were one of the worst terror incidents in India. In the modern world, the security of citizens depend upon the success of anti-terrorist operations by security forces in all major countries around the world, not just India. This proposal focuses on a critical operational unit in successful anti-terror response - the police control room. These attacks provide a unique and time-limited opportunity to examine and understand the information processing performed by information first responders under conditions that are far from equilibrium. Intellectual merit Though critical to emergency response, little is known about the information processing limitations of trained informational first responders and their supporting technology during a severe, novel and unexpected disaster. Early findings suggest that exposure to terrorist attacks impairs memory performance and raises anxiety levels. This study will inform theory about the emergence of information processing limitations in such situations. The simultaneous terrorist operations at multiple sites in this incident provide an opportunity to understand the consequences of multiple streams of information on information processing in these situations. Unlike the 9/11 attacks in the US, the 11/26 attacks were an ongoing, drawn out event with the situation unfolding in real time, creating unique decision-making challenges. Further, this proposal looks at information processing within police control rooms in developing countries, an under-studied area with implications for security for US citizens. The project will be completed in collaboration with police research organizations in India. Structural equation modeling will be used to analyze paths and causal relationships from the data. Analysis of variance and covariance, and t-tests would be used. Broader impact The broader impact of the research will be to improve society's ability to understand the impact of extreme stress and time pressure on the dimensions of performance of informational first responders. This will help in the development of organizational and technical support systems for processing front-line information to improve our ability to withstand terror attacks and other forms of severe, unexpected disasters. IIS CSE NY HPCC 9216 0104000 Information Systems Lawrence Brandt Standard Grant security INFO INTEGRATION & INFORMATICS 7364 SUNY at Buffalo Rao, H. Raghav 30100 0926148 September 1, 2009 Collaborative Research: RoboBees: A Convergence of Body, Brain and Colony "This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)." RoboBees: A convergence of body, brain, and colony J. Ayers, G. Barrows, D. Brooks, S. Combes, L. Mahadevan, G. Morrisett, R. Nagpal, S. Ramanathan, G.-Y. Wei, M. Welsh, R.J. Wood, T. Zickler This project entails the creation of a coordinated colony of robotic bees, RoboBees. Research topics are split between the ?body?, ?brain?, and ?colony?. Topics within the ?body? include all aspects of the flight apparatus, propulsion, and power systems. The ?brain? involves research on the electronic nervous system equivalent of a bee?s brain including circuits for sensing and decision-making. Finally, research within the ?colony? entails communication and control algorithms that will enable performance well beyond the capabilities of an individual. Each of these research areas is drawn together by the challenges of recreating various functionalities of natural bees. One such example is pollination: Bees coordinate to interact with complex natural systems by using a diversity of sensors, a hierarchy of task delegation, unique communication, and an effective flapping-wing propulsion system. Pollination and other agricultural tasks will serve as challenge thrusts throughout the life of this project. Such tasks require expertise across a broad spectrum of scientific topics. The research team includes experts in biology, computer science, electrical and mechanical engineering, and materials science, assembled to address fundamental challenges in developing RoboBees. Beyond pollination and assisted agriculture, coordinated robotic insects will have substantial impact upon rescue workers for search and rescue and hazardous environment exploration applications. High fidelity environmental monitoring, traffic monitoring, and mobile sensor networks are just a few examples of the future impact of coordinated RoboBees. Since each RoboBee component must be developed from scratch, technological fallout will be prevalent throughout research on the body, brain, and colony. This new technology and the exciting and tangible nature of robotic bees present a tremendous opportunity to catalyze young minds and encourage their participation in science and engineering. An integral part of this program is the development of a museum exhibit, in partnership with the Museum of Science, Boston, which will explore the life of a bee and the technologies required to create RoboBees. ¬ For more information, please visit: http://robobees.seas.harvard.edu IIS CSE HPCC Standard Grant 9218 algorithms MA Kenneth C. Whang Harvard University 0000912 Computer Science Wood, Robert Radhika Nagpal 6890 ITR EXPEDITIONS 6894 J. Gregory Morrisett Gu-Yeon Wei 9301955 0925751 September 1, 2009 Collaborative Research: RoboBees: A Convergence of Body, Brain and Colony "This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)." RoboBees: A convergence of body, brain, and colony J. Ayers, G. Barrows, D. Brooks, S. Combes, L. Mahadevan, G. Morrisett, R. Nagpal, S. Ramanathan, G.-Y. Wei, M. Welsh, R.J. Wood, T. Zickler This project entails the creation of a coordinated colony of robotic bees, RoboBees. Research topics are split between the ?body?, ?brain?, and ?colony?. Topics within the ?body? include all aspects of the flight apparatus, propulsion, and power systems. The ?brain? involves research on the electronic nervous system equivalent of a bee?s brain including circuits for sensing and decision-making. Finally, research within the ?colony? entails communication and control algorithms that will enable performance well beyond the capabilities of an individual. Each of these research areas is drawn together by the challenges of recreating various functionalities of natural bees. One such example is pollination: Bees coordinate to interact with complex natural systems by using a diversity of sensors, a hierarchy of task delegation, unique communication, and an effective flapping-wing propulsion system. Pollination and other agricultural tasks will serve as challenge thrusts throughout the life of this project. Such tasks require expertise across a broad spectrum of scientific topics. The research team includes experts in biology, computer science, electrical and mechanical engineering, and materials science, assembled to address fundamental challenges in developing RoboBees. Beyond pollination and assisted agriculture, coordinated robotic insects will have substantial impact upon rescue workers for search and rescue and hazardous environment exploration applications. High fidelity environmental monitoring, traffic monitoring, and mobile sensor networks are just a few examples of the future impact of coordinated RoboBees. Since each RoboBee component must be developed from scratch, technological fallout will be prevalent throughout research on the body, brain, and colony. This new technology and the exciting and tangible nature of robotic bees present a tremendous opportunity to catalyze young minds and encourage their participation in science and engineering. An integral part of this program is the development of a museum exhibit, in partnership with the Museum of Science, Boston, which will explore the life of a bee and the technologies required to create RoboBees. ¬ For more information, please visit: http://robobees.seas.harvard.edu IIS CSE HPCC Standard Grant 9218 algorithms MA Northeastern University Kenneth C. Whang 0000912 Computer Science 6890 ITR EXPEDITIONS Ayers, Joseph 698045 6894 0925663 April 1, 2009 Supporting Students Attending the International Conference on User Modeling, Adaptation, and Personalization This is funding to support travel by 6-9 students currently enrolled in PhD programs in the United States to present their accepted papers and posters, and to take part in the Doctoral Consortium, at the First International Conference on User Modeling, Adaptation, and Personalization (UMAP 2009), to be held in Trento, Italy, on June 22-26, 2009. UMAP, the premier user modeling conference in the world, is a merger of the long-running and successful biennial conference series on User Modeling (UM, 1986-2007) and the Adaptive Hypermedia and Adaptive Web-Based Systems (AH, 2000-2008); the former provided a forum in which academic and industrial researchers from the many fields involved in user modeling research (artificial intelligence, education, psychology, linguistics, human-computer interaction, and information science) could exchange their complementary insights on user modeling issues, while the latter provided a forum for dissemination of adaptive technology for hypermedia and other web-based systems. User modeling has been found to significantly enhance the effectiveness and usability of software systems in a variety of areas. A user model is an explicit representation of properties of a particular user; a system that constructs and consults user models can adapt diverse aspects of its performance to individual users. Applications for user modeling range from electronic commerce and intelligent learning environments to health care and assistive technologies. Relevant platforms for user modeling include mobile and wearable systems, and smart environments, as well as individual desktop systems, groupware, adaptive hypermedia, and other web-based systems. The UMAP 2009 Doctoral Consortium will provide a unique opportunity for PhD students partway through their dissertation research to receive valuable feedback from top researchers in the field. Participants will present their work to the conference (15 minutes, which may include a short demo if appropriate), with additional time allocated to questions and for discussion (15 minutes). Both during the question/discussion period and in subsequent informal interactions, committee members and other conference participants will provide constructive comments on the student's work and attempt to address the aspects of the work on which s/he requested advice. Student papers will be published in the adjunct proceedings of the conference, while summaries will be included in the proceedings. In allocating NSF funds to participants, the Doctoral Consortium Co-chairs will give preference to students who can reasonably prove financial hardship, and they will also strive for diversity (gender, racial, ethnic, disabilities, institutional, etc.) among the selected students. Broader Impacts: Bringing young and creative researchers to UMAP 2009 will help advance an important and socially valuable research field. NSF funding will significantly impact the careers of the next generation of User Modeling researchers, by enabling a number of them to take part in an important event they would otherwise have to miss. The students will have an opportunity to gain wider exposure in the community for their innovative work, and to obtain feedback and guidance from senior members of the research community. Participation will also help foster a sense of community among these young researchers, by allowing them to create a social network both among themselves and with senior researchers at a critical stage in their professional development. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 network human-computer interaction artificial intelligence education 9150 University of Delaware DE HUMAN-CENTERED COMPUTING 7367 7367 Carberry, Mary 14800 0925660 April 1, 2009 Workshop at the ACM CHI 2009 Conference: Human-centered Computing in International Development What does the desktop metaphor mean for communities that do not use or value desks? How does the QWERTY keyboard perform in communities whose language has no "Q", "W", "E", "R", "T", nor "Y"? What is the point of a personal computer in a context where technologies are not held for a person but are shared by a whole community? Computer applications and PC appliances have traditionally been designed by and for Western high-income populations. But today the Internet and internet-enabled computers have become a truly global phenomenon, reaching out to many of the most remote and marginalized communities. This workshop will be held on Saturday and Sunday, April 4-5, as an official part of the ACM 2009 Conference on Human Factors in Computing Systems (CHI 2009) in Boston. The workshop will explore interaction design in the context of international development, and in particular will address interaction design for parts of the world that are often marginalized by current systems and applications designers. The PI believes that to extend the boundaries of existing practice in designing information and communication technologies, design should be with, for, and of the marginalized communities he plans to set centre stage in the workshop discussions. The workshop will provide a forum to exchange experiences, to explore differences between developed and developing world contexts, to develop new partnerships, and to learn from each other's experiences. NSF funding will enable approximately ten international delegates to attend from low-income countries, from which participation would otherwise be impossible; without the active involvement of these participants, the outcomes of the workshop would be far less useful and effective. The annual CHI conference, sponsored by the Association for Computing Machinery's Special Interest Group on Computer-Human Interaction (ACM SIGCHI), is a leading international forum for the presentation and discussion of human-computer interaction (HCI) research and practice. The conference is attended by over 2,000 professionals from around the world. Research papers presented at the conference are heavily-refereed and widely cited; they constitute some of the most scientifically respected research publications in the field of HCI. Broader Impacts: A review of the HCI literature exposes the broad intellectual gap that exists between problems of computer interaction and studies of the development impact of computer technologies in low-income countries. The PI expects that this workshop, building on the outcomes of two previous meetings at CHI 2007 (funded in part by NSF) and CHI 2008 (which led to establishment of a website http://hci4d.org that provides a platform for exchange of information, ideas, and experiences in this domain), will be instrumental in developing additional intellectual momentum around these topic areas. Position papers generated as part of the workshop and summary articles will be published in a special issue of the journal Information Technology and International Development, as well as in ACM Interactions, Interfaces magazine, and Usability News. The PI is also working on a story-based template, which will provide an online repository for reporting findings and observations that can be easily searched and shared. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 Carnegie-Mellon University PA human-computer interaction HUMAN-CENTERED COMPUTING 7367 7367 21000 Kam, Matthew 0925597 July 1, 2009 Promoting Inter/Multidisciplinary Education and Research, The International Joint Conference on Bioinformatics, Systems biology and intelligent computing Bioinformatics, Systems Biology and Intelligent Computing are synergistic interactive research disciplines that hold great promise for the advancement of research and development in complex biomedical systems, agricultural, environmental, pharmaceutical and medical sciences as well as public health, drug design, genomics and so on. Research and development in these two are impacting the science and technology of fields such as medicine, food production, forensics, agriculture, pharmacy, engineering and bio-mathematical and biophysical sciences by advancing fundamental concepts in molecular biology, in genomics and in medicine, by helping us understand living organisms at multiple levels, by developing innovative implants and prosthetics, by new medical image technologies, and by improving tools and techniques for the detection, prevention and treatment of diseases. The International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS 2009) provides a common platform for the cross fertilization of ideas, and to help shape knowledge and scientific achievements by bridging these two very important and complementary disciplines into an interactive and attractive forum. Keeping this objective in mind, IJCBS 2009 is aimed at promoting inter/multidisciplinary education and research by offering a number of keynote, tutorial and cutting-edge research lectures. IJCBS 2009 will host special sessions focusing on the interdisciplinary and multidisciplinary education and research in order to foster collaboration between the bioinformatics, systems biology and intelligent computing domains in the USA and China. IJCBS09 will provide travel support for a number of selected USA students and scholars to attend the event. IIS CSE HPCC 9216 Standard Grant 10000 Sylvia J. Spengler education TX bioinformatics INFO INTEGRATION & INFORMATICS 7364 7364 Texas A&M University-Commerce Yang, Jack Chaoyang (Joe) Zhang Mary Yang 0925511 June 1, 2009 Collaborative Research - Visual Analytics Science and Technology Challenge This award supports the Visual Analytics Science and Technology (VAST) Challenge Workshop 2009. This workshop is bringing together individuals who participate in the VAST 2009 Challenge Competition taking place from February 2009 to July 2009 to present their results and discuss their experiences. Research on visual analytics is very timely, and it is clearly attracting a lot of interest in the research community. VAST 2009 is expected to contribute to the following goals: (1) focus the community on working on useful and realistic visual analytics problems, (2) compare and contrast systems developed or used by the participants, and (3) gather lessons learned from testing novel methodologies and metrics for evaluation. The workshop will bring visual analytics developers and evaluators together for a full day to present the results of the Challenge, and discuss lessons learned about evaluation. It will expose many researchers -- in particular students -- to the challenges of evaluating complex systems for use by analysts. Papers summarizing the challenge and workshop results will be widely disseminated, including the VAST 2009 Website (http://www.cs.umd.edu/hcil/VASTchallenge09/). IIS CSE HPCC 9216 Standard Grant Maria Zemankova MA INFO INTEGRATION & INFORMATICS 7364 University of Massachusetts Lowell visual analytics Grinstein, Georges DATA INTEROPERABILITY NETWORKS 7701 24717 0925482 June 1, 2009 Collaborative Research - Visual Analytics Science and Technology Challenge Workshop This award supports the Visual Analytics Science and Technology (VAST) Challenge Workshop 2009. This workshop is bringing together individuals who participate in the VAST 2009 Challenge Competition taking place from February 2009 to July 2009 to present their results and discuss their experiences. Research on visual analytics is very timely, and it is clearly attracting a lot of interest in the research community. VAST 2009 is expected to contribute to the following goals: (1) focus the community on working on useful and realistic visual analytics problems, (2) compare and contrast systems developed or used by the participants, and (3) gather lessons learned from testing novel methodologies and metrics for evaluation. The workshop will bring visual analytics developers and evaluators together for a full day to present the results of the Challenge, and discuss lessons learned about evaluation. It will expose many researchers -- in particular students -- to the challenges of evaluating complex systems for use by analysts. Papers summarizing the challenge and workshop results will be widely disseminated, including the VAST 2009 Website (http://www.cs.umd.edu/hcil/VASTchallenge09/). IIS CSE HPCC 9216 Standard Grant Maria Zemankova MD University of Maryland College Park INFO INTEGRATION & INFORMATICS 7364 Plaisant, Catherine 9102 24999 visual analytics DATA INTEROPERABILITY NETWORKS 7701 0925357 March 15, 2009 Workshop: Doctoral Consortium for ASSETS 2009 This is funding to support a consortium (workshop) of approximately 12 promising doctoral students from the United States and abroad, along with about 5 distinguished research faculty. The event will take place on Sunday, October 25, immediately preceding and in conjunction with the Eleventh ACM SIGACCESS Conference (ASSETS 2009), to be held Monday-Wednesday, October 26-28, 2009, in Pittsburgh, and sponsored by the ACM Special Interest Group on Computers and Accessibility. The ASSETS conference is the premier forum for presenting research results and innovations in software and technology designed to address the special needs of people with disabilities of all kinds. Researchers and developers from both academia and industry around the world will meet to exchange ideas and present reports on the latest work relating to speech, motor, hearing, and vision impairments, cognitive limitations, emotional and learning disabilities, and aging. A key component of building this community is through its youth. The ASSETS 2009 doctoral consortium will provide an opportunity for graduate students from diverse backgrounds (computing, engineering, psychology, architecture, etc.) to come together and explore their research interests in an interdisciplinary workshop, under the guidance of the PI and a panel of other distinguished experts in the field, so that they can see the broader spectrum of research and development approaches to assistive technologies and universal usability, and also experience the community in which they can pursue their endeavors. Student participants will make formal presentations of their work during the consortium, and will receive constructive feedback from the faculty panel. The feedback is designed to help students understand and articulate how their work is positioned relative to related research, whether their topics are adequately focused for thesis research projects, whether their methods are correctly chosen and applied, and whether their results are appropriately analyzed and presented. Thus, the consortium will help shape ongoing and future research projects aimed at assistive technologies and universal access, will promote scholarship and networking among new researchers in this emerging interdisciplinary area, and will expose these promising young researchers to a larger community. A session has been set aside during the main conference to allow doctoral consortium participants to present to the entire conference, and one student from the doctoral consortium will be selected to deliver the closing plenary presentation. The organizers will take special steps to promote participation from institutions with relatively large numbers of students from underrepresented groups. More information is available online at http://www.sigaccess.org/assets09. Evaluation of the consortium will be conducted, and the results made available to the organizers of future such events. Broader Impacts: The doctoral consortium will help expand the participation of young researchers pursuing graduate studies in this field, by providing them an opportunity to gain wider exposure in the community for their innovative work and to obtain feedback and guidance from senior members of the research community. It will further help foster a sense of community among these young researchers, by allowing them to create a social network both among themselves and with senior researchers at a critical stage in their professional development. Because the students and faculty constitute a diverse group across a variety of dimensions, including nationality/cultural and scientific discipline, the students' horizons are broadened to the future benefit of the field. IIS CSE Ephraim P. Glinert HPCC architecture Standard Grant 9215 University of Washington WA network networking vision HUMAN-CENTERED COMPUTING 7367 7367 Ladner, Richard 34235 0925238 May 1, 2009 Architectural Robotics - An International Workshop This is funding to support a one-day international workshop of researchers from academia and industry, along with graduate students, to be held in conjunction with the 11th International Conference on Ubiquitous Computing (Ubicomp 2009), which will take place September 30-October 3, 2009, in Orlando, Florida, and which is sponsored by the Association for Computing Machinery (ACM). Ubicomp is the premier outlet for novel research contributions that advance the state of the art in the design, development, deployment, evaluation and understanding of ubiquitous computing systems. Ubicomp is an interdisciplinary field of research and development that utilizes and integrates pervasive, wireless, embedded, wearable and/or mobile technologies to bridge the gaps between the digital and physical worlds. More information about the conference is available online at http://www.ubicomp.org/ubicomp2009. This interdisciplinary workshop, the first of its kind in the United States, will bring together researchers from a variety of scientific domains, from engineering, and from architecture, to identify opportunities and challenges in the emerging field of architectural robotics, that is to say robotics technologies embedded in the built environment. Throughout history the emergence of new technologies has reshaped our built environment and so, society. Workshop participants, about 20 of them supported under this award, will share their research and teaching, and explore issues relating to the design of complex engineered systems that respond to human needs and wants. They will wrestle with questions such as: What new vocabularies of design need to be cultivated, and what theories of self-reconfigurability defined, in order to lay the foundation for sophisticated algorithms that will sense and infer the occupancy, activities, and external conditions of a building so as to trigger change that improves life, enhances existing places, and supports human interaction? And: How can we educate students from different academic backgrounds to collaborate productively in teams, and what tools could further teaching and learning in the design and implementation of architectural robotics? The PIs expect the workshop to launch a viable new research community, by serving as a catalyst for future knowledge exchange, collaboration and growth. To these ends, event outcomes will include publication of a collection of position papers and set of references (including academic papers), as well as Web sites and other media that pertain to this new field of research. Broader Impacts: The gradual embedding of robotics throughout the built environment will have a broad impact on society as these technologies support and, in some cases, augment everyday work, school, entertainment, and leisure. Early applications will likely be in health care, in support for persons with physical disabilities, in the empowerment of a growing populating wishing to age in place, and in intelligent work spaces that are responsive to changing needs, that consume less floor space, and that reduce energy costs. The educational activities in the workshop will explore methods of teaching multidisciplinary classes that bridge the academic cultural gaps that separate engineering, human-centered design, and architecture and its allied design and art practices. The PIs will make every effort to ensure a diverse group of participants including representatives of underrepresented groups in science and engineering such as minorities, women, and persons with disabilities. IIS CSE Ephraim P. Glinert HPCC architecture Standard Grant 9215 algorithms 9150 ubiquitous wireless robotics Clemson University SC HUMAN-CENTERED COMPUTING 7367 7367 Mark Gross Green, Keith 32062 0923897 April 1, 2009 Student Panel at IEEE Virtual Reality 2009 This is funding to support attendance by approximately 4 senior graduate students and recent PhDs from VR research groups in academia, industry, the military, and government, at the IEEE Virtual Reality 2009 conference, so that they can participate as panelists on a student panel. IEEE VR is the premier international conference and exhibition in virtual reality. It provides a unique forum for interaction among leading experts in VR and closely-related fields such as augmented reality, mixed reality, and 3D user interfaces, who come together to share their work and in turn to be exposed to the latest work from a worldwide contingent of researchers, as well as to renew friendships and make new ones. The IEEE Virtual Reality 2009 conference will be held in Lafayette, Louisiana, on March 14-18, 2009. Virtual Reality is reaching a crossroads, moving from hype-fueled field to one of serious application, evaluation, and study. And those who will transform the field are the senior graduate students and recent graduates in VR research groups and companies across the world. These "next-gen" VR pioneers need a stronger voice in the field, and the "current-gen" VR researchers and developers need to hear that voice. IEEE VR has a long tradition of strong student participation, with an extensive student volunteer program, fair student conference rates, and strong student interest. However, aside from presenting papers students have had few opportunities to play an active role in the conference. The student panel, which has been accepted for inclusion in the 2009 conference program, will provide a platform for students to participate in forming longer-term visions. This panel will be the third of its kind, following in the footsteps of very successful and well attended predecessors in 2007 and 2008. This year's discussion will focus on two main topics: building a career in VR, and evolving the IEEE VR conference. A moderator will lead the discussion with a focus on involvement by students, but while preference will be given to questions from students in the audience senior researchers will also be encouraged to contribute advice. The PI has committed to being proactive in order to ensure diversity among the panelists, including members of traditionally under-represented groups and women. Broader Impacts: The IEEE VR 2009 student panel will provide a unique vehicle for broadening participation by under-represented groups in the conference, while also affording an opportunity for young researchers to network, cultivate ideas, and create a sense of community ownership. The ideas of the future pioneers of the VR field will be of significant interest to all conference attendees. The establishment of this annual series of student panels at the IEEE VR conferences will serve as a model to other conference organizing committees. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 network MA Worcester Polytechnic Institute augmented reality HUMAN-CENTERED COMPUTING 7367 Lindeman, Robert 6149 0922766 March 15, 2009 Student Travel Support to Japan: 2009 IEEE Int Conf Robotics and Automation and Lab Visits This project is partially funding up to 30 US graduate students to attend the 2009 IEEE International Conference on Robotics and Automation in Kobe, Japan, in May 2009. It is also supporting student visits to laboratories in Japan before and after the conference. The purpose of this project is to increase exposure of US graduate students in robotics to research and researchers in Japan; to help establish meaningful relationships between US students and their counterparts in Japan; and to encourage future collaborations by planting the seeds of international collaboration early in these students' careers. In this sense, the goals of this project have much in common with the NSF East Asia and Pacific Summer Institutes (EAPSI). Indeed, one outcome of this project is expected to be increased interest in the EAPSI and postdoctoral fellowship opportunities in Japan. Other outcomes will be measured by student trip reports and surveys. This project is also producing a database of US robotics labs willing to host future visitors from Japan as part of reciprocal programs. IIS CSE HPCC Northwestern University IL Standard Grant 9215 database robotics Lynch, Kevin 5978 Paul Yu Oh ROBUST INTELLIGENCE 7495 7495 5921 OTHER GLOBAL LEARNING & TRNING 49896 7731 0922161 March 1, 2009 ACM CHI 2009 Workshop: Human-Computer Interaction Doctoral Research Consortium This is funding to support this year's HCI doctoral research consortium (workshop) of approximately 15 promising doctoral students from the United States and abroad, along with distinguished research faculty. The event will take place in conjunction with the ACM 2009 Conference on Human Factors in Computing Systems (CHI 2009), which will be held April 4-9 in Boston, and sponsored by the Association for Computing Machinery's Special Interest Group on Human-Computer Interaction (SIGCHI). Goals of the workshop include building a cohort group of new researchers who will then have a network of colleagues spread out across the world, guiding the work of new researchers by having experts in the research field give them advice, and making it possible for promising new entrants to the field to attend their research conference. Student participants will make formal presentations of their work during the workshop, and will receive feedback from the faculty panel. The feedback is geared to helping students understand and articulate how their work is positioned relative to other human-computer interaction research, whether their topics are adequately focused for thesis research projects, whether their methods are correctly chosen and applied, and whether their results are appropriately analyzed and presented. Student participants will present their work to the doctoral consortium on April 4-5, with follow up activities planned during the technical program of the conference. Extended abstracts of the students' work will be published in the CHI 2009 Extended Abstracts, which has wide print and electronic distribution. SIGCHI's conference management committee will evaluate the doctoral consortium, and the results will be made available to the organizers of future consortia. The CHI doctoral consortia, which began in 1986, have been highly successful in providing a forum for the initial socialization into the field of young doctoral scholars, and many of today's leading HCI researchers participated as students in earlier consortia. Broader Impacts: The annual CHI doctoral consortia traditionally bring together the best of the next generation of HCI researchers, allowing them to create a social network both among themselves and with senior researchers at a critical stage in their professional development. Because the students and faculty constitute a diverse group across a variety of dimensions, including nationality/cultural and scientific discipline, the students' horizons are broadened to the future benefit of the field. This year, in an effort to further broaden the impact of the event, the organizers have undertaken to use NSF funds to support participation by no more than one student from any one institution, and to also consider gender in the participant selection process with a target of a 50/50 split. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 Carnegie-Mellon University PA network human-computer interaction 30000 HUMAN-CENTERED COMPUTING 7367 7367 John, Bonnie 0922106 April 1, 2009 Group09 Doctoral Consortium This award supports a Doctoral Consortium at the 2009 ACM GROUP Conference to be held May 10-13, 2009. The ACM GROUP conference brings together researchers from the fields of organizational behavior, information systems, social informatics, information sciences, and computer supported cooperative work (CSCW). As such the conference is a critical link between the research communities supported by CISE/IIS and the broader social, behavioral, and management sciences. The focus of the GROUP Doctoral Consortium is the intellectual content of the students' doctoral dissertations. These dissertations represent state-of-the-art research in the study and development of organizational systems, information systems, social informatics, and computer supported cooperative work. The Doctoral Consortium creates a social network among the next generation of researchers and several senior researchers. Students and faculty are anticipated to be a diverse group on several dimensions (nationality, scientific discipline, gender, institutional affiliation, under-represented minority status), therefore participation in the consortium will broaden the students' intellectual and social perspectives at a critical stage in their professional development. IIS CSE HPCC Standard Grant 9215 network University of Michigan Ann Arbor MI cscw HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald Finholt, Thomas 17683 0921810 June 1, 2009 Learning-Based Mobile Robotic Sensor Networks Dynamic mobile sensor networks are a distributed collection of mobile robots, each of which has sensing, computation, wireless communication and mobility capabilities. In a traditional wireless sensor network, although mobile robots are being used as sensor nodes, their dynamics and mobility are not fully exploited to improve the quality of collaborative sensing. This project explores research challenges in learning-based robotic wireless sensor networks by exploiting techniques in multi-robot research and wireless sensor networks with mobility taken into consideration. The proposed research aims to develop distributed learning algorithms and adaptive sensing coordination strategies for monitoring properties of mobile wireless sensor networks and adjusting locations of mobile sensor nodes to achieve better quality of collaborative sensing and environmental inference. The impact of this research lies in the novelty and synergism among networking, machine learning, signal processing and control to adaptively control the network structure, data routing, and signal processing in the mobile robotic sensor network to achieve better collaborative sensing. Expected results from this research will find applications in homeland security, environmental monitoring and sampling, and autonomous mobile robots for better collaborative sensing and information processing. The proposed project will serve as an excellent vehicle to recruit graduate students, especially those from underrepresented populations, to pursue their doctoral degree, and to recruit high-school students and encourage them to select engineering/computer science for their choice of career. IIS CSE HPCC Standard Grant 9215 Purdue University IN network algorithms machine learning security networking wireless Paul Yu Oh 299999 ROBUST INTELLIGENCE 7495 7495 Lee, C.S. George 7916 0920869 January 1, 2009 SGER: Finding Interesting Patterns through Analysis of Complex Prediction Models With data mining techniques it is possible to train accurate prediction models for large high-dimensional data. Unfortunately, complex prediction models per se are not easy to understand. To make them 'digestible', analysts need simpler patterns that summarize the complex functions extracted by the model. The number of such function summaries is overwhelming. Each slice of a lower-dimensional subspace of the original data space could contain an interesting function summary. The goal of this project is to develop techniques for finding the most 'interesting' function summaries automatically and efficiently. This is done in three steps. First, by formalizing the notion of interestingness for a wide variety of pattern types. Second, by developing a declarative language for specifying these interestingness measures. With a declarative language analysts define what they find interesting, but they need not specify how to find it efficiently. Third, an optimizing compiler for a small language fragment handles the performance efficiency. A major research challenge is to strike the right balance between expressiveness of the language and making it amenable to effective query optimization. The results of this project will pave the way for powerful exploratory analysis tools. They will also enable future research on optimizers and user-friendly interfaces for the declarative language. The approach will be validated using the rich data resources being organized by the ornithological community in the Avian Knowledge Network (AKN). This will have a tremendous impact on the ability to identify the most significant environmental variables that affect biodiversity on the planet. For example, land managers could discover the possible impact of their decisions on an ecosystem's health. A component of the language will be available to the public through Web services on the AKN Web site (http://www.avianknowledge.net/content). Additional results will be disseminated through the project Web site (http://www.cs.cornell.edu/~mirek/Projects/FunctionSummaries). This will enable a broad audience, from researchers to land managers or bird watchers, teachers or school children to derive novel knowledge from the data resources gathered. IIS CSE HPCC 9216 Standard Grant network Maria Zemankova 9237 MA data mining Northeastern University INFO INTEGRATION & INFORMATICS 7364 compiler Riedewald, Mirek 56047 0919004 April 1, 2009 Workshop: Doctoral Consortium at the Fourth International Conference on Communities and Technologies (C&T 2009) This award supports a Doctoral Consortium to be held in conjunction with the 2009 Conference on Communities and Technologies, June 24-27, 2009. The Communities and Technologies (C&T) conference addresses communities as social entities comprised of actors who share something in common. Further, C&T addresses the ways that computing and communication technologies influence, enable or hinder, communities and the actions that they take. The Doctoral Consortium focuses on the intellectual contributions of the diverse set of doctoral students who are competitively selected to attend. The Doctoral Consortium will help build a future set of community informatics researchers by providing an intellectually intense pre-conference event that attracts and energizes a key set of faculty advisors and doctoral candidates. The Doctoral Consortium will build on the diversity of community informatics researchers and help establish their connection to the broader computing research community. Developing this diversity is valuable for the NSF and for the vitality of the CISE/IIS research community. IIS CSE HPCC Standard Grant 9215 PA Carroll, John Pennsylvania State Univ University Park HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald 12035 0918684 May 1, 2009 Student Support for the AIED 2009 Artificial Intelligence in Education Conference This is funding to support travel by 18 intermediate and advanced doctoral students to participate in the AI-ED Doctoral Student Consortium, at the upcoming International Artificial Intelligence in Education Conference (AI-ED), to be held to be held in Brighton, United Kingdom from July 6 to 10, 2009. The AI-ED International Conference is the premier biennial event for promoting promotes rigorous research and development of interactive and adaptive learning environments for learners of all ages; AI ED will be the 7th event in the series. The interdisciplinary areas that AI-ED represents, comprising cognitive science, computer science, and educational technology, are critical research domains that enhance the effectiveness and usability of software learning systems. Active participation of young researchers in this conference is very important, both for the health of the field and for the researchers themselves. The AI-ED Doctoral Consortium provides a unique opportunity for PhD students partway through their dissertation research to receive valuable feedback and individual mentoring from top researchers in the field. The PI and co-PI both hold active leadership roles within AI-ED, and both hold over $2 million of NSF grants in the areas of human-language and advanced learning technologies. The AI-ED Doctoral Consortium Committee will be comprised of the two PIs together with two other senior professors/researchers. Broader Impact: Bringing young and creative researchers to AI-ED will help advance an important and socially valuable interdisciplinary research field. For many graduate students, the cost of attending the AI-ED conference exceeds their travel budget. Thus, NSF funding will significantly impact the careers of the next generation of AIED researchers, by enabling a number of them to take part in an important event they would otherwise have to miss; in particular, those who lack funding from other sources (e.g., advisor's grants). The students will have an opportunity to gain wider exposure in the community for their innovative work, and to obtain feedback and guidance from senior members of the research community. Participation will also help foster a sense of community among these young researchers, by allowing them to create a social network both among themselves and with senior researchers at a critical stage in their professional development. The PI and co-PI have indicated that they will act to assure participation by members of traditionally under-represented institutions, and will pay close attention to inclusion of minorities and women. IIS CSE HPCC Standard Grant 9215 network artificial intelligence education 9150 cognitive science University of Memphis TN HUMAN-CENTERED COMPUTING 7367 7367 Arthur Graesser David W. McDonald 21600 Azevedo, Roger 0918459 September 15, 2009 Doctoral Consortium Support - JCDL 2009 This proposal requests funding to support a doctoral consortium for Ph. D. students to be held just prior to the Joint Conference for Digital Libraries (JCDL) held this year in Austin, Texas. The doctoral consortium is a workshop for Ph.D. students who are in the early phases of their dissertation work. It is international in scope. The primary goal is to help students develop their thesis proposal and a plan for subsequent research by providing constructive and thoughtful feedback on their work to date and ideas for future work. Students provide formal written papers for critical review, and then present their research at the workshop, which takes place the day before the conference begins. The consortium is led by prominent professors and experienced practitioners in the field of digital library research and development. Leaders come from all over the world, and work at universities, research foundations, and in industry. The JCDL doctoral consortium provides students with an opportunity to meet and interact intellectually with people who would be otherwise difficult to meet, as well as to discuss their ideas with a broad and diverse set of people. IIS CSE HPCC 9216 Standard Grant Stephen Griffin TX University of Texas at Austin INFO INTEGRATION & INFORMATICS 7364 7364 12000 Winget, Megan 0918376 May 1, 2009 Workshop on Doctoral Education in the iSchools As scholarly digital content has proliferated over the past decade, the field of library and information science has had to undergo radical curriculuum changes to deal with primary library tasks designed for print and analog media. The major educational and research changes related to digital information have led to a number of leading institutions to form interdisciplinary "iSchools" and begin a dialogue to adapt collectively to the new forms of information and access practices. This proposal is for a workshop to gather selected iSchool administrators and faculty focusing on pedagogical issues to discuss and reach common agreement on primary curriculum goals. This workshop is designed to present and analyze individual existing approaches and construct implementation, evaluation and dissemination plans to guide future activities. IIS CSE HPCC Standard Grant 9215 Stephen Griffin education MD University of Maryland College Park 7484 INFO INTEGRATION & INFORMATICS 7364 Druin, Allison 45000 Paul Jaeger Jennifer Golbeck 0917837 December 1, 2008 Collaborative Research: Major: Puppet Choreography and Automated Marionettes Puppet choreography is a highly-developed language for controlling mechanically complex marionettes. It has evolved over centuries into a largely standardized form that allows puppeteers to address issues that arise as a result of the complex systems with which they are working. The project looks at how puppeteers address complex tasks in their choreographic descriptions of plays and using that understanding to solve questions of importance to computer science and engineering. These goals will be achieved by creating an automated puppet play, which will use insights about puppet choreography to implement embedded control of mechanically complex marionettes engaged in complex coordination tasks. This work will impact a broad spectrum of activities, including integrating the choreographic structure of programming into two innovative classes, introducing puppeteers to technical computer science and engineering problems, and the introduction of puppetry as programming to children involved in a local YMCA. Students in these classes will also be statistically assessed for their ability to transfer the choreographic techniques to novel problems as well as the puppeteers? ability to apply their expertise to high-level engineering problems in order to gain insight into educational aspects of the creative process. IIS CSE HPCC Northwestern University IL Standard Grant 9215 9251 HUMAN-CENTERED COMPUTING 7367 7367 Mary L. Maher 7655 CreativeIT 7788 Murphey, Todd 374410 0917833 June 1, 2009 Imaging and Image Analyses Applied to Historical Objects This workshop will explore a range of issues related to new and emerging technologies for advanced research for imaging related to capturing, digitizing and analyzing historical objects. These include historical documents, visual images, and three-dimensional (3D) artifacts, many of which are to be found in widely-scattered collections and having restricted accessibility. By digitizing historical objects, critical attributes are both preserved and at the same time enable studies that are impossible with the traditional approaches. Such studies might have been impossible up to now either because a research question requires study of large quantities of data preserved in collections around the world or because the study wishes to examine details invisible to the human eye, such as the remains of erased texts preserved in palimpsests, miniature scripts on cylinder seals, etc. or patterns that are hard to compare objectively (e.g., patterns of quilt images). A team at the University of Illinois, Urbana-Champaign and the National Center for Supercomputing Applications organize and convene the event in collaboration and with support from the Humanities, Arts, Science, and Technology Advanced Collaboratory (HASTAC). IIS CSE HPCC 9216 IL Standard Grant Stephen Griffin University of Illinois at Urbana-Champaign INFO INTEGRATION & INFORMATICS 7364 7364 Bajcsy, Peter Anne Hedeman 24845 0917818 July 1, 2009 HCC:Small:Collaborative Research: Exploring the Use of Immersive Virtual Reality Technologies for Scientific Research, Communication, and Outreach Abstract The goals of this study are to explore the utility of the emerging immersive virtual reality (VR) technologies and virtual worlds (VWs) for scientific and educational uses in the framework of astrophysics and related fields. This work will be done under the auspices of the Meta Institute for Computational Astrophysics (MICA), the first professional scientific organization based exclusively in VWs, representing an experiment in scientific organization and communication by itself. However, the results should be more broadly applicable to most other fields of science and scholarship. Demonstrating the potential and the utility of these emerging technologies to the academic community at large will help engage and facilitate a broader participation of scientists and educators in these developments. Specifically, the research will explore the following topics: (1) The uses of VWs as scientific collaboration and communication environments, ranging from individual and group discussions, to seminars and scientific conferences and workshops. (2) Novel ways of user interaction and interfacing with numerical simulations, in terms of the setup, adjustments, and interpretation of results, especially in an interactive/collaborative setting. (3) Immersive and interactive visualization of highly complex, multi-dimensional data sets, for a direct visual exploration, data mining, and as publishable presentations in electronic media. (4) Uses of WVs as a novel, interactive educational and public outreach platform. (5) An initial exploration of the data architectures and structures for the next generation of the Web with a VR user interface, and interaction of human avatars and intelligent software agents. (6) Investigate VW developments in the non-proprietary Opensim/Opengrid environments. This effort will develop new modalities of scientific research and communication using new VR and VWs technologies in the domain of astrophysics but having positive impacts for comparable fields of science as well. It can open qualitatively new ways in which scientists interact among themselves, with their data, and with their numerical simulations, and thus foster some genuine new "computational thinking" approaches to science and scholarship. Virtual worlds will become a platform to conduct rigorous research activities in the fields of computational astrophysics and data-intensive astronomy, seeking to determine the potential of these new technologies, as well as to develop a new set of best practices for scholarly and research activities enabled by them, and by a combination of the existing Web-based and the new VR technologies. The central idea behind this project is that immersive VR and VWs are potentially transformative technologies on par with the Web itself, which can and should be used for serious purposes, including science and scholarship. By conveying this idea to professional scientists and scholars, and by leading by example, the project aims to engage a much broader segment of the academic community in utilizing, and developing further these technologies. The work will include an active and multi-facetted program of education and public outreach, which will foster the public understanding of both science and information technology. IIS CSE HPCC Standard Grant 9215 William Bainbridge data mining visualization education NJ HUMAN-CENTERED COMPUTING 7367 7367 Hut, Piet Institute For Advanced Study 23412 0917816 July 1, 2009 HCC:Small:Collaborative Research: Exploring the use of immersive virtual reality technologies for scientific research, communication, and outreach Abstract The goals of this study are to explore the utility of the emerging immersive virtual reality (VR) technologies and virtual worlds (VWs) for scientific and educational uses in the framework of astrophysics and related fields. This work will be done under the auspices of the Meta Institute for Computational Astrophysics (MICA), the first professional scientific organization based exclusively in VWs, representing an experiment in scientific organization and communication by itself. However, the results should be more broadly applicable to most other fields of science and scholarship. Demonstrating the potential and the utility of these emerging technologies to the academic community at large will help engage and facilitate a broader participation of scientists and educators in these developments. Specifically, the research will explore the following topics: (1) The uses of VWs as scientific collaboration and communication environments, ranging from individual and group discussions, to seminars and scientific conferences and workshops. (2) Novel ways of user interaction and interfacing with numerical simulations, in terms of the setup, adjustments, and interpretation of results, especially in an interactive/collaborative setting. (3) Immersive and interactive visualization of highly complex, multi-dimensional data sets, for a direct visual exploration, data mining, and as publishable presentations in electronic media. (4) Uses of WVs as a novel, interactive educational and public outreach platform. (5) An initial exploration of the data architectures and structures for the next generation of the Web with a VR user interface, and interaction of human avatars and intelligent software agents. (6) Investigate VW developments in the non-proprietary Opensim/Opengrid environments. This effort will develop new modalities of scientific research and communication using new VR and VWs technologies in the domain of astrophysics but having positive impacts for comparable fields of science as well. It can open qualitatively new ways in which scientists interact among themselves, with their data, and with their numerical simulations, and thus foster some genuine new "computational thinking" approaches to science and scholarship. Virtual worlds will become a platform to conduct rigorous research activities in the fields of computational astrophysics and data-intensive astronomy, seeking to determine the potential of these new technologies, as well as to develop a new set of best practices for scholarly and research activities enabled by them, and by a combination of the existing Web-based and the new VR technologies. The central idea behind this project is that immersive VR and VWs are potentially transformative technologies on par with the Web itself, which can and should be used for serious purposes, including science and scholarship. By conveying this idea to professional scientists and scholars, and by leading by example, the project aims to engage a much broader segment of the academic community in utilizing, and developing further these technologies. The work will include an active and multi-facetted program of education and public outreach, which will foster the public understanding of both science and information technology. IIS CSE HPCC Standard Grant 9215 PA William Bainbridge data mining visualization education HUMAN-CENTERED COMPUTING 7367 Drexel University 7367 McMillan, Stephen Enrico Vesperini 165085 0917814 July 1, 2009 HCC:Small:Collaborative Research: Exploring the Use of Immersive Virtual Reality Technologies for Scientific Research, Communication, and Outreach The goals of this study are to explore the utility of the emerging immersive virtual reality (VR) technologies and virtual worlds (VWs) for scientific and educational uses in the framework of astrophysics and related fields. This work will be done under the auspices of the Meta Institute for Computational Astrophysics (MICA), the first professional scientific organization based exclusively in VWs, representing an experiment in scientific organization and communication by itself. However, the results should be more broadly applicable to most other fields of science and scholarship. Demonstrating the potential and the utility of these emerging technologies to the academic community at large will help engage and facilitate a broader participation of scientists and educators in these developments. Specifically, the research will explore the following topics: (1) The uses of VWs as scientific collaboration and communication environments, ranging from individual and group discussions, to seminars and scientific conferences and workshops. (2) Novel ways of user interaction and interfacing with numerical simulations, in terms of the setup, adjustments, and interpretation of results, especially in an interactive/collaborative setting. (3) Immersive and interactive visualization of highly complex, multi-dimensional data sets, for a direct visual exploration, data mining, and as publishable presentations in electronic media. (4) Uses of WVs as a novel, interactive educational and public outreach platform. (5) An initial exploration of the data architectures and structures for the next generation of the Web with a VR user interface, and interaction of human avatars and intelligent software agents. (6) Investigate VW developments in the non-proprietary Opensim/Opengrid environments. This effort will develop new modalities of scientific research and communication using new VR and VWs technologies in the domain of astrophysics but having positive impacts for comparable fields of science as well. It can open qualitatively new ways in which scientists interact among themselves, with their data, and with their numerical simulations, and thus foster some genuine new "computational thinking" approaches to science and scholarship. Virtual worlds will become a platform to conduct rigorous research activities in the fields of computational astrophysics and data-intensive astronomy, seeking to determine the potential of these new technologies, as well as to develop a new set of best practices for scholarly and research activities enabled by them, and by a combination of the existing Web-based and the new VR technologies. The central idea behind this project is that immersive VR and VWs are potentially transformative technologies on par with the Web itself, which can and should be used for serious purposes, including science and scholarship. By conveying this idea to professional scientists and scholars, and by leading by example, the project aims to engage a much broader segment of the academic community in utilizing, and developing further these technologies. The work will include an active and multi-facetted program of education and public outreach, which will foster the public understanding of both science and information technology. IIS CSE HPCC Standard Grant 9215 CA William Bainbridge data mining visualization education California Institute of Technology HUMAN-CENTERED COMPUTING 7367 7367 Djorgovski, Stanislav 199908 0917773 November 15, 2008 Collaborative Research III-COR: From a Pile of Documents to a Collection of Information: A Framework for Multi-Dimensional Text Analysis Many information workers are swamped with unfamiliar collections of text. One challenge is to obtain an accurate overview of a large text collection, such as the public comments collected in ''''''''notice and comment'''''''' rulemaking. No single tool currently provides a sufficiently diversified picture of such a corpus, and no adequate theory exists to help people explore and form a deep and nuanced understanding of such a text collection. This research seeks to develop a computational framework that allows further exploration of this problem from multiple, integrated perspectives. All the assembled perspectives will be brought together into a single overall supra-document structure that is dynamically constructed under user guidance. In this structure, hierarchical topic clusters will be cross-linked by opinion and argumentation links, using two classes of text analysis engines: one for topics and subtopics, and the other for argument structures. The research team will design, develop, build, and systematically test an overall text exploration framework, an application to support federal regulation writersone called the Rule-Writers Workbench. There is a strong collaboration with Federal government officials who will provide data and participate in user testing. The three PIs have successfully collaborated on a related project under previous NSF funding. Intellectual Merit: This is a sustainable collaboration between computer science and political/social science research, rooted in a challenging and important real world application and informed by years of end user research. Dynamic, user-driven subtopic definition and clustering algorithms coupled with language modeling are an innovative yet reachable set of goals. The framework to be developed will be grounded in the humanities disciplines'' expertise in rhetoric, discourse structure, and subjectivity. Broader Impacts: The Rule-Writers Workbench will allow federal government regulation writers to employ a suite of technical tools that perform independent analyses of public responses to proposed regulations, including near-duplicate detection and clustering, user-based topic selection from dynamically extracted keywords, opinion identification, and subtopic clustering. These capabilities will open new avenues for federal comment analysis. IIS CSE HPCC 9216 Lawrence Brandt Standard Grant algorithms MA University of Massachusetts Amherst INFO INTEGRATION & INFORMATICS 7364 Shulman, Stuart 7364 H233 213114 0917708 May 1, 2009 SGER: 2- and 3-D Visualization of Ecological Phenomena - Transition to the Petabyte Age The informatics problem facing scientists in the Petabyte Age has three parts: 1) finding, accessing, and loading massive amounts of data, 2) figuring out the appropriate data reduction or abstraction to make sense of the combined data set, and 3) operationally processing those data. n this SGER, the focus is on the second of these sub-problems ? how scientists deal cognitively with petabyte size data sets of their own and others? provenance. Natural language processing applications saw orders of magnitude improvement when statistical processing/machine learning was applied to massive data sets. Some linguists observe that these improvements level off at some point, and that subsequent improvements come only after domain knowledge (in that case, linguistic theory) is also applied to the processing. There is a similar situation in science. While some application areas in a Petabyte Age might require only non domain-specific machine learning to predict phenomena (what Chris Anderson calls ?agnostic statistics?), others will require the deeper understanding of phenomena that most scientists seek. In other words, for some domains, and in most sciences, it is not enough to answer ?what? ? one also needs to answer ?how?. This work is high risk because there is little research on the extent to which visualization of natural phenomena can be made "cognitively" consonant across disparate spatial and temporal scales, Further, because of disparity between the scientific- and information-visualization communities, it is unclear how to connect analytics with visualization of ecological phenomena. Finally, the data integration necessary for the proposed visualization requires managing much larger volumes of data, and many more different kinds of data. The intellectual merit of the proposed work lays in new conceptual data structures and data representations for scientific visualization that can be used to generate domain specific visualization templates from which a range of specific visualizations for a domain could be drawn. The broader impacts of the proposed work are three-fold: 1) potential application of the work beyond the realm of environmental science and climate change to that of natural resource management and policy, and to other sciences, 2) educational impact to computational thinking in terms of curricular development at Evergreen College, which will be disseminated via the NSF CPATH project Northwest Distributed Computer Science Department (NWDCSD), and 3) free open-source distribution of the software tool to the scientific community. IIS CSE HPCC 9216 Standard Grant WA 9251 9237 machine learning visualization Sylvia J. Spengler 7484 INFO INTEGRATION & INFORMATICS 7364 Cushing, Judith Evergreen State College 9102 92977 0917676 August 15, 2008 CAREER: Mobility Control for Robotic Sensor Networks This project focuses on the development of mobility strategies for Robotic Sensor Networks (RSNs) which are networks of robots equipped with communication, computation and sensing capabilities. For RSN technology to be utilized in critical applications such as emergency response and environmental monitoring, mobility algorithms for operation in dynamic and complex environments are needed. In this project, three novel mobility problems which arise in many RSN applications are introduced. These problems are general enough to capture the interplay between communication, sensing and mobility. Yet, they can be succinctly formulated as geometric optimization problems. This work will focus on solving these mobility problems which will yield provably correct solutions for numerous RSN applications. In addition, bounds on the performance of a given RSN in fundamental problems such as tracking, collaborative sensing and estimation will be established. The output of this research will be a significant step toward enabling the use of fully autonomous RSNs for crucial applications in emergency response, energy and environmental monitoring, and health care automation. Sensing and actuation play important roles in the evolution of information technology. The project will contribute to this evolution through the development of novel distributed sensing and control algorithms. IIS CSE Continuing grant HPCC 9215 9251 algorithms 1045 OTHR 0000 University of Minnesota-Twin Cities MN Paul Yu Oh ROBUST INTELLIGENCE 7495 7495 Isler, Ibrahim 158620 0917666 March 15, 2009 SGER: EScience in the All-Digital Library Digital library developments currently emphasize traditional services, such as searching, archiving, and access to digital information (e.g., the HathiTrust and Google Book Search), but fail to recognize the potential for eScience to identify latent information that is buried in digital collections and scientific databases. This SGER is exploring the new forms of scientific research that are possible in an all-digital library. As a case study we are using Cornell's Mann Library, which will be one of the first large scientific libraries to digitize at least 80 percent of its book collections. As yet, only a few scientists are aware of the opportunities that an all-digital library offers. The first part of the study is gathering information from scientists about the potential for information-driven research in their domains, and the tools and services that they need. The second part of the study is undertaking pilot experiments to gain insights into the practical difficulties in carrying out information-intensive research on digital book collections. This will use an initial collection of 100,000 digitized books to which we are providing tools for indexing, simple data mining, and extraction of subsets. Public dissemination of the results is an integral part of this study. The aim is not simply to explore eScience in the all-digital library, but to enable scientists to carry out eScience research without the help of computing specialists. For this they need support from universities, libraries, and the government. This support must be built on a well-informed analysis of the technical, human, and organizational factors that will maximize the benefits of the all-digital library. IIS CSE NY HPCC 9216 Standard Grant 9237 data mining Cornell University 7484 INFO INTEGRATION & INFORMATICS 7364 120000 James C. French Arms, William 0917397 September 1, 2009 RI: Small: Kernelization with Outer Product Instances Thus far kernel methods have been mainly applied in cases where observations or instances are vectors. We are lifting kernel methods to the matrix domain, where the instances are outer products of two vectors. Matrix parameters can model all interactions between components and therefore take second order information into account. We discovered that in the matrix setting a much larger class of algorithms based on any spectrally invariant regularization can be kernelized. Therefore we believe that the impact of the kernelization method will be even greater in the matrix setting. In particular we will show how to kernelize the matrix versions of the multiplicative updates. This family is motivated by using the quantum relative entropy as a regularization. Most importantly we will use methods from on-line learning to prove generalization bounds for multiplicative updates that grow logarithmic in the feature dimension. This is important because it lets us use high dimensional feature spaces. We will apply our methods to collaborative filtering. In this case an instance is defined by two vectors, one describing a user and another describing an object. The outer products of such pairs of vectors become the input instances to the machine learning algorithms. The multiplicative updates are ideally suited to learn well when there is a low-rank matrix that can accurately explain the preference labels of the instances. The kernel method greatly enhances the applicability of the method because now we can expand the user and object vectors to high-dimensional feature vectors and still obtain efficient algorithms. IIS CSE HPCC Standard Grant 9215 CA algorithms machine learning University of California-Santa Cruz Douglas H. Fisher ROBUST INTELLIGENCE 7495 455000 Warmuth, Manfred 7495 7923 0917381 September 15, 2009 NeTS: Small: Collaborative Research: Multi-Resolution Analysis & Measurement of Large-scale, Dynamic Networked Systems with Applications to Online Social Networks Many large-scale networked systems such as Online Social Networks (OSNs)are often represented as annotated graphs with various node or link attributes. Such a representation is usually derived from a snapshot that is obtained through measurements. These graph representations enable researchers to characterize the connectivity of these systems using graph analysis. However, captured snapshots of large networked systems are likely to be distorted. Furthermore, commonly-used graph analysis characterizes the connectivity of a graph in an indirect fashion and generally ignores graph dynamics. This multi-disciplinary research program designs, develops and rigorously evaluates theoretically grounded techniques to accurately measure and properly characterize the connectivity structure of large-scale and dynamic networked systems. More specifically, the project examines various graph sampling techniques for collecting representative samples from large and evolving graphs. It also investigates how multiscale analysis can be used as a powerful technique to characterize the key features of the connectivity structure of large dynamic networked systems at different scales in space and time. The developed techniques will be used to characterize fundamental properties of the friendship and various interaction connectivity structures in different OSNs. This project promises to identify the underlying technical and social factors that primarily drive the structural properties and dynamic nature of OSN-specific connectivity structures. It will produce new models for friendship and interactions in OSNs, a large archive of anonymized datasets and new tools for OSN measurement, simulation and analysis. The latter will be incorporated into newly-developed courses in Computer Science and Sociology, and will be freely distributed. IIS CSE HPCC Standard Grant 9218 simulation OR University of Oregon Eugene 0000912 Computer Science David W. McDonald NETWORK SCIENCE & ENGINEERING 7794 7794 7923 7363 Rejaie, Reza 354999 0917379 October 1, 2009 III: Small: Do-It-Yourself forms-driven workflow web applications Emerging Do-It-Yourself database-driven web application projoect aims to (1) enable non-programmers to rapidly build custom data management and workflow applications and (2) to promote a novel pattern of interaction between application owners and programmers. Their beneficiaries are organizations, in need of long tail web applications, that cannot afford the time and money needed to engage into the conventional code development process. Do-It-Yourself platforms must maximize two metrics that present an inherent trade-off: the simplicity of specification and the application scope, which characterizes the class of applications that can be built using the platform?s specification mechanism. The proposal introduces two scopes with interesting trade-off features. Namely, in the "All-SQL" scope and the (more limited) "forms-driven workflows" scope each application page consists of a report (modeled by a nested query) and forms and actions in the report's context, leading to updates. The limitations have practically minor effects on the scope but they enable simple specification and automatic optimizations, studied in the proposal, such as: 1. automatic creation of reports by choosing between the candidates using information theoretic criteria relying on constraints captured in the limited models. 2. summarization of applications as workflow specifications by analyzing the dependencies between updates and queries. Vice versa, the proposal shows how simple workflow primitives translate to queries (reports) and updates (forms and actions). The proposal also provides an unlimited model of web applications, where programmers introduce code components and interface them with the "limited" part via queries and updates. The proposed models of database-driven web applications will impact the education of both Computer Science (CS) and non-CS students that need to comprehend web applications at a high conceptual level. For further information see the project web page at http://www.db.ucsd.edu/forward IIS CSE HPCC 9216 Standard Grant CA education database 0116000 Human Subjects University of California-San Diego INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 Papakonstantinou, Yannis Alin Deutsch 7923 499444 0917366 September 15, 2009 HCC: Small: Supporting Males' and Females' Problem-Solving Strategies in End-User Debugging This project will examine two issues related to end-user debugging of computer programs. First, what strategies do male and female end-user programmers try to use for debugging, and with which do they succeed? Second, how should end-user programming environments go about supporting and guiding these debugging strategies? Most of what is programmed must eventually be debugged. The support of problem-solving tasks such as debugging must extend all the way to the heart of problem solving, to strategy. Although once only professional programmers developed software, today it is common for end users to create some of their own software. Common examples are spreadsheet systems, in which end users program by creating and changing formulas, and web application builders, in which end users program by demonstrating the desired behavior and/or making dataflow connections among computation tools dragged in from a palette. Numerous other examples exist. In fact, in the U.S. alone, there are millions of end users doing such forms of programming every day - many more than professional programmers. Unfortunately, however, evidence is beginning to accumulate that some females are not benefiting as much as males from these empowering devices. There have been recent reports of gender differences in end users' willingness to approach and adopt new software features related to debugging, differences in attitudes toward software features, and differences in end-user programmers' playful tinkering with features. Other results of gender differences in software-based problem solving are also emerging. These findings suggest that there are factors within the software itself that may be subtly undermining females' effectiveness in many software-based problem-solving tasks. This project contributes to the effective use of information technology in evolving, heterogeneous socio-technical systems, enabling more people to take full advantage of the power of computing. It will accomplish this by investigating the underlying strategies males and females use successfully to solve problems with software, and then investigating how to support these strategies. This work will also produce new advances in how to guide and encourage computational thinking to help males and females solve everyday problems that arise in computing, combining empirical methods with proof-of-concept prototypes. In addition, it will contribute to education programs, and in particular will involve talented female high-school students as research interns, to encourage them in the direction of computer science. IIS CSE Continuing grant HPCC 9215 William Bainbridge education OR 0116000 Human Subjects Oregon State University HUMAN-CENTERED COMPUTING 7367 7367 Burnett, Margaret 7923 163154 0917362 July 1, 2009 HCC-Small: TextSL: A Virtual World Interface for the Visually Impaired The immersive graphics, the large amount of user-generated content, and the social interaction opportunities offered by the greater sophistication of virtual worlds are leading to an explosive growth in their popularity, with millions of participating users. Unfortunately, however, virtual worlds are not accessible to users who are visually impaired. In this project the PI's goal is to alleviate this deficiency by developing a virtual world interface for visually impaired users called TextSL, which is able to extract a textual representation from a virtual world that can be read with a screen reader. Users interact with TextSL by means of a command-based mechanism inspired by multi-user dungeon games. A prototype of the system that allows for a basic form of access to the popular virtual world of Second Life has shown that such an approach is feasible. But the prototype lacks two of the most important features offered by virtual worlds: the ability to create content, and the ability to interact with interactive objects. Additional problems identified by the PI's preliminary work are that less than half of the objects in Second Life have a meaningful descriptive name, which makes the majority of objects invisible to visually impaired users, and that virtual worlds contain such vast amounts of content that merely converting it somehow into a textual description may easily overwhelm the user. To address such challenges, the PI will develop a game based on the "games-with-a-purpose" paradigm that allows sighted users to make virtual worlds accessible to visually impaired users by labeling objects without a name, while also establishing a taxonomy of objects that can be used to provide a more usable form of feedback. He will create a new 3D object recognition algorithm based on the "bag-of-features" approach that takes advantage of the unique way objects are represented in Second Life, and which can efficiently and automatically recognize large numbers of objects without a name. And he will also develop techniques that are able to provide feedback on a user's environment without overwhelming the user with large amounts of information, that are able to convey textually a user's interaction with an interactive object, and that support creation of 3D objects using command or haptic based methods. Broader Impacts: This research will increase the quality of life for the millions of people who are visually impaired, by allowing them to participate more fully in the information society. In particular, the social interaction offered by virtual worlds will benefit people who are visually impaired as well as other individuals whose disabilities often lead to both physical and social isolation. Furthermore, as virtual worlds are increasingly used as educational platforms, access for all to this technology is doubly critical. The interaction models of virtual worlds resemble those of games, so project outcomes may also provide new insights into how to make games accessible to the visually impaired. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 9150 0116000 Human Subjects graphics HUMAN-CENTERED COMPUTING 7367 7367 University of Nevada Reno NV Folmer, Eelke 7923 George Bebis 499332 0917349 July 1, 2009 III-COR-Small: Towards More Flexible, Expressive and Robust Stream Systems This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Current stream engines are mostly stand-alone systems, whereas in many applications, stream processing will be one component of a larger information system. Coupling with other components, such as user interfaces, transactional data and archives will be increasingly important. For stream queries to be robust over extended execution periods, they must have the means to adapt to both internal and external changes. Changes in input rates, time lags and data distributions can cause shifts in internal memory and processing loads. Operators must adapt to these changes both local and in concert with other operators. Variations in client demands create opportunities for improved resource use to which operators must adapt. For a stream processing system to be robust in the face of changing workloads and possible system faults, the architecture must have levels of flexibility and adaptivity not currently existing. The team proposes three approaches to developing the necessary flexibility. They will use a formal analysis that will provide precise notions of time and progress, in order to provide criteria and metrics for a variety of situations. In addition, they will elaborate operator and architecture design activities that will then be implemented and evaluated in two different data stream systems. In addition to faculty at Portland State University and graduate students there, the team has an ongoing collaboration with AT&T Research, who will provide access for testing the new system. The techniques developed on this project will broaden the number of applications that can be reasonably served with data stream systems, and by working with commercial systems, the adoption of those techniques into next-generation products will be accelerated. In addition to a new course in steam systems, the team works with high school interns each summer, who are recruited through the Saturday Academy program at Portland State. IIS CSE HPCC 9216 architecture Standard Grant Sylvia J. Spengler OR INFO INTEGRATION & INFORMATICS 7364 7364 Portland State University David Maier 6890 7923 Tufte, Kristin 389793 0917340 September 15, 2009 DC:Small: "Synergizing statistical machine learning and stochastic system modeling with application to real systems". The scale and complexity of highly distributed data intensive systems is approaching a point where traditional performance evaluation techniques are becoming difficult to apply. Specifically, use of traditional stochastic performance evaluation methods encounters difficulties in (1) complexity (i.e., scale of the models and intractability of corresponding solution techniques) and (2) parameter estimation (i.e., needed by the models). In this project we seek to address these two challenges through the use of machine learning techniques. Such techniques have not been traditionally employed in this area, but have emerged recently as a possible direction. We envision that this will lead us not only to better machine learning approaches but will also facilitate merging of machine learning-based techniques with more traditional approaches to performance evaluation, where we anticipate obtaining better results than can be obtained through either approach alone. The broader impacts of this work will be to enable a deeper understanding of the role, advantages, and limitations of machine learning approaches in performance evaluation of large-scale systems as well as their relationship with more traditional approaches. Broader impact also includes improved interdisciplinary education at the graduate and undergraduate levels and diversity efforts. IIS CSE HPCC Standard Grant 9215 CA machine learning education University of Southern California James C. French 7793 DATA-INTENSIVE COMPUTING 7793 7923 7751 Golubchik, Leana Fei Sha 157000 0917333 September 1, 2009 III: Small: Information Systems Under Schema Evolution: Analyzing Change Histories and Management Tools The significant progress made by database research on schema mapping (e.g., omposition, invertibility), data exchange, and query rewriting, can provide breakthrough solutions for the Database Schema Evolution problem. But as of today, information systems are sorely lacking the methods and tools needed to cope with the problem, and to reduce the cost of data migration, rework of queries, application rewriting, and downtime created by schema changes. In fact, this old problem has been made worse by the success of scientific databases (e.g., Ensembl) and web information systems (e.g., Wikipedia)---where the fast evolution of applications and requirements characterizing the web and the scientific discovery process is exacerbated by the number and diversity of users and organizations cooperating on these endeavors.. Fortunately, the openness of these public-domain information systems (vs. corporate ones), and the abundance of their interesting evolution histories make it possible to built a comprehensive testbed to determine the strengths, limitations, and potentials of candidate methods and tools proposed for the problem. Thus, this project is building: (i) an open-source curated repository containing evolution histories from key information systems, (ii) benchmarks for a comprehensive set of tools tested therein, and (iii) instruments to collect and analyze evolution histories. These are then used to (a) compare and evaluate existing approaches, methods and tools, and (b) entice researchers to evaluate and improve their techniques and add their test cases to the benchmark. A transformative impact can be expected upon schema mapping research and applications, inasmuch as theoretical solutions are now validated and improved on real-life case-studies. These in turn are expected to transform and improve significantly scientific databases and web information systems. For further information see the project web page: http://www.cs.ucla.edu/~zaniolo/nsf0917333.html IIS CSE Continuing grant HPCC 9216 University of California-Los Angeles CA database Zaniolo, Carlo INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 7923 157583 0917321 September 1, 2009 HCC:Small:Computational Studies of Social Nonverbal Communication This research will create a new generation of computational tools, called contextual prediction models, for analyzing and modeling social nonverbal communication in human-centered computing. This computational study of nonverbal communication not only encompass the recent advances in machine learning, pattern analysis and computer vision, but goes further by developing and evaluating new algorithms and probabilistic models specifically designed for the domain of social and nonverbal communication. The ability to collect, analyze and ultimately predict human nonverbal cues will provide new insights into human social processes and new human-centric applications that can understand and respond to this natural human communicative channel. This new endeavor will advance through the development of prediction models and their accompanying selection algorithms and feature representations for predicting human nonverbal behavior given a social context (such as the immediately preceding verbal and nonverbal behaviors of a conversational partner). The investigator's previous work has demonstrated the feasibility of using machine learning approaches to model nonverbal communication. Probabilistic sequential models were shown to improve performance of nonverbal behavior recognition during human-robot interactions and make possible the natural animation of virtual humans. This project addresses three fundamental challenges directly: feature representation (optimal mathematical representation of social context), feature selection (subset of social context relevant to prediction of nonverbal behaviors) and probabilistic modeling (efficiently learning the predictive relationship between social context and nonverbal behaviors). This research will evaluate and test the generalization of the computation tools using a large corpus of natural interactions in different settings (human-human, human-robot and human-computer) and domains (e.g., storytelling, interview, and meetings). These prediction models will have broad applicability, including the improvement of nonverbal behavior recognition, the synthesis of natural animations for robots and virtual humans, the training of cultural-specific nonverbal behaviors, and the diagnoses of social disorders (e.g., autism spectrum disorder). The code resulting from this work will be made available to the research community through an open-source Matlab toolbox. The outcome of this research effort will produce state-of-the-art computational models more accessible to researchers who aim to analyze social nonverbal communication and develop natural and productive human-centered computing technologies. IIS CSE Continuing grant HPCC 9215 CA algorithms William Bainbridge machine learning vision animation computer vision University of Southern California HUMAN-CENTERED COMPUTING 7367 pattern analysis 7367 7923 Morency, Louis-Philippe 210620 0917318 September 1, 2009 RI: Small: Learning Biped Locomotion In a not too distant future, assistive robots will become a natural part of the human society, in hospitals, schools, elder care facilities, inner city urban areas, and eventually homes. While wheeled robots, e.g., a humanoid torso on a mobile platform, can cover a range of tasks that assistive robots will be needed for, eventually, legged robots will be the most suitable, as legs increase the effective workspace of a robot and allow maneuvering more complex terrains like steps, curbs, and cluttered and rough terrains in general. This project investigates biped locomotion with a Sarcos humanoid robot. In contrast to most other projects in biped locomotion, it emphasizes walking over uneven and rough terrain, obstacle avoidance, recovery from unexpected perturbation, and learning methods for motor control, as these issues seem to be the most important for working in dynamic and partially unpredictable human environments. Another focus is on dexterous movement control, i.e., control with a maximal amount of compliance and minimal negative feedback gains, using advanced operational space controllers with internal model control. Dexterous, compliant control will increase safety of the robot when accidentally impacting with humans or obstacles, and it will also allow the robot to recover more easily from external perturbation simply by ?giving in?. Such a control approach requires departing from the traditional high-gain position controlled humanoid systems, and focuses on torque control, reactive instantaneous control instead of finite horizon optimization, as well as efficient motion planning and learning methods. IIS CSE HPCC Standard Grant 9215 CA 450000 University of Southern California Schaal, Stefan Paul Yu Oh ROBUST INTELLIGENCE 7495 7923 0917308 August 1, 2009 HCC: Small: Collaborative Research: Graph and Pattern Design on Surfaces Abstract ? Zhang/Wonka The research investigates theory and efficient algorithms for pattern design on surfaces. Patterns on surfaces appear in many natural phenomena such as leaves, animal textures, and terrains as well as man-made objects such as origami, glass ornaments, and facades. Patterns can also be used to describe networks, such as street layouts, power grids, aqueducts, and sensor networks. Pattern design has a wide range of applications in art and entertainment, architecture, engineering, medicine, and city planning. In addition, theory and techniques developed in the research can benefit domains such as computational geometry and vector and tensor field visualization. There are several fundamental challenges when it comes to pattern design on surfaces. First, there is a lack of unified mathematical formulations of patterns in terms of both symmetries and orientations contained in the patterns. Consequently, the aforementioned applications are typically addressed as being unrelated despite the intrinsic links between them. Second, many past approaches to these problems lack hierarchical control. This is required so that the user can design high level information down to occasionally low level specifics and the layout algorithms fill in the rest procedurally. In this research, the investigators explore a unified framework that allows hierarchical design of patterns on surfaces. In this framework, orientation and symmetry information is specified everywhere in the domain through tensor field design. Next, the tensor field which contains desired orientation and symmetry information is used to generate a complex which can be a point set, a graph, a tiling, or any combination of them. Finally, additional details are added onto the complex through texture and geometry synthesis, or sub-patterns are added inside the cells of the complex. Ideas from various mathematical domains such as dynamical systems, tensor calculus, differential geometry, and algebraic topology are borrowed and applied in this research. IIS CSE HPCC architecture Standard Grant 9215 algorithms visualization OR computational geometry Oregon State University GRAPHICS & VISUALIZATION 7453 7453 Lawrence Rosenblum 7923 Zhang, Eugene 249976 0917286 September 1, 2009 HCC: Small: Rethinking Simulation in Computer Graphics Abstract ? Keyser (0917286) Simulations play an important role in many graphical applications, ranging from entertainment to virtual environments used for training. These applications demand greater and greater realism, and this in turn creates a need for more believable and more efficient simulations. While there have been major improvements in simulation technology, many of these techniques can be quite slow, and simply applying greater computing power is not sufficient to meet the demands of real-time systems. The focus of this research is on finding ways to create real-time simulations by taking different approaches to the simulation framework used in computer graphics. The researchers explore ways to replace simulation by statistical data, simplify the theory of effects-based simulations, and incorporate these approaches into a system that trades off accuracy and speed to meet the requirements of a given problem. This work can change the manner in which simulation is performed in graphics and other applications. This research deals with developing methods for real-time simulation by taking fundamentally different approaches to the simulation problem. First, the researchers investigate ways of replacing full physics-based simulations with statistically-based capture of simulation effects. Rather than simulating at run-time, statistics are gathered regarding simulation behavior across several samples, from either full simulations or other sources. At run-time, the simulation is replaced by a result generated according to the statistical distribution. Second, the researchers develop ways of simplifying the theory of certain simulation systems to allow simplified simulations that still capture the important effects, while leaving the less important details to be handled by other (less computationally-involved) means. Third, the researchers investigate ways to develop level-of-detail simulations that allow a tradeoff between fidelity and efficiency, supporting both a highly accurate simulation and a faster simulation with guaranteed performance. The statistical and simplified approaches are incorporated into this level-of-detail simulation. IIS CSE HPCC Standard Grant 9215 simulation computer graphics graphics Texas Engineering Experiment Station TX GRAPHICS & VISUALIZATION 7453 7453 Lawrence Rosenblum 7923 Keyser, John 469010 0917266 September 1, 2009 RI: Small: Scalable Roadmap-Based Methods for Simulating and Controlling Behaviors of Interacting Groups: from Robot Swarms to Crowd Control Simulating and controlling communities of characters that can interact with each other and their environment, and dynamically react to changes, is a challenging problem with many important applications ranging from homeland security (e.g., simulation of disaster scenarios and responses), to civil crowd control (e.g., planning exit strategies for sporting events), to education and training (e.g., providing immersive museum exhibits and training systems). While there are existing methods that attempt to address the simulation aspect, there is a lack of methods that support interaction of multiple types/groups of agents and little work has been done on the control or steering aspect. This work aims to address these challenges by integrating roadmap-based planning with agent-based modeling. This hybrid approach enables the development of methodology for modeling group interactions which are also influenced by constraints imposed by the environment (e.g., wide or narrow corridors) and techniques, including interfaces that enable planning and experimentation, that can scale to large numbers of agents. The results of this work will be shared with the community via publications and open source software. An anticipated outcome of this research is a tool for simulation and control of large crowds at major events (e.g., sporting events, political rallies, emergency evacuations of a building, region, or city). This could allow emergency response planners to investigate the crowd response when officials are placed in particular positions, or architects to study how evacuation times are affected by widening or narrowing corridors. IIS CSE Continuing grant HPCC 9215 simulation education security Texas Engineering Experiment Station TX agent-based Paul Yu Oh ROBUST INTELLIGENCE 7495 9102 7495 Amato, Nancy 7923 Lawrence Rauchwerger 145839 0917232 September 1, 2009 HCC: The Effect of Tiled Display on Performance in Multi-Screen Immersive Virtual Environments New immersive display systems are emerging as platforms for presenting three dimensional data and virtual worlds. However, little effort has been spent on evaluating these systems or providing guiding design principles from a human factors point of view. The PIs argue that interconnected tiled screens have the potential to be as effective as more costly continuous image systems. Demonstrating this potential would in effect significantly drive down the cost of 3D immersive visualization, paving the way for much broader application areas than are now possible (imagine, for example, a science teacher able to show her students a 3D visualization of DNA and to have them interact with the model on a low cost immersive display). To this end, in this project the PIs will compare performance and user interaction on a low-cost, multi-screen spatially immersive visualization system against a more expensive continuous image platform. Their hypothesis is that the low-cost system will present a perceptually equivalent visual experience, despite image seams introduced by the connecting display screens. Psychophysical experimentation will compare the two systems through human judgments based on performance. Project outcomes will also provide insights into optimal hardware and software display configuration when building large multi-screen displays. The PI has an extensive background in conducting experiments involving human subjects, while the Co-PI brings expertise on building and configuring next generation immersive displays, including one of those to be used in this work, which was developed under a prior NSF award. The team has access at Texas A&M to two complementary immersive systems, putting them in a unique position to conduct this research. Broader Impacts: The knowledge and insights gained from this work should help make immersive visualization systems available in areas currently unable to afford such systems or to justify the expense, and thus advance discovery and understanding by enhancing the infrastructure for research, while also promoting teaching, training and learning by engaging more (and more diverse) students in science and technology. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 visualization 0116000 Human Subjects TX HUMAN-CENTERED COMPUTING 7367 7367 Texas A&M Research Foundation 7923 McNamara, Ann Frederic Parke 266172 0917228 September 1, 2009 III: Small: Collaborative Research: Mining and Optimizing Ad Hoc Workflows Ad hoc workflows are everywhere in service industry, scientific research, as well as daily life, such as workflows of customer service, trouble shooting, information search, etc. Optimizing ad hoc workflows thus has significant benefits to the society. Currently the execution of ad hoc workflows is based on human decisions, where misinterpretation, inexperience, and ineffective processing are not uncommon, leading to operation inefficiency. The goal of this research project is to design and develop fundamental models, concepts, and algorithms to mine and optimize ad hoc workflows. The project includes novel research on the following key areas: (1) Network Modeling and Structure Mining. A network model is built that statistically captures the execution characteristics of ad hoc workflows, and is optimized to improve the execution of new workflows with respect to different optimization objectives. (2) Workflow Artifact Mining. The network model built on workflow executions is then extended with workflow artifact mining to realize an optimization system that is able to take advantage of both executions and text contents. (3) Role Discovery and Relation Assessment. A computational framework is built to analyze the roles and relationships of agents involved in ad hoc workflow executions in order to further optimize workflows. Advances from this project include models to represent ad hoc workflows, algorithms for mining hidden collaborative models, and techniques that optimize ad hoc workflow processing. The project bridges two emerging research areas: service science and network science, and enriches the principles and technologies of data mining. It also enhances research infrastructure through the collaboration of team members from different areas (data mining, database, and network). This research is tightly integrated with education through student mentoring and curriculum development. Publications, software and course materials that arise from this project will be disseminated on the project website: URL: http://www.cs.ucsb.edu/~xyan/smartflow.htm IIS CSE HPCC 9216 Standard Grant CA network algorithms data mining education database INFO INTEGRATION & INFORMATICS 7364 Frank Olken University of California-Santa Barbara 7364 Yan, Xifeng 7923 248607 0917199 September 1, 2009 HCC: Small: A Physical Vocabulary for Human-Robot Interaction We are the victims of our own success. We can now deploy mobile robots in real-world environments and have them operate completely autonomously for extended periods of time. We no longer have to surround our robots with graduate student wranglers to keep them functional, and to keep the general public at a safe distance. These technical successes mean that members of the general public must now interact directly with robots, without the aid of an interpreter. But members of the public are poorly equipped for such interactions, since they are unfamiliar with real robots and how they work. Thus, the interactions often go poorly; the robot is hindered in performing its task, and the human is unhappy. For people to be comfortable interacting with a robot, they must feel that they understand what it's thinking, what it's trying to do, and the actions that it will take. Moreover, people must be able to deduce this information from observing the robot for a short period of time, just as we do with other humans that we encounter. The fundamental problem here is that humans communicate a wealth of information by means of a non-verbal "vocabulary" in which body language (how we stand, how we hold our arms, etc.), eye contact, nods, and other subtle cues ostensibly not essential to the task at hand play significant roles. We do this naturally, and without conscious effort. Taken in context, this information allows us to infer another person's state of mind, goals, and intentions with surprising accuracy; this, in turn, allows us to predict how a given interaction will unfold, and gives us some control over it. Because people take this ability for granted, they suffer when it is absent, as is currently often the case when interacting with a mobile robot. The PI intends to address this deficiency in the current project. He argues that to make human-robot interactions as natural as possible, we must equip robots with our physical vocabulary and ensure that they use it appropriately, following social norms. To achieve this goal the PI will turn to the performing arts, where actors are trained to express themselves physically. A good actor can convey a vast amount of information about a character's state of mind, goals, and intentions by simply walking across the stage in a particular way. The actions may be styled, larger-than-life, or subtle, but they are intended to convey information about the character's internal mental state. The techniques that actors employ have been honed and refined for hundreds of years and tested for effectiveness on the general public. In this research, the PI will exploit such insights and skills to develop a physical vocabulary that can communicate beliefs, intentions, and goals to humans interacting with a robot, thereby enabling people to better predict the robot's actions. Finally, the PI will rigorously evaluate these actions to verify that they are actually useful. Broader Impacts: Robots are becoming more and more a part of our lives, and members of the public will be forced to deal with them sooner or later. If we have an understanding of the physical aspects of these interactions, the integration of robots into our everyday lives will be made much less painful and distressing. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 0116000 Human Subjects Washington University MO HUMAN-CENTERED COMPUTING 7367 7367 7923 Smart, William 487209 0917175 July 15, 2009 Small: Statistical Measurement, Modeling, and Inference on Natural 3D Scenes This project investigates two deeply commingled and significant scientific questions on the statistical distributions of range, disparity, chrominance and luminance in natural 3D images of the world: (1) developing a comprehensive database of co-registered luminance, chrominance, range, and disparity images of natural scenes; and (2) conducting eye movement studies on stereoscopic images.. On the acquired database, the research team studies and models the bivariate statistics of luminance, chrominance, range, and disparity . In the eye movement studies, the locations of visual fixations are measured as they land in range space against where they land in luminance, chromatic, and disparity space, making it possible to develop gaze-contingent models of the statistics of luminance, chrominance, range, and disparity. The results of these studies have broad significance in vision science and image processing. To exemplify this, new approaches to computational stereo and to stereo image quality assessment are developed. New computational stereo algorithms are developed using appropriate prior and posterior distribution models on disparity. Further, new algorithms are developed for stereopair image quality assessment using the statistical models that we will develop. These new algorithms dramatically impact the emerging 3-D digital cinema, gaming, and television industries, allowing for automatic assessment of 3D presentations to human viewers. The developed 3D range-luminance databases are made available via public web portals, and the results of the work are published in the highest-profile vision science and image science journals. IIS CSE Continuing grant HPCC 9215 algorithms database vision TX University of Texas at Austin ROBUST INTELLIGENCE Jie Yang 7495 7495 7923 Cormack, Lawrence Alan Bovik 324783 0917170 September 1, 2009 RI: Small: Collaborative Research: Word Sense and Multilingual Subjectivity Analysis Approaches to subjectivity and sentiment analysis often rely on manually or automatically constructed lexicons. Most such lexicons are compiled as lists of words, rather than word meanings ("senses"). However, many words have both subjective and objective senses as well as senses of different polarities, which is a major source of ambiguity in subjectivity and sentiment analysis. The proposed work addresses this gap, by investigating novel methods for subjectivity sense labeling, and exploiting the results in sense-aware subjectivity and sentiment analysis. To achieve these goals, three research objectives are targeted. The first is developing methods for assigning subjectivity labels to word senses in a taxonomy. The second is developing contextual subjectivity disambiguation techniques to effectively make use of the word sense subjectivity annotations. The third is applying these techniques to multiple languages, including languages with fewer resources than English. The project will have broader impacts in both research and education. First, it will make subjectivity and sentiment resources and tools more widely available, in multiple languages, to the research community, which will help advance the state of the art in automatic subjectivity analysis, which in turn will benefit end applications. Second, several educational goals will be pursued: training graduate and undergraduate students in computational linguistics; augmenting artificial intelligence courses with projects based on the proposed research, which will offer students hands-on experience with natural language processing research; and reaching out to women and minorities to increase their exposure to text processing technologies and access to research opportunities. IIS CSE HPCC Standard Grant 9215 artificial intelligence education TX Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 9102 Mihalcea, Rada University of North Texas 7495 7923 224796 0917154 September 1, 2009 TC: Small: Scalable Censorship Resistant Overlay Networks Freedom of speech is a founding principle of democratic society, and the Internet has become one of the most effective and common means of conveying expression that is likely to be controversial or suppressed. One threat to the freedom of speech online is the now widespread practice of Internet censorship by both private and state interests. These censors use a variety of social and technological means to limit availability or expression of information, stifling the democratic process. This project is focused on developing overlay networks that promote freedom of speech by circumventing social and technological censorship measures. The project will develop new software and protocols for secure overlay networks that satisfy three distinct security goals: relationship privacy, membership hiding, and blocking resistance. A secondary focus of the project is on understanding these security goals and the relationships between them, and investigating the extent to which existing systems satisfy these properties. The project will train undergraduate and graduate students to perform and apply research from a variety of disciplines (including cryptography, networking, algorithms, and coding theory). The project also develops new intellectual infrastructure by introducing the new notion of membership hiding as a security problem. Finally, the results of the project are expected to include the broad dissemination of free software that promotes freedom of expression on the Internet, against regimes that strongly oppose such expression. IIS CSE Continuing grant HPCC 9218 algorithms Sylvia J. Spengler security University of Minnesota-Twin Cities MN networking privacy cryptography 7923 TRUSTWORTHY COMPUTING 7795 Hopper, Nicholas Yongdae Kim 175415 0917151 September 1, 2009 RI: Small: Region-based Probabilistic Models for Descriptive Scene Interpretation This project focuses on the task of providing a consistent, semantic interpretation of all components of an image of an outdoor scene. The image is automatically segmented into large regions, each of which is a coherent scene component that is labeled with a rough geometric configuration (distance from the camera and surface normal), and with one of a subset of semantic classes, which include both background classes (such as water, grass, or road) and specific object classes (such as person, car, cow, or boat). The approach is based on the development of a holistic probabilistic model (a Markov random field) whose parameters are automatically learned from data. The model exploits both scene features and contextual relationships between scene components (e.g., cows are typically found on grass and boats on water). It also utilizes object shape and appearance models to identify specific object instances in the image. To address the complexities of reasoning using these richly structured models, new probabilistic inference algorithms are developed. This project helps train graduate and undergraduate students within the PI's group, as well as students in an annual project class in this area. The project also develops significant infrastructure, including an extensive data set of labeled images and efficient inference algorithms, which are freely distributed to the research community. The ability to provide a coherent interpretation of a scene composition is an important step towards automatic image annotation, with benefits both for image retrieval and for providing image summaries to visually impaired users. IIS CSE HPCC Standard Grant 9215 CA algorithms Stanford University ROBUST INTELLIGENCE 7495 499999 Qiang Ji 7923 Koller, Daphne 0917149 September 15, 2009 III: Small: Techniques for Integrated Analysis of Graphs with Applications to Cheminformatics and Bioinformatics A number of scientific endeavors generate data that can be modeled as graphs: high-throughput biological experiments on protein interactions, high throughput screening of chemical compounds, social networks, ecological networks and food-webs, database schemas and ontologies. Access and analysis of the resulting annotated and probabilistic graphs are crucial for advancing the state of scientific research, accurate modeling and analysis of existing systems, and engineering of new systems. This project aims to develop a set of scalable querying and mining tools for graph databases by integrating techniques from databases and data mining. The proposed research work is theoretical as well as empirical. New theoretical ideas and algorithms are being developed and these are being applied to the domains of Cheminformatics and Bioinformatics. The first research thrust examines primitives for graph data management and graph mining. A declarative query language for graphs is being investigated. This language is based on a formal language for graphs and a graph algebra, and separates the concerns of specification and implementation. Scalability of techniques for similarity search on graphs and mining for significant patterns is being investigated as a part of this thrust. The second research thrust applies the developed techniques to the domain of Cheminformatics. Specific tasks that are being examined are search for similar compounds, mining for significant motifs, diversity analysis, and analysis of macromolecular complexes. The final research thrust applies the developed methods to the domain of Bioinformatics. There has been an explosion of data of widely diverse biological data types, arising from genome-wide characterization of transcriptional profiles, protein-protein interactions, genomic structure, genetic phenotype, gene interactions, gene expression, proteomics, and other techniques. Techniques being developed can integrate and analyze data from multiple sources and models efficiently, while accelerating (interaction and function) prediction, and pathway discovery. Further information about the project can be found at the project web page http://www.cs.ucsb.edu/~dbl/0917149.php. IIS CSE HPCC 9216 Standard Grant CA algorithms data mining database bioinformatics INFO INTEGRATION & INFORMATICS 7364 Frank Olken University of California-Santa Barbara 7364 Singh, Ambuj 7923 488261 0917141 September 1, 2009 RI: Small: Recursive Compositional Models for Vision Detecting and recognizing objects from real world images is a very challenging problem with many practical applications. The past few years have shown growing success for tasks such as detecting faces, text, and for recognizing objects which have limited spatial variability. Broadly speaking, the difficulty of detection and recognition increases with the variability of the objects ? rigid objects being the easiest and deformable articulated objects being the hardest. There is, for example, no computer vision system which can detect a highly deformable and articulated object such as a cat in realistic conditions or read text in natural images. This project develops and evaluates computer vision technology for detecting and recognizing deformable articulated objects. The strategy is to represent objects by recursive compositional models (RCMs) which describe objects into compositions of subparts. Preliminary work has shown that these RCMs can be learnt with only limited supervision from natural images. In addition, inference algorithms have been developed which can rapidly detect and describe a limited class of objects. This project starts with single objects with fixed pose and viewpoint and proceeds to multiple objects, poses, and viewpoints. Theoretical analysis of these models gives insight and understanding of the performance and computational complexity of RCMs. The expected results are a new technology for detecting and recognizing objects for the applications mentioned above. The results are disseminated by peer reviewed publications, webpage downloads, and by university courses. IIS CSE HPCC Standard Grant 9215 University of California-Los Angeles CA algorithms vision computer vision ROBUST INTELLIGENCE 7495 Yuille, Alan 7495 Qiang Ji 399999 7923 0917123 September 1, 2009 RI: Small: Scaling Up Inference in Dynamic Systems with Logical Structure Stochastic inference problems arise naturally in many applications that interact with the world, such as natural language processing (NLP), robot control, analysis of social networks, and environmental engineering. Current real-world applications require inference mechanisms that can scale to thousands and more interactions. This project is scaling up and improving precision of automated inference and learning in dynamic partially observable domains, with later application to decision making, by combining the complementary computational strengths of logical and probabilistic methods. The inference and learning algorithms being developed are being assessed by theoretical and experimental means, with aims of improving question answering from text narratives and environment-state estimation for mobile robots. Examples of societal benefits are enabling people to access and examine details hidden in large amounts of textual information as well as enabling more helpful and autonomous mobile robots. IIS CSE HPCC IL Standard Grant 9215 algorithms University of Illinois at Urbana-Champaign 450000 Douglas H. Fisher ROBUST INTELLIGENCE 7495 7495 Amir, Eyal 7923 0917122 September 1, 2009 RI: Small: Efficient Reinforcement Learning for Generic Large-Scale Tasks Recent advances in autonomous agents research are pushing our society closer to the brink of the widespread adoption of autonomous agents in everyday life. Applications that incorporate agents already exist or are quickly emerging, such as domestic robots, autonomous vehicles, and financial management agents. Reinforcement learning (RL) of sequential decision making is an important paradigm for enabling the widespread deployment of autonomous agents. However, a few notable successes notwithstanding, state-of-the-art reinforcement learning algorithms are not yet fully capable of addressing generic large-scale applications. This project is advancing in four directions to scale-up application of RL systems. Specifically, the project is (1) developing algorithms to automatically structure the input, output, and policy representations for learning; (2) introducing parallelizable reinforcement learning algorithms so as to exploit modern parallel architectures; (3) unifying abstraction and hierarchical reasoning with model-based learning for the purpose of enabling intelligent exploration of large-scale environments; and (4) enabling reinforcement learning algorithms to benefit from low-bandwidth interactions with human users. Finally, we intend to unify the four research thrusts above into a single algorithm and conduct empirical evaluation on real-world/large-scale applications, to include biped robot balancing and walking, robot soccer in simulation and with real robots, and a full-size autonomous vehicle capable of planning paths in an urban environment. In addition to research advances and implications for improving national infrastructure, the project will contribute to undergraduate and graduate curriculum development. IIS CSE HPCC Standard Grant 9215 algorithms simulation TX University of Texas at Austin Douglas H. Fisher ROBUST INTELLIGENCE 7495 Stone, Peter 7495 7923 485000 0917109 August 1, 2009 RI:Small:RUI: Towards the Next Generation of Stereo Algorithms This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This project provides challenging test data and benchmarks designed to advance stereo vision methods to a level of practical relevance. It aims to bridge the gap between the sophisticated but brittle methods that perform best on current benchmarks and the robust but simple methods employed in real-world applications. The project provides new high-resolution datasets with accurate ground truth, taken with different cameras under different lighting conditions, and depicting complex indoor and outdoor scenes with non-Lambertian surfaces and outliers such as moving people, reflections, and shadows. The project explores novel algorithmic approaches for dealing with such challenges, including ways to leverage resolution, deriving color and noise models on the fly, and designing local region-growing techniques that allow deferring global optimization from the pixel level to the region level. Undergraduate students are actively involved in all components of this research. The project has strong potential impact along several fronts. The datasets and benchmarks resulting from this work serve as catalyst for new research and enable machine learning approaches. The algorithmic contributions allow harnessing the explosion of images available online. Robust matching techniques that can handle the variety of images available on the Internet enable a host of new applications with broad impacts on the population at large, including visual localization and navigation, as well as automatic 3D reconstruction and visualization of whole cities. Finally, the project exposes undergraduates at a liberal-arts college in rural Vermont to the world of research, experimentation, and discovery. IIS CSE HPCC Standard Grant 9215 algorithms machine learning visualization 9150 vision Middlebury College VT ROBUST INTELLIGENCE Jie Yang 7495 7495 Scharstein, Daniel 6890 7923 245000 0917104 September 1, 2009 CIF: Small: Nonintrusive Digital Speech Forensics: Source Identification and Content authentication Current digital media editing software allows malicious amateur users to perform imperceptible alterations to digital speech communications This creates a serious threat to the "knowledge life cycle". The proposal seeks to develop theories, methods and tools for extracting and visualizing evidence from digital speech content for the purpose of media source identification and content authentication. The strategy will be based on the important paradigm of nonintrusive media forensics. The hypothesis is that the physical devices and associated signal processing chains may leave behind intrinsic "fingerprint" that are detectable by statistical methods. The hypothesis clearly indicates the purpose of the project. IIS CSE HPCC 9216 Standard Grant Stephen Griffin MD University of Maryland College Park INFO INTEGRATION & INFORMATICS 7364 7364 9102 Espy-Wilson, Carol 7923 499943 0917072 September 1, 2009 DC: Small: Semantic Analysis of Large Multimedia Data Sets This research addresses interactivity and scalability in automatically analyzing large collections of video and multiple video streams processed continuously. This work is developing mechanisms to enable real-time interactive video search for user defined concepts using intelligent, active processing clusters and methods for performing high-accuracy semantic video analysis from large amounts of weakly-labeled video over distributed computing resources. The methods leverage modern cluster file systems where data is stored on the local disks of the compute servers, and the location of data is made available to the runtime system to allow co-location of compution and storage. The specific research objectives are to allow co-location of compute and storage through a runtime for parallel stream processing that parallelizes data processing and machine learning tasks across a cluster of multi-core compute nodes. The project also extends distributed versions of graphic model algorithms to speed computation of both the basic low-level signal processing steps and for the semantic analysis based on weakly labeled video data as currently available on the web. The main outcome is to demonstrate vastly accelerated, complete processing of parallel live video streams into a retrieval database with immediate search capabilities and accessing cluster resources during interactive search. The goal of this work is to develop principles for interactive applications driven by real-time processing of high-rate streaming data. The processing architecture and modules developed in this work will enable computer vision and multimedia developers to efficiently apply and test their own methods within this framework. IIS CSE HPCC 9216 architecture Standard Grant Carnegie-Mellon University PA algorithms machine learning distributed computing database vision 450000 computer vision multimedia James C. French Hauptmann, Alexander 7793 DATA-INTENSIVE COMPUTING 7793 7923 0917070 September 1, 2009 Global Graphs: A Middleware for Data Intensive Computing It is often the case that the time and effort required to develop effective and efficient software on high-end computing systems is the bottleneck in many areas of science and engineering. This project is building a novel middleware framework called Global Graphs that targets this bottleneck. Global Graphs takes a data-structure centric view of shared data where graph-based dynamic data structures drive the development of the rest of the system. A key scientific outcome of this proposed framework is to allow the programmer to have multiple views of the shared data as well as multiple views of the control and tasking model. This flexibility can be leveraged along a discrete scale of data and process views depending on whether the goal is to develop a quick prototype for validating ideas on small scale problems, or the goal is efficient realization on large scale problems, or something in between these two extremes. An additional outcome will be the development of a performance feedback engine that will provide the programmer insights into parts of the program to focus on for performance tuning. The proposed work has important implications for a range of domains requiring the processing of large scale datasets, including data mining, scientific computing and XML data management. The broader outcomes of this work will be to train capable undergraduate and graduate students. The PIs are actively encouraging under-represented minorities to participate in this effort. IIS CSE HPCC 9216 Standard Grant OH data mining 0116000 Human Subjects Ohio State University Research Foundation middleware scientific computing INFO INTEGRATION & INFORMATICS 7364 James C. French Parthasarathy, Srinivasan 7793 Ponnuswamy Sadayappan 499997 0917069 August 15, 2009 III:Small:RUI:Integrating Image and Non-Image Geospatial Data This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This project develops novel methods for integrating image and non-image geospatial data to (1) advance the state-of-the-art of automated remote sensed image analysis and, in turn, (2) improve the coverage and fidelity of the non-image repositories. A characteristic of remote sensed imagery which has not been sufficiently exploited by the analysis community is that the images can be georeferenced to extensive repositories of non-image geospatial data such as maps and geographic dictionaries termed gazetteers. In particular, these associations represent a rich source of labeled data needed to train the analysis algorithms. The first part of this project develops a framework for learning appearance models for a large set of geospatial objects indexed by an extensive gazetteer in an unsupervised fashion. Besides the standard challenges such as choice of features and form of the model, this problem is made interesting by the fact that current gazetteers only specify the spatial footprint of indexed objects using a single point location. Methods are explored for simultaneously estimating the model parameters and the spatial extents of the known objects. The second part investigates using the learned models to update the gazetteer from imagery. This includes estimating the spatial extents of known object instances as well as detecting unknown or novel instances. The project has research and educational synergies with a Spatial Analysis Research Center that the PI is establishing at the new University of California at Merced. The PI plans to open imagery so that evaluation datasets can be made publicly available through the project website (http://vision.ucmerced.edu/projects/integrating/). IIS CSE HPCC 9216 Standard Grant CA Maria Zemankova algorithms vision INFO INTEGRATION & INFORMATICS 7364 7364 University of California - Merced 6890 7923 Newsam, Shawn 396684 0917062 September 1, 2009 III:Small:Integrated Problem Diagnosis and Repair in Databases and Storage Area Networks Databases are typically used as a subsystem in a larger system that contains Web servers, application servers, and network-attached storage servers. Such complex systems experience some form of change all the time, e.g., an update to a Java module in the application server, a statistics update in the database, or a RAID rebuild in a storage volume. Such changes in different subsystems can cause an overall performance degradation whose cause is hard to diagnose. The diagnosis task is all the more daunting because enterprise environments have isolated administration teams and tools for each subsystem. This project is developing an integrated tool called DIADS that automates complex administrative tasks like problem diagnosis, what-if analysis, orchestrating disaster recovery, and online tuning when a database is used as a subsystem in a larger system. DIADS contains two technical innovations. Problem diagnosis involves reconstructing system behavior at various points of time using historic and current monitoring data collected from the system. However, the amount and quality of monitoring data available from production systems is constrained by the need to keep monitoring overhead low. DIADS uses an abstraction called Annotated Plan Graph to represent and reason about database behavior in the context of a larger system. Annotated Plan Graphs are generated from light-weight monitoring data. The other innovation in DIADS is a suite of workflows for administrative tasks that combine machine-learning techniques with domain knowledge from system experts. For example, for problem diagnosis, the machine-learning part of the workflow provides core techniques to handle large and noisy streams of monitoring data, while the domain-knowledge part acts as checks-and-balances to guide the diagnosis in the right direction. This unique design enables DIADS to function effectively even in the presence of multiple concurrent problems as well as noisy monitoring data prevalent in production environments. DIADS is being prototyped for research and educational purposes in a datacenter setting with PostgreSQL databases and an enterprise-level storage area network. For further information see the project web page at http://www.cs.duke.edu/~shivnath/diads.html IIS CSE Continuing grant HPCC 9216 network NC database INFO INTEGRATION & INFORMATICS 7364 Frank Olken Duke University 7364 Babu, Shivnath 7923 117697 0917059 September 1, 2009 NetSE: Small: A Framework to Identify Relationships Among Students in School Bullying Resulting from using Digital Communication Media Bullying among students at a school has become a serious social problem. In the project, researchers at University of California, Irvine and at KDDI Research and Development Laboratories study school bullying using digital communication media (such as cell phones, short messaging systems, emails, blogs) and create a sociological based network framework to help teachers and parents identify whether school bullying may exist among students. The project models interactions among students as a relationship network, constructs a relationship network from usage statistics of digital communication media, and identifies whether unique structural features exist in a relationship network that may indicate bullying among students. The framework is designed based on two key hypotheses: (1) school bullying imposes distinct structural features in a relationship network, and (2) a relationship network is constructed with some degree of accuracy from usage statistics of digital communication media without examining privacy-violating information. The framework only uses usage statistics that maintain privacy of communication, and it extracts such usage statistics from those publicly available, those collected by and traditionally made only available internally to a service provider, and those that the framework directly monitors. The PIs collaborate with researchers in sociology and social psychology to empirically verify the hypotheses used in the framework, design the framework and empirically examine the framework. The intellectual merit of the project includes considering privacy of communication and applying sociology and social psychology knowledge in framework design. The broader impact of the project includes advancing understanding of school bullying, creating a framework that helps teachers and parents, and sharing of the project findings and artifacts with a large segment of the research community. IIS CSE HPCC Standard Grant 9218 CA network 300000 privacy University of California-Irvine 0000912 Computer Science David W. McDonald NETWORK SCIENCE & ENGINEERING 7794 7794 7923 Suda, Tatsuya 0917040 July 15, 2009 HCC-Small:Interactive Auditory Displays Interactive Auditory Displays PI: Ming C. Lin Co-PIs: Gary Bishop and Dinesh Manocha Department of Computer Science University of North Carolina at Chapel Hill An auditory display utilizes sound to communicate information to a user and offers an alternative means of visualization. By harnessing the sense of hearing, audio rendering can further enhance a user's experience in a multimodal virtual world. Acoustic realism has many areas of applicability including virtual reality, computer gaming, training systems, desktop interfaces, education, and scientific visualization. We are conducting an ambitious research program to develop interactive auditory displays. Our goal is to develop new algorithms for physics-based sound synthesis and sound propagation for interactive applications including computer gaming, training systems, and enabling technologies. The approach involves the fusion of both geometry (for high frequencies) and physics (for low frequencies) to model sound propagation and the development of techniques for acoustic levels of detail. To this end we are developing efficient numerical algorithms based on domain decomposition and exploiting modern architecture features to further accelerate the overall performance. We are also evaluating the performance of our algorithms on different applications. In addition to acoustic simulation, our research is generating a fundamental scientific foundation and interactive performance methods for solving wave/sound propagation problems in highly complex domains that span many scientific and engineering disciplines. IIS CSE Continuing grant HPCC architecture 9215 algorithms simulation visualization education NC University of North Carolina at Chapel Hill GRAPHICS & VISUALIZATION 7453 7453 Lawrence Rosenblum 7923 Lin, Ming Gary Bishop Dinesh Manocha 328894 0916993 September 1, 2009 HCC: Small: View of Privacy in Older Adults This research addresses open questions on the interaction of aging, financial security in an information society, and elder perceptions of the risks of digital information technologies. The end technological goal is to improve the design of security and privacy technologies for older Americans. Note that security on the Internet is designed by younger, risk-seeking technologists who are primarily male. Those over 65 who require a secure Internet are by definition older, also risk-averse, rarely technologists, and more often than not female. It is not surprising that there is a mismatch between the mental models of secure system designers and this growing group of users. In terms of method, this research will include an examination of the value of multimedia combined with the scale enabled by traditional surveys. Specifically, the research will evaluate the inclusion of multimedia interactive technologies in terms of enabling inclusion of the nuance previously possible only in smaller scale qualitative work. The implications of computer security being systematically ill-suited for elders are profound, and improving this mismatch is the broader implication of this work. Never has so much wealth been accessible with such ease by so many predators. Organized crime reaches out from remote jurisdictions, where American law enforcement cannot reach back. The combination of the concentration of wealth among Americans over age 65, the global unsecured network, availability of personally identifiable information used for authentication, increasingly organized online crime, and the disproportionate lack of technical expertise among elders can create a perfect digital storm. Yet this problem is profoundly under-studied. Without computer security technology built to serve elders, these risks cannot be effectively, systematically addressed. IIS CSE HPCC Standard Grant 9218 IN network security 0116000 Human Subjects privacy Indiana University multimedia 9102 Camp, L. Jean David W. McDonald Katherine Connelly Lesa Lorenzen-Huber 399999 7923 TRUSTWORTHY COMPUTING 7795 0916951 September 1, 2009 RI: Small: Simplifying Text for Individual Reading Needs A surprisingly large number of Americans read below their grade level, either because of limited education or because their native language is not English. Low reading levels impact a child?s progress in school and an adult?s job opportunities as well as limiting information access. This project aims to improve access by developing new language processing technology for selecting and transforming text to obtain material at lower reading levels, extending current paraphrasing work that focuses on summarization as compression to include explanatory expansions. In addition, the goal is to develop adaptive models that can be tuned to a specific domain and an individual's needs. The approach involves analyzing corpora of comparable text collected from the web, developing models of paraphrasing aimed at generating simplified English, developing a discourse-sensitive clause selection method for expanding or omitting details, and exploring representations of language that facilitate domain and user adaptation. The language processing contributions of this work include development of text resources to support language technology in education applications, new representations of reading difficulty, and advances in automatic methods of paraphrasing. The broader impact of this project includes making information more accessible to people with limited English reading proficiency. In addition, students working on the project will have the opportunity to interact with teachers from a local school so as to better understand the impact of their work and guide their approach, and their work will be showcased in University of Washington diversity-oriented outreach programs. IIS CSE HPCC Standard Grant 9215 University of Washington WA education 450000 Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 9102 Ostendorf, Mari 7495 7923 0916948 September 1, 2009 III: Small: Collaborative Research: Reconstruction of Haplotype Spectra from High-Throughput Sequencing Data Recent advances in high-throughput sequencing (HTS) technologies provide opportunities to study genome structure, function, and evolution at an unprecedented scale, and are profoundly transforming genomic research. However, fully realizing the potential of HTS technologies requires sophisticated data analysis methods. This research project is aimed at developing efficient computational methods for reconstructing the full spectrum of haplotype sequences from HTS data. Working in collaboration with molecular biologists from the University of Connecticut Health Center and the Centers for Disease Control, the investigators will develop methods enabling three novel applications of HTS, namely (a) reconstruction of diploid genome sequences, including complete haplotype sequences of each CNV copy, (b) reconstruction of alternative splicing isoform sequences and their frequencies, and (c) reconstruction of viral quasispecies sequences and their frequencies. Major outcomes of the project will include the development of a comprehensive analytical toolkit for these problems, and high-quality open source software implementations that will be made available free of charge to the research community. The project will provide opportunities for participation of undergraduate and graduate students in bioinformatics research at UCONN and Georgia State University, and will especially encourage participation of women and underrepresented groups. IIS CSE Continuing grant HPCC 9216 Sylvia J. Spengler bioinformatics INFO INTEGRATION & INFORMATICS 7364 University of Connecticut CT data analysis 7364 Mandoiu, Ion 7923 Yufeng Wu 94340 0916944 September 1, 2009 HCC: Small: Interface Interactivity and User Engagement: A Communications Perspective Interactivity has become ubiquitous in the digital media landscape. From scrollbars in mobile texting devices to customization options in Web portals to chat functions on social-networking sites, there has been a proliferation of interactive tools affording enhanced user interaction with the system. While studies in the past have assessed the efficacy of individual tools, we have precious little generalizable knowledge about the larger concept of interactivity. How does interactivity affect user experience? Does it always ensure richer user engagement with the medium? The research will experimentally investigate three species of interactivity corresponding to the three central elements of communication -- source, medium, and message. Data will be used to articulate three ordinal levels of each type of interactivity, which will then be operationalized and used in further experiments to detect the combined effects of the three types of interactivity on user engagement as well as other outcomes of interest for both power users and regular users of Web interfaces. The research will assess the validity of conceptualizing interactivity in terms of multiple loci and more importantly, explore theoretical mechanisms by which interactivity is believed to affect user engagement. A scientific understanding of the psychological effects of interactivity is quite critical for a society that is becoming saturated with interactive digital media. Dissemination of the proposed work will likely spawn a new wave of theoretically driven interactivity research. Research results will feed directly into design of interfaces for a variety of purposes, from learning systems to serious games. The proposed comparison between power users and regular users will shed light on interactivity's potential to bridge the digital divide. Equipment budgeted for lab studies will enhance infrastructure for research and education at Penn State's Media Effects Lab. IIS CSE HPCC Standard Grant 9215 PA education ubiquitous 0116000 Human Subjects networking Pennsylvania State Univ University Park HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald 7923 Sundar, Shyam 432313 0916918 September 1, 2009 RI: Small: Robust Automatic Speech Recognition in Highly Reverberant Environments Speech processing systems, including automatic speech recognition and speaker identification, are the key enabling technologies that permit natural interaction between humans and intelligent machines such as humanoid robots, automated information providers, and similar devices. For example, it is now commonplace to encounter speech-based intelligent agents handle at least the initial part of a query in many types of call center applications. While we have made great progress over the past two decades in overcoming the effects of additive noise in many practical environments, the failure to develop techniques that overcome the effects of reverberation in homes, classrooms, and public spaces is the major reason why automated speech technology remains unavailable for general use in these venues. Reverberation remains one the most difficult unsolved problems in speech recognition in open acoustical environments. This project develops novel approaches that combat the effects of reverberation through two complementary perspectives: contemporary knowledge of human auditory processing and state-of-the art application of statistical source separation techniques that build on techniques in image and music processing. The synergistic development of these approaches is expected to provide substantially improved speech recognition and speaker identification accuracy in reverberant acoustical environments, along with a principled structure that enables us to understand on a much deeper level why the solutions to these problems are effective. This work is expected to have an enormous impact in extending the applicability of automatic recognition of natural and casual speech to highly reverberant environments. IIS CSE HPCC Standard Grant 9215 Carnegie-Mellon University PA 450000 Tatiana D. Korelsky Stern, Richard ROBUST INTELLIGENCE 7495 Rita Singh 7495 7923 0916875 September 15, 2009 TC: Small: Collaborative Proposal: User-Controlled Persona in Virtual Community Today we have organizational and software procedures that facilitate the exchange of interpersonal information in social networking sites, instant messaging, bulletin boards, online role?playing games, computer?supported collaborative work, and online education. All of these applications fit into the larger category of social media that support virtual communities. As we increasingly rely on this cyberspace, the issue of privacy protection in social media is critically important. The objective of this project is to develop an advanced framework called U-Control for Digital Persona and Privacy Management to manage and release personal information considering a notion of digital persona based user privacy preferences and associated risks in disclosing such private information over virtual communities. Successful completion of this project will result in the development of a protection framework and architecture to address the privacy challenges in social media-based virtual communities. This research effort is also expected to set an important direction to the future research in the area of virtual communities and online privacy solutions. This research has the potential for broad societal impact by providing IIS CSE Continuing grant HPCC architecture 9218 education networking privacy NM Amy Baylor 7923 TRUSTWORTHY COMPUTING 7795 Shin, Dongwan New Mexico Institute of Mining and Technology 62452 0916874 September 1, 2009 III: Small: Towards Scalable and Comprehensive Uncertain Data Management Due to the importance of uncertain data for a large number of applications, there has been significant recent interest in database support for uncertain data. Existing work in this area includes new models for uncertain data, prototype implementations, and efficient query processing algorithms for specific types of queries. Despite the recent efforts, several important aspects of uncertain data management remain unexplored. This project addresses two of these areas: Query Optimization and Support for Non-Relational Operators. The first goal is about efficient execution of uncertain data queries. As with traditional data, efficient execution is necessary for ensuring the viability of uncertain data management systems. However, due to the complications of ensuring correct results, and the need for CPU-intensive operations over probability distributions, the goal is critical and challenging. In this project, automatic query optimizations are developed, through query rewriting rules that involve probability threshold operators, corresponding access methods, and cost estimation functions. The difficulty of handling uncertainty when dealing with non-relational operators has been expressed in many domains. The project aims to advance the capability of tracking the exact impact of uncertain inputs as data is processed by arbitrary programs, leveraging advanced techniques from the area of program analysis. A key problem with traditional Monte Carlo based solutions lies in correctly identifying independence in the output of Monte Carlo simulations. Data lineage tracing, which identifies the set of inputs used to compute an output value, is used to address the challenge. Furthermore, a program dependence tracing based approach is devised to trace the propagation of uncertainty during execution of arbitrary binary code. The technique does not rely on Monte Carlo simulations, and does not require access to source code or domain knowledge. For further information see the project web page: http://www.cs.purdue.edu/homes/sunil/uncertainty IIS CSE Continuing grant HPCC 9216 Purdue University IN algorithms database INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 Prabhakar, Sunil 7923 Xiangyu Zhang 164590 0916870 September 1, 2009 RI: Small: Cooperative Coevolutionary Design and Multiagent Systems Cooperative coevolution is a potent approach to doing large-scale stochastic optimization. The unsolved game-theoretic challenges inherent in this computational method are complex and of significant interest to the evolutionary computation community. This project is advancing the state of the art in coevolution and is applying it to significantly larger problems than commonly found in the literature. These challenges, and their solution, have potentially transformative impact on other co-adaptive environments such as multiagent reinforcement learning, estimation of distribution algorithms, agent modeling, and swarm robotics. Coevolution has strong applicability to fields that use multiagent system models, including multirobotics, biology, economics, land use, and political science. Better models in these fields can positively affect society, policy, homeland security, and the environment. IIS CSE HPCC Standard Grant 9215 algorithms VA security robotics Douglas H. Fisher George Mason University ROBUST INTELLIGENCE 7495 455000 7923 Luke, Sean 0916868 August 15, 2009 RI: Small: Learning-Based Systems for Single-Image Photometric Reconstruction This project focuses on developing algorithms and datasets that can transform photometric reconstruction systems from hand-designed systems into learning-based systems that are optimized on real-world data. Photometric reconstruction systems derive cues from the perceived intensity of different locations on a surface. Shape-from-shading, where the surface is assumed to have a diffuse reflectance, is a well-known example of photometric reconstruction. This project produces the datasets and methods necessary to use machine learning techniques to build models for photometric reconstruction. This learning-based approach enables systems to be optimized on real-world data so that they produce the most accurate results possible. In addition, this learning-based approach enables the development of more sophisticated methods with more parameters than typically used in hand-designed systems. The ability to find optimal parameters in an automated fashion can not only improve existing approaches, such as by incorporating image data more effectively, but can also enable the development of algorithms that push the boundaries of current systems. In particular, algorithms are developed for estimating the shape of objects without knowing the illumination or even trying to explicitly model it. The power of the learning approach cannot be realized without data for training and testing. A major task in this work is the construction of a database of images and ground-truth 3D reconstructions of the objects in the images. The 3D models can be found using an example-based photometric stereo technique. IIS CSE HPCC Standard Grant 9215 algorithms machine learning database University of Central Florida FL ROBUST INTELLIGENCE 7495 7495 Qiang Ji Tappen, Marshall 7923 363005 0916859 September 1, 2009 DC:Small: ProActive: A RAID Protection Activator for High Availability Partial or complete disk failures are becoming so common and frequent in modern-day large-scale data centers that they are now considered the norm rather than the exception. It is thus of paramount importance to develop novel approaches to effectively and significantly supplementing and improving the existing RAID protection mechanisms, with the goal of providing high reliability and availability for RAID-structured storage systems so that they are capable of tolerating partial, complete, and multiple disk faults while delivering acceptable, non-stop services to the users. This project seeks to develop a holistic framework, called a RAID protection activator (ProActive), to address the fundamental and ever-increasing availability challenge facing RAID-structured storage systems. ProActive exploits application workload intensity and data/parity management and intelligently leverages rich available spare storage resources in large-scale data centers to address the efficiency problem of the existing state-of-the-art availability mechanisms for RAID. ProActive will develop solutions to handle the increasingly more frequent partial and complete disk failures in RAID-structured storage systems based on the design goals of significantly supplementing and improving existing fault-detection, fault-tolerance, and fault-recovery mechanisms. The broader impact of the project lies in its (1) research development that impacts and enriches fields as diverse as high-performance computing, availability and reliability in high-end computing systems; (2) infrastructure development that enhances research and education at UNL and, through accessibility via public domain, the high performance and data-intensive computing community; and (3) educational development that exposes graduate and undergraduate students to cutting edge research in highly available storage systems. IIS CSE HPCC Standard Grant 9215 education 9150 University of Nebraska-Lincoln NE James C. French high-performance computing 7793 DATA-INTENSIVE COMPUTING 7793 7751 Jiang, Hong 474739 0916858 September 15, 2009 HCC: Small: Learning-by-Explaining to a Virtual Human The primary goal of this project is to better understand the cognitive and social factors in a learning paradigm called learning-by-explaining and to build a virtual training partner to promote better learning in this paradigm. Learning-by-explaining is an effective learning technique used by human tutors that has yet to be exploited by the intelligent tutoring system community. In this technique, students are encouraged to explain a concept either to another or themselves. Decades of research shows that generating such explanations can lead to deep understanding of the learning material, and that these learning effects are particularly strong when the explanations are delivered in a social context (i.e., explaining to a peer or tutor), as opposed to explaining to oneself. These effects have even been observed when the "other" is a computer generated character. There are competing views on why learning-by-explaining works. Cognitive theories emphasize how the act of generating an explanation helps student recognize gaps and conflicts in their mental models and creates opportunities for mental model revision and that learning partners facilitate this process by identify missing knowledge and prompting for further clarification. In contrast, social theories argue that the presence and behavior of the explainee motivates learners to invest more effort into the learning tasks, resulting in learning gains. This project aims to gain better understanding of the interplay between the cognitive and socio-relational feedback, how they impact the learning-by-explaining process and how to build an explainee agent that facilitates learning-by-explaining. The project seeks to answer two research questions: 1) Will cognitive feedback or socio-relational feedback facilitate learning-by-explaining? 2) How to build an effective virtual training partner "a virtual explainee" in the learning-by-explaining context? This project will inform the development of next generation intelligent tutoring systems and has the potential to significantly enhance basic understanding of the design of human centered computing. It explores factors related to effective multi-modal interfaces and helps to identify crucial factors that impact social impressions and effective interaction which may facilitate a new generation of more human-centric approaches to human learning. The project will also support the research activities of underrepresented groups (the senior researcher is a woman and the work will support the training of an intern from a HCBU-MI). It will enhance infrastructure for research and education by making advanced research tools and corpora freely available to the research community. These tools provide a novel method to study and enhance the effectiveness of computer-mediated and human-computer interaction, allowing the experimental manipulation of key mediating factors in such interactions. The work will advance discovery and understanding while promoting teaching and learning as it will be performed within the context of USC's Centers for Creative Technologies, a university-affiliated federal laboratory with a core mission to develop and disseminate advanced virtual reality training technology with an extensive track record in transitioning technology into the classroom. IIS CSE Ephraim P. Glinert Continuing grant HPCC 9215 CA human-computer interaction education 0116000 Human Subjects University of Southern California HUMAN-CENTERED COMPUTING 7367 7367 Gratch, Jonathan 7923 181314 0916855 September 15, 2009 NetSE: Small: Collaborative Research: Multi-Resolution Analysis & Measurement of Large-scale, Dynamic Networked Systems with Applications to Online Social Networks Many large-scale networked systems such as Online Social Networks (OSNs)are often represented as annotated graphs with various node or link attributes. Such a representation is usually derived from a snapshot that is obtained through measurements. These graph representations enable researchers to characterize the connectivity of these systems using graph analysis. However, captured snapshots of large networked systems are likely to be distorted. Furthermore, commonly-used graph analysis characterizes the connectivity of a graph in an indirect fashion and generally ignores graph dynamics. This multi-disciplinary research program designs, develops and rigorously evaluates theoretically grounded techniques to accurately measure and properly characterize the connectivity structure of large-scale and dynamic networked systems. More specifically, the project examines various graph sampling techniques for collecting representative samples from large and evolving graphs. It also investigates how multiscale analysis can be used as a powerful technique to characterize the key features of the connectivity structure of large dynamic networked systems at different scales in space and time. The developed techniques will be used to characterize fundamental properties of the friendship and various interaction connectivity structures in different OSNs. This project promises to identify the underlying technical and social factors that primarily drive the structural properties and dynamic nature of OSN-specific connectivity structures. It will produce new models for friendship and interactions in OSNs, a large archive of anonymized datasets and new tools for OSN measurement, simulation and analysis. The latter will be incorporated into newly-developed courses in Computer Science and Sociology, and will be freely distributed. IIS CSE HPCC Standard Grant 9218 simulation NC Duke University 0000912 Computer Science David W. McDonald Maggioni, Mauro NETWORK SCIENCE & ENGINEERING 7794 7794 7923 95000 7363 0916852 September 1, 2009 III: Small:Using Data Mining and Recommender Systems to Facilitate Large-Scale Requirements Processes Problems related to requirements definitions account for numerous project failures and translate into significant amounts of wasted funds. In many cases, these problems originate from inadequacies in the human-intensive task of eliciting stakeholders' needs, and the subsequent problems of transforming them into a set of clearly articulated and prioritized requirements. These problems are particularly evident in very large projects such as the FBI Virtual Case File or NASA's Space Station, in which knowledge is dispersed across thousands of different stakeholders. On one hand, it is desirable to include as many people as possible in the elicitation and prioritization process, but on the other hand this can quickly lead to a rather chaotic overload of information and opinions. The work proposed under this grant will develop a new framework that utilizes data mining and recommender systems techniques to process and analyze high volumes of unstructured data in order to facilitate large-scale and broadly inclusive requirements processes. The proposal is based on the observation that the requirements elicitation process of many large-scaled industrial and governmental projects is inherently data-driven, and could therefore benefit from computer-supported tools based on data mining and user modeling techniques. INTELLECTUAL MERIT The proposed research will lead to a robust requirements elicitation framework and an associated library of tools which can be used to augment the functionality of wikis, forums, and specialized management tools used in the requirements domain. Specifically, this research will enhance requirements clustering techniques by incorporating prior knowledge and user-derived constraints. A contextualized recommender system will be designed to facilitate appropriate placement of stakeholders into requirements discussion forums generated in the clustering phase. BROADER IMPACT The proposed work has potential for broad impact across organizations that develop stakeholder-intensive systems. Technology transfer can be expected due to collaborations with organizations such as Siemens and Google planned as an integral part of this research. Educational materials will be developed specifically for requirements engineering and recommender systems courses, and will be broadly disseminated. Key Words: Recommender systems; Data mining; Clustering; Requirements engineering; Requirements elicitation. IIS CSE HPCC 9216 IL Lawrence Brandt Standard Grant data mining DePaul University INFO INTEGRATION & INFORMATICS 7364 7364 9102 Bamshad Mobasher 7923 Huang, Jane 499892 0916845 September 1, 2009 HCC: Small: Dynamic Skeletal Part Hierarchies for Sketching 3D Shapes and Their Animations Abstract 0916845 Nealen, Andrew HCC: Small: Dynamic Skeletal Part Hierarchies for Sketching 3D Shapes and Their Animations Rutgers University New Brunswick Software for creating, modifying and animating digital 3D shapes is a key component in many computer applications: computer aided design, engineering, computer animation and digital games to name a few. This proposal addresses research into dynamic hierarchical part hierarchies, which combine the interrelated topics of modeling and animating digital shapes into a coherent whole, as well as simple and effective sketch-based computer tools, with the ultimate goal of making these tasks more accessible to amateurs and professionals alike. Advances in consumer computer graphics, as well as the current inþux of user generated digital content, both in virtual 3D worlds as well as on the Internet, illustrate the demand for easy-to-use 3D content creation software. Recent research in shape modeling and animation, as well as sketch-based interfaces, has led to the development of experimental techniques, which clearly demonstrate their viability. Yet, most of these are highly specialized and domain specific, and they do not deal with these problems as a whole. The proposed research will build upon this previous work, but put an emphasis on developing new sketch-based interfaces, algorithms and shape representations that allow combining previously disjoint, yet intrinsically connected tasks of shape modeling and animation. IIS CSE HPCC Standard Grant 9215 Stephen Griffin algorithms NJ computer graphics graphics animation Rutgers University New Brunswick GRAPHICS & VISUALIZATION 7453 7923 Nealen, Andrew 499272 0916829 September 1, 2009 RI: Small: Modeling and Recognition of Landmarks and Urban Environments The goal of this project is to design a scalable and robust system for modeling and representing the spatiotemporal and semantic structure of large collections of partially geo-referenced imagery. Specifically, the project is aimed at Internet photo collections of images of famous landmarks and cities. The functionalities of the system include 3D reconstruction, browsing, summarization, location recognition, and scene segmentation. In addition, the system incorporates human-created annotations such as text and geo-tags, models scene illumination conditions, and supports incremental model updating using an incoming stream of images. This system is designed to take advantage of the redundancy inherent in community photo collections to achieve levels of robustness and scalability not attainable by existing geometric modeling approaches. The key technical innovation of the project is a novel data structure, the iconic scene graph that efficiently and compactly captures the perceptual, geometric, and semantic relationships between images in the collection. The key methodological insight of this project is that successful representation and recognition of landmarks requires the integration of statistical recognition and geometric reconstruction approaches. The project incorporates statistical inference into all components of the landmark modeling system, and includes a significant layer of high-level semantic functionality that is implemented using recognition techniques. Potential applications with societal impact include virtual tourism and navigation, security and surveillance, cultural heritage preservation, immersive environments and computer games, and movie special effects. Datasets and code produced in the course of the project will be made publicly available. The project includes a significant education component through undergraduate and graduate course development. IIS CSE HPCC Standard Grant 9215 education NC security University of North Carolina at Chapel Hill ROBUST INTELLIGENCE Jie Yang 7495 computer games 9102 7923 Frahm, Jan-Michael Svetlana Lazebnik 449179 0916812 September 1, 2009 RI: Small: Novel structured regression approaches to high-dimensional motion analysis The ability to estimate motion of objects from video is a fundamental scientific problem that arises in many tasks: finding out how the human body moves, tracking vehicles movements on a highway or the motility of schools of fish. Despite many advancements the problem remains hard because of sudden, often highly nonlinear changes and the high dimensionality of the object's configuration spaces. Much prior work has focused on building complex physics-based models, in an "analysis-by-synthesis" paradigm dominated by expert's domain knowledge. When such knowledge is lacking, the resulting models may produce inaccurate predictions. To address these issues, this project investigates a new paradigm of using limited amounts of carefully collected data to learn direct predictive models of high-dimensional motion. We approach the problem as that of the structured regression, a novel generalization of traditional statistical methods that specifically exploits the spatio-temporal structure of the data to avoid the need for "analysis-by-synthesis". This research will result in a set of robust techniques and computational algorithms that support this new modeling framework. The tools and techniques developed here will have wide applicability in many areas of technology and industry that rely on design of accurate prediction models in complex space-time domains, leading to more general and sustainable forecasting solutions. Through engagement of graduate and undergraduate students in key research activities, the project also provides advanced technical training vital for success of a new generation of computer scientists. IIS CSE HPCC Standard Grant 9215 algorithms NJ Rutgers University New Brunswick ROBUST INTELLIGENCE 7495 Pavlovic, Vladimir Qiang Ji 7923 370929 0916807 August 1, 2009 RI Small: On Robot Motor Capability for Skill Learning Endowing humanoid robots with the ability of skill learning will enable them to be versatile and skillful in performing various tasks. The problem of transferring human skills to humanoid robots raises tremendous research interest in studying human and robot motor skills. Our research aims at developing a quantitative measure of robot motor capability of a humanoid root motor system for the application of transferring human skills to a humanoid robot. An in-depth study of basic intrinsic properties of robot motor capability based on information theory will be performed to derive a pseudo index of motor performance. This pseudo index of motor performance is derived from robot kinematics, dynamics, and control with the speed-accuracy constraint taken into consideration. With the speed-accuracy constraint, the motor performance of a robot is optimized to accomplish a task. The research results demonstrate an increased understanding of robot motor capability that shows the capability and limitation of a robot for learning skills from human demonstration. The project plans to verify the result on an existing humanoid robot. The project results will provide new insights in humanoid robot motor learning and control, and launch a new research direction on human-robot interaction when both humans and robots better know their respective motor capabilities. The broader impacts include incorporation of research results into existing graduate and undergraduate robotics courses, and outreach activities of a weeklong robotics summer camp and a lab open house for high-school students. The research results will be disseminated in professional conferences, workshops and archival journal publications. IIS CSE Continuing grant HPCC 9215 Purdue University IN robotics Paul Yu Oh ROBUST INTELLIGENCE 7495 7495 Lee, C.S. George 7923 242917 0916750 September 1, 2009 RI: Small: Statistical Modeling of Dynamic Covariance Matrices Suitable models for dynamic covariance matrices can be extremely useful in several application domains, such as in text mining and topic modeling, where one can study the evolving correlation between topics; in financial data ranging from stock/bond returns to interest rates and currencies, where the paramount importance of tracking evolving covariances has been widely acknowledged; in environmental informatics to study trends in dynamic covariance among disparate variables from the atmosphere as well as the biosphere. In such domains, it is not sufficient to simply compute the sample covariance at each time step; the goal is to discover any trends there may be in the evolution of the covariance structure. This project introduces and investigates a novel family of Dynamic Wishart Models (DWMs), which has the same graphical model structure as the Kalman filter, but tracks evolution of covariance matrices rather than state vectors. Similar to the use of multivariate Gaussians in Kalman filters, the models use the Wishart and inverse Wishart family of distributions on covariance matrices. Unlike Kalman filters, an analytic closed form filtering may not be possible in DWMs, but the models still have enough structure to allow efficient approximate inference algorithms. The project focuses on approximate inference for filtering, smoothing, and related problems in the context of DWMs; develop suitable numerically stable recursive updates in order to prevent numerical loss in positive definiteness; and investigate generalizations of DWMs including mixture models for tracking complex covariance dynamics. The development of effective tracking algorithms for covariances will permit the modeling of dynamic systems where the states really represent the varying relationships between multiple entities. The key contribution of the research is in leveraging the existing literature of dynamic latent state vectors to create equally powerful methods for dynamic latent covariance matrices. Such a transformation will have direct impact on applications in text analysis and topic modeling, financial data analysis, social network analysis, environmental informatics, and several other domains, and will spawn new opportunities for bringing together researchers and students across these disciplines, thereby broadening participation in computer sciences. IIS CSE HPCC Standard Grant 9215 network algorithms University of Minnesota-Twin Cities MN Douglas H. Fisher data analysis ROBUST INTELLIGENCE 7495 455000 7495 Banerjee, Arindam 7923 Daniel Boley 0916749 September 1, 2009 HCC: Small: Perceiving and Enacting Actions in Simulated Environments: The Role of Perceptual Motor Features and Individual Differences The long-term practical objective of this research project is to develop simulated training environments that mesh with the constraints of perceiving and enacting actions in the real world. Simulated environments differ from real environments in a number of aspects. In particular, there are significant differences in perceptual and motor features between these environments. Advances in embodied cognitive science have consistently demonstrated how the body, and the environment which it inhabits, are tightly coupled with the mind, and together they form a complex system for perception and action. The central questions being investigated in this research involve identifying the conditions that promote perceiving and enacting actions in simulated training environments, and include questions such as: (a) whether high fidelity simulation environments and perceptual motor cues signaling risk are necessary ingredients to enhance training effectiveness; (b) whether performance pressure is a necessary component in training paradigms; (c) the extent to which bodily interactivity with the training environment is necessary; and (d) the extent to which individual differences in perception and action contribute to training effectiveness of simulated environments. This research project also begins to investigate whether encoding events in language that selectively focus on particular conceptual components can be used as an effective mechanism to guide visual attention. Simulation environments will be modeled after real world events, and will vary the degree of user control and level of immersion. Research participants will have their eye movements recorded, mark off when a meaningful event ends and another begins, perform perceptual mental simulation tasks by identifying the correct bodily movements, or manipulate user controlled simulation environments. This research project investigates how people spontaneously engage in riskier behaviors due to differences between simulated environments and the real world, whether putting people under some pressure is critical for successful training in simulated environments, and what type of physical interactivity with simulated environments is essential for optimal performance. This research also investigates whether differences in cognitive abilities and personality types have effects on performance in simulated environments. This project uses multiple methods in an attempt to understand along which dimensions training in simulated environments can effectively transfer to real world practice. This research pushes forward the frontiers of perceiving and enacting actions in psychology, computer science, robotics, and human computer interactions. The results will form an empirical basis promoting the development of improved methods for training in simulated worlds. Improved designs of simulated worlds for training and modeling are increasingly important in many areas of our society including: impacts in education, military, law enforcement and emergency response organization training; smart environments for the better treatment of disabled or special needs populations; and entertainment industries and the arts. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 simulation education 0116000 Human Subjects robotics TX cognitive science 500000 HUMAN-CENTERED COMPUTING 7367 7367 Texas A&M University-Commerce Lu, Shulan Tracy Henley Derek Harter 7923 0916736 September 1, 2009 III: Small: Managing Large-scale Uncertain Data Repositories Increasing numbers of real-world application domains are generating data that is inherently noisy, incomplete, and probabilistic in nature. Examples of such data include measurement data collected by sensor networks, observation data in the context of social networks, scientific and biomedical data, and data collected by various online cyber-sources. The data uncertainties may be a result of the fundamental limitations of the underlying measurement infrastructures, the inherent ambiguity in the domain, or they may be a side-effect of the rich probabilistic modeling typically performed to extract high-level events from sensor and cyber data. Similarly, when attempting to integrate heterogeneous data sources ("data integration") or extracting structured information from text ("information extraction"), the results are approximate and uncertain at best. However, there is currently a lack of data management tools that can reason about large volumes of uncertain data, and hence the information about the uncertainty is often either discarded or reasoned about only superficially. In this project, we are building a complete probabilistic data management system, called PrDB, that can manage, store, and process large-scale repositories of uncertain data. PrDB unifies ideas from "large-scale structured graphical models" like probabilistic relational models (PRMs), developed in the machine learning literature, and "probabilistic query processing", studied in the database literature. PrDB framework is based on the notion of "shared factors", which not only allows us to express and manipulate uncertainties at various levels of abstractions, but also supports capturing rich correlations among the uncertain data. PrDB supports a declarative SQL-like language for specifying uncertain data and the correlations among them. PrDB also supports exact and approximate evaluation of a wide range of queries including inference queries, SQL queries, and decision-support queries. The cross-disciplinary research undertaken during this project will enable us to simultaneously address the challenges in the areas of probabilistic databases and machine learning, and allow us to transfer the key technologies developed between those areas, thus advancing the research in both areas. It will enable the development of a significant and high-impact new class of real-world applications, in a variety of domains including health informatics, social media management, World Wide Web, and scientific databases. The PrDB system source code, and the datasets generated during the project, will be released using an appropriate open source license, at the project web site: http://www.cs.umd.edu/db/PrDB.html IIS CSE Continuing grant HPCC 9216 machine learning MD database University of Maryland College Park INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 Lise Getoor Deshpande, Amol 7923 161768 0916733 August 1, 2009 III:Small:Integrated Digital Library Support for Crisis, Tragedy, and Recovery Today people make novel uses of social networking and other internet software to respond to tragic events in creative and dynamic ways. Since shortly after the April 16, 2007 shootings at Virginia Tech, this research group has integrated digital library, data and text mining, information visualization, and social network analysis techniques to help with understanding and recovery from this tragic school crisis. This proposal is meant to research and develop a next- generation domain specific digital library software suite, the Crisis, Tragedy and Recovery (CTR) -toolkit, building upon 17 years of work on digital libraries, as well as expertise in information retrieval, data and text mining, database management, human-computer interaction, and sociology. Advanced intelligent information integration methods have not been sufficiently applied to this domain. The impact of events is felt over extended periods, requiring longitudinal perspectives to understand their complexity and inter-dependencies. Consequently, with the Internet Archive and other partners, the group will begin to create CTRnet, an integrated distributed digital library network for providing a rich suite of CTR-related services. Such work will help ensure that further tragic events might be better understood and prevented. IIS CSE HPCC 9216 Standard Grant Stephen Griffin network human-computer interaction visualization VA Virginia Polytechnic Institute and State University database Fox, Edward networking information retrieval INFO INTEGRATION & INFORMATICS 7364 information visualization 500000 7364 Donald Shoemaker Naren Ramakrishnan 7923 Andrea Kavanaugh Steven Sheetz 0916720 September 1, 2009 RI: Small: AquaSWARM: Small Wireless Autonomous Robots for Monitoring of Aquatic Environments The goal of the AquaSWARM project is to design and develop small, energy-efficient, autonomous underwater robots as sensor-rich platforms for dynamic, long-duration monitoring of aquatic environments. A novel concept of gliding robotic fish is investigated, which merges the energy-efficient design of underwater glider with the high maneuverability of robotic fish. Gliding motion, enabled by pitch and buoyancy control, is exploited to realize dive/ascent and large-distance horizontal travel. Soft actuation materials-based flexible tail fins are used to achieve maneuvers with high hydrodynamic efficiency. The research is focused on understanding gliding design for small robotic fish, and addressing the energy efficiency issue from a systems perspective. Schools of such autonomous robots are deployed in lakes at the Michigan State University Kellogg Biological Station to detect harmful algal blooms (HABs) and validate models for HAB dynamics. The project is expected to result in cost-effective, underwater robots that can perform uninterrupted, long-duration (several months), long-travel (hundreds of miles) operation in aquatic environments. This will provide a novel, viable, versatile, cyber-physical infrastructure for aquatic environmental monitoring, with applications ranging from understanding the impact of global warming, to environmental protection, drinking water reservoir safety, and seaport security. The project also offers an interdisciplinary training environment for graduate and undergraduate students, and provides outreach opportunities to inspire pre-college students and train highly qualified teachers. Robotic fish-based HAB detection will also be used as a tool to engage communities at local lakes and stimulate their interest in novel technology and environmental issues. IIS CSE HPCC Standard Grant 9215 MI security wireless Michigan State University Paul Yu Oh ROBUST INTELLIGENCE 7495 7923 Tan, Xiaobo Elena Litchman 409999 0916691 September 1, 2009 III: Small: Automatic Incremental Design for Next-Generation Database Systems Data management systems are undergoing a sea change. Specialized engines with specialized physical storage structures are emerging to address the extreme data volume and performance requirements of modern applications. At the same time, the complexity of these systems, the applications in which they are used and the platforms on which they are built is increasing. Thus, there is a growing need for automatic database design for these emerging database systems. Unfortunately, previous work in automatic design cannot be used directly. While the conceptual framework of previous research is useful, the specifics must be reworked in order to adequately take advantage of the opportunities and address the challenges that these new structures present. Furthermore, most design tools only create complete designs. There is also a need for automatic designers that can produce a new design that is sensitive to the cost of migrating an old design to a new one. Such an incremental designer would often generate a sub-optimal design if that design will produce 80% of the benefit with 20% of the work. The PIs propose to investigate this incremental automatic design paradigm for newly emerging database systems. Specifically, the project addresses three data management platforms: column stores for OLAP, main memory, cluster-based systems for OLTP, and an extension to row stores for exploiting correlations in data attributes. The PIs expect to extend the work on current design tools by demonstrating workable incremental designers as described above. There is a strong need for such tools, and if this research is successful, it should enable successful deployment of many new-style data managers. Moreover, incremental design ideas based on sound cost-benefit analysis are applicable to other data-intensive computing environments and constitute an important direction towards truly autonomic computing. Further information on the project can be found on the project web page: http://database.cs.brown.edu/projects/auto/ IIS CSE HPCC 9216 Standard Grant 9150 database Brown University RI Zdonik, Stanley INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 499999 7923 Ugur Cetintemel 0916690 September 1, 2009 III: Small: Collaborative Research: Modeling, Detection, and Analysis of Branching Structures in Medical Imaging Detection and analysis of branching structures and/or texture is very challenging; it arises in many areas of science and engineering (e.g., medical images, chemical compounds, etc). The objective of this proposal is to develop novel approaches to model, detect, and analyze branching structures obtained from multimodality data. Such representation and analysis tools are expected to make many complex problems more tractable. Examples include identifying and recognizing a large number of structure classes; discovering new relationships among structure, texture, and function or pathology; evaluating hypotheses; developing modeling tools; assisting with surgical design; and managing medical image data efficiently. Specifically, the investigators plan to explore three research topics under this project: (1) To develop descriptors of branching structures and texture, and knowledge discovery tools that will enable hypotheses generation and evaluation and improve modeling of branching structures; (2) To design automated algorithms and a flexible framework to detect branching structures. The investigators are especially interested in addressing challenges of occlusion and topology change; (3) To demonstrate the applicability of the proposed tools to breast imaging by building a prototype database of images from various modalities and associated clinical data that will provide advanced analysis and visualization capabilities. Though the investigators use breast imaging as the driving application, the proposed project is expected to provide software and data resources that can assist clinical tasks and scientific discoveries in general. Developing automated tools to effectively characterize, detect, and classify tree-like structures in images would provide great insight into the relationship between the branching topology and function or pathology. The investigators plan to further contribute to the medical/scientific community by disseminating the related software and annotated data sets. The educational goals include incorporating research findings to graduate courses at Temple (data mining course and medical image analysis seminar) and at the University of Pennsylvania (medical image analysis course). IIS CSE Continuing grant HPCC 9216 PA algorithms data mining visualization Sylvia J. Spengler database University of Pennsylvania INFO INTEGRATION & INFORMATICS 7364 7364 7923 Bakic, Predrag 62230 0916688 September 15, 2009 TC: Small: Collaborative Proposal: User-Controlled Persona in Virtual Community Today we have organizational and software procedures that facilitate the exchange of interpersonal information in social networking sites, instant messaging, bulletin boards, online role?playing games, computer?supported collaborative work, and online education. All of these applications fit into the larger category of social media that support virtual communities. As we increasingly rely on this cyberspace, the issue of privacy protection in social media is critically important. The objective of this project is to develop an advanced framework called U-Control for Digital Persona and Privacy Management to manage and release personal information considering a notion of digital persona based user privacy preferences and associated risks in disclosing such private information over virtual communities. Successful completion of this project will result in the development of a protection framework and architecture to address the privacy challenges in social media-based virtual communities. This research effort is also expected to set an important direction to the future research in the area of virtual communities and online privacy solutions. This research has the potential for broad societal impact by providing user-controlled sharing of personal attributes and the efficiency of existing business models and operations of such. IIS CSE Continuing grant HPCC architecture 9218 education Arizona State University AZ networking privacy Ahn, Gail-Joon Amy Baylor 7923 TRUSTWORTHY COMPUTING 7795 89476 0916687 July 15, 2009 RI: SMALL: Category-Driven Affordance Prediction For Autonomous Robots This research program is developing theory and algorithms that will enable a robot to learn through training and experimentation how to predict object and environmental affordances from sensor data. These affordances determine which actions a robot may perform when interacting with a given object, and thus define the capabilities of the robot at any given time. For example, a doorway affords the possibility to leave one room and enter another, and a handle attached to an object affords the ability to grasp it. The approach being developed leverages a graphical model approach to learn visual categories ? to learn the world contains entities such as doors and handles ? that provide a powerful intermediate representation for affordance prediction and learning. This is in contrast to the classical direct perception approach in which the agent learns a direct mapping from image features to affordances. The models and theory are being validated on two robot platforms and tasks: an outdoor mobile robot performing navigation and pursuit/evasion tasks, and an indoor robot manipulator performing assembly/disassembly tasks. The importance and broader impact of this research lies in empowering robots to actively and effectively learn about its environment given little human training. Because pre-programmed sensing capabilities are typically brittle ? not accounting for the variability of the world in which the robot is actually operating ? and because extensive human training and supervision is too labor intensive, such learning paradigms are essential for the development of robots that operate effectively in the human world. IIS CSE Continuing grant HPCC 9215 algorithms GA Tech Research Corporation - GA Institute of Technology GA INFO INTEGRATION & INFORMATICS 7364 Paul Yu Oh ROBUST INTELLIGENCE 7495 7364 GRAPHICS & VISUALIZATION 7453 7495 7453 7923 Bobick, Aaron James Rehg 290138 0916686 September 1, 2009 RI: Small: Algorithms for Sampling Similar Graphs Using Subgraph Signatures Abstract below: Graphs and networks are a natural representation across a wide range of disciplines and domains. Statistical tools have recently been brought to bear on the analysis of graphs, yielding rich dividends in various application areas. The aim of this project is to use tools from statistics and graph theory to develop algorithms that generate similar graphs efficiently. Since graph data is often expensive to collect, it is desirable to synthetically generate graphs. To be widely applicable however, the generated graphs need to both preserve the semantics of the original data (i.e., be drawn from the same distribution) and be efficient to compute. Two key questions form the core emphasis of the current project. First, how does one measure similarity between two graphs? Second, how can this notion of similarity be used to generate new graphs? On the topic of similarity, the project will investigate representations to preserve global properties, propose new, efficient, representations for signatures, and explore sampling techniques and their convergence behavior. On the topic of generation of new graphs, the project will develop an exponential random graph model using signatures, investigate feature selection via regularization, propose novel methods to sample from the exponential random graph model and novel techniques to produce proposal graphs, and provide rigorous empirical validation across a range of application areas. The project will facilitate the study of large complex structures of the kind frequently encountered in domains like theoretical ecology, social networks, and chemo-informatics, allowing researchers in these domains to leverage statistical network analysis tools to identify significant patterns and understand algorithm performance. For further information see the project web page: URL: http://www.stat.purdue.edu/~vishy/graphs.html IIS CSE HPCC 9216 Standard Grant Purdue University IN network algorithms INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7923 Swaminathan, Vishwanathan Jennifer Neville Sergey Kirshner 494538 0916676 September 1, 2009 III: Small: Inference of Causal Regulatory Relationships from Genetic Studies III:Small: Inference of Causal Regulatory Relationships from Genetic Studies Inference of biological networks from high-throughput genomic data is a central problem in bioinformatics where many different types of methods have been proposed and applied to a wide diversity of datasets. Several recent studies have collected data which contain both genetic variation information as well as gene expression information from a set of genetically distinct strains of an organism which have several advantageous properties for inferring causal regulatory relationships between genes. A principled way of representing causal relationships is using graphical causal models and a rich theory of inference of such models from observational data and interventions has been developed. However, this theory assumes full knowledge of the joint distribution which is equivalent to having very large samples and so is only guaranteed to work asymptotically. In this proposal, the team will extend causal inference methods in several directions motivated by applications to genetic views of genomics datasets where there are relatively small samples. In particular they will apply their new methods to detecting the presence and absence of causal relationships between yeast genes. While the focus of this proposal is on applying the developed techniques to a specific problem in bioinformatics, the causal inference issues addressed in this proposal are the general issues faced when applying causal inference to finite samples. Many of the approaches developed in this proposal will be applicable to a wide range of problems. The resulting methods developed in this proposal will be made available to the scientific community through publicly available software. The project involves the training of a graduate and undergraduate students. The collaborative nature of the project will expose the students to the medical and genetics worlds, and at the same time, it will improve their abilities to design and implement solutions to complex algorithmic and statistical problems. The research will be converted into course materials for the interdisciplinary course, Computational Genetics, which is taken by both undergrad and graduate students as well as students from the medical school. IIS CSE Continuing grant HPCC 9216 University of California-Los Angeles CA Sylvia J. Spengler bioinformatics INFO INTEGRATION & INFORMATICS 7364 7364 Eskin, Eleazar 7923 170065 0916663 September 1, 2009 DC:Small: Energy-aware Coordinated Caching in Cluster-based Storage Systems As the computing capacity increases rapidly in large-scale cluster computing platforms, power management becomes an increasingly important concern. This project focuses on the research of reducing disk and memory power consumption through energy-aware cooperative caching in cluster-based storage systems. The project leverages I/O characteristics of scientific applications and dynamic power management features of disk drives and memory chips to reduce I/O energy consumption. This project involves three components: (1) investigate program context based pattern detection to predict I/O activities in the operating systems, (2) investigate disk energy aware cooperative cache management schemes, and (3) prototype the management schemes and incorporate into cluster-based file systems. This project has broader impact through its contributions to the energy-aware computing, graduate education, and undergraduate education via an existing NSF-REU site award. IIS CSE HPCC Standard Grant 9215 education 9150 University of Maine ME James C. French operating systems 7793 DATA-INTENSIVE COMPUTING 7793 7751 Zhu, Yifeng 139303 0916649 September 1, 2009 NetSE: Small: Privacy-preserving Architectures for Social Networking Services This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Online social networks (OSNs) have become central to the lives of millions of people worldwide. Unfortunately, existing OSNs cede responsibility for user data to a single administrative entity, and are inherently prone to violations of usersʼ privacy. This sensitive user data creates an attractive target for hackers and can be abused by internal administrators. This work argues that more decentralized alternatives to the dominant OSN architecture can provide a better balance between features and privacy. By endowing OSN participants with full ownership and control of their personal data, including control over which machines are allowed to store information and who is allowed to access it, decentralized OSN architectures have the potential to reduce the risk of a large-scale privacy breach while providing OSN features, efficiency, and availability that are competitive with more centralized schemes. The work will provide insights into the fundamental tension between privacy and features in OSN-service architectures. Implementation and evaluation of methods based on these insights will lead to greater understanding of the features, efficiency, and availability that decentralized OSN architectures can support. The work will enable scientific inquiry into a wide range of social phenomena through the development of privacy-preserving methods and infrastructure for collecting location data. The work will also strengthen ties between computer science and on-campus initiatives for integrating undergraduate education and research through the Duke SmartHome. IIS CSE HPCC architecture Standard Grant 9218 education NC networking privacy Duke University 0000912 Computer Science David W. McDonald 6890 NETWORK SCIENCE & ENGINEERING 7794 7794 7923 Cox, Landon 498176 0916639 September 1, 2009 RI: Small: Ensemble Modeling of Speech Signals for Automatic Speech Recognition This project is aimed at developing a new framework of ensemble modeling of speech signals to address the long standing challenge of robust and accurate recognition of spontaneous speech. Toward this goal, random forests based allophonic clustering is used to construct ensemble models of allophones by random sampling the variables underpinning the allophonic variations; data sampling is used to enrich the diversity of the ensemble models by balancing within-set data sufficiency and between-sets data diversity; functional discriminative training is used to further optimize the efficiency and accuracy of the ensemble models. Experimental evaluations of these methods are performed on a standard speech recognition task to facilitate direct assessments of their efficacy by the speech research community. The ensemble modeling approach promises higher accuracy performance and lower computation costs than the current multiple system integration approach, owing to the improved likelihood scores contributed by the ensemble models in local steps of decoding search. The approach as advocated in this project opens up a new paradigm for investigating the many issues in speech acoustic modeling, it offers a new way for ensemble modeling of structured data generally, and therefore it has the potential of significantly impacting the fields of speech recognition and other machine learning applications. The research findings are disseminated via journal publication, conference presentation, and a website. The methods of this project have broad applications in speech recognition and structured data classification, and particularly they are employed to improve the accuracy performance of a telemedicine automatic captioning system. IIS CSE HPCC Standard Grant 9215 machine learning MO 100000 Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 9102 7495 University of Missouri-Columbia 7923 Zhao, Yunxin 0916624 September 1, 2009 III: Small: Collaborative Research: Modeling, Detection, and Analysis of Branching Structures in Medical Imaging Detection and analysis of branching structures and/or texture is very challenging; it arises in many areas of science and engineering (e.g., medical images, chemical compounds, etc). The objective of this proposal is to develop novel approaches to model, detect, and analyze branching structures obtained from multimodality data. Such representation and analysis tools are expected to make many complex problems more tractable. Examples include identifying and recognizing a large number of structure classes; discovering new relationships among structure, texture, and function or pathology; evaluating hypotheses; developing modeling tools; assisting with surgical design; and managing medical image data efficiently. Specifically, the investigators plan to explore three research topics under this project: (1) To develop descriptors of branching structures and texture, and knowledge discovery tools that will enable hypotheses generation and evaluation and improve modeling of branching structures; (2) To design automated algorithms and a flexible framework to detect branching structures. The investigators are especially interested in addressing challenges of occlusion and topology change; (3) To demonstrate the applicability of the proposed tools to breast imaging by building a prototype database of images from various modalities and associated clinical data that will provide advanced analysis and visualization capabilities. Though the investigators use breast imaging as the driving application, the proposed project is expected to provide software and data resources that can assist clinical tasks and scientific discoveries in general. Developing automated tools to effectively characterize, detect, and classify tree-like structures in images would provide great insight into the relationship between the branching topology and function or pathology. The investigators plan to further contribute to the medical/scientific community by disseminating the related software and annotated data sets. The educational goals include incorporating research findings to graduate courses at Temple (data mining course and medical image analysis seminar) and at the University of Pennsylvania (medical image analysis course). IIS CSE Continuing grant HPCC 9216 PA algorithms data mining visualization Sylvia J. Spengler database INFO INTEGRATION & INFORMATICS 7364 Temple University 7364 Megalooikonomou, Vasileios 7923 Haibin Ling 102732 0916614 September 1, 2009 III:Small: Commugrate -- A Community-based Data Integration System The goal of this project is to build Commugrate, a community-driven data integration system that capitalizes on the information gained from the interactions of communities of humans with data sources. Commugrate tackles key challenges raised by the increase in the number of information sources used in science, engineering, and industry, as well as the need for large-scale data integration solutions to enable effective access to these sources. These challenges include schema matching and mapping, record-linkage, and data repair. More specifically, Commugrate (i) utilizes both direct and indirect contributions from different types of human communities with a focus on the latter contributions, (ii) solves key data integration issues using new evidences like usage and behavior data which have not been previously used, (iii) adopts a new technique for schema matching, which defines a new class of its own, namely usage-based schema matching, (iv) introduces the first of its genre technique for record linkage based on entities' behavior, and (v) provides an adaptive feedback system to improve the quality of the data by making the best use of users feedback. Commugrate has a broad impact across multiple segments of society as data integration is by far the most important and in the same time vexing issue in many areas in sciences, engineering, and industry. Furthermore, leveraging users' interactions with data sources, especially indirect interaction, may provide several benefits and help solve many intractable data integration tasks which cannot be done without human intervention. PhD students will pursue research in this project. Publications, technical reports, software and experimental data from this research will be disseminated via the project web site at http://www.purdue.edu/cybercenter/commugrate. IIS CSE HPCC 9216 Standard Grant Purdue University IN INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 Ahmed Elmagarmid Ouzzani, Mourad 7923 498373 0916607 September 1, 2009 RI: Small: Computational Models of Context-awareness and Selective Attention for Persistent Visual Target Tracking Although persistent and long-duration tracking of general targets is a basic function in the human vision system, this task is quite challenging for computer vision algorithms, because the visual appearances of real world targets vary greatly and the environments are heavily cluttered and distractive. This large gap has been a bottleneck in many video analysis applications. This project aims to bridge this gap and to overcome the challenges that confront the design of long-duration tracking systems, by developing new computational models to integrate and represent some important aspects in the human visual perception of dynamics, including selective attention and context-awareness that have been largely ignored in existing computer vision algorithms. This project performs in-depth investigations of a new computational paradigm, called the synergetic selective attention model that integrates four processes: the early selection process that extracts informative attentional regions (ARs), the synergetic tracking process that estimates the target motion based on these ARs, the robust integration process that resolves the inconsistency among the motion estimates of these ARs for robust information fusion, and the context-aware learning process that performs late selection and learning on-the-fly to discover contextual associations and to learn discriminative-ARs for adaptation. This research enriches the study of visual motion analysis by accommodating aspects from the human visual perception and leads to significant improvements for video analysis. It benefits many important areas including intelligent video surveillance, human-computer interaction and video information management. The project is linked to educational activities to promote learning and innovation through curriculum development, research opportunities, knowledge dissemination through conferences and the internet as well as other outreach activities, and the involvements of underrepresented groups. IIS CSE HPCC Northwestern University IL Standard Grant 9215 algorithms human-computer interaction vision computer vision ROBUST INTELLIGENCE Jie Yang 7495 Wu, Ying 7495 7923 375986 0916599 July 15, 2009 RI: Small: General Knowledge Bootstrapping from Text The goal of this project is to extend methods of extracting general knowledge from texts, so as to obtain not only simple "factoids" such as "A door can be open" or "A person may respond to a question" (exemplifying the millions of outputs of the U. Rochester KNEXT system), but also general, conditional knowledge such as that "If a car crashes into a tree, the driver may be hurt or killed". Such conditional knowledge is crucial for intelligent agents that can understand language and make commonsense inferences. The approach employed in the project involves bootstrapping of two principal sorts: (1) abstraction from simple factoids, both individually and collectively; (2) use of already-derived factoids to boost the performance of a natural language parser/interpreter, enabling (a) extraction of more complex conditional facts from miscellaneous texts, and (b) direct interpretation of general conditional facts stated in English in sources such as Common Sense Open Mind or WordNet glosses. The evaluation methodology for the derived knowledge involves both direct human judgement and judgement of inferences automatically generated with the aid of the extracted knowledge, using the EPILOG inference engine at U. Rochester. The general knowledge obtained in this work will be made available to the broader AI community, and will advance the state of the art both in natural language understanding and in knowledge-dependent commonsense reasoning (for example, in question answering). It will also provide evidence relevant to the hypothesis that language understanding is a process dependent not only on a few thousand syntactic rules, but also on millions of pattern-like items of general knowledge that bias the parsing and interpretation process. IIS CSE NY Continuing grant HPCC 9215 Schubert, Lenhart Gregory Carlson University of Rochester Tatiana D. Korelsky INFO INTEGRATION & INFORMATICS 7364 ROBUST INTELLIGENCE 7495 7364 7495 7923 291155 0916565 September 1, 2009 DC: Small: Data Streaming through a Complexity-Theoretic Lens The modern, highly digitized world is replete with human activities that generate copious streams of data on a seemingly-continuous basis. There is knowledge to be gained by suitably analyzing, mining and monitoring the wealth of information in these data streams. However, due to the sheer scale of the data involved, traditional algorithmic thinking is inadequate and one needs data streaming algorithms that can process inputs memory-efficiently in one, or a few, scanning passes, ideally operating in sync with data generation. The area of data streaming algorithms, though dating back to about 1980, has truly come alive only in the last decade or so, with a wealth of algorithms having been developed for, e.g., various statistical analyses, geometric problems and graph-theoretic problems. An area rich with algorithmic ideas deserves sound theoretical underpinnings. Accordingly, this project shall investigate a number of fundamental questions in this area, focusing on the delineation of what can and cannot be achieved in this important computational model. By investigating foundational questions, rather than focusing too much on particular applications, the project's approach shall be that of algorithms-and-complexity theory. Some representative research goals of this project are as follows. (1) Refining our understanding of the space complexity of various statistical measures, especially in the multi-pass setting. (2) Understanding the power of randomness in order-dependent streaming problems. (3) Proving lower bounds in stronger variants (extensions) of the basic stream model. (4) Attacking fundamental questions in communication complexity that lie at the heart of current open questions in data stream complexity. Results obtained in the course of the project will be disseminated at international conferences, workshops and seminars at various institutions. The project's educational component shall consist of the development of a graduate-level course on data stream algorithms, covering the basics of the field and leading up to the most significant recent discoveries. IIS CSE HPCC 9216 Standard Grant algorithms 9150 NH Dartmouth College James C. French 7752 7793 DATA-INTENSIVE COMPUTING 7793 7923 COMPLEXITY & CRYPTOGRAPHY Chakrabarti, Amit 336456 7927 0916557 August 1, 2009 RI: Small: A Simple but General Hand "This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)." Robot hands are usually simple, with just two or three fingers, perhaps a single actuator, and most often no sensors at all. These simple hands are also very specific in their function, such as picking up a specific part. Research on more general robot hands usually focuses on complex hands, often resembling human hands. This project is developing simple hands with general capabilities. The approach is inspired by a variety of simple hands, such as a prosthetic hook, which have proven generality when controlled by a human, yet have never demonstrated great generality when controlled by an autonomous robot. In particular the project is developing hands that can blindly capture objects among clutter, and testing these hands both in a factory automation application and in a home assistive robotics application. Results from this study will be broadly applicable. Every advance in hand design enables new applications, so development of new principles broadly advancing the generality of hands will be useful. Specific cases are advancing the nation's manufacturing workforce productivity, and enabling the elderly to live independently. Results will be disseminated by scholarly publication of new principles, analysis, and experimental results, as well as distribution of analytical software, planning and control software, and hand designs. IIS CSE HPCC Standard Grant 9215 Carnegie-Mellon University PA robotics Mason, Matthew Paul Yu Oh ROBUST INTELLIGENCE 7495 7495 6890 7923 515079 0916555 September 1, 2009 RI: Small: Coordinating Language Modeling, Computer Vision, and Machine Learning for Dramatic Advances in Optical Character Recognition The goal of this research is to develop new methods for improving the performance of optical character recognition (OCR) systems. In particular, the PI investigates "iterative contextual modeling", an approach to OCR in which high confidence recognitions of easier document portions are used to help in developing document specific models. These models can be related to appearance--for example a sample of correct words can be used to develop a model for the font in a particular document. In addition, the models can be based on language and vocabulary information. For example, after recognizing a portion of the words in a document, the general topic of the document may be detected, at which point the distribution over likely words in the document can be changed. The ability to modify character appearance distributions and language statistics and tune them specifically to the document at hand is expected to produce significant increases in the quality of OCR results. IIS CSE HPCC Standard Grant 9215 MA machine learning University of Massachusetts Amherst vision computer vision ROBUST INTELLIGENCE 7495 Andrew McCallum 7495 Qiang Ji Learned-Miller, Erik 7923 463395 0916553 September 15, 2009 DC: Small: An Integrated Architecture for Federated Search This project is developing a general solution to two new federated search problems. Massive web datasets are made more manageable by dividing them into topic-oriented "shards," and then searching only a few shards per query, thus reducing computational costs dramatically. Integration of focused "vertical search services" into general-purpose search portals is made more manageable by a framework that uses multiple techniques to characterize the contents of each resource and track how its content and query traffic change over time. Introducing resource definition policies and diverse information into federated search requires solving a variety of new resource representation, resource selection, and result merging problems. This research is also addressing the requirements of dynamic resources, and looks beyond average case analysis to characterize the range of accuracy that a federated search service experiences. Reducing the computational costs of searching massive web corpora enables greater academic study of large web datasets, and lowers costs for web search companies. A comprehensive framework for integrating specialized information services in web portals makes it easier for commercial web search portals to deploy new search services. New algorithms are being disseminated in the open-source Lemur Toolkit, thus making it very accessible. Datasets are being published in a form that enables them to be recreated precisely or closely by other researchers. Queries and relevance judgments are being published so that they may be used by other researchers. This project is an extension of research done in IIS-0841275, SGER: Multi-Tier Indexing for Web Search Engines. Project URL: http://www.cs.cmu.edu/~callan/Projects/IIS-0916553/ IIS CSE HPCC 9216 architecture Standard Grant Carnegie-Mellon University PA algorithms INFO INTEGRATION & INFORMATICS 7364 James C. French Callan, Jamie 7793 TRUSTWORTHY COMPUTING 7795 499671 0916515 September 1, 2009 III: Small: Data-Centric Business Processes: Specification and Static Analysis Businesses and other organizations increasingly rely on business process management, and in particular the management of electronic workflows underlying business processes. These workflows are often centered around a database. They are typically very complex and prone to costly bugs, which leads to a critical need for computer-aided design and static analysis tools. Such tools would result in enhanced functionality, as well as increased confidence in the robustness and correctness of complex business processes. They would potentially benefit a wide variety of applications ranging from e-commerce to digital government to healthcare and scientific applications. Classical software verification techniques applicable to static analysis include model checking and theorem proving. However, both have serious limitations: Model checking requires finite-state abstraction, which results in serious loss of semantics for both the business process and verified properties. Theorem proving is incomplete and requires expert user feedback. This project proposes an alternative approach to static analysis. Instead of applying general-purpose techniques with only partial guarantees of success, it aims to identify restricted but sufficiently expressive classes of business processes for which sound and complete static analysis can be performed in a fully automatic way. Moreover, the target of verification consists of semantically rich data-aware workflows, in contrast to the traditional process-centric workflows. Data awareness is a feature of central importance in applications such as the above, which has recently led to the emergence of a new approach to workflow specification in which data is pre-eminent. This approach, introduced by IBM, focuses on data records, known as "business artifacts" or simply "artifacts", that correspond to key business-relevant objects, their life cycles, and the services (or tasks) that are invoked on the artifacts. IBM's artifact-centric approach has been demonstrated to yield substantial improvements to the operations of medium- and large-sized businesses. The main objective of the present project is to develop new tools for the high-level specification and verification of such data-centric business processes. The project investigates the trade-offs between the expressiveness of the specification language and the feasibility of the analysis tasks. It aims to establish tractability boundaries for static analysis and to develop practical algorithms and heuristics. Tools are to be developed for carrying out analysis tasks, that will be integrated with Siena, IBM's prototype for compiling artifact-centric business process specifications into workflow support code. The technical problems raised are intellectually quite challenging, and bring into play techniques from logic, automata theory, computational complexity, algorithms, and computer-aided verification. The resulting static analysis tools have high potential for becoming a transformational technology in the area of business processes. For further information see the project web page http://db.ucsd.edu/artifacts IIS CSE Continuing grant HPCC 9216 CA algorithms database verification University of California-San Diego INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 Alin Deutsch Vianu, Victor 7923 135467 0916489 September 1, 2009 DC: Small: Stream Clustering Algorithms in Mixed Domains with Soft Two-way Semi-Supervision One way to form a model of massive data sets is to use clustering techniques that summarize the data by several cluster representatives. However, clustering huge data sets is a very challenging problem whose difficulty increases further when the data is dynamic. We are developing scalable and robust stream summarization methods to provide a concise summary of huge multi-dimensional data streams that keep track of each discovered cluster or component of the summary through time, and that store only milestones corresponding to the occurrence of significant changes in these cluster representatives. Moreover to handle possibly diverse data formats and different sources of data, we are using a semi-supervised framework for (i) combining diverse representations of the data, in particular where data comes from different sources, some of which may be unreliable or uncertain; and (ii) exploiting optional external concept set labels to guide the clustering of the main data set in its original domain. Our methods have tremendous impact on applications that deal with streaming data in general, and more specifically on monitoring data streams in real-life dynamic settings. For example, as more and more everyday activities move online, network and Web data has been increasing at a rapid pace that precludes standard and classical data analysis methods, and call instead for real time analysis. The same can be said about the deluge of data that is being or about to be generated by new and future sensor networks, astronomical observatories, and missions in space. Thus, new research efforts and paradigms are needed and will have a strong impact on our ability to digest and make sense of this data. IIS CSE HPCC 9216 Standard Grant network algorithms 9150 450000 University of Louisville Research Foundation Inc KY James C. French data analysis Nasraoui, Olfa 7793 DATA-INTENSIVE COMPUTING 7793 7923 0916488 September 1, 2009 III: Small: Efficient Ranking and Aggregate Query Processing for Probabilistic Data When dealing with massive quantities of data, ranking and aggregate queries are powerful techniques for focusing attention on the most important answers. Many applications that produce such massive quantities of data inherently introduce uncertainty in the same time, for example, probabilistic match in data integration, imprecise measurements from sensors, fuzzy duplicates in data cleaning, inconsistency in scientific data. Hence, the importance of these queries is even greater in probabilistic data, where a relation can encode exponentially many possible worlds. Uncertainty opens the gate to many possible definitions for ranking and aggregate queries. This project systematically examines the underlying properties associated with the rich semantics of ranking and aggregate queries for large amounts of probabilistic data. More importantly, this project investigates the issue of how to design novel and scalable algorithms for processing these queries efficiently in various settings, such as the offline, centralized environment, distributed systems and the streaming model. With the emergence of probabilistic data in many important application domains, the demand for understanding and processing the ranking and aggregate queries efficiently from the scientific community and beyond (e.g., government and military agencies) is expected to intensify in the coming years. The results of this project lay down a firm foundation for this important problem. For further information, such as publications, data sets and source code, please see the project website at http://www.cs.fsu.edu/~lifeifei/rankaggprob IIS CSE Continuing grant HPCC 9216 algorithms FL INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 Florida State University 7923 Li, Feifei 106006 0916463 September 1, 2009 DC: Small: Efficient Algorithms for Data-intensive Bio-computing The field of bioinformatics and computational biology is experiencing a data revolution unlike any other scientific computing field. Experimental techniques to procure data have increased in throughput, improved in accuracy, and reduced in costs. The preponderance of data has limited the scalability of existing software tools. In a pursuit to understand the complexities and challenges that stem from designing algorithms for data-intensive biocomputing, this project is developing new approaches for two major problems in protein bioinformatics: i) identification of protein families and homology clusters; and ii) peptide identification from large-scale mass spectrometry data. The former requires large-scale graph analysis and the latter requires large-scale database search. The project is investigating a multi-faceted approach which involves designing space-efficient algorithms for massively parallel machines, developing algorithmic heuristics for reducing the time to solution, evaluating the MapReduce paradigm as an alternate computing model, and deploying multicore architectures for fine-grain parallelism. Project outcomes will include new algorithms and open-source software libraries for large-scale protein bioinformatics, including a more generic library for data-intensive biocomputing. The project is addressing a critical need for scalable methods in protein bioinformatics and in doing so will usher in state-of-the-art computing models and concepts from both software and hardware into mainstream biocomputing. Broader impacts include creating interdisciplinary research opportunities for undergraduate and graduate students, and new interdisciplinary curricula at high school, undergraduate and graduate levels. Education materials will be disseminated through a partnership program with Shodor Education Foundation, Inc. Project homepage: http://www.eecs.wsu.edu/~ananth/DataIntensive-Biocomputing/ IIS CSE HPCC 9216 Standard Grant WA algorithms education database computational biology Washington State University scientific computing bioinformatics James C. French 435000 7793 DATA-INTENSIVE COMPUTING 7793 7923 Kalyanaraman, Anantharaman Partha Pande William Cannon 0916459 September 1, 2009 HCC: Small: Helping People Negoiate Uncertain Information Online This proposal explores how individuals decide what online information to trust given the uncertain information they often encounter. In today's society, life experiences are often processed in an online world. Online resources provide information and support for parenting, health, hobbies, news, and more. However, online resources are often incomplete, may include a diversity of opinions, and may be inaccurate. In a connected series of research studies exploring general theoretical questions, this project focuses on a compelling and common example of uncertainty online - the uncertainty about treatments for a chronic health condition. Chronic disease is a leading cause of ill health world wide, and one in ten Americans lives with a life-altering chronic condition. Unlike acute conditions (such as a high fever), chronic conditions, such as HIV, diabetes, arthritis, and Lyme disease, are prolonged and rarely cured completely. For this reason, management of chronic conditions lies much more in the hands of the patient. This research will study online health resources and individuals' use of these online resources using interview data, survey data, and text analysis of thousands of pages and posts available in online content. First, it will characterize the uncertainty of different types of online resources. Second, it will focus on how uncertainty impacts a specific community characterized by highly uncertain and even controversial information about the disease process and treatment (the Lyme disease community). And third, the research will test the generalizability of results in a complementary setting (such as individuals with chronic arthritis). The empirical work will answer the following questions: 1) How does the existence of incomplete, divergent and/or conflicting information affect the health choices made by individuals with chronic illness? 2) What factors (community, time, exposure to information) are critical to an individual with chronic illness deciding whether to believe in a specific viewpoint? The results will drive the design of two technological interventions that can improve people's ability to understand and decide among online resources: (1) A tool to extract and highlight key parameters of decision making derived from the research, that will crawl relevant sources and extract information such as patient consensus, medical research timeline, and risks. (2) A tool to classify online resources in terms of viewpoint, leveraging machine-learning techniques such as co-training to learn classifications on the fly. The second tool will inform the first, but also provide an interface for sorting and filtering online information and compare the information cloud associated with different viewpoints. The results of this research will add to existing knowledge about how the Internet can support individuals with chronic conditions, and contribute to the development of curriculum for courses on human-computer interaction in the medical area. IIS CSE HPCC Standard Grant 9215 Carnegie-Mellon University PA William Bainbridge human-computer interaction 0116000 Human Subjects HUMAN-CENTERED COMPUTING 7367 7367 Mankoff, Jennifer 7923 492079 0916443 September 1, 2009 RI:Small: Relational learning and inference for network models Networks are everywhere. Discovering the underlying principles of the networks has great impact on our understanding of complex systems in many scientific, engineering, and social research areas. Nowadays, the availability of network data, such as online social networks from facebook.com or protein-protein interaction data, give researchers unprecedented opportunities to quantitatively study these complex systems. In this project, the PI brings together problems, ideas and techniques from different areas including machine learning, statistics, biology and social sciences, to develop novel computational tools and statistical models for common problems in network inference and learning. The research activities include i) designing nonparametric Bayesian models to discover latent classes from relational data, ii) developing relational Bayesian models, coupled with efficient deterministic approximate inference methods, to predict missing links and node labels, and iii) examining network dynamics at different substructure levels. The developed models, algorithms, and tools for analyzing network data are available to the public via publication and web distribution, disseminating to other machine learning researchers and helping computational biologists and socials scientists analyze massive network data that are being generated with an unprecedented fast speed. The PI incorporates the research results into the graduate-level interdisciplinary courses he teaches and recruits graduate and undergraduate students to conduct research for this project. IIS CSE HPCC Standard Grant 9215 Purdue University IN network algorithms machine learning ROBUST INTELLIGENCE 7495 7495 Qiang Ji 7923 Qi, Yuan 358544 0916441 September 1, 2009 RI: Small: Acquisition and Modeling of Dense Nonrigid Shape and Motion The objective of this project is to advance the state-of-the-art in acquiring and modeling dynamic non-rigid objects. The specific examples of non-rigid objects that the project is to focus on include: human faces, hands, soft tissues, cloths, and animals. The PI seeks to address the following two fundamental questions: (1) How can non-contact optical methods be used to measure dense 3D surface motion without physically modifying the appearance of the surface? (2) What physical and/or biological properties can be inferred from the acquired dense 3D motion data? The research team addresses these two questions by two simple but general ideas, namely the space-time approach and data-driven models. The space-time approach builds upon space-time stereo, and enables accurate optical measurements of 3D surface motion, as well as automatic registration of shape sequences among different dynamic objects. The data-driven models are used for both material recognition and deformation-EMG correlation. The project has a wide range of scientific impacts, including generating data for 2D face alignment and 3D face recognition in biometrics, generating data for 3D face emotion recognition in human computer interaction, measuring human body deformation in biomechanics, modeling soft tissues for orthopedics and computer-aided surgery, and building virtual human models for entertainment and education. These scientific impacts translate into benefits to society, for example, by building more accurate biometric systems to secure our country, innovating surgery procedure to reduce health insurance cost, and creating 3D digital replicas of great teachers to make our education available anywhere, anytime, at a lower cost. IIS CSE HPCC Standard Grant 9215 human computer interaction education WI University of Wisconsin-Madison ROBUST INTELLIGENCE Jie Yang 7495 GRAPHICS & VISUALIZATION 7453 7495 7453 Zhang, Li 7923 308250 0916439 August 1, 2009 III: Small: Generalization of the Association Analysis Framework Association analysis finds patterns that describe the relationships among the binary attributes (variables) used to characterize a set of objects. A key strength of association pattern mining is that the potentially exponential nature of the search space can often be made tractable by using support based pruning of patterns i.e., eliminating patterns supported by too few transactions. Despite the well-developed theoretical foundation of association mining, this group of techniques is not widely used as a data analysis tool in many scientific domains. For example, in the domain of bioinformatics and computational biology, while the use of clustering and classification techniques is common, techniques from association analysis are rarely employed. This is because many of the patterns required in bioinformatics and other domains are not effectively captured by the traditional association analysis framework and its current extensions. Although such patterns can be found by techniques such as bi-clustering and co-clustering, these approaches suffer from a number of serious limitations, most notably, an inability to efficiently explore the search space without resorting to heuristic approaches that compromise the completeness of the search. To address the challenges mentioned above, the team will extend the traditional association analysis framework. They propose two novel frameworks for directly mining patterns from real-valued data that, unlike biclustering and co-clustering, are able to discover all patterns satisfying the given constraints and do not suffer from the loss of information caused by discretization and other data transformation approaches. They will also extend association analysis based approaches to work with data that has class labels by effectively using the available class label information for pruning the exponential search space and finding low-support patterns that discriminate between the two data classes. To evaluate the results of the work, they will develop robust evaluation methodologies for evaluating the patterns obtained from the proposed frameworks. The proposed work promises to extend the power of association analysis to a wide range of new applications in health and life sciences, such as the discovery of biomarkers and functional modules from single nucleotide polymorphism and gene expression data, with potential applications in personalized medicine and the development of drugs and bio-fuels. IIS CSE HPCC 9216 Standard Grant Sylvia J. Spengler University of Minnesota-Twin Cities MN computational biology bioinformatics INFO INTEGRATION & INFORMATICS 7364 data analysis 7364 Kumar, Vipin 499996 Michael Steinbach 7923 0916425 September 15, 2009 DC: Small: One Thousand Points of Light: Accelerating Data-Intensive Applications By Proxy A large class of distributed data-rich applications, including distributed data mining, distributed workflows, and Web 2.0 Mashups, are increasingly relying on cloud services to meet their data storage and computing demands. However, today, these applications are responsible for combining data and results from different specialized cloud services individually, which can lead to significant performance and reliability bottlenecks, due to the lack of appropriate resources connecting the applications to multiple clouds, resulting in a significant impediment to their successful deployment. This project proposes a cloud proxy network that allows optimized and reliable data-centric operations to be performed at strategic network locations. In this model, proxies may take on several data-centric roles: interacting with cloud services, routing data to each other, caching data for later use, and invoking compute-intensive data operators for intermediate processing. The proposed solution will enable an efficient coupling of cloud services to yield improved end-to-end performance and reliability for newly emerging data-intensive applications. This project will explore the potential of the proxy network architecture by evaluating its merits using a volunteer approach, focusing on four main research challenges: Proxy Performance, Proxy Reliability, Proxy Information Sparsity, and Proxy Selection. The broader impact of this project is to amplify the effectiveness and productivity of diverse scientific, social, and engineering communities for enabling data-driven scientific inquiry in a performance-efficient and reliable manner. Proxy middleware will be released to the wider community towards this end. Educating a new generation of students in data-centric computing through major curriculum innovation is also proposed. IIS CSE HPCC architecture Standard Grant 9215 network data mining University of Minnesota-Twin Cities MN 450000 middleware James C. French 7752 7793 DATA-INTENSIVE COMPUTING 7793 7923 Weissman, Jon Abhishek Chandra 7751 0916421 September 1, 2009 III: Small: FoLIO - Framework for Longitudinal Image-based Organization This proposed project will develop a framework for organizing images that allows the specific types of relationships between those images to be represented, manipulated, highlighted, enhanced, and studied. The technical challenges involve building new representational and algorithmic systems to capture the major "longitudinal categories" that relate heterogeneous images to each other within collections. The problem of relationship between images is normally posed through registration, which is most often highly contextualized. This work will capture the steps necessary to specify registration as a metadata construction that enables a range of granularities in mapping images to each other, and heterogeneous relationship across organizational categories such as time (diachronic), multi-modal, and instances related by a semantic object. The work is highly interdisciplinary and results can be generalized to other problem sets. IIS CSE Continuing grant HPCC 9216 Stephen Griffin 9150 INFO INTEGRATION & INFORMATICS 7364 KY University of Kentucky Research Foundation Seales, William 7364 82523 7923 0916409 July 15, 2009 RI: Small: The Dynamics of Information Flow in Embodied Cognitive Systems This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). There is a growing realization within the behavioral and brain sciences that the embodiment and situatedness of intelligent agents plays an essential role in their behavior. However, it is still a significant open challenge to understand the complex interactions between an agent's nervous system, its body and its environment. This project focuses on mathematical methods for analyzing the flow of information in models of situated and embodied cognitive agents. Specifically, the investigator will (1) develop new information-theoretic tools that characterize the flow of information over time throughout the system, (2) test and refine these methods on evolved model brain-body-environment systems, and (3) use these techniques and models to explore the unique advantages of embodiment and situatedness for a cognitive agent. The new analysis techniques build on the notion of conditional mutual information between random variables at specific points in time. For example, conditioning the mutual information between a stimulus feature and a state variable at time t on the information that state variable contains about the stimulus feature at time t-1 allows one to compute a measure of information gain. Similar measures can be used to compute the information loss or retention. This basic approach will be extended in several different directions. Information factoring, which builds on the notion of transfer entropy, will allow the interactions between system components to be characterized in terms of directional information flow. Information backtracking will offer a refined portrait of the structure of these interactions by tracing backwards in time from particular informational configurations of the system to determine the flows that produce them. Specific information spectra will be used to explore the informational relationships between particular values taken on by components of the system, allowing the structure of their interactions to be probed at a finer level of detail. These methods will be applied to evolved models of relational categorization, referential communication, and visually guided behavior, allowing the following questions to be explored: How do embodied agents extract, store, and suppress information? How do embodied agents integrate information about multiple features? What is the relationship between informational and dynamical properties? Finally, these techniques will be used to characterize the capabilities of embodied and situated agents, including information self-structuring, information offloading, and embodied information transfer. These analysis methods are expected to have applications not only to brain-body-environment systems, but also to other complex biological and social networks. IIS CSE HPCC Standard Grant 9215 IN Kenneth C. Whang Indiana University ROBUST INTELLIGENCE 7495 7495 6890 7923 Beer, Randall 443211 0916401 September 1, 2009 III: Small: Collaborative Research: Reconstruction of Haplotype Spectra from High-Throughput Sequencing Data Recent advances in high-throughput sequencing (HTS) technologies provide opportunities to study genome structure, function, and evolution at an unprecedented scale, and are profoundly transforming genomic research. However, fully realizing the potential of HTS technologies requires sophisticated data analysis methods. This research project is aimed at developing efficient computational methods for reconstructing the full spectrum of haplotype sequences from HTS data. Working in collaboration with molecular biologists from the University of Connecticut Health Center and the Centers for Disease Control, the investigators will develop methods enabling three novel applications of HTS, namely (a) reconstruction of diploid genome sequences, including complete haplotype sequences of each CNV copy, (b) reconstruction of alternative splicing isoform sequences and their frequencies, and (c) reconstruction of viral quasispecies sequences and their frequencies. Major outcomes of the project will include the development of a comprehensive analytical toolkit for these problems, and high-quality open source software implementations that will be made available free of charge to the research community. The project will provide opportunities for participation of undergraduate and graduate students in bioinformatics research at UCONN and Georgia State University, and will especially encourage participation of women and underrepresented groups. IIS CSE Continuing grant HPCC 9216 Sylvia J. Spengler GA Georgia State University Research Foundation, Inc. bioinformatics INFO INTEGRATION & INFORMATICS 7364 data analysis 7364 7923 Zelikovskiy, Aleksandr 81525 0916372 August 15, 2009 RI: SMALL: Statistical Linguistic Typology This project considers the unification of two view of language: that from natural language processing and that from linguistic typology. Our view is that typological information is both useful for solving real-world natural language processing thats and automatically derivable from language data. This research first explores how to use typological knowledge to improve performance on problems such as dependency parsing and machine translation for low density langauges. Intuitively, our statistical models waste time exploring a hypothesis space that is too big: the space of realistic grammars is much smaller than the space of all grammars. The second part of this research considers the automatic acquisition and boostrapping of typological knowledge from raw text. The outcome of this research is: (a) improved statistical models for hard natural language processing problems; and (b) a larger library of typological universals that have been derived automatically from data. Our outcomes are empirically evaluated on the raw language processing tasks and in terms of the quality of the universal implications mined from data, but comparing them with known repositories of universals. Our results will impact the fields of natural language processing and linguistics. From the research side, this research will find applications in a wider variety of problems than the ones we intend to study; in particular, the use of linguistic universals in natural language processing technology will fundamentally change the way multilinguality is addressed in this field. From a linguistics perspective, the goal of this project is to shed new light on linguistic universals. This should impact not only the area of typology, but also the study and preservation of endangered languages. By automatically identifying typological features and implications from data, the process of documenting endangered languages could be made more efficient: leading to a smaller loss of knowledge of these languages. IIS CSE Continuing grant HPCC 9215 University of Utah UT Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 7495 LINGUISTICS 1311 Daume, Hal 7923 134030 0916345 August 1, 2009 III: Small: Fast Subset Scan for Anomalous Pattern Detection This work will develop new methods for fast and scalable detection of anomalous patterns (subsets of the data that are interesting or unexpected) in massive, multivariate datasets. There will be a focus on real-world applications such as an emerging disease outbreak or a pattern of smuggling activity with complex, subtle, and probabilistic patterns that are difficult to spot with existing techniques. The research is based on two key insights. First, the pattern detection problem can be framed as a search over all subsets of the data, in which can be defined a measure of the "anomalousness" of a subset and then maximize this measure over all potentially relevant subsets. Second, it has been discovered that, for many spatial detection methods (including Kulldor's spatial scan statistic and many recently proposed variants), one can perform an exact search which efficiently maximizes the measure of anomalousness over all subsets of the data. The research team will explore this new combinatorial optimization method, investigate how it can be extended to constrained subset scans and to more general multivariate pattern detection problems, and examine how it can be incorporated into a subset scan framework, enabling the creation a variety of fast, scalable, and useful methods for anomalous pattern detection. Intellectual Merit The research team will develop, implement, and evaluate a general probabilistic framework for efficient detection of anomalous patterns in both spatial and non-spatial datasets. The proposed work will address these challenging and important research questions: 1)How can one define a useful measure of the "anomalousness" of a subset of the data, and efficiently optimize this measure over all subsets to find the most anomalous patterns? 2) What are the necessary and sufficient conditions for a set function F (S ) to satisfy the "linear- time subset scanning" (LTSS) property, enabling exact unconstrained optimization of F (S ) over all 2 N subsets of N records while only requiring O(N ) subsets to be evaluated? 3) How can one extend fast subset scanning methods to general multivariate datasets, and incorporate search constraints such as proximity, connectivity, and self-similarity? 4) How can one deal with uncertainty about the effects of an anomalous pattern by searching over subsets of "input" and "output" attributes as well as subsets of records? Broader Impact Development and testing will be prioritized in three areas: 1) early detection of disease outbreaks, 2) detecting illicit container shipments, and 3) identifying anomalous trends in social networks. These applications will allow the demonstration the value of these methods across a wide spectrum of domains. Through existing collaborations, the algorithms will be incorporated into deployed systems for health and crime surveillance that contribute directly to the public good. The Principle Investigator's lab has over 5 years of history offering free machine learning software, and the software implementations of all algorithms developed through this grant will be made publicly available. The bulk of the funding will go to training graduate students who will become the next generation of researchers to explore new methods for anomalous pattern detection. Key Words: anomalous patterns; pattern detection; fast subset scan; scan statistics; optimization. IIS CSE HPCC 9216 Lawrence Brandt Standard Grant Carnegie-Mellon University PA algorithms machine learning INFO INTEGRATION & INFORMATICS 7364 7364 7923 Neill, Daniel 499991 0916307 September 1, 2009 TC: Small: Towards Automating Privacy Controls for Online Social Networks For millions of Internet users today, controlling information access on Online Social Networks (OSNs) such as Facebook and LinkedIn is a difficult challenge. Privacy controls in current systems do not provide the necessary level of flexibility and usability to their users. Some systems like MySpace and LinkedIn allow users to grant all-or-nothing access control to their profiles. While simple to use, these controls are imprecise and can easily leak data to unintended recipients or prevent the legitimate sharing of data. In contrast, OSNs like Facebook provide extremely powerful controls that are unfortunately too complex for most users to configure. This proposal addresses the need for privacy control policies that are both powerful and simple to use. The proposed work provides simple and powerful privacy policies by using machine learning techniques to automatically infer user preferences from observed user behavior. The work also proposes "privacy lenses," a generalized mechanism to debug privacy policies by viewing user information through the access controls of any specified user. These technical solutions will be implemented on the Facebook social network as a third-party application. In addition, the data gathered from the deployed application will provide evidence to either validate or refute the perplexing phenomenon known as the "privacy paradox," where users take little action to protect their privacy despite expressing strong concerns about online privacy. The proposed project addresses a significant problem fundamental to protecting online information. By allowing the social network to "learn" what users want based on their actions, the PIs remove the complexity of managing privacy policies, thereby giving non-technical Internet users a simple and intuitive way to customize their preferences. The work is novel in its use of machine learning techniques to infer user preferences, and can change the way privacy policies are constructed for a wide variety of Internet applications. By gathering user data from a large-scale social network, the project will also provide significant support to improve understanding of the motivations behind users actions concerning online privacy. Finally, the proposed work will integrate sophisticated experimental networking research techniques with detailed human studies, adding an additional dimension to traditional experiments performed by social scientists. IIS CSE HPCC Standard Grant 9218 CA network machine learning 0116000 Human Subjects networking privacy University of California-Santa Barbara David W. McDonald Zhao, Ben 7923 TRUSTWORTHY COMPUTING 7795 Miriam Metzger 485508 0916292 September 1, 2009 HCC: Small: "Peer Review for Scientific Testimony." In the United States, Congress has in place a number of institutions and services to provide members and committees with up-to-date, expert knowledge. The acquisition of expert scientific testimony for congressional members and committees costs Congress over $2 billion per year. Clearly, science and technology testimony is important to legislative and policy making bodies. This project studies the quality of expert scientific testimony. The challenge we propose to address is this: Can the quality of the expert testimony of scientists, engineers, and technologists be improved through the introduction of new ideas and new technologies of peer review and peer production? In the design of the proposed system we will integrate new insights and techniques of peer-production from studies of Open Source Software development; game-based co-production of useful information by non-experts; and, an emerging subfield of knowledge management, online expertise sharing between experts and non-experts. The combination and extension of these techniques will provide new tools for exploring online, peer-production processes. The research results will create a set of concrete tools for providing science, technology, and engineering input into legal, legislative or policy decision making processes. In terms of the possible, pedagogical impacts of the project, its budget and plan have been developed to foster collaborative working relationships between faculty, staff, graduate students, undergraduates, university and non-university based scientists and engineers from a diverse set of fields. Information provided to or discovered by Congress has a profound effect on the legislative process. The acquisition of policy information for its members and committees costs Congress well over $2 billion per year of which about $100 million is spent on House and Senate committees. If the proposed project is successful, we will have a set of concrete, implemented proposals to show how Congress and the communities of science, technology, and engineering could be better integrated using tools and techniques of information technology. In terms of the possible, pedagogical impacts of the project, its budget and plan have been developed to foster collaborative working relationships between faculty, staff, graduate students, undergraduates, university and non-university based scientists and engineers from a diverse set of fields, in addition to, potentially, congressional staff and members of Congress. IIS CSE HPCC Standard Grant 9215 CA 0116000 Human Subjects University of California-Santa Cruz HUMAN-CENTERED COMPUTING 7367 7367 Sack, Warren David W. McDonald 7923 387400 0916289 September 1, 2009 GV:Small: Lagrangian Visualization Methods for Very Large Time-Dependent Vector Fields Lagrangian methods assume a central role in the analysis and visualization of vector fields resulting from simulation and measurement across many application domains. These methods provide key insight into vector field structures and dynamics, but are based on the expensive computation of integral curves. Applied to large-scale problems and data sets, they are burdened heavily by enormous computational cost. To improve this situation, the problem-specific computation of integral curves employed in vector field visualization techniques is replaced by a two-stage process consisting of an adaptive pre-computation of integral curve sets and methods for interpolation within these sets, effectively transferring the vector field representation to the Lagrangian domain. Hence, the computational burden is isolated into a pre-computation stage. The obtained Lagrangian representation is stored into efficient out-of-core data structures. Integration-based visualization algorithms can leverage the resulting fast interpolation of integral curves, whose approximative characteristics are examined in detail, from this pre-computed data. This generic framework permits enhancement of existing integration-based visualization methods to become interactive and provides a basis for research into novel efficient and interactive vector field visualization for very large vector fields. Taking advantage of these properties, new visualization tools are developed to study transport processes in vector fields using Lagrangian analysis. To increase the impact of this research and distribute it to a large community of scientists and engineers, the developed algorithms are integrated with an open-source visualization package. These new techniques are integrated into coursework and student projects that enable students to study new methods of analysis of flow computations. Information concerning these new methods are found on the project website (http://idav.ucdavis.edu/~joy/NSF-IIS-0916289). IIS CSE Continuing grant HPCC 9216 9218 CA Maria Zemankova algorithms simulation visualization University of California-Davis 9217 GRAPHICS & VISUALIZATION 7453 Joy, Kenneth Christoph Garth 148518 0916286 September 1, 2009 IIS: III: Small: Conformal Geometry for Computer Vision This project investigates the computational conformal geometric methods applied for computer vision. The research team develops novel algorithms for shape analysis, surface matching and registration based on Ricci flow, and compared with conventional vision methods thoroughly. The basic of idea of the research is to conformally deform all surfaces as one of three canonical shapes, and then perform matching and registering these 3D surfaces in 2D planes. The research team uses the Ricci for shape analysis to preserve the intrinsic geometric characteristics, and to map the surfaces to the canonical shapes.. Ricci flow is the process to deform the metric proportional to the curvature, such that the curvature evolves according to a heat diffusion process. The research team conducts thoroughly comparison with conventional vision methods through extensive experiments using real world datasets. The research benefits many computer vision and visualization applications with methods for computing and visualizing conformal structures and conformal invariants on surfaces. The project also provides opportunity for training students in the areas of differential geometry, hyperbolic geometry, Riemannian geometry, computer vision. IIS CSE NY HPCC Standard Grant 9215 algorithms visualization SUNY at Stony Brook vision computer vision 100000 ROBUST INTELLIGENCE Jie Yang 7495 7364 7495 Dimitrios Samaras Gu, Xianfeng 7923 0916280 September 1, 2009 RI: Small: RUI: Resource-light Morphosyntactic Tagging of Morphologically Complex Languages This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The main goal of this project is to develop a tagging method which neither relies on target-language training data nor requires bilingual dictionaries and parallel corpora. The main assumption is that a model for the target language can be approximated by language models from one or more related source languages. Exploiting cross-lingual correspondence leads to a better understanding of 1) what linguistic properties are crucial for morphosyntactic transfer; 2) how to measure language similarity at different levels: syntax, lexicon, morphology; 3) how this method applies to pairs that do not belong to the same family; 4) what determines the success of the model, and 5) how to quantify its potential for a given language pair. By exploiting cross-language relationships, the size, and hence cost, of the training data are significantly reduced. This project is a new cross-fertilization between theoretical linguistics (especially typology and diachronic linguistics) and natural language processing. The practical contribution is a robust and portable system for tagging resource-poor languages. With this new approach, it is be possible to rapidly deploy tools to analyze a suddenly critical language. This approach can also enhance NSF's initiatives in documenting endangered low density languages as it leverages exactly the type of knowledge that a field linguist and a native speaker could provide. Additional benefits include high quality annotated data, automatically derived multilingual lexicons, annotation schemes for new languages, new typological generalizations, and graduate and undergraduate researchers with significant experience of highly practical work on difficult and underrepresented languages. IIS CSE HPCC Standard Grant 9215 NJ Tatiana D. Korelsky 9102 7495 Montclair State University 6890 Feldman, Anna 7923 TRUSTWORTHY COMPUTING 7795 169174 0916272 September 1, 2009 RI: Small: Spacetime Reconstruction of Dynamic Scenes from Moving Cameras The proliferation of camera-enabled consumer items, like cellular phones, wearable computers, and domestic robots, has introduced moving cameras, in staggering numbers, into everyday life. These cameras record our social environment, where people engage in different activities and objects like vehicles or bicycles are in motion. State-of-the-art structure from motion algorithms cannot reliably reconstruct these types of scenes. The overarching focus of this work is to develop the theory and practice required to robustly reconstruct a dynamic scene from one moving camera or simultaneously from several moving cameras. To achieve this, the PI is developing a theory of imaging in dynamic scenes. A useful ?device? for analyzing dynamic scenes is to visualize them as constructs in spacetime, analogous to static structures in space. Much of the progress in multi-view geometry in static scenes has centered on the development of tensors that embody the relative positions of cameras in space. The dimensional analogue is being used to define corresponding analogues for multi-view geometry in dynamic scenes. A goal in this work is to derive geometric relationships within a system of independently moving cameras. To reconstruct unconstrained dynamic scenes, factorization approaches are being extended to spacetime to simultaneously reconstruct nonrigid structure from multiple moving cameras. The algorithms that result from this research create the space for a host of new technologies in several industries such as autonomous vehicle navigation, distributed visual surveillance, aerial video monitoring and indexing, cellphone interface, urban navigation, coordination and planning for autonomous robots, and human-computer interface. IIS CSE HPCC Standard Grant 9215 Carnegie-Mellon University PA algorithms ROBUST INTELLIGENCE 7495 7495 Qiang Ji 7923 Sheikh, Yaser 445771 0916250 September 1, 2009 III: Small: Accurate Protein Threading via Tree-Decomposable Graph Modeling This protein threading project at University of Georgia develops high throughput computer programs for accurate protein tertiary structure prediction based on a new conformational graph modeling of protein amino acid tertiary interactions. The modeling approach enables the following three novel, effective components together to achieve the project goal: (1) a very efficient sequence-structure alignment method that can incorporate sophisticated energy functions to improve fold recognition accuracy, (2) a simultaneous backbone prediction and side chain packing method to improve threading alignment accuracy, and (3) a semi-threading method to improve the accuracy of new structure prediction. This project also enriches the interdisciplinary education programs at University of Georgia, allowing computer science students to implement protein threading programs and visualization tools, and bioinformatics and biology students to develop threading methods, test data, and evaluate and disseminate results. For further information see the project web page at URL: http://www.uga.edu/RNA-Informatics/?p=projects IIS CSE HPCC 9216 Standard Grant visualization education GA bioinformatics INFO INTEGRATION & INFORMATICS 7364 Frank Olken 500000 University of Georgia Research Foundation Inc 7364 7923 Cai, Liming 0916235 July 15, 2009 CCF: AF: Small: Volumetric Mesh Mapping With the rapid development of volumetric acquisition and computational technologies in numerous applications, such as industrial inspection and medical imaging, there is a growing need for tools for processing such datasets to analyze the topology and geometry, including volumetric mapping to canonical structures, volumetric registration, volumetric feature extraction, geometric database indexing, volumetric parameterization, etc. In this project the PI and his team will develop rigorous algorithms for computing the topology and geometry for general mesh volumes, a nontrivial and fundamentally more challenging problem than for surfaces. To capture the tunnels, handles, and voids of a volume, homology and cohomology groups need to be computed; to describe the tangling, twisting, and linking patterns among the handles, tunnels, and voids, fundamental groups need to be computed as well. Because it is NP-hard to verify whether two fundamental groups are isomorphic, conventional algebraic topological methods are inadequate and geometric structures such as Ricci flows need to be incorporated. Thus, a major focus of this project is development of computational algorithms for Ricci flows. On the other hand, it is highly desirable to map one or more volumes to a canonical domain, in order to support database indexing and volume registration, yet it is an open problem to obtain canonical geometric structures for volumes using computational methodologies. The PIs are confident that Ricci flows are the key to solving this problem, too. Project outcomes will include rigorous computational algorithms for processing volumetric data based on 3-manifold topology and geometric structures, which will be developed in a sequence of interrelated steps as follows: design and implementation of algorithms to compute topological invariants, including homology, cohomology groups, and fundamental groups; design and implementation of algorithms to compute canonical geometric structures, Riemannian metrics with constant section curvatures for discrete 3-manifolds, based on curvature flow and differential forms; design and implementation of algorithms to incorporate canonical geometric structures to the topological invariants, such as finding the closed geodesics and minimal surfaces as homotopy class representatives; design and implementation of algorithms for volumetric mapping to canonical structures, volumetric registration, volumetric feature extraction, and volumetric parameterization; design and implementation of parallel version of the above algorithms, and use of a GPU cluster for speed up; and investigation of the complexity, the stability, and the convergence rate of the above algorithms. Broader Impacts: The PIs will build and disseminate a concrete set of software tools for computing and visualizing the topology and geometric structures for mesh volumes, including volumetric parameterization, volumetric registration, volumetric mapping to canonical structures, fundamental groups computation, and topological and geometric feature extraction. Diverse disciplines such as engineering, science, medicine, computer graphics, vision, scientific computing, and mathematics, as well as a host of industrial applications, will directly benefit from these tools, which can be used for volumetric texture mapping, spline volume construction, volumetric deformation, etc. IIS CSE NY Ephraim P. Glinert HPCC 9216 Standard Grant 9218 9215 algorithms database SUNY at Stony Brook vision computer graphics graphics scientific computing 500000 9217 GRAPHICS & VISUALIZATION 7453 7453 Kaufman, Arie Xianfeng Gu 0916217 September 1, 2009 HCC: Small: Energy Signature of Interaction Techniques for Low Power Bi-Stable Displays Information Appliances Looking back at Weiser's 1991 vision of ubiquitous computing, many of his predictions have been surpassed, often by several orders of magnitude: information appliances sport ever more powerful processors, store in excess of 64 GB of data onto solid state chips, and have constant high-bandwidth access to the network. Yet one prediction that has not been realized, as anybody who uses mobile computers today can attest, is the ability to use information appliances for several days before recharging them. While battery life is the cornerstone (without sufficient battery life the burden of maintaining multiple mobile devices outweighs the advantages), three emerging technologies may significantly alter the energy footprint of information appliances: bi-stable displays which only consume energy when they are refreshed; Magnetic RAM (MRAM) which combines the speed of SRAM, the density of DRAM, and the non-volatility of FLASH memory; and a new generation of powerful embedded processors that support aggressive power saving strategies, and which offer a preview of the potential of energy efficient asynchronous processors. Combined, these technologies will make it possible to create systems where power consumption is near zero while quiescent, a significant departure from the behavior of current devices. The PI's goal in this project is to investigate how the various interaction techniques we know of today might benefit a low energy architecture enabled by the aforementioned emerging technologies. The team has extensive experience with hardware design, hardware simulation, and empirical evaluation. Project outcomes will contribute to a better understanding of the parameters influencing the design of very low power interfaces, and will include: an openly available hardware test bed for evaluating interaction technique energy signatures; the first systematic evaluation of the energy footprint of command selection and navigation techniques to complement the extensive performance data already gathered; an evaluation of the potential of sensor-assisted techniques to reduce the energy consumption of information appliances; and the first evaluations of the potential of asynchronous design to enable very low power information appliances. Evaluations will be performed in the lab and through longitudinal deployments, considering a variety of tasks, to further increase the external validity of the results. Broader Impacts: Project outcomes will establish the empirical foundations for very low energy interface designs that will help researchers and designers better understand the energy implications of various interaction techniques. They will offer researchers and practitioners the tools and toolkits they need to quickly implement and evaluate the overall energy footprint of a design, and thus will significantly lower the barrier to entry into this research area. Furthermore, they will support new curricula that focus on energy consumption. Given that information appliance use is accelerating, and since the distinction between information appliances and personal computers is blurring, this work will have a great impact on reducing the overall energy consumed for our everyday information needs. IIS CSE NY Ephraim P. Glinert HPCC architecture Standard Grant 9215 network simulation ubiquitous 0116000 Human Subjects vision Cornell University HUMAN-CENTERED COMPUTING 7367 7367 Guimbretiere, Francois 7923 Rajit Manohar 499519 0916200 September 15, 2009 RI: Small: An Advanced Learning Paradigm: Learning Using Hidden Information Modern machine learning is limited in its ability to use diverse information during training. This project is developing algorithms in the SVM family that allow extra information to be used effectively during training, with the understanding that this extra information will not be available during actual operation. Examples of extra information include structural homologies between proteins in a system designed to predict structure from amino acid sequences; and values for a financial time series between the time where a prediction is made and the time of the value being predicted. Preliminary testing has shown that such extra information can dramatically reduce prediction error in the learned system compared with current generation machine learning methods that cannot use this extra information. This project encompasses analytic research to establish performance bounds on our new algorithms, and to explore the relationships of this work to human learning. The project also includes experimental work, including construction of novel training and testing datasets; software implementation of the algorithms; and training, testing and analysis of experimental results. Areas of application include handwritten character recognition; 3-D protein structure prediction; non-linear time series prediction, for example of financial time series; and prediction of likelihood of hospital readmittance for elderly patients. This project aims to give greater insight into the nature of learning, whether in humans or machines, and seeks to formally take into account data that is today seen as only peripheral to the learning task, and impossible for current machine learning algorithms to use. The project will produce technical articles, a book, and teaching materials explaining this research. In addition the project will produce sharable software that implements the best version of the algorithm devised during the life of the project. IIS CSE NY HPCC Standard Grant 9215 algorithms machine learning Columbia University ROBUST INTELLIGENCE 7495 Waltz, David Qiang Ji 7923 499071 Vladimir Vapnik 0916186 September 1, 2009 DC: Small: EEGMine: A Distributed Framework for Learning on EEG Data obtained from Epilepsy Patients The Center for Computational Learning Systems (CCLS) is collaborating with the Computational Neurophysiology Laboratory (CNL) in the Department of Neurology, Columbia University Medical School (CUMC) to develop a distributed framework for data management and machine learning on intracranial EEG data obtained from patients suffering from epilepsy. Drs. Schevon and Emerson have initiated a trial of a dense, two-dimensional microelectrode array which can record over long periods of time at a sampling rate of up to 30 kHz per channel. To date approximately 30 TB of data has been collected. The large volume of complex EEG data compels us to rethink how we will deal with this "data avalanche." The design of a data center for storage and analysis is particularly challenging since traditional methods of storing data on a single server do not allow machine learning algorithms to be computed within a reasonable time. Further, due to the conditions under which the data is collected, noise of multiple types and sources is pervasive; the data must be extensively cleaned and potential seizure precursors carefully labeled. The project is investigating mechanisms to develop a cluster architecture (using Apache Hadoop) for the EEGMine Data Center that incorporates reliable storage and backup; developing a library of machine learning algorithms (EEGMine- ML library) and addressing their scalability issues, potentially leveraging the MapReduce programming paradigm. This research will have immediate impact for both epilepsy and computer science research. Because of the uniqueness and value of human-derived microelectrode EEG data, it would be beneficial for the seizure prediction community to enable data sharing and long-distance collaborations. The most practical means of sifting through terabytes of complex EEG data is to combine distributed storage on a cluster with local processing to prepare data and generate meta-data that can be used as inputs for machine learning algorithms thus enabling identification of physiologically significant patterns. From an education perspective, the project will benefit the EWarn Research Group which is part of CCLS and CUMC by training them in signal processing, machine learning and basics of EEG. Website Address: http://www1.ccls.columbia.edu/~dutta/EEGMine IIS CSE NY HPCC architecture Standard Grant 9215 algorithms machine learning education Columbia University INFO INTEGRATION & INFORMATICS 7364 James C. French 7793 DATA-INTENSIVE COMPUTING 7793 7923 Dutta, Haimonti David Waltz Catherine Schevon Ronald Emerson 439998 0916161 October 1, 2009 RI: Small: Universal Automated Reasoning by Knowledge Compilation A long term goal motivating this project is the development of a universal computational engine to support reasoning across different applications of intelligent systems (e.g., planning and diagnosis). The project is founded on an approach that compiles knowledge bases into a taxonomy of tractable forms, which result from imposing various conditions, such as decomposability and determinism, on Negation Normal Form (NNF). Certain queries, which are generally intractable, become tractable on the compiled NNF forms. To implement a task like planning or diagnosis, all one needs to do is compile their knowledge base to the most succinct subset of NNF that provides polytime support for the queries required by the task. The project will focus in particular on algorithms for imposing various conditions on NNF compilations, using both top-down and bottom-up compilation techniques, to support a larger set of tractable forms. This will lead to developing and evaluating a more powerful inference engine than traditional SAT solvers, supported by a more comprehensive set of queries and transformations on knowledge bases. It will also lead to extending the compilation approach to a larger class of AI applications. IIS CSE HPCC Standard Grant 9215 University of California-Los Angeles CA algorithms Douglas H. Fisher ROBUST INTELLIGENCE 7495 7495 Darwiche, Adnan 7923 474999 0916154 September 1, 2009 RI-Small: Statistical Decoding Models to Improve the Performance of Motor Cortical Brain-Machine Interfaces The goal of this project is to develop statistical models to accurately and efficiently decode population neuronal activity in the motor and premotor cortex. The study focuses on motor behavior as it is easily measured and strongly correlated with neuronal activity. Recent advances in motor cortical brain-machine interfaces have shown that research animals and paralyzed human patients were able to perform rudimentary actions with external devices such as robotic limbs and computer cursors. Neural decoding, which provides control commands to external devices, plays a key role in such interfaces by converting brain signals (e.g., spiking rates of a population of neurons) to kinematic states (e.g., hand position, hand movement direction). Current decoding models are often based on the strong assumption that the neural signal sequence is a stationary process. This assumption, however, does not take into account the significant dynamic variability of spiking activity over time. Moreover, these methods have either focused on decoding the entire trajectory or on the occurrence times of a few "landmarks" during the movement. Effective coupling of these two complementary strategies can be expected to improve the decoding performance by better exploiting the nature of the landmark-defined movement. This project will develop computational methods to address these two issues. For the non-stationarity, the research team will develop adaptive versions of state-of-the-art decoding methods such as particle filters and point process filters that can capture the varying patterns in neural signals and update the model accordingly. To couple trajectory decoding and time decoding, landmark times will be identified from the neural activity, and then incorporated into the kinematic model. The team will use simultaneous recordings from multi-electrode arrays in the primary motor cortex, the dorsal premotor cortex, and the ventral premotor cortex that were recorded during behavior or visuo-motor tasks. Improved decoding methods are expected to have significant impacts on neural prosthetics. IIS CSE HPCC Standard Grant 9215 Kenneth C. Whang FL ROBUST INTELLIGENCE 7495 Florida State University 7923 Wu, Wei 135592 0916152 September 1, 2009 III: RI: Small: RUI: Tracking Predators and Bullies Via Chat Log Transcripts Cyber-violence is increasing exponentially as social networking applications such as Instant Messaging, Facebook, and MySpace are developed, deployed, and reach increasingly younger users. These young users frequently fall victim to cyber-predators and cyber-bullies. To address this ongoing concern, this research project will study of the communicative strategies employed by both aggressors and victims in cases of online predation and cyber-bullying. The primary outcome of this project is the development of theoretical communicative models and technology for the detection of online predation and cyber-bullying. In addition to flagging aggressive communication and notifying parents, the open-source software developed with these funds will suggest appropriate responses so a teen or tween can immediately defend him or herself against an aggressor. The response software will also subtly teach effective defense communication strategies that children can take into other situations (in the real or online world). Existing data resources for research in this area are scant and problematic. The data to be collected and disseminated as part of this project can be used to for future research in the development of theoretical communicative models of online predation and response, and of cyber-bullying communication and response. A data set for research in resolving multiple Internet identities will also be created and distributed. This research project will bring together two fields, Computer Science and Media and Communication Studies, which have been dramatically impacted by the explosive growth of Internet social networking sites. The study will build upon existing web-centered technologies and tools, as well as theories of communication that were developed by close analysis of other media forms, such as television and print media. Existing machine learning algorithms will be enhanced by the development and integration of communicative theories that have been updated for online interactions. The focus on cyber-violence, especially cyber-violence directed at children, supplies a socially relevant test-bed that has suffered from neglect by researchers in Computer Science, primarily due to the lack of standard data sets. This project will provide collections of annotated data that will be used by other researchers in both fields. IIS CSE Continuing grant HPCC 9218 PA algorithms machine learning 0116000 Human Subjects networking 9102 Amy Baylor 7923 TRUSTWORTHY COMPUTING 7795 Kontostathis, April Lynne Edwards Ursinus College 158842 0916148 September 1, 2009 III: SMALL: FoLIO-Framework for Longitudinal Image-based Organization This proposed project will develop a framework for organizing images that allows the specific types of relationships between those images to be represented, manipulated, highlighted, enhanced, and studied. The technical challenges involve building new representational and algorithmic systems to capture the major "longitudinal categories" that relate heterogeneous images to each other within collections. The problem of relationship between images is normally posed through registration, which is most often highly contextualized. This work will capture the steps necessary to specify registration as a metadata construction that enables a range of granularities in mapping images to each other, and heterogeneous relationship across organizational categories such as time (diachronic), multi-modal, and instances related by a semantic object. The work is highly interdisciplinary and results can be generalized to other problem sets. IIS CSE Continuing grant HPCC 9216 Stephen Griffin 9150 SC INFO INTEGRATION & INFORMATICS 7364 7364 7923 Blackwell, Christopher Furman University 113874 0916131 July 15, 2009 HCC:Small:FlowBase: A Realtime Simulation System of Turbulent Fluids Driven by Flow Pattern Database This project develops an innovative real-time simulation system of turbulent fluids, FlowBase, by employing a novel flow pattern database. A collection of pre-generated small flow patterns are managed by a database system with efficient storage and searching strategy, query and transaction handling, to enable fast and effective retrieval. The patterns are integrated with real-time ongoing simulation according to physical and topological parameters of the ongoing global flow. The FlowBase system is implemented with GPU (graphics hardware) acceleration on single PC or clusters. Furthermore, the system provides focus+context flow pattern integration that only appends turbulent flow details in focus areas, and also offers flexibility by adding patterns according to the computational resource budget. In this way, the system overcomes the resource limit for large-scale simulations and provides turbulent flow details for modeling visually-satisfying and physically-correct fluid phenomena. The FlowBase system can rapidly provide vivid and spirited fluid behavior to create realistic experiences which are a decisive factor in many educational and entertainment applications, including virtual reality based education, advertisement and entertainment industry. It also benefits prediction and training applications, where fast computation and visual interaction of turbulent fluids are highly critical, such as contaminant dispersion prediction, emergency responder training, flight simulators, and urban and environmental design. The research outcome of the pattern database, novel algorithm, modeling techniques, computational and visualization tools are made publicly available through publications and software packages. IIS CSE Continuing grant HPCC 9215 OH simulation visualization education database 0116000 Human Subjects graphics Jie Yang GRAPHICS & VISUALIZATION 7453 7453 7923 Zhao, Ye Kent State University 128719 0916129 August 1, 2009 HCC: Small: Collaborative Research: Asynchrony and Persistence for Complex Contact Simulations IIS - 0916129 HCC: Small: Collaborative Research: Asynchrony and Persistence for Complex Contact Simulations Grinspun, Eitan Columbia University Collaborative Proposals IIS - 0914833 Guibas, Leonidas J Stanford University ABSTRACT This proposal addresses the challenge of complex contact simulations by working entirely in an asynchronous setting. The project operates along three lines, combining tools from Asynchronous Variational Integrators (AVIs) and Kinetic Data Structures (KDSs) with a novel effort to perform and exploit qualitative analysis of contact simulation data. The first investigation will show that AVIs, meant to handle the continuous aspects of the physics, can be ntegrated well with KDSs, meant to handle discrete geometric events. The second research component addresses the fundamental problem of event scheduling when future trajectories are uncertain. Methods will be explored that improve event detection times, reduce the number of auxiliary events that have to be processed, and allow events to be processed in parallel. The third research task will be to initiate a study of the qualitative behavior of contact simulation by building a hierarchy of coarser models which can then be used for better resource allocation, for the validation of various approximations, and in improved simulation design to attain desired effects. IIS CSE NY Continuing grant HPCC 9215 Stephen Griffin simulation Columbia University GRAPHICS & VISUALIZATION 7453 7923 Grinspun, Eitan 81969 0916119 July 1, 2009 The role of sensory information for performance in virtual environments across the lifespan The practical application of 3D virtual environment technology has made significant progress over the last decade, as advances in the computer industry allow for faster, more complex operations to be accomplished with much less hardware and at decreased cost. As a consequence, applications once limited to experimental laboratories are reaching the design phase for mainstream deployment. However, successful integration of 3D virtual environments into the daily activities of the full spectrum of society is hindered by the fact that such systems do not yet provide an environment in which human performance matches performance in the natural world. One of the limiting factors is the failure to properly take into account the human psychological and sensorimotor systems, and how these systems change with age. It is well known, for example, that vision is the dominant source of sensory information, and that changes in sensorimotor processing and perception result in alterations in the use of vision for motor control at various stages of the lifespan. That human-computer interaction is significantly affected by such changes where standard computer interfaces are concerned is well documented, but there is a paucity of information on human-computer interaction across the lifespan in 3D virtual environments. The PI argues that to succeed in the creation of valid 3D virtual environments for the full array of potential users, age-specific sensorimotor requirements must be systematically determined. To this end, the PI will in this project seek to establish sensory feedback parameters for motor control in a desktop 3D virtual environment. A multi-disciplinary approach based on methodologies from human motor control, biomechanics and neuroscience will be used. Specifically, the PI will investigate the quantity, quality, and timing of visual feedback employed in simple tasks. Further inquiry into the interaction of task difficulty and sensory feedback will be undertaken. Finally, for each of the basic experimental investigations, the between subjects factor of age group and the within subjects factors of manipulated sensory feedback will be analyzed. Basic motor control measurement and predictive modeling techniques will be employed to understand how sensory feedback is used, to quantify the negative effects of lag, and to develop methods for improving the presentation of sensory information to users of various age groups. These developments will then be incorporated into the system, and experiments will be extended to more complex and functional tasks. Broader Impacts: By gaining a comprehensive understanding of the role of graphic information for interaction in computer-generated environments across the lifespan, this research will establish evidence-based recommendations to designers of virtual environments regarding how to provide the most effective sensory feedback to users of all ages. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 human-computer interaction virtual environment WI 0116000 Human Subjects vision University of Wisconsin-Madison HUMAN-CENTERED COMPUTING 7367 7367 Mason, Andrea 7923 497668 0916116 September 1, 2009 RI: Small: Enhancing Nonmonotonic Declarative Knowledge Representation and Reasoning by Merging Answer Set Programming with Other Computing Paradigms Answer Set Programming (ASP) is a recent form of declarative programming that has been applied to many knowledge-intensive tasks, such as product configuration, planning, diagnosis, and information integration. Like other computing paradigms, such as SAT (Satisfiability Checking) and CP (Constraint Programming), ASP provides a common basis for formalizing and solving various problems, but is distinct from others in that it focuses on knowledge representation and has proved to be useful for rapid prototyping. While the research on ASP has produced many promising results, it has also identified serious limitations. The project aims at overcoming the limitations by merging ASP with other computing paradigms, such as satisfiability checking, first-order logic and constraint programming, and exploring the synergy between them. This project is expected to provide a transformative understanding of ASP's relation to other computing paradigms, to enhance ASP's reasoning capability and broaden the areas in which it is effective. Within knowledge representation, the study will clarify the role of ASP as a major knowledge representation formalism with effective computation methods that combines various methods available in other computing paradigms. IIS CSE HPCC Standard Grant 9215 Arizona State University AZ Douglas H. Fisher ROBUST INTELLIGENCE 7495 7495 Lee, Joohyung 275000 7923 0916102 September 1, 2009 III: Small: Computational Infrastructure for the Identification of Copy Number Variations from SNP Microarrays It was recently discovered that copy number variations (CNVs) in human genome are quite common, and have important implications on phenotype. Currently, the primary platforms for large-scale detection and characterization of CNVs are SNP (single nucleotide polymorphism) microarrays. The current state-of-the-art in computational identification of CNVs from microarray data relies mostly on model-based approaches (e.g., Hidden Markov Models). However, such methods require extensive training data, which may not be always available. Furthermore, since these methods use common CNVs to train their models, they are not as successful in identifying rare CNVs, which are believed to make up a substantial proportion of all CNVs in the human population. The objective of this project is to develop optimization based algorithms and software for the identification and genotyping of CNVs, with a view to enabling fast and accurate identification of different types of CNVs (rare and common), without the requirement of training data. The proposed framework develops a novel computational approach by explicitly formulating CNV identification as a series of optimization problems that incorporate multiple factors, including sensitivity to noise, rarity/commonality of CNVs, genotypic specificity, and parsimony. This formulation enables development of efficient algorithms that treat identification of rare and common CNVs as different problems with different objective functions. Availability of the resulting software to the community will enable more efficient and accurate identification of CNVs in large samples, facilitating advances in understanding the role of CNVs in a range of complex phenotypes, including HIV, autism, schizophrenia, mental retardation, and many others. Furthermore, the computational innovations introduced by this project are likely to find applications in next generation sequencing. IIS CSE HPCC 9216 Standard Grant OH algorithms Sylvia J. Spengler INFO INTEGRATION & INFORMATICS 7364 7364 Case Western Reserve University 7923 Koyuturk, Mehmet Thomas LaFramboise 497603 0916099 September 1, 2009 III: Small: Optimizing News and Opinion Aggregators for Diversity Observers have raised alarms about increasing political polarization of our society, with opposing groups unable to engage in civil dialogue to find common ground or solutions. Aggregators such as Digg, Reddit, and Google News rely on ratings and links to select and present subsets of the large quantity of news and opinion items generated each day. If a majority of the raters or linkers share a political viewpoint, minority viewpoints may get little representation in the results, creating an echo chamber for the majority. Even if a site selects items based on votes or links from people with diverse views, algorithms based solely on popularity may lead to a tyranny of the majority that effectively suppresses minority viewpoints. This work is the first attempt to formalize several different instances of the general concept of diversity of viewpoints and to devise algorithms that optimize for these measures. The techniques are likely to be applicable to other domains where selecting a diverse set of items is valuable, such as search engine results and audience voting on questions to ask of a conference speaker or public official. The goals of this research are to: 1) form alternative measures of diversity for result sets; 2) develop algorithms for selecting result sets that jointly optimize for diversity and popularity; 3) assess the impacts of alternative selection and presentation methods on people's willingness to use an aggregation service, their exposure to diverse opinions, and the size of their argument repertoires. The results of the project will provide a better understanding of alternative notions of what it means for a set of items to be diverse or balanced, and the range of reactions that different people have to varying levels and presentations of diversity. Insight into people's preferences for acceptable support and challenge may also allow for the creation of news and opinion aggregators that cause people to choose to expose themselves to greater diversity, thus reducing polarization and enhancing democracy. Results, including open source software, will be distributed via the project web site: (http://si.umich.edu/balance/). IIS CSE HPCC 9216 Standard Grant Maria Zemankova algorithms University of Michigan Ann Arbor MI 0116000 Human Subjects INFO INTEGRATION & INFORMATICS 7364 7364 Resnick, Paul 7923 499312 0916098 August 1, 2009 HCC: III: Small: MyDome - Defining the Computational and Cognitive Potential of Interactive Simulations in an Immersive Dome Environment The MyDome project will bring 3D virtual worlds for group interaction into planetaria and portable domes. Advances in computing have evolved the planetarium dome experience from a star field and pointer presentation to a high-resolution movie covering the entire hemispherical screen. The project will further transform the dome theater experience into an interactive immersive adventure. MyDome will develop scenarios in which the audience can explore along three lines of inquiry: (1) the past with archeological reconstructions, (2) the present in a living forest, and (3) the future in a space station or colony on Mars. These scenarios will push the limits of technology in rendering believable environments of differing complexity and will also provide research data on human-centered computing as it applies to inquiry and group interactions while exploring virtual environments. The project proposes to engage a large portion of the population, with a special emphasis on the underserved and under-engaged but very tech-savvy teenage learner. Research questions addressed are: 1. What are the most engaging and educational environments to explore in full-dome? 2. What on-screen tools and presentation techniques will facilitate interactions? 3. What are the limitations for this experience using a single computer, single projector mirror projection system as found in the portable Discovery Dome? 4. Which audiences are best served by exploration of virtual hemispherical environments? 5. How large can the audience be and still be effective for the individual learner? What techniques can be used to provide more people with a level of control of the experience and does the group interaction enhance or diminish the engagement of different individuals? 6. What kind of engagement can be developed in producing scientific and climate awareness? Does experiencing past civilizations lead to more interest in other cultures? Does supported learning in the virtual forest lead to greater connection to and understanding of the real forest? Does the virtual model space experience excite students and citizens about space exploration or increase the understanding of the Earth's biosphere? The broader impacts of the project are (1) benefits to society from increasing public awareness and understanding of human relationships with the environment in past civilizations, today?s forests and climate change, and potential future civilizations in space and on Mars; (2) increasing the appeal of informal science museums to the tech-savvy teenage audience, and (3) significant gains in awareness of young people in school courses and careers in science and engineering. The partners represent a geographically diverse audience and underserved populations that include rural (University of New Hampshire), minority students (Houston Museum of Natural Science) and economically-distressed neighborhoods (Carnegie Museum of Natural History). Robust evaluation will inform each program as it is produced and refined, and will provide the needed data on the potential for learning in the interactive dome environment and on the optimal audience size for each different type of inquiry. IIS CSE HPCC Standard Grant 9215 William Bainbridge 9150 University of New Hampshire NH HUMAN-CENTERED COMPUTING 7367 7367 7923 Schloss, Annette Kerry Handron Carolyn Sumners 499841 0916053 September 1, 2009 DC: Small: Collaborative Research: Shape Representation of Large Geometries via Convex Approximation Geometric models composed of millions (or more) of facets are common today due to improved technologies for generating high-resolution complex models. The large size makes it infeasible to perform some fundamental geometric operations on these models. For instance, more than 1.4 billion geometric union operations are required to compute the Minkowski sum of the David model. Re-designing existing algorithms for large models would require significant time and effort, and may not always be possible. This project is investigating approximate convex decomposition (ACD), an alternative representation for large geometries that approximately represents the original model using a set of convex objects. By using the much smaller convex approximation in place of the original model, ACD allows existing (inefficient) methods and software to perform efficiently for large geometries without designing and implementing new algorithms. An important goal of this project is to develop simple algorithms that not only allow efficient reconstruction but also allow practical implementation. This project will make significant contributions to fundamental problems in geometric computing, such as Minkowski sum, continuous motion collision detection, general penetration depth estimation, and swept volume. Beyond these fundamental geometric operations, this project will provide new ways to handle geometric problems in several areas of robotics (e.g., environment/map representation, motion planning and grasp planning), in pattern recognition (e.g., structural salient feature recognition, visual-based part decomposition and motif identification in protein structures), and in computer graphics (e.g., data compression, physically-based simulation and skeletonization). The software developed by this project will be provided to the public domain. IIS CSE HPCC 9216 Standard Grant algorithms simulation computer graphics graphics robotics Texas Engineering Experiment Station TX Amato, Nancy 7752 7793 DATA-INTENSIVE COMPUTING 7793 Sankar Basu 199961 0916046 September 1, 2009 RI: Small: Collaborative Research: Word Sense and Multilingual Subjectivity Analysis Approaches to subjectivity and sentiment analysis often rely on manually or automatically constructed lexicons. Most such lexicons are compiled as lists of words, rather than word meanings ("senses"). However, many words have both subjective and objective senses as well as senses of different polarities, which is a major source of ambiguity in subjectivity and sentiment analysis. The proposed work addresses this gap, by investigating novel methods for subjectivity sense labeling, and exploiting the results in sense-aware subjectivity and sentiment analysis. To achieve these goals, three research objectives are targeted. The first is developing methods for assigning subjectivity labels to word senses in a taxonomy. The second is developing contextual subjectivity disambiguation techniques to effectively make use of the word sense subjectivity annotations. The third is applying these techniques to multiple languages, including languages with fewer resources than English. The project will have broader impacts in both research and education. First, it will make subjectivity and sentiment resources and tools more widely available, in multiple languages, to the research community, which will help advance the state of the art in automatic subjectivity analysis, which in turn will benefit end applications. Second, several educational goals will be pursued: training graduate and undergraduate students in computational linguistics; augmenting artificial intelligence courses with projects based on the proposed research, which will offer students hands-on experience with natural language processing research; and reaching out to women and minorities to increase their exposure to text processing technologies and access to research opportunities. IIS CSE HPCC Standard Grant 9215 PA artificial intelligence education University of Pittsburgh Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 9102 Wiebe, Janyce 225000 7495 0916040 September 1, 2009 DC: Small: Collaborative Research: Shape Representation of Large Geometries via Convex Approximation Geometric models composed of millions (or more) of facets are common today due to improved technologies for generating high-resolution complex models. The large size makes it infeasible to perform some fundamental geometric operations on these models. For instance, more than 1.4 billion geometric union operations are required to compute the Minkowski sum of the David model. Re-designing existing algorithms for large models would require significant time and effort, and may not always be possible. This project is investigating approximate convex decomposition (ACD), an alternative representation for large geometries that approximately represents the original model using a set of convex objects. By using the much smaller convex approximation in place of the original model, ACD allows existing (inefficient) methods and software to perform efficiently for large geometries without designing and implementing new algorithms. An important goal of this project is to develop simple algorithms that not only allow efficient reconstruction but also allow practical implementation. This project will make significant contributions to fundamental problems in geometric computing, such as Minkowski sum, continuous motion collision detection, general penetration depth estimation, and swept volume. Beyond these fundamental geometric operations, this project will provide new ways to handle geometric problems in several areas of robotics (e.g., environment/map representation, motion planning and grasp planning), in pattern recognition (e.g., structural salient feature recognition, visual-based part decomposition and motif identification in protein structures), and in computer graphics (e.g., data compression, physically-based simulation and skeletonization). The software developed by this project will be provided to the public domain. IIS CSE HPCC 9216 Standard Grant algorithms simulation VA computer graphics graphics robotics George Mason University 7752 7793 DATA-INTENSIVE COMPUTING 7793 Lien, Jyh-Ming Sankar Basu 295652 0916038 September 1, 2009 RI: Small: Semi-Supervised Learning for Non-Experts This project develops semi-supervised machine learning algorithms that are practical, and at the same time guided by rigorous theory. In particular, the project is developing learning theory that quantifies when and to what extent the combination of labeled and unlabeled data is provably beneficial. Based on the theory, novel algorithms are being developed to address issues that currently hinder the wide adoption of semi-supervised learning. The new algorithms will be able to guarantee that using unlabeled data is at least no worse, and often better, than supervised learning. The new algorithms will also be able to learn from unlimited amounts of supervised and unsupervised data as they arrive in real-time, something humans can do but computers cannot so far. This project has a number of broader impacts: (1) An open-source software will be an enabling tool for new discoveries in science and technology, by making machine learning possible or better in situations where labeled data is scarce. Since the software specifically targets non-machine-learning-experts, the impact is expected to be across the whole spectrum of science and technology that utilizes machine learning. (2) It advances our understanding of the learning process via new machine learning theory, which can be applied to both computers and humans. (3) The proposal contains projects ideally suited to engage students in computer science education and research. IIS CSE HPCC Standard Grant 9215 algorithms machine learning education WI University of Wisconsin-Madison Douglas H. Fisher ROBUST INTELLIGENCE 7495 7495 Zhu, Xiaojin 7923 414417 0916027 September 1, 2009 III: Small: RIOT: Statistical Computing with Efficient, Transparent I/O Recent technological advances enable collection of massive amounts of data in science, commerce, and society. These datasets bring us closer than ever before to solving important problems such as decoding human genomes and coping with climate changes. Meanwhile, the exponential growth in data volume creates an urgent challenge. Many existing analysis tools assume datasets fit in memory; when applied to massive datasets, they become unacceptably slow because of excessive disk input/output (I/O) operations. Across application domains, much of advanced data analysis is done with custom programming by statisticians. Progress has been hindered by the lack of easy-to-use statistical computing environments that support I/O-efficient processing of large datasets. There have been many approaches toward I/O-efficiency, but none has gained traction with statisticians because of issues ranging from efficiency to usability. Disk-based storage engines and I/O-efficient function libraries are only a partial solution, because many sources of I/O-inefficiency in programs remain at a higher, inter-operation level. Database systems seem to be a natural solution, with efficient I/O and a declarative language (SQL) enabling high-level optimizations. However, much work in integrating databases and statistical computing remains database-centric, forcing statisticians to learn unfamiliar languages and deal with their impedance mismatch with host languages. To make a practical impact on statistical computing, this project postulates that a better approach is to make it transparent to users how I/O-efficiency is achieved. Transparency means no SQL, or any new language to learn. Transparency means that existing code should run without modification, and automatically gain I/O-efficiency. The project, nicknamed RIOT, aims at extending R---a widely popular open-source statistical computing environment---to transparently provide efficient I/O. Achieving transparency is challenging; RIOT does so with an end-to-end solution addressing issues on all fronts: I/O-efficient algorithms, pipelined execution, deferred evaluation, I/O-cost-driven expression optimization, smart storage and materialization, and seamless integration with an interpreted host language. RIOT integrates research and education, and continues the tradition of involving undergraduates through REU and independent studies. As a database researcher, the PI is committed to learning and drawing from work from programming languages and high-performance computing. Findings from RIOT help create synergy and seed further collaboration with these communities. To ensure practical impact on statistical computing, RIOT has enlisted collaboration from statisticians and the R core development team on developing, evaluating, and disseminating RIOT. Further information can be found at: http://www.cs.duke.edu/dbgroup/Main/RIOT IIS CSE HPCC 9216 Standard Grant algorithms education NC database INFO INTEGRATION & INFORMATICS 7364 Frank Olken Duke University data analysis high-performance computing 500000 programming languages 7364 Yang, Jun 7923 0916019 October 1, 2009 HCC: Small: The role of social network sites in facilitating collaborative processes Collaboration, when it works, optimizes the contributions of individuals, often resulting in better decisions, outcomes, and experiences than individuals working alone. Social network sites (SNSs) offer new opportunities for collaboration due to their social and technical affordances. SNS profiles enable the display of identity information, which can act as a social lubricant and help individuals initiate conversations and find common ground. Within SNSs, contact lists lower the transaction costs associated with interaction. Finally, SNSs enable access to a larger pool of individuals (and their wider and more diverse knowledge base) while also providing a context in which social capital processes serve as a mechanism for encouraging collaboration, advice-giving and information-sharing. This project will develop and test a model of SNS-enabled collaboration motivated by the following research questions: What forms of collaboration are enabled by SNSs? How do the features of SNSs affect these processes? Who uses these sites to collaborate and why? This study will examine SNS-facilitated collaborative instances using quantitative and qualitative data to provide insight into users motivations, perceptions, and conceptual frameworks. First, we will examine examples of ad-hoc collaboration among college undergraduates to explore how relationship initiation and collaboration occur, both in SNS and face-to-face contexts. Second, aggregate behavioral patterns on Facebook will be analyzed to discover and investigate modes of collaboration on the site. IIS CSE Continuing grant HPCC 9215 network MI 0116000 Human Subjects Michigan State University HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald 7923 Ellison, Nicole Clifford Lampe 149565 0916014 September 1, 2009 RI: Small: Exploiting Geometric and Illumination Context in Indoor Scenes The research objective is to investigate methods to recover geometry and perform spatial reasoning in rooms. This project aims to recover the room space, illumination, and object layout from an image. Together, these elements capture the layout of the room walls, the location of objects in the image and the 3D space, and a lighting representation that allows illumination artifacts to be explained and rooms to be relit with inserted objects. The work takes an integrated approach, exploiting constraints within and between spatial representations. The project also aims to leverage knowledge of room geometry to better reason about surface utility, enabling advanced spatial analysis of indoor scenes. This research unifies ideas from geometry, multiple view computer vision, shading, and statistics to recover complex spatial representations from single views. The work further aims to create tools for object insertion and removal and scene completion, allowing the average person to more easily create the photograph that she wants or an interior designer to quickly sketch a photorealistic prototype of a new concept. The recovered spatial information also enables mobile robots to find walkable paths through cluttered rooms and to understand how objects can be physically manipulated and placed, which is essential for assistive household robotics. Other anticipated applications include surveillance, security, and transportation safety. The project contributes to education through student projects, course development, and workshops and tutorials involving a broader audience. IIS CSE HPCC IL Standard Grant 9215 education University of Illinois at Urbana-Champaign security vision 450000 robotics computer vision Forsyth, David ROBUST INTELLIGENCE Jie Yang 7495 7923 Derek Hoiem 0916001 September 1, 2009 RI-small: Simultaneous Groupwise Nonrigid Registration, Segmentation and Smoothing of 3D Shapes and Images The goal of this research is the investigation of a computational framework that allows for group-wise joint segmentation, smoothing and registration of images and shapes. With recent advances in sensor technology, images (shapes and other types of data) are being generated in abundance, and there is now a need for algorithms that operate and process images in collections instead of individually. In particular, this requires segmentation, smoothing and registration, three most important image processing operations, to be formulated in new ways that emphasize the relational aspects of their inputs. In addition, image data in computer vision applications are usually sampled from low-dimensional manifolds embedded in high-dimensional features spaces, and an important problem is to construct versatile and expressive computational models that exploit their geometries for solutions. The proposed computational framework addresses these two issues by formulating a variational framework that unifies smoothing, segmentation and registration. Specifically, it uses hypergraphs to model the multiple geometric relations among the inputs, and the three operations are integrated in one single discrete variational framework defined over a hypergraph. The proposed framework provides a foundation for several principled joint segmentation and registration algorithms for images and shapes that can guarantee crucial properties such as compatibility, consistency, unbiasedness and symmetry. Furthermore, it also provides a new and more discriminative numerical signature for 2D and 3D shapes that can be important for many shape-related vision applications such as shape recognition, shape retrieval and image-based medical diagnosis. IIS CSE HPCC Standard Grant 9215 algorithms vision computer vision FL University of Florida ROBUST INTELLIGENCE Jie Yang 7495 7923 HO, JEFFREY Baba Vemuri 253749 0915990 August 1, 2009 HCC: Small: Collaborative Research: Graph and Pattern Design on Surfaces Abstract ? Zhang/Wonka The research investigates theory and efficient algorithms for pattern design on surfaces. Patterns on surfaces appear in many natural phenomena such as leaves, animal textures, and terrains as well as man-made objects such as origami, glass ornaments, and facades. Patterns can also be used to describe networks, such as street layouts, power grids, aqueducts, and sensor networks. Pattern design has a wide range of applications in art and entertainment, architecture, engineering, medicine, and city planning. In addition, theory and techniques developed in the research can benefit domains such as computational geometry and vector and tensor field visualization. There are several fundamental challenges when it comes to pattern design on surfaces. First, there is a lack of unified mathematical formulations of patterns in terms of both symmetries and orientations contained in the patterns. Consequently, the aforementioned applications are typically addressed as being unrelated despite the intrinsic links between them. Second, many past approaches to these problems lack hierarchical control. This is required so that the user can design high level information down to occasionally low level specifics and the layout algorithms fill in the rest procedurally. In this research, the investigators explore a unified framework that allows hierarchical design of patterns on surfaces. In this framework, orientation and symmetry information is specified everywhere in the domain through tensor field design. Next, the tensor field which contains desired orientation and symmetry information is used to generate a complex which can be a point set, a graph, a tiling, or any combination of them. Finally, additional details are added onto the complex through texture and geometry synthesis, or sub-patterns are added inside the cells of the complex. Ideas from various mathematical domains such as dynamical systems, tensor calculus, differential geometry, and algebraic topology are borrowed and applied in this research. IIS CSE HPCC architecture Standard Grant 9215 algorithms visualization Arizona State University AZ computational geometry GRAPHICS & VISUALIZATION 7453 Wonka, Peter 7453 Lawrence Rosenblum 7923 249601 0915977 September 1, 2009 RI:Small:Robust Image Matching with Deformations and Lighting Variation This project is to develop new, effective distance metrics for comparing two images. These metrics account for two effects. First, pixels can change their position, deforming from one image to another. Second, pixels may change their intensity. In many vision problems, intensity changes are primarily due to lighting variation. The research team first addresses the effect of illumination changes, which enables to develop a new, powerful, robust distance for measuring the effects of lighting variation in an image. The research team combines this with both existing and new methods to develop a robust distance that accounts simultaneously for image deformations and intensity variations. Computing this distance separates these two effects, providing a correspondence between images. This can be used to track objects moving relative to a light, to match images taken at different times of day, or to recognize objects seen under different lighting, from different viewpoints, with variations in their shape. This new metric provides a theory of computation for deformation and lighting that encodes our notion of image similarity. However, it is still a considerable challenge to find ways to effectively compute with such an image metric. Therefore, the research team also develops computationally effective algorithms based on this new metric. These algorithms improve performance in numerous applications such as face recognition, autonomous navigation, and optical flow and tracking, in which variations in lighting and shape cause significant challenges for existing methods. IIS CSE Continuing grant HPCC 9215 algorithms MD vision University of Maryland College Park theory of computation ROBUST INTELLIGENCE Jie Yang 7495 7495 Jacobs, David 7923 107116 0915971 September 1, 2009 RI: Small: Modeling Cities by Integrating 3D and 2D Data This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The problem of automated 3D reconstruction and modeling of urban environments is of great interest and importance in the fields of computer vision and graphics. This is due to the fact that accurate 3D city models are paramount in the further development of a variety of fields. This project acquires vast amounts of data via the latest generation in laser scanning technology and high-resolution cameras in order to achieve the goal of 3D city modeling. That data is registered in a common frame of reference based on current techniques that are improved in order to facilitate voluminous point clouds. One of the major challenges is the complexity of the acquired dataset that needs to be simplified for efficient rendering and higher-level recognition algorithms. Current simplified methods produce reduced sets of triangles that lack higher-order labels. This project segments and classifies the data into coarse and fine urban elements (such as facades, windows, vegetation, etc.), discovers symmetries among the elements, and fills missing data. It also integrates image-based and laser-based models in order to enhance the acquired geometry, and to produce a seamless visualization result. The above will also aid recognition processes, alternative visualizations and truly photorealistic representation of the 3D scene. This work is disseminated through collaborations with the geography and film-and-media departments of Hunter, as well as other agencies (such as museums). It has significant impact for urban planning, architecture, and archeology applications, and in systems such as street map visualization, as well as in film and construction industries. IIS CSE NY HPCC architecture Standard Grant 9215 algorithms visualization SPECIAL PROJECTS - CISE 1714 vision graphics computer vision Jie Yang Stamos, Ioannis CUNY Hunter College GRAPHICS & VISUALIZATION 7453 7453 6890 7923 474963 1714 0915956 September 1, 2009 III: Small: Avoiding Contention on Multicore Machines To take full advantage of the parallelism offered by a multicore machine, one must write parallel code. Writing parallel code is difficult. Even when one writes correct code, there are numerous performance pitfalls. For example, an unrecognized data hotspot could mean that all threads effectively serialize their access to the hotspot, and throughput is dramatically reduced. This project aims to provide a generic framework for performing certain kinds of concurrent operations in parallel. Infrastructure is provided to perform those operations in a scalable way over the available threads in a multicore machine, automatically responding to hotspots and other performance hazards. The goal is not to squeeze the last drop of performance out of a particular platform. Rather, with the planned system a programmer can, without detailed knowledge of concurrent and parallel programming, develop code that efficiently utilizes a multicore machine. The project involves the development of algorithms and data structures designed for the efficient parallel execution of generic code fragments. The primary focus is on data intensive operations as would typically be found in an in-memory database engine. Critical research questions include how to design generic multi-threaded operators that can be applied to a range of computations, how to avoid cache thrashing, and how to implement the framework in a way that works on a variety of hardware platforms. Performance improvements in throughput of an order of magnitude are expected relative to naive solutions that suffer from contention. The project aims to achieve performance close to that of hand-tailored expert-written parallel code, with far less coding effort. This project has immediate applications in both commercial and public-domain database systems where performance improvements would enhance the experience of database system users, and reduce hardware and energy requirements for a given level of performance. Programmability improvements would allow programmers without expertise in parallel programming to effectively use multicore machines. The project also provides the focus for an advanced-level course on database system implementation for multicore machines. The software infrastructure will be made available for research use by others. IIS CSE NY HPCC 9216 Standard Grant algorithms database Columbia University INFO INTEGRATION & INFORMATICS 7364 Frank Olken Ross, Kenneth 7364 7923 499978 0915933 August 1, 2009 III: Small: Collaborative Research: Coordinated Visualization for Comparative Analysis of Cross-Subject, Multi-measure, Multi-dimensional Brain Imaging Data The amount of medical imaging data has been growing at an unprecedented rate in recent years due to the rapid advancement in medical imaging devices and technologies. In many medical application areas, assessment of similarity and disparity from multimodality, multi-dimensional data across subjects plays a central role. Current software in exploration and visualization of a collection of multimodality, multidimensional cross-subject data impedes the effective utilization and better understanding of acquired, large-scale data. The overall objective of this research is to design and develop a unique coordinated visualization framework, based on advanced geometric computing as well as data and visual abstraction, for integrating, interpreting and comparative analysis of cross-subject, multidimensional, multi-measure brain imaging data. This developed visualization framework extends the state-of-the-art in both information visualization and medical visualization. It employs novel geometric feature analysis for better supporting surface matching and shape comparison, and generalizes data warehousing technology to spatially varying information deep inside multi-dimensional medical images. The research outcomes are disseminated through traditional publications as well as the Internet. This research project provides a useful multimodality imaging analytics framework which contributes to diverse application domains, such as clinical diagnosis of neurological disorders, drug efficacy analysis through quantitative image analysis, and basic neuroscience. In addition, the sharing of data and software tools has both clinical and educational values for students, physicians, researchers, and the general public. The integration of the research and education components promotes further interactions between computer science and neuroscience. IIS CSE HPCC Standard Grant 9215 MI visualization education 0116000 Human Subjects information visualization 250000 Jie Yang 7364 GRAPHICS & VISUALIZATION 7453 Wayne State University Hua, Jing 7453 7923 0915910 July 15, 2009 III: Small: Exploring Data in Multiple Clustering Views This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The primary objective of this research is to formulate a framework for a new paradigm for clustering: discovering all possible non-redundant multiple clustering views from data. Typical clustering algorithms only find one clustering solution, but many real and complex data are multi-faceted by nature. Data can be interpreted in many different ways. Given the same data, what is interesting to a physician will be different from what is important to an insurance agency. This research will provide new formulations, algorithms, and tools for exploratory data analysis that are widely applicable to many domains. The PI will apply the new algorithms in detection of skin lesions, including cancers/ This will involve the automatic segmentation of the dermis and epidermis junction in skin images, automated detection of machine sounds, and in developing algorithms for multiple non-redundant clustering of text and natural images. Additionally, this project will provide research experiences for both undergraduate and graduate students in the classroom and in the lab. The PI will work with the Society of Women Engineers to inspire female students to pursue careers in engineering and computer science, and to ensure that under-represented groups are involved in this research. IIS CSE HPCC 9216 Standard Grant algorithms MA Sylvia J. Spengler Northeastern University 0116000 Human Subjects INFO INTEGRATION & INFORMATICS 7364 data analysis 7364 Dy, Jennifer 6890 7923 470112 0915876 July 15, 2009 RI: Small: Robot Grasping of Deformable Objects Deformable objects are ubiquitous in our daily life. The ability to manipulate them is an important measure of the robot's intelligence and dexterity. This project investigates several fundamental issues related to robot grasping of deformable objects (which has remained an underdeveloped research area): (i) efficient and interactive modeling of grasp formation; (ii) analysis and synthesis of two-finger squeeze grasps with understanding of the role of elasticity; (iii) resistance to and absorption of external forces; and (iv) sensing and force control for grasp achievement. A graphical interface called GraspDeform is under development not only for simulation purpose, but also to assist the design, evaluation, and implementation of grasping strategies over a robot platform. The PI hopes to acquire in-depth understanding about the geometry and mechanics of grasping in the presence of deformation, by examining issues like contact, strain energy, elasticity constants, and friction. The project intends to demonstrate that a robot hand can reliably grasp various deformable objects. Results are expected to considerably widen up research on manipulation of deformable objects, influence deformable modeling in computer graphics, and have potential applications in medical and home robotics. They will be disseminated to the research community via the development of an interactive website on deformable grasping. IIS CSE Continuing grant HPCC 9215 simulation Iowa State University IA ubiquitous computer graphics graphics robotics Paul Yu Oh ROBUST INTELLIGENCE 7495 Jia, Yan-Bin 7495 7923 122260 0915865 September 1, 2009 III:Small : MOSAIC - Advanced Querying Paradigms For Supporting Discovery Oriented Tasks on the Semantic Web The proposal is to support multi-stage queries by integrating knowledge from across different queries, creating a "knowledge mosaic." This approach, if successful could radically improve and extend web search from one of primarily fact-finding to one of fact-finding and problem solving using a set of related queries which find pieces of information which are then "stitched together" such that relationships and links between the information can be better revealed. The approach to problem-solving becomes one of using a set of related queries which find out portions of information on graph models, building the knowledge mosaic using RDF and other graph-based Semantic Web technologies and extending a proposed new query language SPARQ2L The resultant view keeps track of what knowledge has been found, and attempts tto combine it in useful ways to provide a more complex knowledge view for the information the user seeks to discover. The research could have pronounced impact on how search and discovery is performed on the web. IIS CSE Continuing grant HPCC 9216 NC North Carolina State University INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 7923 Ogan (Anyanwu), Kemafor 155997 0915862 September 1, 2009 RI: Small: Intelligent Compressive Multi-Walker Recognition and Tracking (iSMART) through Pyroelectric Sensor Networks Although high-cost, data-intensive multi-camera systems have been widely used for mobile human tracking and recognition, the pyroelectric infrared (PIR) sensor has a variety of advantages including dramatically low costs, chemical stability, high sensitivity to human body thermal variation, and extremely low sensory data throughput. This project implements an Intelligent Compressive Multi-Walker Recognition and Tracking (iSMART) testbed based on PIR Sensor Networks (PSN). The novelties of iSMART include three aspects: (1) Context-aware region-of-interest (RoI) exploration to achieve an inherent tradeoff between area of sensor coverage and degree of information acquisition resolution. This research uses strict mathematical models to measure RoI context. (2) Decentralized inference / learning for in-network intelligence. This project develops a belief-propagation-based distributed inference scheme with data-to-object association for continuous tracking and recognition of multiple walkers. It uses orthogonal-projection-based distributed learning for sensor calibration and feature model training. (3) Networked, compressive sampling structures and sensing protocols. This project extends the latest progress in compressive and multiplex sensing theories to guide the design of novel networked sensor receiver pattern geometries and decentralized sensing protocols. The above research efforts will lead to a novel low-cost, high fidelity wireless distributed sensing system for multiple walker recognition and tracking. As an alternative to video camera systems, iSMART systems can be widely deployed to automatically monitor airports, customs / harbors, and other critical national infrastructures. This project will also generate interesting hands-on labs on intelligent sensor / sensor networks and class projects for both undergraduate and graduate students. IIS CSE HPCC Standard Grant 9215 network 9150 wireless Paul Yu Oh ROBUST INTELLIGENCE 7495 AL University of Alabama Tuscaloosa 7923 Hao, Qi Fei Hu 325933 0915801 September 1, 2009 III: Small: Quality Assessment of Computational Protein Models The PI plans to develop a new and robust hierarchical multi-resolution framework that can be used for assessement of model uncertainty, especially that associated with prediction of protein structure. The approaches also enable respresentation of the structure in ways that enable rapid searching of structure space. It is important to establish quality estimation methods for predicted structures so that they can be used wisely by knowing the limitations of the model. Protein tertiary structure prediction has made steady progress in the past decade. However, current prediction methods are still not capable of producing highly accurate models on a regular basis. Practical use of prediction methods by biologists is limited not only the accuracy of current prediction methods but also the lack of error estimation of the models they produce. Moderately accurate models are still useful for many purposes, including design of site-directed mutagenesis experiments and structure-based function prediction, if the possible error range is understood. Resulting quality assessment methods will also contribute to improvement of protein structure prediction methods. In addition, structure models of proteomes of model organisms will be constructed with quality assessment data and will be made available to the public through the Internet. The proposed project leverages Purdue University's efforts in interdisciplinary computational life science and engineering by training graduate students and undergraduate students of different backgrounds through interdisciplinary coursework and direct involvement in the project. IIS CSE HPCC Standard Grant 9215 Purdue University IN Sylvia J. Spengler INFO INTEGRATION & INFORMATICS 7364 7364 7923 Kihara, Daisuke 327606 0915788 August 1, 2009 III: Small: Supporting Investigative Analysts and Researchers in Sense-making across Large Document Collections through Visual Analytics People routinely encounter and seek to make sense of large collections of data that include both unstructured reports or stories and loosely-structured logs or spreadsheets. In many cases, information of relevance is scattered about a large number of documents and it is the task of an analyst to read the documents and "put the pieces together." For instance, police investigators when sifting through a multitude of observations, case reports, and witness testimonies must develop a coherent view of the events that really occurred. Academic researchers investigating a new domain pour over large numbers of paper abstracts, citations, and articles to develop a better understanding of the state of work in that area. The process of connecting individual pieces of information such as those discussed above into a more coherent narrative is a component of investigative analysis, the main focus of this project. One common element of analytic sense-making activities is that they are cognitively very challenging, frequently involving large collections of data that tax a person's memory, deduction, reasoning, and general analytic capabilities. Investigative analysis today is made even more challenging by the ever-increasing torrent of data available in a world where one can access vast databases and conduct internet searches that in seconds return a quantity of documents no human can read and assimilate in a reasonable amount of time. But technological means of augmenting human memory and analytic reasoning hold great potential as investigative aids. This project explores the development of computational systems to make investigative analysts more effective and more efficient. The PI's approach centers on providing multiple visual representations of the individual pieces of data gathered during the investigation, to help highlight connections or potential connections among them and to help analysts determine the next pieces of data to examine from a large collection of evidence. The PI will draw upon his experience in information visualization and visual analytics to design and create a system to help analysts, and upon his experience in human-computer interaction to evaluate whether the system is effective. The work will include fundamental research on challenges such as the representation of reliability and uncertainty in a visualization display, the development of collaborative system capabilities so that analysts can work together, and the integration of sophisticated automated textual analysis capabilities with the human-directed exploration approach that visual interfaces provide. Careful evaluation of all the new analytic capabilities will accompany their design as well. Broader Impacts: Investigative analysis is a fundamental activity in law enforcement and in intelligence activities that are important to our national security. This project will invent next-generation visual analytic techniques and technologies that can be used to develop investigative analysis systems in the future. Other domains such as news reporting, academic research, and business intelligence also require investigative analysis, so this project has the potential to impact those fields as well. IIS CSE Ephraim P. Glinert Continuing grant HPCC 9215 human-computer interaction visualization GA Tech Research Corporation - GA Institute of Technology GA security 0116000 Human Subjects information visualization Stasko, John GRAPHICS & VISUALIZATION 7453 visual analytics 7453 7923 218691 0915775 September 15, 2009 TC:Small: A Formal Inter-Disciplinary Study of the Impact of Security Awareness Efforts on User Behavior Given the diverse and complex nature of computer security, a natural response of the academic and industrial community has been to study how one can create technical solutions to the problem. Although the technical solutions to various problems can be quite effective, the underlying premise of many of the solutions is predicated upon an informed awareness of the user of the importance of avoiding risky behavior. While there has been considerable rigor undertaken with regards to the evaluation of the efficacy of the various technical approaches, the human aspect of computer security has received relatively minor attention with largely cursory / anecdotal evaluation. The unfortunate result of this lack of rigorous scientific data is the use of under-funded and ad hoc awareness security awareness initiatives that offer limited benefit to the security of the enterprise. This work will leverage the unique aspects of the university-environment to conduct a multi-scale (time, observation group, data granularity) formal set of experiments regarding the efficacy of security awareness techniques. Moreover, the inter-disciplinary effort will bring to bear the application of formal experiments to explore the usage of negative, positive, and targeted communication interventions drawn from theoretical considerations of existing criminology, psychology, and information system literature. Stated in an alternative manner, organizations dedicate significant financial and human resources to information security awareness programs designed to raise user knowledge about safe computing practices and information security risks. Unfortunately, despite the fact that many organizations are expending significant resources on awareness, organizations have little if any guidance or scientific evidence to construct effective strategies. Should strategies focus on positive or negative strategies? Are post cards or hallway posters or training classes more effective? Are awareness campaign effects temporary or long term? The focus of this work will be to provide that rigorous scientific basis by exploring how effective awareness techniques are in the ?wild? of the university environment, unimpeded by normal network security controls. A key broader impact of the work will be the creation of basic guidelines for the construction of security awareness programs. The net result will be dramatically improved cost efficiency of security awareness techniques and hence, significant improvement in the national cyber-security infrastructure. IIS CSE Continuing grant HPCC 9218 IN network security 0116000 Human Subjects University of Notre Dame Amy Baylor 7923 TRUSTWORTHY COMPUTING 7795 Striegel, Aaron Charles Crowell John D'Arcy 159354 0915754 September 1, 2009 RI: Small: Modeling Coarticulation for Automatic Speech Recognition This project focuses on applying a model used in text-to-speech synthesis (TTS) to the task of automatic speech recognition (ASR). The standard method in ASR for addressing variability due to phonemic context, or ?coarticulation,? requires a large amount of training data and is sensitive to differences between training and testing conditions. Despite the effective use of stochastic models, current ASR systems are often unable to sufficiently account for the large degree of variability observed in speech. In many cases, this variability is not due to random factors, but is due to predictable changes in the speech signal. These factors are currently modeled in order to generate speech via TTS, but they are not yet modeled in order to recognize speech, largely because of non-local dependencies. We apply the Asynchronous Interpolation Model (AIM) used in TTS to the task of speech recognition, by decomposing the speech signal into target vectors and weight trajectories, and then searching weight-trajectory and stochastic target-vector models for the highest-probability match to the input signal. The goal of this research is improve the robustness of ASR to variability that is due to phonemic and lexical context. This improvement will increase the use of ASR technology in automated information access by telephone, educational software, and universal access for individuals with visual, auditory, or speech-production challenges. More effective models of coarticulation may increase our understanding of both human speech perception and speech production. Results from this project are disseminated through technical papers and the CSLU Toolkit software package. IIS CSE Continuing grant HPCC 9215 Oregon Health and Science University OR Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 7495 7923 Hosom, John-Paul 145019 0915705 August 15, 2009 HCC: Small: First Days: Improving Maternal and Infant Health with Persuasive Technology This proposal focuses on the development of motive and persuasive technologies (MPT) for personal health and health decision making. In particular persuasive technologies could help influence mothers to make better health choices. Motive technologies can improve community health worker job performance. MPT is a relatively new area and much foundational work remains to be done. This project draws on classical theories of persuasion from psychology, economics, rhetoric, and the recent literature on persuasive technologies to inform the approach. The nascent MPT literature has shown that many classical influence mechanisms ?work? via information and communication technology. But relatively little work has compared the relative power of these mechanisms, which is important for MPT to grow as a design science. And very little work has been done on persuasion in developing regions where MPT has great potential impact - the diffusion of innovation literature has shown that personal choice is the main challenge to development in many poor communities. The goal of this project is to compare the relative power of different persuasive mechanisms in the context of maternal and infant health care. The results of this work will have profound impacts on underserved communities in the US. For example, recent work by the PI on language learning among immigrant farm workers in the US suggests that they face many of the challenges that the poor in developing regions face. There are estimated to be 13 million such workers in the US. Poor education, lack of trust and participation in the formal health system, conflicts with traditional practices, all reproduce the challenges seen in health care in developing regions. Maternal and infant health are among the most basic indicators of social development. They comprise two of the eight Millennium Development Goals defined by the United Nations. The research will shed light on the challenges in delivering maternal and infant health care to poor communities, and on finding practical means to overcome them. Success in the project would pave the way to widespread use of MPT in health care, and will inform its application to other areas such as agriculture, education, and sustainable development. IIS CSE HPCC Standard Grant 9215 CA education 0116000 Human Subjects University of California-Berkeley 500000 HUMAN-CENTERED COMPUTING 7367 Canny, John 7367 David W. McDonald 7923 0915665 July 15, 2009 HCC: Small: RUI: Parameterization and Collection of Demonstrative Gestures for Interactive Virtual Humans The research goal of this project is to develop new techniques for producing realistic and parameterized humanlike gestures based on motion data acquired from real performances. This work proposes novel computational models and interactive interfaces, and focuses on whole-body demonstrative gestures for interactive training and assistance applications with autonomous virtual humans. Although gesture modeling has made substantial advances in recent years, less attention has been given to parameterized demonstrative gestures which can be modified to refer to arbitrary locations in the environment. This particular class of gestures is critical for a number of applications. Typical examples of such gestures include pointing to and demonstrating how to operate particular devices or objects. To ensure the system's effectiveness, this project includes cognitive studies for guiding the development of the computational gesture model. To ensure the achievement of realistic results, motion capture data obtained from real performers executing gestures for demonstration tasks within real scenarios will be employed. The proposed framework will also account for gestures captured interactively from a low-cost wearable set of motion sensors, enabling the interactive customization of gestures needed for programming interactive virtual human demonstrators for a broad range of applications. This project will significantly advance research on gesture modeling with two key contributions: (1) a novel computational model which integrates blending of realistic full-body gestures from motion capture with motion modification techniques for achieving precise arbitrary placement of the hands at the gesture stroke time, and (2) a new user interface for gesture modeling based on direct demonstrations which will truly enable a seamless human-centered user interface for programming autonomous characters. The approach constitutes a substantial step toward achieving autonomous virtual assistants which can meaningfully and effectively demonstrate tasks and procedures. The interactive interface component of this project has the transformative potential to enable gesture programming to become accessible to the non-specialized user, and therefore to enable virtual humans to become widely employed as a powerful communication medium. This project has the potential to impact the basic and broad research problem of modeling human movement and cognition, which is a central topic in information technology. This project will in addition benefit other researchers by producing a unique type of demonstrative gestures database which will be made available from a public project webpage. It will also provide unique educational opportunities for students and contribute to interdisciplinary educational programs based on new courses being developed or improved around the topics of the research. IIS CSE HPCC Standard Grant 9215 CA William Bainbridge database 0116000 Human Subjects 9229 HUMAN-CENTERED COMPUTING 7367 University of California - Merced 7923 Kallmann, Marcelo Teenie Matlock 499071 0915624 July 15, 2009 HCC-Small: Technology at the Margins: The Urban Homeless as a Lens on the Needs of Users Outside the Mainstream This research will explore a specific marginalized population--the urban homeless in the U.S.--as a lens through which to pursue both a broadening of the methodological repertoire of Human-Computer Interaction as well as specific technological innovations aimed at overcoming aspects of the digital divide in a community. This research will provide a source of primary empirical data through ethnographic inquiry focusing on both the urban homeless and the social service networks that support them; this empirical dataset will be used to drive the creation, deployment, and evaluation of a series of technological interventions that leverage mobile technologies for collaboration between services and their homeless clients, personalized information delivery, and user-generated content creation and dissemination. Research in human-computer interaction has long focused on "environments of plenty" such as workplaces with dedicated networks and computers, and trained support staff to ensure that everything works as intended. Recent work, however, has pointed out the degree to which focusing outside this narrow slice can result in innovative technology that is more broadly useful. This research project will substantially contribute to this thread of work by exploring the challenges and opportunities for technological intervention in a community outside the "environment of plenty": focusing upon the urban homeless in the U.S. This research will provide important data about the opportunities for technology use among the homeless and those who work with them; it will also lead to the creation of new technology and services to support mobile phone-based information delivery. This research will further broaden our understanding of the role computing can play in under-served communities. IIS CSE HPCC Standard Grant 9215 human-computer interaction GA Tech Research Corporation - GA Institute of Technology GA 0116000 Human Subjects HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald Edwards, Keith 7923 499249 0915598 August 1, 2009 HCC: Small: Plan-Based Models of Narrative Structure for Virtual Environments The primary objective of this research is to develop new cognitively informed plan-based models of narrative action and to demonstrate that these models can be used both to control a virtual environment and to make effective predictions about the results of users' mental models of the stories that they characterize. Motivated by psychological models of plans and plan reasoning, this research builds on prior work in plan generation and plan-related communication to develop an architecture for creating understandable interaction in narrative-oriented virtual environments. The specific research program can be divided into two high-level thrusts: 1) Developing new generative knowledge representation schemes for the control of narrative action, focusing on the structures of conflict and goal dynamics. 2) Formally validating the results from the items via large-scale empirical evaluations. This work will develop computational models of narrative, focusing on elements of creativity in narrative (as defined roughly by coherence and expectation violation). The project will explore the hypothesis that creativity in the design of many artifacts (and in the design of narrative in particular) is not only a property of the algorithms used to create the artifacts but also a property of how the artifacts are experienced or understood by human users. This work will have a significant impact on the theory and understanding of the relationships between computation and cognition, particularly in the context of narrative. Because of the multidisciplinary nature of the research objectives, the project will produce significant advances in both computer science and cognitive science. It is anticipated that the resulting model will serve as a foundation for a new generation of tools that support mixed-initiative virtual world design, particularly focusing on the generation of narrative systems. In addition, the research will explore the use of the models to create customized, context-sensitive storylines for computer game-based learning environments. The project will contribute to the infrastructure of science and education by training new researchers (graduate research assistants) in an area that is broadly multidisciplinary (computer science, cognitive science and narrative theory). These new researchers will gain from the project a unique integrated view of the contributing disciplines. The project will train undergraduates through involvement in formal and informal research exposure efforts supported in part by REU supplements. IIS CSE HPCC architecture Standard Grant 9215 algorithms William Bainbridge virtual environment education NC 0116000 Human Subjects North Carolina State University cognitive science Young, Robert HUMAN-CENTERED COMPUTING 7367 7367 7923 497860 0915594 February 1, 2009 Workshop: i-Conference Doctoral Research Colloquium This award supports the Doctoral Colloquium program at the 2009 iConference to be held on the campus of the University of North Carolina at Chapel Hill February 8 - February 11, 2009. This workshop will bring together 15 dissertation-stage doctoral students in diverse information and informatics subfields for one day of presentations, interaction, and feedback with five faculty members selected from among distinguished information researchers. The iConference Doctoral Research Colloquium is designed to assist doctoral students to become stewards of the information and informatics disciplines, by providing an opportunity for interaction with distinguished research faculty and for the development of a global cohort group of new researchers. This project provides support for the travel, lodging and registration of students, plus the registration for the participating faculty mentors, as well as the direct expenses of putting on the Doctoral Colloquium at the meeting. IIS CSE HPCC Standard Grant 9215 NC University of North Carolina at Chapel Hill HUMAN-CENTERED COMPUTING 7367 David W. McDonald Pomerantz, Jeffrey 17176 0915560 July 1, 2009 HCC: Small: Modular Tactile Feedback for Whole-Body Motion Guidance Computers have progressed from their origins as isolated rooms of electronic components to being distributed, highly connected, mobile personal devices that are increasingly intertwined with everyday human experience. But computers haven't yet permeated the domain that is most intuitive and essential for their users, namely that of three-dimensional space and naturalistic human movement. In this research the PI will test the hypothesis that graded whole-body tactile feedback can help humans learn or relearn important body postures and motions. To this end, she will augment commercial human motion tracking with a suit of modular tactile actuators (tactors) that provide spatially-registered naturalistic real-time feedback on the way in which each limb segment should be moved, emulating the light touch of a physical therapist, teacher, or coach. Through collaboration with a clinical researcher the PI will focus in this project on rehabilitation for apraxic stroke patients. Our current understanding of stroke indicates that these patients cannot accurately estimate the pose of their limbs when performing purposeful movements, so the PI will augment their motion practice with continuous tactile guidance about the 3D location and magnitude of any configuration errors. Determining the efficacy of this approach will advance our knowledge of healthy vs. impaired human motor control, and will also improve our understanding of the way in which humans process certain types of tactile signals. Development of the novel modular tactor system will provide insights on the effectiveness of voice-coil tactors and the range of sensations they can create. In the course of testing the project's primary hypothesis, the PI will employ human-subject experiments to determine which system design methods best succeed at helping stroke patients recover. The project will be organized into low- and high-level thrusts, each of which will be spearheaded by a doctoral student. The PI's prior work on haptic contact feedback will help her successfully lead this project, as will the support and resources of relevant experts at the University of Pennsylvania, at nearby Moss Rehabilitation Research Institute, and at Engineering Acoustics, Inc., a leading tactor company. Broader Impacts: This project will have immediate relevance to stroke rehabilitation, with excellent potential for positive impact on society in the longer term through application to a variety of exciting topics in human-centered computing, especially computer-mediated scenarios in human motion guidance, such as athletic motion training and haptic virtual environments. The PI will strive to conduct this research so as to enhance its appeal to students from groups that are typically underrepresented in computer science and engineering, especially women. IIS CSE Ephraim P. Glinert Continuing grant HPCC 9215 PA 0116000 Human Subjects University of Pennsylvania HUMAN-CENTERED COMPUTING 7367 Kuchenbecker, Katherine 7923 163630 0915542 July 15, 2009 RI: Small:Robotic Inspection, Diagnosis, and Repair This project develops a framework for deploying multi-robot teams on man-made structures for continual inspection, structural health monitoring, and minor repair tasks. Within this framework, sensory information obtained by a coordinated group of mobile robots is pooled, resulting in a diagnosis of the state of the structure on which the robots roam. In addition, methodologies for endowing the robots with the ability to self-diagnose are being explored. These topics involve the integration of advanced probabilistic, geometric, and mechanics-based computations. The results of this research are expected to enhance the robustness, lifetime, and range of applicability of robotic systems. Research results will be leveraged by collaborating with external research labs. The robots and structures in the testbed being developed in this project, which are scaled down models of real-world systems, are ideal for student projects. Participation by undergraduates and local high-school students on research projects in the PI's lab will serve as a model for increasing interest in engineering research among these groups. IIS CSE Continuing grant HPCC 9215 MD Johns Hopkins University Paul Yu Oh Chirikjian, Gregory ROBUST INTELLIGENCE 7495 7495 7923 119357 0915527 August 1, 2009 HCC-Small: Displaying Prosodic Text for Reading Aloud with Expression Reading aloud is a complex motor, perceptual, cognitive and linguistic feat that takes years to learn and master. Text is problematic for developing readers because punctuation does not reliably mark phrase units or appropriate pause structure; commas do not always necessitate a pause, and question marks do not always necessitate rising intonation. Young readers who are learning these conventions are left to decode the author's intended prosody by trial and error; even those who have accurate decoding skills often experience difficulty chunking text into meaningful units. As a result, they read in a word-by-word manner with insufficient prosodic variation, which adversely impacts their ability to comprehend what they have read aloud. Traditional reading instruction and software programs emphasize rapid, accurate decoding and word recognition; little or no emphasis is placed on facilitating expressive, prosodic oral reading. Yet prosodic cues such as fundamental frequency F0 (perceived as pitch/intonation), intensity (perceived as loudness), and duration (perceived as length), convey a wide range of linguistic and affective functions that link the speech code to underlying semantic and syntactic content, which is crucial for language comprehension. In this project, the PI will explore a number of innovations to enable developing readers to read aloud with expression. She will design an interactive reading interface that displays prosodically varying text to help children read aloud fluently with appropriate expression. Prosodic targets (F0 contour, intensity envelop, and word and pause duration) will be derived from recordings made by a fluent adult reader and translated into textual manipulations using novel semi-automated acoustic-to-graphic mappings. The software will provide auditory and visual cues corresponding to the model adult production; near-real time visual and auditory feedback of the child's own production will enable self-monitoring to further support learning. The resulting electronic media will resemble a children's book, displaying a story image along with the corresponding prosodic text, and will include additional listening and recording functions. The software will be assessed using a repeated measures design, in which 32 children aged 6-8 years will read age and grade-level appropriate stories with and without the prosodic text. The PI's hypothesis is that providing explicit visual cues pertaining to the underlying prosodic targets will improve oral reading fluency, including accuracy, rate, and expressiveness. The additional cues may also provide the scaffolding to support comprehension of spoken text. Efforts to scale the prosodic text rendering techniques to a larger set of spoken content will be undertaken. Project outcomes will contribute to the fields of, digital signal processing, speech acoustics, speech and language development, reading acquisition, visual typography, and human-computer interaction. Broader Impacts: The ultimate goal of this project is to inspire young readers to make the words on the page "come alive" through their expressive realization of the text. The PI expects the tools and methodologies developed in this work will also be applicable to improving spoken prosody for non-native speakers, for individuals with speech impairments, and for those with learning disabilities. IIS CSE Ephraim P. Glinert Continuing grant HPCC 9215 human-computer interaction MA Northeastern University 0116000 Human Subjects HUMAN-CENTERED COMPUTING 7367 Patel, Rupal 7923 167194 0915472 August 1, 2009 HCC:Small:Collaborative Research:Design and Evaluation of Socially Engaging Avatars This project will employ a cyclical two-step process to develop a computational model that embeds dynamic expression and socially engaging non-verbal gestures into talking avatars, and experimentally tests its usability within digital virtual environments involving human-digital agent interaction. Specifically, the research objectives of this project include: (1) synthesis of expressive talking faces and modeling of dynamic facial expressions, (2) synthesis of socially engaging non-verbal facial gestures, and (3) in-depth usability studies on resultant avatars. Digital immersive virtual environment technology has enormous implications for human-computer interaction. Many qualities of digital human representations, particularly those of human-appearing agents, are important for social engagement and social influence. In particular, non-verbal behaviors play a critical role. Among such behaviors, arguably the most important are facial expressions of emotion, which are critical for meaningful renderings of digital agents. To date, computational models that would permit such renderings are less than optimal. Indeed, an applicable and systematic computational model for rendering spontaneous, on-the-fly non-verbal facial gestures and integrating them with speech has not been created. The success of this proposed project will remove a major barrier to the widespread application of useful digital human representation technology for all applications in which computer-mediated communication can play a role, including commerce, education, health, engineering, and entertainment applications. In addition, it will have far-reaching scientific implications, providing a computationally tractable mechanism for embedding human qualities into computer-controlled entities that are used in other scientific and engineering fields. IIS CSE HPCC Standard Grant 9215 CA William Bainbridge human-computer interaction virtual environment education 0116000 Human Subjects University of California-Santa Barbara HUMAN-CENTERED COMPUTING 7367 Blascovich, James 7367 7923 255396 0915462 August 1, 2009 HCC: Small: Simulating and Animating Materials with Dynamic Geometry Abstract ? O?Brien (0915462) This research project focuses on numerical and geometric methods for physically realistic simulation of objects and materials whose shapes are grossly deforming or changing. These methods use several techniques for dynamic mesh generation, wherein unstructured tetrahedral volume meshes and triangular surface meshes evolve or change through time to accommodate the movement of a highly deformable material. This research is leading to unprecedented simulation capabilities to evolve the meshes used for numerical techniques such as finite element methods and finite volume methods, while maintaining high-quality tetrahedra and triangles. These new algorithms will enable the simulation of phenomena that could not previously be modeled well because of the difficulty of simulating materials whose shapes change radically, such as body tissues during surgery or ballistics undergoing high-speed impacts. The technical contributions of this research fall into two classes. The first contribution is numerical methods that use dynamic mesh generation to bring better accuracy to simulations of elastoplastic solids and viscous fluids undergoing plastic flow, cutting, and fracture. The simulation and dynamic mesher are being coupled so that they locally conserve mass, energy, and momentum as a mesh evolves, and so that the refinement and anisotropy of the mesh are tailored to the physical problem. The second contribution is extensions of dynamic geometry algorithms developed by the researchers that more accurately model surface evolution, that enable a finer surface resolution than volume resolution, and that easily handle topological changes and self-collisions. IIS CSE HPCC Standard Grant 9215 CA algorithms simulation University of California-Berkeley 500000 GRAPHICS & VISUALIZATION 7453 7453 Lawrence Rosenblum 7923 O'Brien, James Jonathan Shewchuk 0915438 September 1, 2009 III-Core-Small: Collaborative Research: Mining and Optimizing Ad Hoc Workflows Ad hoc workflows are everywhere in service industry, scientific research, as well as daily life, such as workflows of customer service, trouble shooting, information search, etc. Optimizing ad hoc workflows thus has significant benefits to the society. Currently the execution of ad hoc workflows is based on human decisions, where misinterpretation, inexperience, and ineffective processing are not uncommon, leading to operation inefficiency. The goal of this research project is to design and develop fundamental models, concepts, and algorithms to mine and optimize ad hoc workflows. The project includes novel research on the following key areas: (1) Network Modeling and Structure Mining. A network model is built that statistically captures the execution characteristics of ad hoc workflows, and is optimized to improve the execution of new workflows with respect to different optimization objectives. (2) Workflow Artifact Mining. The network model built on workflow executions is then extended with workflow artifact mining to realize an optimization system that is able to take advantage of both executions and text contents. (3) Role Discovery and Relation Assessment. A computational framework is built to analyze the roles and relationships of agents involved in ad hoc workflow executions in order to further optimize workflows. Advances from this project include models to represent ad hoc workflows, algorithms for mining hidden collaborative models, and techniques that optimize ad hoc workflow processing. The project bridges two emerging research areas: service science and network science, and enriches the principles and technologies of data mining. It also enhances research infrastructure through the collaboration of team members from different areas (data mining, database, and network). This research is tightly integrated with education through student mentoring and curriculum development. Publications, software and course materials that arise from this project will be disseminated on the project website: URL: http://www.cs.ucsb.edu/~xyan/smartflow.htm IIS CSE HPCC 9216 Standard Grant network algorithms data mining education database Arizona State University AZ INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 Chen, Yi 7923 249817 0915327 September 1, 2009 Small: RI: Broad-Coverage High-Accuracy Machine Translation into Morphologically-Rich Languages Machine Translation (MT) into morphologically-rich languages poses unique challenges that have so far not been adequately addressed in state-of-the-art approaches. Even the best available MT systems into languages such as Arabic frequently produce translations that are disfluent and lack proper grammatical structure. This project explores novel approaches that address these issues by the development of a statistical MT framework that incorporates deeper levels of modeling of syntax and morphology. While the methods explored are largely language independent, the research is conducted and experimentally evaluated within the context of a large-scale English-to-Arabic MT system constructed using vast corpora available from LDC. The research in this project focuses on novel approaches for combining syntactic and non-syntactic translation resources that are automatically acquired from vast amounts of parallel data and on exploring several alternative pathways for the integration of information provided by a high-accuracy morphological analysis and generation engine for Arabic into the MT framework. The project also explores methods for improving the syntax of MT output in Arabic using syntactic transfer rules that model syntactic divergences between English and Arabic. The goal is to develop an English-to-Arabic MT system that produces significantly more fluent, grammatical and accurate Arabic output than the current best systems, as measured by MT evaluation metrics (such as BLEU and METEOR), and as judged by human evaluators. The availability of high-accuracy fully-automatic Machine Translation from English into Arabic has high potential value to the Arabic-speaking population at large, by opening up access to all English content available over the web. Such high-quality MT into Arabic may potentially also improve access to markets in the Arabic-speaking world for US and international companies. IIS CSE HPCC Standard Grant 9215 Carnegie-Mellon University PA 450000 Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 7495 Lavie, Alon 7923 0915315 September 1, 2009 Small: The MCDB Database System for Managing and Modeling Uncertainty The MCDB Database System for Managing and Modeling Uncertainty Analysts working with large data sets often use statistical models to ``guess'' at unknown, inaccurate, or missing information associated with the data stored in a database. For example, an analyst for a manufacturer may wish to know, "What would my profits have been if I'd increased my margins by 5% last year?" The answer to this question depends upon the extent to which the higher prices would have affected each customer's demand, which is undoubtedly guessed via the application of some statistical model. The MCDB project is concerned with the design and implementation of a prototype database system called the "Monte Carlo Database System," or "MCDB" for short. MCDB allows an expert-level analyst or statistician to attach arbitrary stochastic models to the database data in order to "guess" the values for unknown or inaccurate data, such as each customer's unseen demand function. These stochastic models reside in the database, and are always up-to-date in the sense that they are parameterized on the current state of the database (using each customer's most recent purchases in the above example). The project attacks a number of key intellectual and scientific challenges. Most of these are related to the fact that for performance reasons, it is not possible to materialize one thousand stochastic instances of a one terabyte data warehouse, and query each of them in sequence. Novel methods for avoiding such materializations are being considered, such as skipping Monte Carlo trials that produce data which will never be used to answer a specific query. The project also considers statistical challenges, such as generating database instances that fall far out in the tail of the answer distribution, which is necessary for specific applications such as risk assessment. Further information is available at http://mcdb.cs.rice.edu. IIS CSE HPCC 9216 Standard Grant database TX INFO INTEGRATION & INFORMATICS 7364 Frank Olken 500000 William Marsh Rice University Jermaine, Christopher 7923 0915268 July 15, 2009 HCC-Small: DHH Cyber-Community - Supporting Deaf and Hard of Hearing Students in STEM Fields Succeeding in mainstream universities (at all levels) involves extra challenges for deaf and hard of hearing students. Skilled sign language interpreters and captioners with advanced domain knowledge are often difficult to find, multiple visual channels of information in the classroom can be hard to juggle, and collaboration both inside and outside the classroom is often strained due to language barriers. Furthermore, translation of advanced STEM (Science, Technology, Engineering, and Mathematics) topics into American Sign Language (ASL) is far from standardized, and often requires discussion between students and interpreters to devise consistent signs. Better access to classroom activities and a consistent, conceptually clear signing system for STEM topics are both vitally needed in order for deaf and hard of hearing students to advance in the sciences. Thus, in this project the PI will design, implement, and evaluate two distinct yet interconnected technologies. The first of these is ClassInFocus, a classroom platform to help students access remote interpreters and captioners, avoid visual dispersion, and facilitate interaction in the classroom. ClassInFocus will utilize the existing high bandwidth internet connections at multiple universities to create more opportunity for finding the best qualified interpreter or captioner for specific STEM topics. Designing, implementing, and evaluating technological solutions that best meet the educational needs of deaf and hard of hearing students is a challenging research problem; this immersive technology that brings many different technical and human resources into the classroom in an accessible and unobtrusive way is new to the field. ASL-STEM Forum, the second thrust of this work, will be an on-line video forum to facilitate discussion about signing for STEM topics. Visual collaboration between members of the community, in this case the Deaf Community, is important to ensure natural, conceptually correct, and community approved language progression. Designing, implementing, and evaluating a cyberinfrastructure solution to facilitate discussion about ASL vocabulary and grammar for STEM content, that will eventually serve as a resource for students, teachers, and interpreters involved in ASL for STEM fields, is another challenging research problem. Using social networking techniques to engage the deaf and hard of hearing community in discussions about ASL topics and vocabulary in STEM fields is entirely new to the field. This research will involve collaboration with the National Technical Institute for the Deaf at Rochester Institute of Technology, Gallaudet University, and the Shodor Education Foundation. Broader Impacts: The new technologies to be developed as outcomes of this research will enable deaf and hard of hearing students to better access the STEM fields by improving the learning environment and the linguistic access to STEM content. Not only does this increase the likelihood that deaf people will attain college and graduate degrees, it will increase the participation of deaf and hard of hearing people in the development and research of new technology. Other students may also benefit from this technology, as digital notes and captions provide alternative access for students with learning disabilities and create opportunities for searchable archives of class content. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 University of Washington WA education 0116000 Human Subjects networking HUMAN-CENTERED COMPUTING 7367 7367 Ladner, Richard 7923 Caroline Solomon Edward Clymer 499889 0915265 September 15, 2009 RI: Small: Exploiting Bilingual Resources to Improve Monolingual Syntactic Tools Natural language processing systems currently degrade when used outside of their training domains and languages. However, when text is analyzed along with translations into another language, the two languages provide powerful constraints on each other. For example, a syntactic construction which is ambiguous in one language may be unambiguous in another. We exploit such constraints by using multilingual models that capture the ways in which linguistic structures correspond between one language and another. These models are then used to accurately analyze both sides of parallel texts, which can in turn be used to train new, better, models for each language alone. Multilingual models are challenging because each language alone is complex, and the correspondences between languages can include deep syntactic and semantic restructurings. Focusing on syntactic parsing, we address these complexities with a hierarchy of increasingly complex models, each constraining the next. Our approach of multilingual analysis improves three technologies: resource projection, wherein tools for resource-rich languages are transferred to resource-poor ones, domain adaptation, wherein tools are transferred from one domain to another, and multilingual alignment, wherein correspondences between languages are extracted for use in machine translation pipelines. In addition to publishing the research results from this work, we also make freely available the multilingual modeling tools we develop. IIS CSE HPCC Standard Grant 9215 CA 100000 Tatiana D. Korelsky University of California-Berkeley ROBUST INTELLIGENCE 7495 7495 Klein, Dan 7923 0915196 August 1, 2009 III: Small:Collaborative Research: Bayesian Model Computation for Large and High Dimensional Data Sets This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5. This grant supports research in adapting and optimizing Markov Chain Monte Carlo methods to compute Bayesian models on large data sets resident on secondary storage, exploiting database systems techniques. The work will seek to optimize computations, preserve model accuracy and accelerate sampling techniques from large and high dimensional data sets, exploiting different data set layouts and indexing data structures. The team will develop weighted sampling methods that can produce models of similar quality as traditional sampling methods, but which are much faster for large data sets that cannot fit on primary storage. One sub-goal will study how to compress a large data set preserving its statistical properties for parametric Bayesian models, and then adapting existing methods to handle compressed data sets. Intellectual Merit and Broader Impact This endeavor requires developing novel computational methods that can work efficiently with large data sets and numerically intensive computations. The main technical difficulty is that it is not possible to obtain accurate samples from subsamples of a large data set. Therefore, the team will focus on accelerating sampling from the posterior distribution based on the entire data set. This problem is unusually difficult because stochastic methods require a high number of iterations (typically thousands) over the entire data set to converge. However, if the data set is compressed it becomes necessary to generalize traditional methods to use weighted points combined with higher order statistics, beyond the well-known sufficient statistics for the Gaussian distribution. Developing optimizations combining primary and secondary storage is quite different from optimizing an algorithm that works only on primary storage. This research effort requires comprehensive statistical knowledge on both Bayesian models and stochastic methods, beyond traditional data mining methods. A strong database systems background in optimizing computations with large disk-resident matrices is also necessary. This research will enable a faster solution of larger scale problems compared to modern statistical packages to solve stochastic models. Bayesian analysis and model management will be easier, faster and more flexible. Broad Impact This research will occur within the context of three separate application areas: cancer, water pollution, and medical data sets with patients having cancer and heart disease. The educational component of this grant will enhance current teaching and research on data mining. In an advanced data mining course students will apply stochastic methods to compute complex Bayesian models on hundreds of variables and millions of records. Data mining research projects will be enhanced with Bayesian models, promoting interaction between statistics and computer science. Keywords: Bayesian model, stochastic method, database system IIS CSE HPCC 9216 Lawrence Brandt Standard Grant data mining database TX INFO INTEGRATION & INFORMATICS 7364 7364 6890 7923 Baladandayuthapani, Veera University of Texas, M.D. Anderson Cancer Center 56873 0915187 September 1, 2009 RI-Small: Probabilistic Models for Structure Discovery in Text This project advances learning methods for obtaining linguistic knowledge from raw or nearly raw text; such knowledge constitutes a core component of natural language processing technology but is difficult to obtain, usually relying on expensive manual annotation of text data. Specifically, this project aims to automate some of the mechanical aspects of developing learning algorithms for linguistic structure (in part by using a empirical Bayesian framework to unify considerable past work by the PI and others), to enrich models with richer linguistic bias (particularly through lexicalization and integration of morphology and syntax), and to apply these techniques to new natural language processing problems (identifying boilerplate and quotation extraction). Another exciting dimension is learning from text collections in multiple languages (not necessarily including translations), which past work has shown can lead to better unsupervised learning. The project will lead to working systems, including generic tools applicable to many problems in natural language processing and machine learning. These tools will provide infrastructure for the PI's courses and will be publicly available to the research community. Research results will be published in leading journals and at major conferences. The project supports one primary graduate student and a post-doctoral researcher. Major impacts of this project will be improvements in the quality of rapidly ported natural language processing tools for new languages and text domains, as well as a deeper scientific understanding of natural language learning by machines. IIS CSE Continuing grant HPCC 9215 Carnegie-Mellon University PA algorithms machine learning Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 7495 Smith, Noah 7923 204570 0915176 September 1, 2009 RI: Small: Statistical Machine Translation Through a Tree Adjoining Grammar with Flexible Parsing Operations Our research involves the development of a syntactic approach for statistical machine translation that extends a tree adjoining grammar (TAG) formalism to the translation problem, and frames translation directly as a parsing problem. The model imposes no constraints on entries in the phrasal lexicon, thereby retaining the flexible lexical entries of phrase-based translation systems; it allows straightforward incorporation of a syntactic language model. The operations used to combine tree fragments into a complete parse tree are generalizations of standard parsing operations found in TAG; specifically, they are modified to be highly flexible, potentially allowing any possible permutation (reordering) of the initial fragments. This allows the model a great deal of freedom in capturing differences in word order between source and target languages. The use of flexible parsing operations raises a couple of challenges that are a major focus of our research. First, efficient decoding algorithms are required for the models. Second, flexible parsing operations allow the model to capture complex reordering phenomena, but in addition introduce many spurious possibilities. We are investigating the use of learned, probabilistic constraints based on information in the source-language sentence, or in a parse tree for the source-language sentence, thereby incorporating syntactic information from the source language. The end goal of the project is to develop new models for translation that improve the fluency or grammaticality of translations, improve the degree to which semantic information (e.g., predicate-argument structure) is preserved in translation, and improve the treatment of differing word orders between source and target languages. IIS CSE HPCC Standard Grant 9215 algorithms MA 450000 Massachusetts Institute of Technology Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 Collins, Michael 7923 0915148 July 15, 2009 RI: Small: Randomized Feedback Motion Planning with Computational Lyapunov Certificates Recent advances in the direct computation of Lyapunov functions using convex optimization make it possible to efficiently evaluate local regions of stability for smooth nonlinear systems. These tools can be combined with randomized motion planning algorithms to obtain new feedback motion planning algorithms which probabilistically cover a bounded reachable state-space with verified regions of stability; efficiently constructing a global feedback controller out of many locally valid controllers. If successful, the proposed work will generate a class of algorithms capable of computing covering feedback policies for nonlinear systems with dimensionality beyond that of dynamic programming. In addition, the algorithms operate directly on the continuous state and action spaces, and thus are not subject to the pitfalls of discretization. By considering feedback during the planning process, the resulting plans are robust to disturbances and quite suitable for implementation on real robots. Through both theoretical and experimental validation of these algorithms, this work aims to have broad impact on the experimental control of nonlinear and underactuated systems, including walking robots, aerial vehicles, and robots which grasp and manipulate the environment. The algorithms and robotic experiments will be integrated in the PI's graduate robotics curriculum and outreach activities, and the algorithms will be disseminated through a software distribution. IIS CSE Continuing grant HPCC 9215 algorithms MA robotics Massachusetts Institute of Technology Paul Yu Oh ROBUST INTELLIGENCE 7495 7495 Tedrake, Russell 7923 144819 0915085 July 1, 2009 HCC: Small: Eye Movement in Stereoscopic Displays, Implications for Visualization In this project the PI will investigate human eye movement within stereoscopic displays, with the goal of improving the perceptual effectiveness of these displays for visualization. While numerous studies have looked at eye motion for conventional displays, there has been very little work on stereo displays. The challenge and the opportunity for eye tracking in stereoscopic displays is that both eyes can be tracked, yielding data on gaze position on the screen and eye vergence, which affords an estimate of where the subject is fixating in three dimensional space (not just in screen space). Such data would shed light on the strategies users employ to explore and interpret information in these displays, which in turn may enable display designers to increase the effectiveness of their displays in transmitting information. This research will include technology development, perceptual experimentation, and visualization system development phases. The PI will initially devise an experimental methodology for binocular eye tracking devices, develop protocols and software, and run preliminary experiments to collect a baseline set of stereo tracking data which establish what tracking precision is possible and provide a basic understanding of how humans perform eye movements on stereo displays. Later, the focus will be on experiments exploring issues in the design of perceptually optimal visualizations on stereoscopic displays; eye tracking will illuminate how users scan such displays, providing insight into the visual strategies used to navigate, interpret, and extract information. Finally, the PI will employ what he has learned to design, construct, and evaluate an eye-tracking enhanced stereo 3D visualization system. Broader Impacts: The potential impacts of this project are as broad as the possible application of stereoscopic displays. For example, the visualization of geographic data often requires the display of layered surfaces; studies have shown that when using a stereoscopic display such visualizations can be designed so that the user's perception of the shapes of two overlapping surface layers is well preserved, but we lack empirical stereo eye tracking data at present to inform and guide this design. In the medical domain, stereoscopic displays have been shown to significantly improve endoscopic performance, in terms of laparoscopic precision, of both novice and experienced surgeons, but surgeons generally need to maintain a fixed viewing position in order to maintain stereoscopic integrity, a problem which could be alleviated by measuring positional and vergence eye movements and using them to dynamically adjust the location of the autostereoscopic "sweet spot." This work will contribute to building a perceptual science underlying stereoscopic display technology, which will be critical to its continuing perfection and application in domains such as science, education and training, healthcare, industry, and consumer products. IIS CSE Ephraim P. Glinert Continuing grant HPCC 9215 visualization education 9150 0116000 Human Subjects Clemson University SC HUMAN-CENTERED COMPUTING 7367 7367 Duchowski, Andrew 7923 Donald House 211173 0915081 August 15, 2009 HCC-Small: Deception Hotspots as a Resource for Supporting Interpersonal Awareness Narratives This study of the function of deception in electronic communications is designed to develop new theories and tools that will significantly improve collaboration in virtual organizations of all types. Virtual organizations - aggregations of individuals, facilities and resources that span geographic and institutional boundaries - are having transformative effects on the ways in which people socialize and collaborate. They enable interaction between individuals with diverse perspectives who might not otherwise work together, the sharing of expensive and scarce resources, and novel ways of accomplishing tasks and solving problems. Despite the unique capabilities that virtual organizations provide to distributed groups, many have faced difficulties in working together at a distance. While virtual organizations provide basic communication tools (e.g., instant messaging or video conferencing), these tools lack support for the subtlety and nuance of initiating and exiting conversations. In particular, systems fail to support the narrative accounts that people use to explain their behavior and availability. The result of this disconnect between social practice and technical implementation is a flood of unwanted interruptions and painstaking decisions about what personal information one is willing to share and with whom. This research addresses this fundamental problem by developing a narrative approach to interpersonal awareness, and by focusing on the role of deception in managing these narratives. Preliminary evidence suggests that one fifth of all lies told in instant messaging are used to initiate or conclude a conversation. These lies represent potentially valuable "hotspots" that signal trouble in one's interpersonal awareness narrative. Focusing on these hotspots, this work addresses 3 issues: 1) How do people use deception to manage their interactions and avoid unwanted interruption? 3) Are there linguistic and sensor-based attributes that indicate deception may be likely? 3) Can this knowledge be used to design and evaluate tools for managing interpersonal awareness narratives and enabling interaction in virtual organizations? This work builds on substantial research in the area of interpersonal awareness and fostering informal interaction in geographically distributed groups. It makes several unique contributions through a focus on interpersonal awareness narratives: 1) systematically examining the conditions under which deception is used as a resource with existing awareness technologies, 2) conceptualizing deceptions as an indicator of a "hot spot" that can be drawn on in supporting interpersonal awareness, 3) identifying behavioral predictors of deception to design systems that reduce unwanted interruptions without blocking those that are useful or important. By managing attentional and awareness needs more fluidly, members of virtual organizations will be able to coordinate their activity and achieve their tasks more effectively, and the organizations as a whole will be better able to meet their business, educational, social or other goals. IIS CSE NY HPCC Standard Grant 9215 William Bainbridge 0116000 Human Subjects HUMAN-CENTERED COMPUTING 7367 7367 Cornell University - State Birnholtz, Jeremy Jeffrey Hancock 7923 460884 0915071 September 1, 2009 RI: Small: Foundations and Applications of Generalized Planning This project is developing automated methods of artificial intelligence (AI) for creating generalized plans that include loops and branches, can handle unknown quantities of objects, and work for large classes of problem instances. One of the key challenges is to reason about plans with loops and to do so without using automated theorem proving, which tends to be intractable. In particular, research is accomplishing the following goals: (1) develop new theoretical foundations for generalized planning; (2) develop effective abstraction mechanisms and new plan representations to support these new capabilities; (3) develop effective algorithms for plan synthesis as well as generalization of sample plans; (4) develop analysis tools to reason about the applicability, correctness and efficiency of generalized plans; (5) extend the framework to include sensing actions, conditional plans, and domain-specific knowledge in the form of partially specified plans; (6) create a new set of challenging benchmark problems and perform a rigorous evaluation of the approach; and (7) increase the interaction between the AI community and other communities, particularly model checking, that study the abstraction mechanisms and theoretical foundations necessary for generalized planning. This new framework may significantly improve the scope and applicability of automated planning systems. IIS CSE HPCC Standard Grant 9215 algorithms MA artificial intelligence University of Massachusetts Amherst Douglas H. Fisher ROBUST INTELLIGENCE 7495 Zilberstein, Shlomo 455000 7495 7923 Neil Immerman 0915054 September 1, 2009 III: Small: BeliefDB - Adding Belief Annotations to Databases In many scientific disciplines today, a community of users is working together to assemble, revise, and curate a shared data repository. As the community accumulates knowledge and the database content evolves over time, it may contain conflicting information and members can disagree on the information it should store. Relational database management systems (RDBMS) today can help these communities manage their shared data, but provide limited support for managing conflicting facts and conflicting opinions about the correctness of the stored data. This project develops a Belief Database System (BeliefDB) that allows users to express belief annotations. These annotations can be positive (agreement) or negative (disagreement), and can be of higher order (belief annotations about other belief annotations). The approach allows users to have a structured discussion about the database content and annotations. A BeliefDB gives annotations a clearly defined semantics that lets a relational database understand and manage them efficiently. Intellectual merits: (i) Definition of a Belief Database Model: The project develops a formalism that extends a relational database with belief annotations on data and on previously inserted annotations. (ii) Design of a Belief Query Language: The project complements the data model with a new query language that extends SQL. (iii) Development of a canonical Belief Database Representation: The projects develops approaches to store and manipulate belief databases on top of a conventional RDBMS. Broader impact: Curated databases and shared data repositories are becoming widespread in the scientific communities. A BeliefDB provides a new data management system that addresses the need of these communities to manage conflicting data. If successful, the project will be one of the pieces that will help data management technology undergo a new paradigm shift, from managing data as content, to supporting a community of users in collaboratively creating partly conflicting database contents. For further information on the project see the project web page:: http://db.cs.washington.edu/beliefDB/ IIS CSE HPCC 9216 Standard Grant University of Washington WA database INFO INTEGRATION & INFORMATICS 7364 Suciu, Dan Frank Olken 500000 7364 7923 Magdalena Balazinska Wolfgang Gatterbauer 0915040 October 1, 2009 DC: Small: The Use of Ternary Associative Memories in Data Intensive Computing A Content Addressable Memory (CAM) is an associative lookup memory containing a set of fixed-width cells that can hold arbitrary data bits. A CAM takes a search key as a query, and returns the address of the entry that contains the key, if any. In a Ternary CAM (TCAM), each data bit is capable of storing one of three states: 0, 1, or *, where a * matches both 0 and 1. This is a powerful primitive, and has found much use in high performance network routers and switches. This project will develop general techniques for using TCAMs to implement sophisticated randomized data structures, and use them in multiple data intensive applications. TCAMs consume a lot more power (around 20 times) compared to Random Access Memories (RAMs). Hence, it is important to study applications where the improvements are of several orders of magnitude, resulting in an overall decrease in power consumption compared to traditional software solutions that only use RAM; this will be a guiding principle of this research. The full potential of TCAMs has not been realized due to a lack of sophisticated applications. This project will help this technology maximize its potential. This research is expected to have impact in multiple data-intensive applications such as network flow processing, document/image-similarity, backups, and WAN compression. Also, the PIs will train graduate students and develop a new class which will describe real-life algorithms for similarity search, stream processing, and pattern matching, incorporating results from this research. IIS CSE HPCC Standard Grant 9215 CA network algorithms Stanford University INFORMATION TECHNOLOGY RESEARC 1640 430000 James C. French 7793 Goel, Ashish 7923 0915038 September 1, 2009 RI: Small: Learning Strategic Behavior in Sequential Decision Tasks Many routine, real-world tasks can be seen as sequential decision tasks. For instance, navigating a robot through a complex environment, driving a car in congested traffic, and routing packets in a computer network requires making a sequence of decisions that together minimize time and resources used. It would be desirable to automate these tasks, yet it is difficult because the optimal decisions are generally not known. Many existing learning methods lead to reactive behaviors that perform well in short term, but do not amount to intelligent high-level behavior in the long term. This project is developing methods for learning strategic high-level behavior. Strategic methods need to (1) retain information from past states, (2) learn multimodal behavior, (3) choose between the different behaviors based on crucial detail, and (4) implement a sequential high-level strategy based on those behaviors. The neuroevolution methods developed in prior work solve the first problem by evolving (through genetic algorithms) recurrent neural networks to represent the behavior. To solve the remaining problems, these methods are being extended in the proposed work with multi-objective optimization, local nodes with cascaded structure, and with evolution of modules and their combinations. Preliminary results indicate that this approach is indeed feasible. In the long term, developed technology will make it possible to build robust sequential decision systems for real-world tasks. It leads to safer and more efficient vehicle, traffic, and robot control, improved process and manufacturing optimization, and more efficient computer and communication systems. It will also make the next generation of video games possible, with characters that exhibit realistic, strategic behaviors: Such technology should lead to more effective educational and training games in the future. The OpenNERO open source software platform developed in this work will be made available to the research community. IIS CSE HPCC Standard Grant 9215 network algorithms TX Miikkulainen, Risto University of Texas at Austin Douglas H. Fisher ROBUST INTELLIGENCE 7495 455000 7495 7923 0915035 August 1, 2009 HCC: Small: Enabling Focus Cues in Stereoscopic Displays Although many different approaches to 3D displays have been explored for decades, the dominant technology in practical use has been of the stereoscopic type, which provides a sense of depth by presenting two perspective images, one for each eye, of a scene from two slightly different viewing positions. A problem inherent to this approach is the lack of correctly rendered focus cues, which stems from the fact that the pairs of stereoscopic images are typically presented on 2D flat surfaces at a fixed distance from the eye. As a result, retinal image blur does not vary with the distances from an eye fixation point to other points at different depths in the simulated scene, but remains consistent with the fixed distance of the display surface. And the eye accommodation depth tends to be at the fixed distance of the 2D display while the eyes are forced to converge at different distances to view objects at different depths. Psychophysical studies have suggested that these incorrect focus cues may contribute to the compressed depth perception and visual discomfort commonly reported when viewing stereoscopic images, which have profound implications for adopting stereoscopic displays for a wide range of applications. In this project, the PI will develop an alternative 3D display based on depth-fused multifocal plane technology that offers more accurate rendering of the focus cues than conventional stereoscopic displays. Instead of focusing on the engineering aspects of developing a better 3D display, she will pursue a human-centered approach wherein human observers participate in the design process for a multi-modal 3D display platform that allows flexible adaptation of the display along multiple display modalities with different levels of focus cue accuracy, from the basic stereoscopic display mode to advanced display modes with correct or partially correct focus cues. The PI will carry out pilot experiments to validate the functions of different display modalities and to evaluate the effects of focus cues on depth perception. Broader Impacts: The new technology will offer a display solution with higher depth perception accuracy, higher stereo acuity, faster task performance, and less eye fatigue than currently available systems. Project outcomes will furthermore provide a much-needed research tool that supports investigation under controlled conditions of the various factors potentially contributing to depth perception accuracy and visual fatigue, as well as exploration of critical health issues such as the consequences for vision development in children of protracted viewing of stereoscopic images. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 AZ 0116000 Human Subjects vision University of Arizona Hua, Hong HUMAN-CENTERED COMPUTING 7367 7367 7923 497600 0914988 September 1, 2009 RI:Small:RUI:Intelligent Soundscape Analysis and Generation This project investigates hierarchical machine intelligence for modeling and composing complex soundscapes. We adapt methods for extracting time-dependent information from text documents to the problem of extracting spectral (graphical) patterns and the probabilities that they occur or co-occur in soundscapes. We are analyzing the spectral patterns that emerge from sound files of many types, including recordings of building interiors with regular foot traffic, musical files, and synthesized sound. A significant part of the research is in devising spectral features that are important for this kind of mapping/identification. Research under this award is also investigating the use of reinforcement learning (RL) to identify time-dependent 'landmarks' from soundscape models, and we employ RL agents to compose large soundscapes from thousands of millisecond length grains of sound in a process called granular synthesis. Systems of RL agents enable us to study distributed time-dependent RL agents in a complex environment, with the ability to produce aural demonstrations of the agents' learning. We also expect the system to produce some compositions that are pleasing in the electronic music sense. This research will have an impact on curricular efforts in Arts and Technology at Smith College, supporting the Computer Science Department's efforts to attract more students, especially to research. IIS CSE HPCC Standard Grant 9215 MA Douglas H. Fisher ROBUST INTELLIGENCE 7495 9102 Franklin, Judy Smith College 7495 7923 266929 0914976 August 1, 2009 HCC: SMALL: Modeling and Exploiting Interaction Context in 3D User Interfaces With each advance in computing technology, researchers develop innovative user interfaces so users can leverage the technology to enhance productivity. 3D immersive virtual environments have long been considered a new frontier in computing; however, lack of effective user interfaces within these virtual worlds has prevented them from being widely used in industry. The PI's prior work has focused on a user interface framework for immersive virtual environments that facilitates context-driven interaction techniques, which have been proven to enhance productivity. One aspect of "context" is the use's workspace: the area and position in the virtual world that the user is most interested in. In this project the PI will build on his previously developed framework to create methods for automatically inferring workspace size and location, and for enabling applications to select the most appropriate navigation technique. The underlying hypotheses are that contextual information can be automatically inferred by observing the user's actions, and that automatically activating specialized interaction techniques suited for the current context will enhance productivity. The hybrid navigation aid to be developed will be based on the well-known WIM and Orbital Viewing approaches. Finding ways to effectively transition between navigation aids based on a changing workspace is a principle objective of this research, which will represent a significant contribution toward making navigation within a virtual world less cumbersome and more efficient. Broader Impacts: This work will lead to more effective user interfaces for immersive virtual environments, which will help unlock their true potential for applications such as architectural design, scientific exploration, and collaborative training applications. The implicit recognition of workspace coupled with the activation and transitioning between navigation techniques will be a valuable addition to the current body of knowledge in 3D user interface design. Although the current project focuses on a single particular area of 3D user interface design, the PI's context-driven framework outlines dozens of instances benefit from this technique. Each of these additional highly focused projects can be undertaken with the equipment funded through this award; together, they constitute excellent opportunities for undergraduate research, which will continue long after the current effort is completed. Virtual environment development inherently requires input from a wide range of specialties (e.g., domain experts to help develop simulation applications and graphics artists to help create content); the PI will take full advantage of his institution's liberal arts focus to bring together students and faculty from a variety of disciplines when carrying out this research. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 simulation virtual environment 0116000 Human Subjects NJ graphics HUMAN-CENTERED COMPUTING 7367 7367 7923 Frees, Scott Ramapo College of New Jersey 98780 0914965 August 1, 2009 HCC:Small:Collaborative Research:Design and Evaluation of Socially Engaging Avatars This project will employ a cyclical two-step process to develop a computational model that embeds dynamic expression and socially engaging non-verbal gestures into talking avatars, and experimentally tests its usability within digital virtual environments involving human-digital agent interaction. Specifically, the research objectives of this project include: (1) synthesis of expressive talking faces and modeling of dynamic facial expressions, (2) synthesis of socially engaging non-verbal facial gestures, and (3) in-depth usability studies on resultant avatars. Digital immersive virtual environment technology has enormous implications for human-computer interaction. Many qualities of digital human representations, particularly those of human-appearing agents, are important for social engagement and social influence. In particular, non-verbal behaviors play a critical role. Among such behaviors, arguably the most important are facial expressions of emotion, which are critical for meaningful renderings of digital agents. To date, computational models that would permit such renderings are less than optimal. Indeed, an applicable and systematic computational model for rendering spontaneous, on-the-fly non-verbal facial gestures and integrating them with speech has not been created. The success of this proposed project will remove a major barrier to the widespread application of useful digital human representation technology for all applications in which computer-mediated communication can play a role, including commerce, education, health, engineering, and entertainment applications. In addition, it will have far-reaching scientific implications, providing a computationally tractable mechanism for embedding human qualities into computer-controlled entities that are used in other scientific and engineering fields. IIS CSE HPCC Standard Grant 9215 William Bainbridge human-computer interaction virtual environment education 0116000 Human Subjects TX HUMAN-CENTERED COMPUTING 7367 7367 University of Houston 7923 Deng, Zhigang 243147 0914934 September 1, 2009 III:Small:Privacy Preserving Data Publishing: A Second Look on Group based Anonymization Group based anonymization is the most widely studied approach for privacy preserving data publishing. This includes k-anonymity, l-diversity, and t-closeness, to name a few. The goal of this proposal is to raise a fundamental issue on the privacy exposure of this approach which has been overlooked in the past and come out with a computationally efficient solution. The group based anonymization approach basically hides each individual record behind a group to preserve data privacy. However, patterns may still be derived or mined from the published anonymized data and be used by the adversary to breach individual privacy. The objective of this research is therefore to develop novel group-based anonymization methods that can defend against such an attack. The first part of the project is to define the attack problem, i.e., the published anonymized data can in fact be mined for privacy attacks. It identifies and formulates the privacy exposure to such an attack. The second part is to conduct a systematic study on the exposure of existing privacy techniques to the attack. The third part is to derive the condition that is able to resist such an attack and develop efficient data publishing algorithms to prevent it from occurring. IIS CSE Continuing grant HPCC IL 9218 University of Illinois at Chicago algorithms Sylvia J. Spengler 0116000 Human Subjects privacy 7364 7923 TRUSTWORTHY COMPUTING Yu, Philip 157358 7795 0914927 September 1, 2009 RI: SMALL: LexE: Using Two-part Lexical Entrainment for More Efficient and Reliable Spoken Dialogue Systems When humans speak to each other and want the dialogue to go well, they adapt to each other?s manner of speaking, using the same words, grammatical constructions and expressions. In order to make fundamental improvements in the performance of spoken dialogue systems, LexE is using subtle techniques to model this adaptation, which is called lexical entrainment. LexE is getting users to adapt their speech to the system, as well as getting the system?s speech to adapt to what the user says. To do this, LexE studies human-human dialogues to find the words and constructions (the ?primes?) that are often adopted by dialogue participants. The spoken dialogue system then uses these primes in its output. The system also detects the expressions that its user employs to refer to objects uses them in its synthetic speech. Two real-user spoken dialogue systems are being used as test platforms for LexE. The first is a bus information system for the Port Authority of Allegheny County; the second is the City of Pittsburgh 311 non-emergency service. By making these publicly-available spoken dialogue systems easier to use, LexE makes them (and other spoken dialogue systems) more accessible to a large part of our population, many of whom, the elderly, for example, get much of their information over the telephone. The techniques developed in this project also provide insights for the education of non-native speakers and for speech therapy, where tutoring systems can imitate the way humans implicitly correct errors in what their interlocutors say. IIS CSE Continuing grant HPCC 9215 Carnegie-Mellon University PA education Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 9102 Eskenazi, Maxine 7495 7923 148065 0914861 August 1, 2009 III: Small-Collaborative: Efficient Bayesian Model Computation for Large and High Dimensional Data Sets This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5. This grant supports research in adapting and optimizing Markov Chain Monte Carlo methods to compute Bayesian models on large data sets resident on secondary storage, exploiting database systems techniques. The work will seek to optimize computations, preserve model accuracy and accelerate sampling techniques from large and high dimensional data sets, exploiting different data set layouts and indexing data structures. The team will develop weighted sampling methods that can produce models of similar quality as traditional sampling methods, but which are much faster for large data sets that cannot fit on primary storage. One sub-goal will study how to compress a large data set preserving its statistical properties for parametric Bayesian models, and then adapting existing methods to handle compressed data sets. Intellectual Merit and Broader Impact This endeavor requires developing novel computational methods that can work efficiently with large data sets and numerically intensive computations. The main technical difficulty is that it is not possible to obtain accurate samples from subsamples of a large data set. Therefore, the team will focus on accelerating sampling from the posterior distribution based on the entire data set. This problem is unusually difficult because stochastic methods require a high number of iterations (typically thousands) over the entire data set to converge. However, if the data set is compressed it becomes necessary to generalize traditional methods to use weighted points combined with higher order statistics, beyond the well-known sufficient statistics for the Gaussian distribution. Developing optimizations combining primary and secondary storage is quite different from optimizing an algorithm that works only on primary storage. This research effort requires comprehensive statistical knowledge on both Bayesian models and stochastic methods, beyond traditional data mining methods. A strong database systems background in optimizing computations with large disk-resident matrices is also necessary. This research will enable a faster solution of larger scale problems compared to modern statistical packages to solve stochastic models. Bayesian analysis and model management will be easier, faster and more flexible. Broad Impact This research will occur within the context of three separate application areas: cancer, water pollution, and medical data sets with patients having cancer and heart disease. The educational component of this grant will enhance current teaching and research on data mining. In an advanced data mining course students will apply stochastic methods to compute complex Bayesian models on hundreds of variables and millions of records. Data mining research projects will be enhanced with Bayesian models, promoting interaction between statistics and computer science. Keywords: Bayesian model, stochastic method, database system IIS CSE HPCC 9216 Lawrence Brandt Standard Grant data mining database TX INFO INTEGRATION & INFORMATICS 7364 7364 University of Houston 6890 7923 Ordonez, Carlos 339023 0914833 August 1, 2009 HCC: Small: Collaborative Research: Asynchrony and Persistence for Complex Contact Stimulations IIS - 0916129 HCC: Small: Collaborative Research: Asynchrony and Persistence for Complex Contact Simulations Grinspun, Eitan Columbia University Collaborative Proposals IIS - 0914833 Guibas, Leonidas J Stanford University ABSTRACT This proposal addresses the challenge of complex contact simulations by working entirely in an asynchronous setting. The project operates along three lines, combining tools from Asynchronous Variational Integrators (AVIs) and Kinetic Data Structures (KDSs) with a novel effort to perform and exploit qualitative analysis of contact simulation data. The first investigation will show that AVIs, meant to handle the continuous aspects of the physics, can be ntegrated well with KDSs, meant to handle discrete geometric events. The second research component addresses the fundamental problem of event scheduling when future trajectories are uncertain. Methods will be explored that improve event detection times, reduce the number of auxiliary events that have to be processed, and allow events to be processed in parallel. The third research task will be to initiate a study of the qualitative behavior of contact simulation by building a hierarchy of coarser models which can then be used for better resource allocation, for the validation of various approximations, and in improved simulation design to attain desired effects. IIS CSE Continuing grant HPCC 9215 Stephen Griffin CA simulation Stanford University GRAPHICS & VISUALIZATION 7453 7923 Guibas, Leonidas 87524 0914808 September 1, 2009 HCC-Small: RSVP IconCHAT - A Brain Computer Interface for Icon-based Communication In this project the PI will address the challenge of empowering people with severe motor and speech impairments (SMSI) to socialize through written and spoken language, by increasing communication rate through a novel and intuitive computer interface. Available augmented communication technologies for the SMSI population typically yield speeds on the order of just one word per minute (based on clinical experience). The PI's objective is to develop an EEG-based brain interface technology based on an intuitive icon-based language generation framework, RSVP iconCHAT, which will achieve increased communication rates for the target population. This technology will exhibit three essential features: rapid serial visual presentation (RSVP) of icons that represent words; a large-vocabulary natural language model with the capability for accurate predictions of intended text in order to control the upcoming sequence of icons to be shown to the subject for confirmation in the RSVP paradigm; and an intent detection mechanism that fuses information from multichannel electroencephalography (EEG) and the generative probabilistic language model. Advanced statistical signal processing, machine learning, and natural language modeling techniques will be employed to achieve communication rates over an order of magnitude higher than the current state-of-the-art. The project will also contribute novel techniques and algorithms for synchronous brain interface design, particularly single-trial ERP detection. Both the brain interface and language model components will learn from previous interactions with the user and exhibit robust cooperative learning behavior in order to maximize language throughput. A Bayesian and information theoretic foundation will support adaptability. The PI notes that his approach is innovative along three dimensions: an intuitive icon-based language representation combined with context-dependent language models will be employed for message construction; a noninvasive brain computer interface that is user-adaptive will be developed and employed to interface with the icon-based platform; and methods for probabilistic information fusion between the brain activity measured by the BCI and the predictive language model will be developed. Broader Impacts: There exists a significant SMSI population due to various reasons such as cerebral palsy (CP), neuromuscular disease (Amyotrophic Lateral Sclerosis, ALS), and severe spinal cord injury leading to locked-in syndrome (LIS). These communities rely on inefficient modes of communication that limit the user's ability to generate acceptable communication rates. Successful achievement of this project's goals will not only provide the target population with an improved face-to-face communication experience with their able-bodied communication partners, but will also enable control of their environment and access to information. In addition, the work will contribute to information fusion from different modalities, optimal data dimensionality reduction, single-trial ERP detection, and human computer communication through a novel interface. Data collected in experiments will be made available to other researchers in order to accelerate verification of outcomes and dissemination of results. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 algorithms MA machine learning verification Northeastern University 0116000 Human Subjects HUMAN-CENTERED COMPUTING 7367 499998 7367 Erdogmus, Deniz 7923 Rupal Patel 0914789 September 1, 2009 RI: Small: Collaborative Research: Infinite Bayesian Networks for Hierarchical Visual Categorization Humans possess the ability to learn increasingly sophisticated representations of the world in which they live. In the visual domain, it is estimated that we are able to identify in the order of 30,000 object categories at multiple levels of granularity (e.g. toe-nail, toe, leg, human body, population). Moreover, humans continuously adapt their models of the world in response to data. Can we replicate this life-long-learning capacity in machines? In this project, the PIs build hierarchical representations of data streams. The model complexity adapts to new structure in data by following a nonparametric Bayesian modeling paradigm. In particular, the depth and width of our hierarchical models grow over time. Deeper layers in this hierarchy represent more abstract concepts, such as ?a beach scene? or ?chair?, while lower levels correspond to parts, such as a ?patch of sand? or ?body part?. The formation of this hierarchy is guided by fast hierarchical bottom up segmentation of the images. To process large amounts of information, the PIs distribute computation across many CPUs /GPUs. They develop novel fast inference techniques based on variational inference, memory bounded online inference, parallel sampling, and efficient data-structures. The technology under development has a large number of potential applications ranging from organizing digital libraries and the worldwide web, building visual object recognition systems, successfully employing autonomous robots and training a ?virtual doctor? by processing worldwide information from hospitals about diseases, diagnosis and treatments. Results are disseminated through scientific publications and publicly available software. IIS CSE HPCC Standard Grant 9215 CA California Institute of Technology Perona, Pietro ROBUST INTELLIGENCE 7495 200000 7495 Qiang Ji 7923 0914783 September 1, 2009 RI:Small:Collaborative Research: Infinite Bayesian Networks for Hierarchical Visual Categorization Humans possess the ability to learn increasingly sophisticated representations of the world in which they live. In the visual domain, it is estimated that we are able to identify in the order of 30,000 object categories at multiple levels of granularity (e.g. toe-nail, toe, leg, human body, population). Moreover, humans continuously adapt their models of the world in response to data. Can we replicate this life-long-learning capacity in machines? In this project, the PIs build hierarchical representations of data streams. The model complexity adapts to new structure in data by following a nonparametric Bayesian modeling paradigm. In particular, the depth and width of our hierarchical models grow over time. Deeper layers in this hierarchy represent more abstract concepts, such as ?a beach scene? or ?chair?, while lower levels correspond to parts, such as a ?patch of sand? or ?body part?. The formation of this hierarchy is guided by fast hierarchical bottom up segmentation of the images. To process large amounts of information, the PIs distribute computation across many CPUs /GPUs. They develop novel fast inference techniques based on variational inference, memory bounded online inference, parallel sampling, and efficient data-structures. The technology under development has a large number of potential applications ranging from organizing digital libraries and the worldwide web, building visual object recognition systems, successfully employing autonomous robots and training a ?virtual doctor? by processing worldwide information from hospitals about diseases, diagnosis and treatments. Results are disseminated through scientific publications and publicly available software. IIS CSE HPCC Standard Grant 9215 CA 300000 University of California-Irvine ROBUST INTELLIGENCE 7495 7495 Welling, Max Qiang Ji 0914666 August 1, 2009 HCC: Small: Conversational Agents in Web-Based Consumer Environments Designed for Older Users Only 35% of seniors (over 65 years of age) in the U.S. use the Internet, reflecting an age-based digital divide when compared with the fact that 90% adults in the ages of 19-29 use the Internet. This project will address this divide through a novel approach that employs conversational agent technology to reduce the critical cognitive and social-psychological barriers hindering older users in web-based consumer environments. The goal of this project is to broaden the engagement of the growing yet underrepresented senior population in Internet technology, a medium that can dramatically improve their quality of life. This goal closely aligns with the human-centered computing objective of transforming the human-computer interaction experience through the development of systems that are aware of the abilities and special needs of people that use them. Research on animated pedagogical agents has revealed promising results on the effectiveness of agents in learning and motivation. However, no previous research program has investigated how agent technology can be designed to promote autonomy and empowerment in the older population, particularly in web-based consumer environments, involving multi-dimensional information processing and complex decision-making. To address this critical gap in the research, this project will systematically investigate, three aspects of agent interactions with older users: 1) locus of control (agent vs. user), 2) interactional style (functional vs. relational), and 3) modality of exchange (unimodal-voice vs. unimodal-text vs. dualmodal-voice + text). This research program will apply a multi-phase, mixed-methods approach involving qualitative studies in the first phase, and a series of controlled experiments with over 400 older users in subsequent phases. The purpose is to examine the effectiveness of the three aspects of agent-user interaction in: 1) reducing cognitive barriers (reducing information load, increasing navigation convenience, enhancing information search and retrieval ease), 2) reducing social-psychological barriers (enhancing control and efficacy, increasing trust, enhancing perception of social support), and 3) increasing Internet technology use intent. The experimental studies will further determine whether users' gender, visual or hearing impairments, and prior Internet competency interact with the three aspects of agent-user interaction to affect the desired outcomes. The findings from this project will generate new knowledge on how multimodal systems employing conversational agents can be designed for the abilities and special needs of older users leading to a potentially transformative and empowering experience for this underrepresented population in information technology. Seniors are increasingly finding the necessity to engage in web-based consumer environments (e.g., online banking, shopping, trading, travel reservations). While the functionality of agents in these domains merits examination, the broader significance of this project lies in its ability to inform the development of agent-mediated interfaces for other applications such as websites that provide important health and medical information to seniors. Anecdotal evidence gathered by the project team from a prototype system has revealed the transformative potential of this technology for older users. This pilot research funded by the Office of Outreach at Auburn University, has extended the impact of this project to constituents in the state of Alabama. In addition to research, outreach will also serve as an important mission in the dissemination of future findings from this project to stakeholders at local and national levels. With the goal of enhancing diversity in research and education, this project will also involve a greater representation of African-American (AA) study participants from surrounding counties in Alabama, and actively engage AA graduate students, who are already part of the PI's lab in the educational goal of this project. This project will further enhance the infrastructure for research and education across two disciplines through an interdisciplinary seminar course will be offered to graduate students in computer science and consumer affairs to enhance the understanding of the future researchers on how humans perceive and use computing artifacts such as conversational agents. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 human-computer interaction education 9150 0116000 Human Subjects HUMAN-CENTERED COMPUTING 7367 7367 AL Auburn University Gilbert, Juan 7923 Veena Chattaraman Wi-Suk Kwon 499757 0914631 August 1, 2009 III: Small:Collaborative Research: Coordinated Visualization for Comparative Analysis of Cross-Subject, Multi-Measure, Multi-Dimensional Brain Imaging Data The amount of medical imaging data has been growing at an unprecedented rate in recent years due to the rapid advancement in medical imaging devices and technologies. In many medical application areas, assessment of similarity and disparity from multimodality, multi-dimensional data across subjects plays a central role. Current software in exploration and visualization of a collection of multimodality, multidimensional cross-subject data impedes the effective utilization and better understanding of acquired, large-scale data. The overall objective of this research is to design and develop a unique coordinated visualization framework, based on advanced geometric computing as well as data and visual abstraction, for integrating, interpreting and comparative analysis of cross-subject, multidimensional, multi-measure brain imaging data. This developed visualization framework extends the state-of-the-art in both information visualization and medical visualization. It employs novel geometric feature analysis for better supporting surface matching and shape comparison, and generalizes data warehousing technology to spatially varying information deep inside multi-dimensional medical images. The research outcomes are disseminated through traditional publications as well as the Internet. This research project provides a useful multimodality imaging analytics framework which contributes to diverse application domains, such as clinical diagnosis of neurological disorders, drug efficacy analysis through quantitative image analysis, and basic neuroscience. In addition, the sharing of data and software tools has both clinical and educational values for students, physicians, researchers, and the general public. The integration of the research and education components promotes further interactions between computer science and neuroscience. IIS CSE HPCC IL Standard Grant 9215 visualization education University of Illinois at Urbana-Champaign 0116000 Human Subjects information visualization 250000 Jie Yang 7364 GRAPHICS & VISUALIZATION 7453 7453 7923 Yu, Yizhou 0914615 September 1, 2009 RI: Small: An Affect-Adaptive Spoken Dialogue System that Responds Based on User Model and Multiple Affective States There has been increasing interest in affective dialogue systems, motivated by the belief that in human-human dialogues, participants seem to be (at least to some degree) detecting and responding to the emotions, attitudes and metacognitive states of other participants. The goal of the proposed research is to improve the state of the art in affective spoken dialogue systems along three dimensions, by drawing on the results of prior research in the wider spoken dialogue and affective system communities. First, prior research has shown that not all users interact with a system in the same way; the proposed research hypothesizes that employing different affect adaptations for users with different domain aptitude levels will yield further performance improvement in affective spoken dialogue systems. Second, prior research has shown that users display a range of affective states and attitudes while interacting with a system; the proposed research hypothesizes that adapting to multiple user states will yield further performance improvement in affective spoken dialogue systems. Third, while prior research has shown preliminary performance gains for affect adaptation in semi-automated dialogue systems, similar gains have not yet been realized in fully automated systems. The proposed research will use state of the art empirical methods to build fully automated affect detectors. It is hypothesized that both fully and semi-automated versions of a dialogue systemthat either adapts to affect differently depending on user class, or that adapts to multiple user affective states, can improve performance compared to non-adaptive counterparts, with semi-automation generating the most improvement. The three hypotheses will be investigated in the context of an existing spoken dialogue tutoring system that adapts to the user state of uncertainty. The task domain is conceptual physics typically covered in a first-year physics course (e.g., Newtons Laws, gravity, etc.). To investigate the first hypothesis, a first enhanced system version will be developed; it will use the existing uncertainty adaptation for lower aptitude users with respect to domain knowledge, and a new uncertainty adaptation will be developed and implemented to be employed for higher aptitude users. To investigate the second hypothesis, a second enhanced systemversion will be developed; it will use the existing uncertainty adaptation for all turns displaying uncertainty, and a new disengagement adaptation will be developed and implemented to be employed for all student turns displaying a second state of disengagement. A controlled experiment with the two enhanced systems will then be conducted in a Wizard-of-Oz (WOZ) setup, with a human Wizard detecting affect and performing speech recognition and language understanding. To investigate the third hypothesis, a second controlled experiment will be conducted, which replaces the WOZ system versions with fully-automated systems. The major intellectual contribution of this research will be to demonstrate whether significant performance gains can be achieved in both partially and fully-automated affective spoken dialogue tutoring systems 1) by adapting to user uncertainty based on user aptitude levels, and 2) by adapting to multiple user states hypothesized to be of primary importance within the tutoring domain, namely uncertainty and disengagement. The research project will thus advance the state of the art in both spoken dialogue and computer tutoring technologies, while at the same time demonstrating any differing effects of affect-adaptive systems under ideal versus realistic conditions. More broadly, the research and resulting technology will lead to more natural and effective spoken dialogue-based systems, both for tutoring as well as for more traditional information-seeking domains. In addition, improving the performance of computer tutors will expand their usefulness and thus have substantial benefits for education and society. IIS CSE HPCC Standard Grant 9215 PA education 0116000 Human Subjects University of Pittsburgh ROBUST INTELLIGENCE 7495 HUMAN-CENTERED COMPUTING 7367 7367 Litman, Diane 7495 Amy Baylor Katherine Forbes-Riley 7923 452745 0914591 February 1, 2009 Supporting Students Attending IUI 2009 Conference This is funding to support participation by approximately 15 graduate students currently enrolled in Ph.D. programs in the United States and abroad in the 2009 International Conference on Intelligent User Interfaces (IUI 2009), to be held in Sanibel Island, Florida, on February 8-11, 2009. Sponsored by ACM, the annual IUI conferences are the premier forum where researchers from academia and industry, who work at the intersection of Human-Computer Interaction (HCI) and Artificial Intelligence (AI), come together to exchange complementary insights and to present and discuss outstanding research and applications whose goal is to make the computerized world a more amenable place. Unlike traditional AI the focus is not so much on making the computer smart all by itself, but rather on making the interaction between computers and people smarter. Unlike traditional HCI, there is a focus on solutions that involve large amounts of knowledge and emerging technologies such as natural language understanding, brain computer interfaces, and gesture recognition. To this end, IUI encourages contributions not only from computer science but also from related fields such as psychology, cognitive science, computer graphics, the arts, etc. IUI 2009 will be the 12th conference in the series; topics of interest this year include user input, generation of system output, ubiquitous computing, help, categories of intelligence, IUI design, and user studies. NSF funds will be used to support two groups of participants: students who are the primary author of a submission that has been accepted as a full paper or poster but whose institution is either unable to provide any funding for conference attendance or able to provide only partial funding that is insufficient to cover the student?s expenses; and other students who would benefit from the conference but who would be unable to attend due to restrictions by their department on funding conference travel for non-authors. The IUI 2009 organizing committee has undertaken to proactively recruit student participants from schools that have not traditionally been well represented in the IUI community, and also that the bulk of students supported (70-80%) will be from U.S. institutions. Broader Impacts: This funding will enable attendance at this conference by students who might otherwise be unable to do so for financial reasons. It will enhance the educational experience of funded participants, by bringing them into contact with leading researchers in the field and by exposing them to the lively discussion during the course of the conference that often leads to opportunities for career advancement. The quality of the conference itself will be enhanced as well, thanks to a broadening of the base of institutions represented and increased diversity of participants. The rich exchange of ideas at IUI has previously proven to be a valuable source of ideas for future research, as well as leading to collaborative efforts; this funding will extend the opportunities for collaboration and provide intellectual stimulus to programs that have previously sent few or no representatives to this conference. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 University of Washington WA human-computer interaction artificial intelligence ubiquitous computer graphics graphics cognitive science HUMAN-CENTERED COMPUTING 7367 7367 Weld, Daniel 14400 0914580 September 1, 2009 Mathematical Models & Computational Algorithms for Image Processing, computer Vision & Computer Graphics Abstract - Thiele This proposal addresses three sets of problems in the intersection of image processing, computer graphics and computer vision: (1) image segmentation with applications to object tracking in videos, (2) inpainting problems, both in the pixel domain and the transform domain, and (3) applications of variational PDE-based models to image processing on manifolds. The approach makes use of powerful concepts from computational mathematics, such as duality, convexification, non-smooth optimization, fast combinatorial optimization, numerical PDE techniques, harmonic analysis, and computational differential geometry. The research is focusing on key issues, such as computational efficiency, feature extraction, object tracking, global analysis, and optimization, and preserving both geometric and texture information, which are keys to further advances. The research will impact many areas ranging from entertainment through homeland security and medical imaging. For image segmentation, typical models are generally nonconvex and admit many local optimal solutions, making them sensitive to initial guesses. Moreover, numerical algorithms can be trapped in local non-optimal minima. To obtain better segmentation algorithms, we are extending a novel convexification technique (through the deep connection between TVL1 models and level sets) developed earlier for the Chan-Vese segmentation model to other segmentation models, such as non-local segmentation models inspired by texture synthesis techniques. For inpainting, recent advances include PDE and geometry techniques, texture synthesis, and a hybrid framework for inpainting missing transform (e.g. wavelets or Fourier). This research is examining the synergy between these methods and deriving models and algorithms that combine the best features of each. For image processing on manifolds, we are using conformal mapping techniques and PDE-based image processing models to derive efficient algorithms for general surfaces. New applications include the automatic tracking of landmarks on general surfaces and the incorporation of shape information in landmark matching. IIS CSE Continuing grant University of California-Los Angeles CA algorithms security OTHR 0000 vision computer graphics graphics computer vision COMPUTATIONAL MATHEMATICS 1271 Lawrence Rosenblum Thiele, Christoph 186276 9263 0914564 September 15, 2009 GV: Small: Collaborative Research: Analysis and Visualization of Stochastic Simulation Solutions This is a collaborative research project conducted by Robert Kirby, University of Utah (IIS-0914564) and Dongbin Xiu, Purdue University (IIS-0914447). In this age of scientific computing, the simulation science pipeline of mathematical modeling, simulation and evaluation is a commonly employed rendition of the scientific method. In addition to the traditional components of the pipeline, there has been a recent surge of interest in uncertainty quantification (UQ). Visualization is the window through which scientists examine their data for deriving new science, and hence visualization methods which depict underlying uncertainty information are crucial. This research addresses the questions of how does one accurately and efficiently post-process stochastic simulation fields and how does one effectively and succinctly convey the results. This is accomplished by developing strategies and techniques for augmenting current visualization techniques used for visualizing spatio-temporal fields with UQ information in a seamless way. The broader impacts of this work are that (1) proper techniques for UQ will have large impact on many scientific disciplines from medical/bioengineering to aeronautics, and (2) developed visualization techniques might be put to use when higher dimensional data is available for each point in space. The educational objectives are focused on training a new generation of scientists who are proficient not in both visualization techniques and in UQ. The project will produce a series of methods and algorithms for stochastic visualization. These pioneering results will be disseminated in archival publications as well as via the project website (http://www.cs.utah.edu/~kirby/StochasticVis.html). Workshops on stochastic methods and tutorial sessions in SIAM and IEEE conferences are also planned to raise the visibility and impact of the project. IIS CSE HPCC 9216 Standard Grant 9218 9215 Maria Zemankova algorithms simulation visualization 0116000 Human Subjects University of Utah UT scientific computing 9217 GRAPHICS & VISUALIZATION 7453 7453 7923 Kirby, Robert 277244 0914488 August 1, 2009 HCC:Small: A New Method for Evaluating Perceptual Fidelity in Computer Graphics For many applications of computer graphics, it is important that viewers perceive an accurate sense of the scale and spatial layout depicted in the displayed imagery. Medical and scientific visualizations need to accurately convey information about the size, shape, and location of entities of potential interest. Architectural and educational systems should give the user an overall sense of the scale of a real or hypothesized environment, along with the arrangement of objects in that space. Simulation and training systems need to allow users to perform tasks with the same or similar facility as in the real world. Despite the importance of achieving a high level of perceptual fidelity in computer graphics, there are as yet no established methodologies for evaluating how well computer graphics imagery conveys spatial information to a viewer. The lack of such methodologies is a significant impediment to creating more effective computer graphics systems, particularly for non-entertainment applications. In this multidisciplinary project involving genuine collaboration between computer scientists and cognitive psychologists, the PI and his team will develop a method for quantifying perceptual fidelity that is both generalizable and task-relevant. This work will be the first systematic use of the concept of perceived affordances, defined as the perception of one's own action capabilities, for characterizing the accuracy of space perception in computer graphics. The methodology involves a verbal indication that a particular action can or cannot be performed in a viewed environment. By varying the spatial structure of the environment, these affordance judgments can be used to probe how accurately viewers are able to perceive action-relevant spatial information. The result is a measure relevant to action, less subject to bias than verbal reports of more primitive properties such as size or distance, and applicable to non-virtual-environment display systems in which the actual action cannot be performed. Broader Impacts: This research will lead to a methodology that significantly impacts displays and rendering methods not yet developed, and will result in qualitative improvements in domain-specific systems that go beyond current practice. Project outcomes will be applicable across a broad range of display technologies and rendering techniques, and will reduce the confounds associated with training and prior experience found in more specialized task performance measures. The nature of this collaboration will lead to an exceptional educational environment, from which students will come away with a depth and breadth of experience which makes them especially well qualified to tackle demanding problems in science and engineering. The investigators have a well established record of involving undergraduates and women in research, and will continue that tradition with this work. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 simulation 0116000 Human Subjects computer graphics graphics Thompson, William University of Utah UT Sarah Creem-Regehr GRAPHICS & VISUALIZATION 7453 7453 7923 Jeanine Stefanucci 498893 0914447 September 15, 2009 GV: Small: Collaborative Research: Analysis and Visualization of Stochastic Simulation Solutions This is a collaborative research project conducted by Robert Kirby, University of Utah (IIS-0914564) and Dongbin Xiu, Purdue University (IIS-0914447). In this age of scientific computing, the simulation science pipeline of mathematical modeling, simulation and evaluation is a commonly employed rendition of the scientific method. In addition to the traditional components of the pipeline, there has been a recent surge of interest in uncertainty quantification (UQ). Visualization is the window through which scientists examine their data for deriving new science, and hence visualization methods which depict underlying uncertainty information are crucial. This research addresses the questions of how does one accurately and efficiently post-process stochastic simulation fields and how does one effectively and succinctly convey the results. This is accomplished by developing strategies and techniques for augmenting current visualization techniques used for visualizing spatio-temporal fields with UQ information in a seamless way. The broader impacts of this work are that (1) proper techniques for UQ will have large impact on many scientific disciplines from medical/bioengineering to aeronautics, and (2) developed visualization techniques might be put to use when higher dimensional data is available for each point in space. The educational objectives are focused on training a new generation of scientists who are proficient not in both visualization techniques and in UQ. The project will produce a series of methods and algorithms for stochastic visualization. These pioneering results will be disseminated in archival publications as well as via the project website (http://www.cs.utah.edu/~kirby/StochasticVis.html). Workshops on stochastic methods and tutorial sessions in SIAM and IEEE conferences are also planned to raise the visibility and impact of the project. IIS CSE HPCC 9216 Standard Grant 9218 9215 Purdue University IN Maria Zemankova algorithms simulation visualization scientific computing 9217 GRAPHICS & VISUALIZATION 7453 7453 7923 Xiu, Dongbin 222521 0914442 September 1, 2009 III: Small: Transforming Long Queries This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Many information needs can be more easily expressed using longer, sentence-length queries, but the inadequacies of current search engines force people to try to think up the right combination of keywords to find relevant documents. This can be very difficult and often leads to search failures. On the other hand, long queries are handled poorly by current search engines. The focus of this project is on developing retrieval algorithms and query processing techniques that will significantly improve the effectiveness of long queries. A specific emphasis is on techniques for transforming long queries into semantically equivalent queries that produce better search results. In contrast to purely linguistic approaches to paraphrasing, query transformation is done in the context of, and guided by, retrieval models. Query transformation steps such as stemming, segmentation, and expansion have been studied for many years, and we are both extending and integrating this work in a common framework. The new query processing techniques for long queries are being developed and distributed using the NSF-funded Lemur toolkit from UMass/CMU, and are being evaluated using a variety of document and query collections from sources such as the web, social media sites such as forums, and TREC, with an involvement of graduate and undergraduate students. The project Web site (http://ciir.cs.umass.edu/research/longqueries) will be used to further disseminate results. Given that search is one of the two most common activities on the web and that new modalities for search, such as voice interfaces and collaborative question answering, are increasing the importance of long queries, this research could have a very broad impact, both in the home and the office. IIS CSE HPCC 9216 Standard Grant Maria Zemankova algorithms MA University of Massachusetts Amherst INFO INTEGRATION & INFORMATICS 7364 Croft, W. Bruce 7364 6890 7923 499829 0913459 September 1, 2009 RI: Small: Qualitative Preferences: Merging Paradigms, Extending the Language, Reasoning about Incomplete Outcomes The most common approach in decision theory involves preferences expressed numerically in terms of utility functions, while optimization over different choices takes into account the probability distribution over possible states of the world. An alternative approach represents preferences in qualitative terms, and is motivated, in part, by difficulties in building good utility functions, ascertaining accurate probability distributions, and related problems. This project is advancing qualitative decision theory by focusing on two promising formalisms for representing and reasoning about qualitative preferences: conditional preference networks (CP-nets) and answer-set optimization (ASO) programs. Both CP-nets and ASO programs offer representations for several classes of preference problems, but each has major limitations. This project addresses these limitations by developing a formalism. ASO(CP) programs, which extend both ASO programs and CP-nets by exploiting key properties of both. The project's major objectives are: to introduce formally ASO(CP) programs by integrating into ASO programs generalized conditional (ceteris paribus) preferences of CP-nets; to establish expressivity of ASO(CP) programs, to study properties of preorders that can be defined by means of ASO(CP) programs, and to address relevant computational issues; to investigate a crucial problem of preference equivalence, essential for automated preference manipulation; to study an extension of ASO(CP) programs to the case of incompletely specified outcomes, essential for practical applications; and, to extend ASO(CP) programs to the first-order language extended with aggregate operators. Representing preferences qualitatively and optimizing over such preferences is a fundamental problem of qualitative decision theory. By integrating and advancing understanding of major types of common preferences that are captured by ASO programs and CP-nets, this project will produce theoretical and practical advances in representation and reasoning about preferences, bringing it to the point where it can be effectively used in practical decision support systems. IIS CSE HPCC Standard Grant 9215 9150 Douglas H. Fisher KY University of Kentucky Research Foundation Truszczynski, Miroslaw ROBUST INTELLIGENCE 7495 7495 7923 384999 0913015 July 15, 2009 RI: Small: Swarms That 'Hear The Shape of a Drum' This proposal identifies a pathway to distributed pattern recognition through parallelization of a particular contour identification algorithm and decentralized data collection using formations of robots. Such a system recognizes patterns in the data it collects autonomously. Anticipated applications range from homeland security, to emergency response, scientific exploration and environmental monitoring. Traditional mobile sensor networks are based on an architecture in which some minimal signal processing is performed on the sensing nodes, while the bulk of information is directed to a network sink for processing and interpretation. The hypothesis here is that the same communication infrastructure that enables motion coordination in formation of robots can be exploited for distributed processing of sensor data and autonomous pattern recognition without human intervention. Thus, information is interpreted in a distributed fashion and without dependence on the capabilities of specialized individual nodes. This method brings forward a robust and autonomous system which can inherently tolerate node and network failures and exhibits collective intelligence in the form of group associative memory. Technical challenges to be overcome are the development of decentralized and provably convergent cooperative motion control designs which can enable targeted data collection, and the scalable implementation of a pattern recognition algorithm based on Dirichle Laplacians along with its integration with spatially distributed Hopfield neural networks. The complete system will be demonstrated by an experimental test-bed with mobile robots capable of recognizing noisy, variable shapes on the laboratory floor. Outreach activities will include undergraduate research and summer programs for secondary school teachers. IIS CSE Continuing grant HPCC architecture 9215 network 9150 security Paul Yu Oh University of Delaware DE ROBUST INTELLIGENCE 7495 7495 Tanner, Herbert 7923 299591 0913004 May 1, 2009 Doctoral Consortium and Student - Author Travel for Petra '09 Conference This is funding to support a Doctoral Consortium of approximately 12 promising doctoral students from U.S. institutions of higher learning along with 3 distinguished research faculty, to be held in conjunction with the Second International Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2009), which will take place July 9-13 in Corfu, Greece. The goal of the PETRA 2009 conference is to bring together experts from diverse domains to address an important social and healthcare issue, namely that as the world's population ages there is an urgent need to develop solutions for in-home care of the elderly, as well as of people with Alzheimer's, Parkinson's, and other disabilities or traumas. PETRA 2009 will provide a unique venue that focuses on combining wireless computing, sensors, and other pervasive computing technologies into assistive environments. The conference aims to span the continuum from data involving genetic and brain imaging to behavioral patterns; it will encompass and merge security and privacy issues with monitoring for both physical and digital safety in assistive environments including the home, work place, hospital, rehabilitation / nursing home, etc. Thus, PETRA 2009 will create a channel for applying basic CS principles to the service of millions of humans in need. More information about PETRA 2009 may be found at www.petrae.org. The goals of the Doctoral Consortium are to increase the exposure and visibility of the participants' work within the community, to help establish a sense of community among this next generation of researchers, and to help foster their research efforts by providing substantive feedback and guidance in a supportive and interactive environment from a group of senior researchers. Student participants in the Doctoral Consortium will make formal presentations of their work and will receive feedback from a faculty panel; the feedback is geared to helping students understand and articulate how their work is positioned relative to other research, whether their topics are adequately focused for thesis research projects, whether their methods are correctly chosen and applied, and whether their results are appropriately analyzed and presented. Doctoral Consortium attendees will have short papers on their work included in the Conference Proceedings, and a summary report on the event will be posted on the conference website. Broader Impacts: The PETRA 2009 Doctoral Consortium will bring together some of the best students, researchers and practitioners in relevant fields, and will thereby afford the younger participants a unique opportunity to gain wider exposure for their innovative ideas while also receiving reinforcement for the importance and value of conducting research with societal impact. The workshop will also allow the junior participants to create a social network both among themselves and with senior colleagues. Since the conference is expected to host a diverse group along several dimensions (such as nationality, scientific discipline, and research specialization), participants' horizons will be broadened and new collaborations will emerge, to the future benefit of the field. The organizing committee will make a concerted effort to attract minority and disabled participants to the event. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 network security wireless TX privacy University of Texas at Arlington HUMAN-CENTERED COMPUTING 7367 7367 Makedon, Fillia Heng Huang Zhengyi Le 19000 0912814 April 1, 2009 NeuroML Development Workshop: Biophysical Single Cell Modeling Award Abstract: The complexity of problems in computational neuroscience requires research from multiple groups across many disciplines to be combined. In order to combine research from multiple groups, there must be an infrastructure for exchanging model specifications; however, the current use of multiple formats for encoding model information has hampered model exchange. NeuroML is a model description language developed in XML (extensible Markup Language) that was created to facilitate data archiving, data and model exchange, database creation, and model publication in computational neuroscience. One of the goals of the NeuroML project is to develop standards for model specifications that will allow for greater simulator interoperability and model exchange. An international workshop will be held in March of 2009 to bring together members of the computational neuroscience community for further development of the NeuroML specifications. The workshop participants will include modelers, software developers and experimentalists with the goal of refining the NeuroML standards for single cell modeling including the modeling of channels and the biophysical description of cells. Workshop outcomes will include a list of updates to the standards for the ChannelML and Biophysics specifications, an agreement from simulator developers for future support of cell and channel descriptions including optimization of simulators for large-scale simulations, and a publicly available summary of the meeting proceedings. IIS CSE HPCC Standard Grant 9215 database Arizona State University AZ Kenneth C. Whang ROBUST INTELLIGENCE 7495 9102 MATHEMATICAL BIOLOGY 7334 7327 Crook, Sharon 10050 0911133 September 1, 2009 RI: Small: A Microgripper with Concurrent Actuation and Force Sensing The goal of this work is to develop a better fundamental understanding of the actuation and force sensing required for micromanipulation (objects from 5-500 microns) by exploring the following hypothesis: it is possible to manipulate micro-sized rigid and flexible objects while measuring physical properties, such as stiffness, by using the same microfinger to act simultaneously as an actuator and sensor. This hypothesis is explored both theoretically and experimentally by creating a set of smaller and smaller microgrippers. Two models, one analytical, the other numeric, of this new microfinger will be developed to predict performance as a function of size. Experiments using actual microgrippers verify the quality of the model. The work impacts science, education, and outreach to minority populations. A compliant microgripper is an enabling technology to manipulate flexible and fragile bio-objects for applications in bioengineering, microbiology and genomics. A microgripper squeezes an oocyte to determine its viability by measuring the stiffness of the cell before subsequent injection of DNA or RNA, turning tedious manual procedures into programmed, automatic sequences, thereby reducing cost. New ways of detecting diseases, such as malaria, by measuring physical properties of cells with the microgripper become possible. Broader impacts include education and outreach. Great emphasis is spent on having supported students give talks about this technology at local middle schools and high schools to entice the next generation to become engineers. These talks improve the professional capabilities of the graduate students while simultaneously demonstrating to young students the purpose of studying math and science. IIS CSE HPCC Standard Grant 9215 education 9150 450000 Paul Yu Oh University of New Mexico NM ROBUST INTELLIGENCE 7495 Lumia, Ron 7923 0911036 September 1, 2009 III: Large: Causal Databases The commercial success of data mining, and the great research interest that this area attracts, prove that there is a need for analyzing and understanding data that goes well beyond classical database queries. Users are often particularly interested in understanding the causal relationship between data items and the reasons for observations. Current database systems cannot explicitly model the causal structure within data (although it is often implicit in the data), and thus offer no specific support for causal queries. In the absence of information about causal relationships, users have to rely on techniques for mining for statistically significant patterns in data. Causal relationships are often simply concluded from statistical dependencies. This can lead to inaccurate conclusions; correlation does not necessarily imply causation. This project creates the foundations for a new breed of databases called causal databases. Causal databases can model causal information, and allow for queries regarding causality and explanations, which are beyond the scope of current databases. They can also take advantage of causal information that is implicit, but unexploited, in some current databases, such as those for large engineering projects. In the project, new database models and query languages for representing and transforming causal information are developed, with particular focus on large engineering databases and scientific databases. In addition, efficient and scalable techniques for processing causality and computing explanations in large causal databases are developed. This involves both work on integrating causality processing into traditional database query processing architectures and the development of special datastream techniques for scaling up to the most data-intensive applications. Further information on the project can be found at the project web page: http://www.cs.cornell.edu/databases/causality/ IIS CSE NY Continuing grant HPCC 9216 data mining database INFORMATION TECHNOLOGY RESEARC 1640 Cornell University Frank Olken 7364 Dan Suciu Koch, Christoph 7925 Joseph Halpern David Lifka 1764846 0911032 September 1, 2009 III: Large: Discovering Complex Anomalous Patterns Many of the most interesting and valuable discoveries that can be made from data arise not from the evaluation of single records, but from identifying a set of records that are anomalous in some interesting way. Together they may indicate for example the emergence of a disease outbreak or new patterns of criminal activity. One can view pattern discovery as an interactive process between data analysis algorithms and human users who have expertise in the domain. This research will develop an integrated framework of probabilistic methods to interact with the user in detecting, characterizing, explaining, and learning anomalous patterns over groups of records. The focus is on the many situations where the data (and the probabilistic patterns to be discovered) are not appropriate for using other existing techniques, such as graph mining or frequent sets. The proposed methods will search over arbitrary subsets of records and evaluate their correspondence to known, potentially very complex, probabilistic patterns, or their failure to match baseline data under various learned statistical models. These methods will assist the user in understanding and modeling the discovered, previously unknown anomalies to be identifiable as a known pattern when encountered in the future. Intellectual Merit This collaborative team of researchers will develop, implement, and evaluate a general, comprehensive, and widely applicable probabilistic framework for pattern discovery. The proposed work will address these challenging and important research questions: - How can machine learning concepts such as classification and anomaly detection be generalized to consider groups of records rather than single records? - How can a detection algorithm simultaneously detect and differentiate between known and currently unknown pattern types? - How can an algorithm explain clearly to a user what pattern was found and why? - How can an algorithm learn new pattern types through feedback from a user? The ability to detect, characterize, explain, and learn patterns from groups of records in massive datasets will provide a qualitatively new approach for advancing discovery of knowledge from data. Broader Impact Although the applications for these algorithms are innumerable, development and testing will be prioritized in the areas of patient care in the intensive care unit (ICU) and aircraft fleet maintenance. Through the team's existing collaborations, the algorithms will also be used during the project in other areas including food safety, scientific discovery in astronomy sky surveys, and detection of geographic hot-spots of criminal activity. Together, these applications will demonstrate the methods' value across a wide spectrum of domains and tasks. Key Words: anomalous patterns; pattern discovery; probabilistic models; incremental learning. IIS CSE Continuing grant HPCC 9216 Lawrence Brandt Carnegie-Mellon University PA algorithms machine learning INFORMATION TECHNOLOGY RESEARC 1640 INFO INTEGRATION & INFORMATICS 7364 data analysis 7364 7925 Dubrawski, Artur Gregory Cooper Jeff Schneider Gilles Clermont Daniel Neill 1948615 0911009 August 15, 2009 RI: Large: An Integrated Approach to Creating Context Enriched Speech Translation Systems The project creates robust, widely-deployable and cost-effective technologies for supporting cross-lingual spoken interaction between people who do not share a common language. The target application supports communication between healthcare personnel who speak English only and patients with limited-English proficiency. The state-of-the-art technologies that enable such cross-lingual interactions are characterized by a pipelined architecture of speech recognition, machine translation and speech synthesis, that largely ignore the rich information present in spoken language beyond those conveyed by words. They also do not take advantage of the humans in the loop for collaboratively managing the interaction. Overcoming these limitations requires improving robust intelligence at all levels ? signal, system, and human ? and set the research goals for this project. The project?s intellectual merit comes from the unique combination of theoretical, computational model-ing and empirical elements: The theoretical framework is centered on notions of social co-presence to de-velop new models for translation-mediated communication. The computational modeling focuses on capturing prosody, dialog and user state from spoken language for enriching the technology components. The empirical work relies on a participatory approach to iterative design and evaluation of the system, working directly with the stakeholders. The broader impact can be seen in the potential for facilitating multilingual efforts ranging from disaster relief and global business operations to servicing diverse immigrant populations notably in health care. The effort brings together engineers, linguists, human communication experts, and medical professionals to tackle a broad range of problems, and offers integrated interdisciplinary research training and mentor-ing. IIS CSE Continuing grant HPCC architecture 9215 CA INFORMATION TECHNOLOGY RESEARC 1640 University of Southern California Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 Narayanan, Shrikanth 7495 7925 Margaret McLaughlin Panayiotis Georgiou 1658964 0910992 September 1, 2009 RI: Large: Collaborative Research: Richer Representations for Machine Translation Research in machine translation of human languages has made substantial progress recently, and surface patterns gleaned automatically from online bilingual texts work remarkably well for some language pairs. However, for many language pairs, the output of even the best systems is garbled, ungrammatical, and difficult to interpret. Chinese-to-English systems need particular improvement, despite the importance of this language pair, while English-to-Chinese translation, equally important for communication between individuals, is rarely studied. This project develops methods for automatically learning correspondences between Chinese and English at a semantic rather than surface level, allowing machine translation to benefit from recent work in semantic analysis of text and natural language generation. One part of this work determines what types of semantic analysis of source language sentences can best inform a translation system, focusing on analyzing dropped arguments, co-reference links, and discourse relations between clauses. These linguistic phenomena must generally be made more explicit when translating from Chinese to English. A second part of the work integrates natural language generation into statistical machine translation, leveraging generation technology to determine sentence boundaries, ordering of constituents, and production of function words that translation systems tend to get wrong. A third part develops and compares algorithms for training and decoding machine translation models defined on semantic representations. All of this research exploits newly-developed linguistic resources for semantic analysis of both Chinese and English. The ultimate benefits of improved machine translation technology are easier access to information and easier communication between individuals. This in turn leads to increased opportunities for trade, as well as better understanding between cultures. This project's systems for both Chinese-to-English and English-to-Chinese are developed with the expectation that the approaches will be applied to other language pairs in the future. IIS CSE Continuing grant HPCC 9215 algorithms INFORMATION TECHNOLOGY RESEARC 1640 University of Colorado at Boulder CO Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 9102 7495 Palmer, Martha James Martin 7925 130439 0910989 August 15, 2009 DC: Large: Collaborative Research: ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis The evolution of the "human Web," powered by HTML and HTTP, has revolutionized the way that people find information, buy things, communicate, and collaborate. Web services and semi-structured data formats are having a similar impact on the "machine Web." XML is enriching our ability to find and interchange information today; industry verticals have created XML-based data exchange standards; and XML backbones have gained adoption in support of service-oriented architectures and software-as-a-service initiatives. Other semi-structured formats, like JSON, are playing similar roles, and XML is increasingly being used for its original purpose of semantic document markup. As a result, the world will soon be awash in a sea of semi-structured information. The ASTERIX project is developing new technologies for ingesting, storing, managing, indexing, querying, analyzing, and subscribing to vast quantities of semi-structured information. The project is combining ideas from three distinct areas - semi-structured data, parallel databases, and data-intensive computing - to create a next-generation, open source software platform that scales by running on large, shared-nothing computing clusters. ASTERIX targets a wide range of semi-structured information, ranging from "data" use cases - where information is well-tagged and highly regular - to "content" use cases - where data is irregular and much of each datum is textual. ASTERIX is taking an open stance on data formats and addressing research issues including highly scalable data storage and indexing, semi-structured query processing on very large clusters, and merging parallel database techniques with today's data-intensive computing techniques to support performant yet declarative solutions to the problem of analyzing semi-structured information. Project website: http://asterix.ics.uci.edu/ IIS CSE HPCC Standard Grant 9215 CA database INFORMATION TECHNOLOGY RESEARC 1640 University of California-Irvine James C. French 7793 DATA-INTENSIVE COMPUTING 7793 7925 Carey, Michael Chen Li 1670974 0910908 September 1, 2009 HCC: Large: Intelligent Tracking Systems that Reason about Group Behavior "This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)." The ability to reason about the complexity of living organisms in diverse environments is one of the hallmarks of intelligence. In this project the PI and her interdisciplinary team of investigators will design computer vision algorithms for intelligent tracking of large groups of living individuals in three-dimensional space. She will develop specific systems for tracking groups of microorganisms, bats, birds, and humans. And she will formulate machine learning methods for analyzing group behavior, specifically the conditions for formation and dispersal of groups, and the interactions of individuals within a group. An important innovative aspect of this research is the systematic and comprehensive approach to reasoning about the motion of large groups of living organisms observed in video data, independently of whether they happen to be humans, animals, or cells. Previous efforts in this area have typically focused on studying the behavior of a single type of organism, and on testing theories of behavior based predominately on simulations, without the appropriate analytical tools to automatically explore and quantify the vast number of visual data sets. This project, on the other hand, will base research findings on the analysis of thousands of trajectories of individual group members moving in 3D space. To this end, the PI and her team will collect video data in the field and in public spaces to ensure optimal data capture conditions. They will use these data to develop robust solutions for the problem of matching hundreds of individual bats, birds, or people from frame to frame. They will generate stereoscopic reconstructions of movement trajectories based on multiple calibrated cameras, and use machine learning to model group behavior and mine the trajectory data. Finally, they will compare the findings of their reasoning system against current theories about the formation of groups and the interactions of individuals within a group. A similar, systematic research strategy will be employed to address understanding of the behavior of single cells. The team will design microscope imaging protocols, develop solutions for the segmentation and tracking of individual cells, and use statistical learning techniques to discover patterns and correlations in the behavior of the cells on physiologically relevant substrates. Broader Impacts: Understanding the processes by which groups of animals and microorganisms behave is crucial to the effective conservation of populations and ecosystems and the management of cellular environments. Project outcomes will advance knowledge across the fields of computer vision, artificial intelligence, behavioral ecology, and biological engineering, and will provide new tools for answering urgent economic and ethical questions, for example about the mortality of birds and bats in wind energy facilities. IIS CSE Ephraim P. Glinert HPCC 9216 Standard Grant 9215 algorithms MA artificial intelligence machine learning 0116000 Human Subjects vision computer vision INFO INTEGRATION & INFORMATICS 7364 Betke, Margrit Trustees of Boston University HUMAN-CENTERED COMPUTING 7367 Stan Sclaroff 6890 7925 Thomas Kunz Joyce Wong 2858292 0910868 September 1, 2009 NetSE: Large: Collaborative Research: Platys: From Position to Place in Next Generation Networks This project develops a high-level notion of context that exploits the capabilities of next generation networks to enable applications that deliver better user experiences. In particular, it exploits mobile devices -- always with a user -- to capture key elements of context: the user's location and, through localization, characteristics of the user's environment. What matters for the user experience is the user's place: a location in conceptual terms such as "at home," "jogging," or "grocery shopping" -- descriptions that combine positions with activities, environmental properties, and the activities of other nearby people. Realizing this notion of place requires that information from devices and infrastructure flow in ways unanticipated in current network architectures. It presumes enabling opportunistic interactions while preserving the users' privacy and designing incentive mechanisms to promote cooperation without exploitation of any. The above architectural concerns lie far beyond traditional network topics such as routing. This project will develop, demonstrate, and evaluate a novel network architecture that gives primacy to user experience. It will lead to theoretical advances in semantic context modeling, mobility tracking at multiple levels of abstraction, collaborative localization, and incentive mechanisms. Networked applications offering enhanced user experience will have significant payoffs for industry and the productivity and quality of life of citizens. A prototype system will implement and evaluate context-aware services in university settings with prospects of expansion to K-12 schools and public facilities. IIS CSE HPCC architecture Standard Grant 9218 network NC privacy North Carolina State University 0000912 Computer Science David W. McDonald NETWORK SCIENCE & ENGINEERING 7794 7794 7925 706167 Rhee, Injong Munindar Singh 0910859 August 15, 2009 DC: Large: Collaborative Research: ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis The evolution of the "human Web," powered by HTML and HTTP, has revolutionized the way that people find information, buy things, communicate, and collaborate. Web services and semi-structured data formats are having a similar impact on the "machine Web." XML is enriching our ability to find and interchange information today; industry verticals have created XML-based data exchange standards; and XML backbones have gained adoption in support of service-oriented architectures and software-as-a-service initiatives. Other semi-structured formats, like JSON, are playing similar roles, and XML is increasingly being used for its original purpose of semantic document markup. As a result, the world will soon be awash in a sea of semi-structured information. The ASTERIX project is developing new technologies for ingesting, storing, managing, indexing, querying, analyzing, and subscribing to vast quantities of semi-structured information. The project is combining ideas from three distinct areas - semi-structured data, parallel databases, and data-intensive computing - to create a next-generation, open source software platform that scales by running on large, shared-nothing computing clusters. ASTERIX targets a wide range of semi-structured information, ranging from "data" use cases - where information is well-tagged and highly regular - to "content" use cases - where data is irregular and much of each datum is textual. ASTERIX is taking an open stance on data formats and addressing research issues including highly scalable data storage and indexing, semi-structured query processing on very large clusters, and merging parallel database techniques with today's data-intensive computing techniques to support performant yet declarative solutions to the problem of analyzing semi-structured information. Project website: http://asterix.ics.uci.edu/ IIS CSE HPCC Standard Grant 9215 CA database INFO INTEGRATION & INFORMATICS 7364 James C. French University of California-Riverside Tsotras, Vassilis 7793 DATA-INTENSIVE COMPUTING 7793 7925 429261 0910846 September 1, 2009 NetSE: Large: Collaborative Research: Platys: From Position to Place in Next Generation Networks This project develops a high-level notion of context that exploits the capabilities of next generation networks to enable applications that deliver better user experiences. In particular, it exploits mobile devices -- always with a user -- to capture key elements of context: the user's location and, through localization, characteristics of the user's environment. What matters for the user experience is the user's place: a location in conceptual terms such as "at home," "jogging," or "grocery shopping" -- descriptions that combine positions with activities, environmental properties, and the activities of other nearby people. Realizing this notion of place requires that information from devices and infrastructure flow in ways unanticipated in current network architectures. It presumes enabling opportunistic interactions while preserving the users' privacy and designing incentive mechanisms to promote cooperation without exploitation of any. The above architectural concerns lie far beyond traditional network topics such as routing. This project will develop, demonstrate, and evaluate a novel network architecture that gives primacy to user experience. It will lead to theoretical advances in semantic context modeling, mobility tracking at multiple levels of abstraction, collaborative localization, and incentive mechanisms. Networked applications offering enhanced user experience will have significant payoffs for industry and the productivity and quality of life of citizens. A prototype system will implement and evaluate context-aware services in university settings with prospects of expansion to K-12 schools and public facilities. IIS CSE HPCC architecture Standard Grant 9218 network NC privacy Duke University 0000912 Computer Science David W. McDonald NETWORK SCIENCE & ENGINEERING 7794 7794 7925 Roy Choudhury, Romit 357999 0910838 September 1, 2009 NetSE: Large: Collaborative Research: Platys: From Position to Place in Next Generation Networks This project develops a high-level notion of context that exploits the capabilities of next generation networks to enable applications that deliver better user experiences. In particular, it exploits mobile devices -- always with a user -- to capture key elements of context: the user's location and, through localization, characteristics of the user's environment. What matters for the user experience is the user's place: a location in conceptual terms such as "at home," "jogging," or "grocery shopping" -- descriptions that combine positions with activities, environmental properties, and the activities of other nearby people. Realizing this notion of place requires that information from devices and infrastructure flow in ways unanticipated in current network architectures. It presumes enabling opportunistic interactions while preserving the users' privacy and designing incentive mechanisms to promote cooperation without exploitation of any. The above architectural concerns lie far beyond traditional network topics such as routing. This project will develop, demonstrate, and evaluate a novel network architecture that gives primacy to user experience. It will lead to theoretical advances in semantic context modeling, mobility tracking at multiple levels of abstraction, collaborative localization, and incentive mechanisms. Networked applications offering enhanced user experience will have significant payoffs for industry and the productivity and quality of life of citizens. A prototype system will implement and evaluate context-aware services in university settings with prospects of expansion to K-12 schools and public facilities. IIS CSE HPCC architecture Standard Grant 9218 network MD privacy University of Maryland Baltimore County Anupam Joshi Finin, Timothy 0000912 Computer Science David W. McDonald NETWORK SCIENCE & ENGINEERING 7794 7794 7925 706167 0910820 August 15, 2009 DC: Large: Collaborative Research: ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis The evolution of the "human Web," powered by HTML and HTTP, has revolutionized the way that people find information, buy things, communicate, and collaborate. Web services and semi-structured data formats are having a similar impact on the "machine Web." XML is enriching our ability to find and interchange information today; industry verticals have created XML-based data exchange standards; and XML backbones have gained adoption in support of service-oriented architectures and software-as-a-service initiatives. Other semi-structured formats, like JSON, are playing similar roles, and XML is increasingly being used for its original purpose of semantic document markup. As a result, the world will soon be awash in a sea of semi-structured information. The ASTERIX project is developing new technologies for ingesting, storing, managing, indexing, querying, analyzing, and subscribing to vast quantities of semi-structured information. The project is combining ideas from three distinct areas - semi-structured data, parallel databases, and data-intensive computing - to create a next-generation, open source software platform that scales by running on large, shared-nothing computing clusters. ASTERIX targets a wide range of semi-structured information, ranging from "data" use cases - where information is well-tagged and highly regular - to "content" use cases - where data is irregular and much of each datum is textual. ASTERIX is taking an open stance on data formats and addressing research issues including highly scalable data storage and indexing, semi-structured query processing on very large clusters, and merging parallel database techniques with today's data-intensive computing techniques to support performant yet declarative solutions to the problem of analyzing semi-structured information. Project website: http://asterix.ics.uci.edu/ IIS CSE HPCC Standard Grant 9215 CA database INFORMATION TECHNOLOGY RESEARC 1640 University of California-San Diego James C. French Deutsch, Alin Yannis Papakonstantinou 7793 7925 599765 0910778 September 1, 2009 RI: Large: Collaborative Research: Richer Representations for Machine Translation Research in machine translation of human languages has made substantial progress recently, and surface patterns gleaned automatically from online bilingual texts work remarkably well for some language pairs. However, for many language pairs, the output of even the best systems is garbled, ungrammatical, and difficult to interpret. Chinese-to-English systems need particular improvement, despite the importance of this language pair, while English-to-Chinese translation, equally important for communication between individuals, is rarely studied. This project develops methods for automatically learning correspondences between Chinese and English at a semantic rather than surface level, allowing machine translation to benefit from recent work in semantic analysis of text and natural language generation. One part of this work determines what types of semantic analysis of source language sentences can best inform a translation system, focusing on analyzing dropped arguments, co-reference links, and discourse relations between clauses. These linguistic phenomena must generally be made more explicit when translating from Chinese to English. A second part of the work integrates natural language generation into statistical machine translation, leveraging generation technology to determine sentence boundaries, ordering of constituents, and production of function words that translation systems tend to get wrong. A third part develops and compares algorithms for training and decoding machine translation models defined on semantic representations. All of this research exploits newly-developed linguistic resources for semantic analysis of both Chinese and English. The ultimate benefits of improved machine translation technology are easier access to information and easier communication between individuals. This in turn leads to increased opportunities for trade, as well as better understanding between cultures. This project's systems for both Chinese-to-English and English-to-Chinese are developed with the expectation that the approaches will be applied to other language pairs in the future. IIS CSE NY HPCC Standard Grant 9215 algorithms INFORMATION TECHNOLOGY RESEARC 1640 Columbia University Tatiana D. Korelsky 9102 McKeown, Kathleen 7495 7925 559999 0910710 September 1, 2009 RI: Large: Collaborative Research: Understanding Uncertainty in Rats and Robots Humans, rats and other vertebrates, relying on their advanced nervous systems, are far superior at dealing with the uncertainties of the world than are artificial systems. Thus, a machine, whose behavior is guided by a neurobiologically inspired system, might demonstrate the flexible, autonomous behavior normally attributed to biological organisms. Biological organisms have the ability to respond quickly to an ever-changing world. Because this adaptability is so critical for survival, all vertebrates have sub-cortical structures, which comprise the neuromodulatory systems, to handle uncertainty and change in the environment. Attention, which is influenced by neuromodulation, plays a significant role in animal's ability to respond to such changes. Different neuromodulatory systems are thought to play important and distinct roles in attention. A collaborative approach, which compares rodent experiments with robots having simulated nervous systems, will examine these attentional systems. These experiments will lead to a better understanding of how animals cope with uncertainty in the environment, and will lead to the design of a robot capable of flexible and complex behavior. This work has the potential of being paradigm-shifting technology that could find its way in many practical applications. In an interdisciplinary approach, a robotic system, whose design is based on the vertebrate neuromodulatory system and its effect on attention, will be constructed and tested under similar experimental conditions to the rat, and then in a more practical application. This approach, which combines computational modeling and robotics with rodent behavioral and electrophysiological experiments, will lead to a better understanding of how areas of the brain allocate attentional resources and cause the organism to respond rapidly to essential events and objects. Two of these neuromodulatory systems, the cholinergic and noradrenergic, are thought to play important and distinct roles in attention. Expected uncertainty, the known degree of unreliability of predictive relationships in the environment, drives activity within the cholinergic system. Unexpected uncertainty, large changes in the environment that violate prior expectations, drives activity within the noradrenergic system. These systems modulate activity in brain areas to properly allocate the attention to stimuli in the environment necessary for adequate learning to occur and fluid behavior to be maintained. This knowledge will be used to construct a robust, intelligent robotic system whose capability to adapt to change, and behave effectively in a noisy, complex environment will rival that of a biological system. IIS CSE HPCC Standard Grant 9215 CA INFORMATION TECHNOLOGY RESEARC 1640 Kenneth C. Whang robotics University of California-Irvine ROBUST INTELLIGENCE 7495 7925 Krichmar, Jeffrey 799849 0910664 July 15, 2009 HCC: Large: Collaborative Research: Design Principles for Information Networks Supporting the Social Production of Knowledge The project seeks to develop design principles for social computing applications in which knowledge is produced by large, loosely coordinated groups of people. The research will be carried out by an inter-disciplinary team that brings together expertise from computing and information science with the social sciences; perspectives from all of these areas will be crucial to developing the next generation of large-scale collaborative information systems on the Internet. A particular focus of the research will be on phenomena that affect the quality of information and discussion in these settings, including deception and the kinds of opinion dynamics that lead to polarization. The research will offer new analysis techniques and computational models for understanding such phenomena, drawing on novel styles of investigation involving large-scale datasets of social interaction and social network activity. Building on this, the research will also formulate design principles (based on ideas from intelligent task routing and games with a purpose) to enable creators of social computing applications to design for particular outcomes in the presence of complex underlying social phenomena. The project is motivated by a profound transformation taking place in the way knowledge is produced and shared; in particular, the way it emerges in a "bottom-up" manner from global social networks that largely self-organize online. This raises profound challenges: at a time when a large proportion of Americans turn first to Internet sources for information about politics, health, commerce, and education, there is still very little understanding among the public as well as within the research community of how to deal with deception and misinformation online, or how to prevent online communities from falling into conflict and polarization. Through its focus on design principles and on these challenges, the project will attempt to create more effective means of online discourse and knowledge production. IIS CSE NY Continuing grant HPCC 9215 network education INFORMATION TECHNOLOGY RESEARC 1640 0116000 Human Subjects Kleinberg, Jon Lillian Lee Cornell University HUMAN-CENTERED COMPUTING 7367 David W. McDonald Daniel Huttenlocher 7925 Geraldine Gay 2061727 0910640 September 1, 2009 HCC: Large: Collaborative Research: Widescale Computer-Mediated Communication in Crisis Response: Roles, Trust & Accuracy in the Social Distribution of Information Information and communication technology (ICT) promises to help reduce impacts of large-scale disruptions from natural hazards, pandemics, and terrorist threat. This research focuses on a critical aspect of large-scale emergency response -- the needs and roles of members of the public. By viewing the citizenry as a powerful, self-organizing, and collectively intelligent force, ICT can play a transformational role in crisis situations. This view of a civil society augmented by ICT is based on socio-behavioral knowledge about how people behave in crisis, rather than on simplified and mythical portrayals. With a critical reframing of emergency response as a socially-distributed information system, the project aims to leverage the knowledge of members of the public through reuse of publicly available computer mediated communications (CMCs) (e.g., community, mapping, and social networking sites; blogs; Twitter). The project will study and integrate that heterogeneous information and -- with techniques of information extraction through natural language processing as well as trust and reputation modeling -- add meta-information to help users assess context, validity, source, credibility, and timeliness to make the best decisions for their highly localized, changing conditions. The results of this research addresses matters of policy, practice and technological innovation, responding directly to needs identified in national policy statements, including Grand Challenge #1 of the National Science and Technology Council's Subcommittee on Disaster Reduction, which calls for the provision of "hazard and disaster information where and when it is needed" (SDR, 2005). At-risk populations are disproportionately affected by crises; the results of this research could mitigate the impacts on these communities. The research is also inclusive of people across different cultures/ethnic groups within the U.S. and from different countries. The project broadens the future STEM workforce, since socio-technical and practical orientations to computational research attract women to study STEM disciplines. The research contributions include cyberinfrastructure-aware applications, techniques, and services built from empirical knowledge of the social structures that produce crisis data. IIS CSE HPCC Standard Grant 9215 CA 0116000 Human Subjects networking University of California-Irvine Mark, Gloria HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald 7925 479270 0910611 September 1, 2009 RI: Large:Collaborative Research: Richer Representations for Machine Translation Research in machine translation of human languages has made substantial progress recently, and surface patterns gleaned automatically from online bilingual texts work remarkably well for some language pairs. However, for many language pairs, the output of even the best systems is garbled, ungrammatical, and difficult to interpret. Chinese-to-English systems need particular improvement, despite the importance of this language pair, while English-to-Chinese translation, equally important for communication between individuals, is rarely studied. This project develops methods for automatically learning correspondences between Chinese and English at a semantic rather than surface level, allowing machine translation to benefit from recent work in semantic analysis of text and natural language generation. One part of this work determines what types of semantic analysis of source language sentences can best inform a translation system, focusing on analyzing dropped arguments, co-reference links, and discourse relations between clauses. These linguistic phenomena must generally be made more explicit when translating from Chinese to English. A second part of the work integrates natural language generation into statistical machine translation, leveraging generation technology to determine sentence boundaries, ordering of constituents, and production of function words that translation systems tend to get wrong. A third part develops and compares algorithms for training and decoding machine translation models defined on semantic representations. All of this research exploits newly-developed linguistic resources for semantic analysis of both Chinese and English. The ultimate benefits of improved machine translation technology are easier access to information and easier communication between individuals. This in turn leads to increased opportunities for trade, as well as better understanding between cultures. This project's systems for both Chinese-to-English and English-to-Chinese are developed with the expectation that the approaches will be applied to other language pairs in the future. IIS CSE NY Continuing grant HPCC 9215 algorithms INFORMATION TECHNOLOGY RESEARC 1640 University of Rochester Tatiana D. Korelsky 7495 Gildea, Daniel 7925 275686 0910586 September 1, 2009 HCC: Large: Collaborative Research: Widescale Computer-Mediated Communication in Crisis Response: Roles, Trust & Accuracy in the Social Distribution of Information Information and communication technology (ICT) promises to help reduce impacts of large-scale disruptions from natural hazards, pandemics, and terrorist threat. This research focuses on a critical aspect of large-scale emergency response -- the needs and roles of members of the public. By viewing the citizenry as a powerful, self-organizing, and collectively intelligent force, ICT can play a transformational role in crisis situations. This view of a civil society augmented by ICT is based on socio-behavioral knowledge about how people behave in crisis, rather than on simplified and mythical portrayals. With a critical reframing of emergency response as a socially-distributed information system, the project aims to leverage the knowledge of members of the public through reuse of publicly available computer mediated communications (CMCs) (e.g., community, mapping, and social networking sites; blogs; Twitter). The project will study and integrate that heterogeneous information and -- with techniques of information extraction through natural language processing as well as trust and reputation modeling -- add meta-information to help users assess context, validity, source, credibility, and timeliness to make the best decisions for their highly localized, changing conditions. The results of this research addresses matters of policy, practice and technological innovation, responding directly to needs identified in national policy statements, including Grand Challenge #1 of the National Science and Technology Council's Subcommittee on Disaster Reduction, which calls for the provision of "hazard and disaster information where and when it is needed" (SDR, 2005). At-risk populations are disproportionately affected by crises; the results of this research could mitigate the impacts on these communities. The research is also inclusive of people across different cultures/ethnic groups within the U.S. and from different countries. The project broadens the future STEM workforce, since socio-technical and practical orientations to computational research attract women to study STEM disciplines. The research contributions include cyberinfrastructure-aware applications, techniques, and services built from empirical knowledge of the social structures that produce crisis data. IIS CSE HPCC Standard Grant 9215 0116000 Human Subjects networking University of Colorado at Boulder CO HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald James Martin Palen, Leysia 7925 Kenneth Anderson Douglas Sicker 2396515 0910562 August 1, 2009 III: Small: Medieval Unicorn: Toward Enhanced Understanding of Virtual Manuscripts on the Grid in the Twenty-First Century Computation has revolutionized the study of historic documents and is growing rapidly in terms of use and importance to interdisciplinary technology/domain groups and individual scholars and researchers. An interdiscipinary team at the University of Illinois proposes to research and develop cyber tools for exploratory visual studies and quantitative analyses of large volumes of midieval manuscipts. The project will focus on visual imagery embedded in thes manuscripts and where the analysis tools will require large amounts of computational resources and scalable algorithms. The test collection will be digitized copies of Froissart's Chronicles, a set of midieval manuscripts available for reserch and teaching over the Worldwide Universities Network grid and accessible through Virtual Vellum ( developed at the University of Sheffield, UK, and funded by the UKs Arts and Humanities and Engineering and Physical Sciences experimental e-Science program.) The broader scientific impacts resulting from the proposed activities are expected to be in new methodologies, scalable algorithms. The work will also provide new exploratory frameworks that will support questions related to studying broad, difficult and complex topics such as the composition and structure (codicology) of manuscripts as cultural artifacts of the book trade in later medieval Paris and identifying the characteristic styles and iconographic signatures of particular artists. The research will contribute to a body of recent scholarship that seek to define how books were made, how they circulated, and what their cultural value was in the late medieval period. IIS CSE HPCC 9216 IL Standard Grant Stephen Griffin network algorithms University of Illinois at Urbana-Champaign INFO INTEGRATION & INFORMATICS 7364 7364 Bajcsy, Peter Kevin Franklin Anne Hedeman 128381 7916 0910532 September 1, 2009 RI: Large: Collaborative Research: Richer Representations for Machine Translation Research in machine translation of human languages has made substantial progress recently, and surface patterns gleaned automatically from online bilingual texts work remarkably well for some language pairs. However, for many language pairs, the output of even the best systems is garbled, ungrammatical, and difficult to interpret. Chinese-to-English systems need particular improvement, despite the importance of this language pair, while English-to-Chinese translation, equally important for communication between individuals, is rarely studied. This project develops methods for automatically learning correspondences between Chinese and English at a semantic rather than surface level, allowing machine translation to benefit from recent work in semantic analysis of text and natural language generation. One part of this work determines what types of semantic analysis of source language sentences can best inform a translation system, focusing on analyzing dropped arguments, co-reference links, and discourse relations between clauses. These linguistic phenomena must generally be made more explicit when translating from Chinese to English. A second part of the work integrates natural language generation into statistical machine translation, leveraging generation technology to determine sentence boundaries, ordering of constituents, and production of function words that translation systems tend to get wrong. A third part develops and compares algorithms for training and decoding machine translation models defined on semantic representations. All of this research exploits newly-developed linguistic resources for semantic analysis of both Chinese and English. The ultimate benefits of improved machine translation technology are easier access to information and easier communication between individuals. This in turn leads to increased opportunities for trade, as well as better understanding between cultures. This project's systems for both Chinese-to-English and English-to-Chinese are developed with the expectation that the approaches will be applied to other language pairs in the future. IIS CSE HPCC Standard Grant 9215 algorithms MA INFORMATION TECHNOLOGY RESEARC 1640 Brandeis University Tatiana D. Korelsky 7495 7925 Xue, Nianwen 559847 0910485 September 1, 2009 RI: Large: Collaborative Research: Understanding Uncertainty in Rats and Robots Abstract Humans, rats and other vertebrates, relying on their advanced nervous systems, are far superior at dealing with the uncertainties of the world than are artificial systems. Thus, a machine, whose behavior is guided by a neurobiologically inspired system, might demonstrate the flexible, autonomous behavior normally attributed to biological organisms. Biological organisms have the ability to respond quickly to an ever-changing world. Because this adaptability is so critical for survival, all vertebrates have sub-cortical structures, which comprise the neuromodulatory systems, to handle uncertainty and change in the environment. Attention, which is influenced by neuromodulation, plays a significant role in animal's ability to respond to such changes. Different neuromodulatory systems are thought to play important and distinct roles in attention. A collaborative approach, which compares rodent experiments with robots having simulated nervous systems, will examine these attentional systems. These experiments will lead to a better understanding of how animals cope with uncertainty in the environment, and will lead to the design of a robot capable of flexible and complex behavior. This work has the potential of being paradigm-shifting technology that could find its way in many practical applications. In an interdisciplinary approach, a robotic system, whose design is based on the vertebrate neuromodulatory system and its effect on attention, will be constructed and tested under similar experimental conditions to the rat, and then in a more practical application. This approach, which combines computational modeling and robotics with rodent behavioral and electrophysiological experiments, will lead to a better understanding of how areas of the brain allocate attentional resources and cause the organism to respond rapidly to essential events and objects. Two of these neuromodulatory systems, the cholinergic and noradrenergic, are thought to play important and distinct roles in attention. Expected uncertainty, the known degree of unreliability of predictive relationships in the environment, drives activity within the cholinergic system. Unexpected uncertainty, large changes in the environment that violate prior expectations, drives activity within the noradrenergic system. These systems modulate activity in brain areas to properly allocate the attention to stimuli in the environment necessary for adequate learning to occur and fluid behavior to be maintained. This knowledge will be used to construct a robust, intelligent robotic system whose capability to adapt to change, and behave effectively in a noisy, complex environment will rival that of a biological system. IIS CSE HPCC Standard Grant 9215 CA INFORMATION TECHNOLOGY RESEARC 1640 Kenneth C. Whang robotics University of California-San Diego ROBUST INTELLIGENCE 7495 7925 Nitz, Douglas Andrea Chiba 800001 0910465 April 1, 2009 SGER: Collaborative Research: Curatorial Work and Learning in Virtual Environments The value of social computing environments has been demonstrated in many contexts, including web search, shared bookmarking and photo sharing, personal social networking, online support groups, and information resources such as Wikipedia. Collaborative game environments have been used as both research environments to study collaboration and as environments for facilitating scientific meetings, and professional organizations and scientific communities also leverage cyberinfrastructure and social network applications to share resources, collect data, and facilitate communication. Virtual worlds have also been applied to support a diverse range of professional and work activities, however, there is so far little empirical evidence on how virtual worlds are adopted and leveraged to promote scientific progress. The exploratory research proposed here aims to explore how virtual worlds can be leveraged in the digital curation community for purposes of improving work practices and training. IIS CSE HPCC Standard Grant 9215 Stephen Griffin network 9237 NC networking INFO INTEGRATION & INFORMATICS 7364 Marchionini, Gary University of North Carolina at Chapel Hill 7364 74935 0910455 March 1, 2009 Student Research Workshop in Computational Linguistics at the ACL 2009 Conference The Association for Computational Linguistics (ACL) is the primary international organization in the field of natural language processing. The ACL's annual conference is the major international conference in this field. This project is to subsidize travel, conference, and housing expenses of students selected to participate in the Association for Computational Linguistics (ACL) Student Research Workshop, which is part of the ACL conference on August 2-7, 2009 in Singapore. The workshop consists of two tracks: full paper presentations and poster presentations. All selected work has only student authors. The full paper sessions are composed of paper presentations followed by a discussion panel led by respected researchers in the field. The workshop is organized and run by students. The Student Research Workshop provides a valuable opportunity for the next generation of natural language processing researchers to enter the computational linguistics community. It allows the best students in the field to take their first important step toward becoming professional computational linguists by receiving critical feedback on their work from external experts, and by making contacts with other students and senior researchers in their field. The students who are involved in running and selecting papers for the workshop also gain valuable opportunities for professional growth and interaction with the researchers on the organizing committee of the main conference. The workshop contributes to the maintenance and development of a skilled and diverse computational linguistics and natural language processing research community. IIS CSE HPCC Standard Grant 9215 Oregon Health and Science University OR Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 7495 Roark, Brian 20100 0910453 July 15, 2009 Collaborative Research: HCC: Large: Design Principles for Information Networks Supporting the Social Production of Knowledge The project seeks to develop design principles for social computing applications in which knowledge is produced by large, loosely coordinated groups of people. The research will be carried out by an inter-disciplinary team that brings together expertise from computing and information science with the social sciences; perspectives from all of these areas will be crucial to developing the next generation of large-scale collaborative information systems on the Internet. A particular focus of the research will be on phenomena that affect the quality of information and discussion in these settings, including deception and the kinds of opinion dynamics that lead to polarization. The research will offer new analysis techniques and computational models for understanding such phenomena, drawing on novel styles of investigation involving large-scale datasets of social interaction and social network activity. Building on this, the research will also formulate design principles (based on ideas from intelligent task routing and games with a purpose) to enable creators of social computing applications to design for particular outcomes in the presence of complex underlying social phenomena. The project is motivated by a profound transformation taking place in the way knowledge is produced and shared; in particular, the way it emerges in a "bottom-up" manner from global social networks that largely self-organize online. This raises profound challenges: at a time when a large proportion of Americans turn first to Internet sources for information about politics, health, commerce, and education, there is still very little understanding among the public as well as within the research community of how to deal with deception and misinformation online, or how to prevent online communities from falling into conflict and polarization. Through its focus on design principles and on these challenges, the project will attempt to create more effective means of online discourse and knowledge production. IIS CSE HPCC Standard Grant 9215 Carnegie-Mellon University PA network education INFORMATION TECHNOLOGY RESEARC 1640 0116000 Human Subjects 375000 HUMAN-CENTERED COMPUTING 7367 David W. McDonald 7925 von Ahn, Luis 0910358 July 1, 2009 SGER: Foundations of Multiagent Control in Complex Environments The ability to control systems operating in dynamic and stochastic environments is a critical bottleneck in many scientific (e.g., exploration robots), military (e.g., surveillance drones) and everyday civilian (e.g., air traffic) domains. For example, the autonomous control of a Micro Air Vehicle (MAV) would allow better search and rescue (e.g., in remote mountainous areas), disaster response (e.g., inside damaged buildings), ecological data gathering (e.g., on tree tops) and military intelligence (e.g., reconnaissance). Such technologically-promising and scientifically-challenging problems are challenging because they possess most of the difficulties of control systems simultaneously, notably that the system (i) has highly non-linear dynamics; (ii) operates in non-stationary environments; (iii) operates in stochastic environments; and (iv) has complex interactions with the environment that are difficult, if not impossible, to model accurately. Research under this award is pursuing an agent-based approach to (single-agent) control that relies on local actions, but local actions need to be carefully coordinated to ensure that the system receives a coherent control signal. There is currently a lack of algorithms that can combine multiple sensors and use distributed computation and actuation to control a complex system without carrying a model of the system or resorting to difficult to implement agent coordination routines. This research will contribute to theoretical foundations of multiagent control, and provide learning and coordination algorithms for controlling systems operating in dynamic and stochastic environments. IIS CSE HPCC Standard Grant 9215 algorithms 9237 OR Oregon State University agent-based Douglas H. Fisher ROBUST INTELLIGENCE 7495 7495 Tumer, Kagan 128769 0910183 April 1, 2009 SGER: Collaborative Research: Curatorial Work and Learning in Virtual Environments The value of social computing environments has been demonstrated in many contexts, including web search, shared bookmarking and photo sharing, personal social networking, online support groups, and information resources such as Wikipedia. Collaborative game environments have been used as both research environments to study collaboration and as environments for facilitating scientific meetings, and professional organizations and scientific communities also leverage cyberinfrastructure and social network applications to share resources, collect data, and facilitate communication. Virtual worlds have also been applied to support a diverse range of professional and work activities, however, there is so far little empirical evidence on how virtual worlds are adopted and leveraged to promote scientific progress. The exploratory research proposed here aims to explore how virtual worlds can be leveraged in the digital curation community for purposes of improving work practices and training. IIS CSE HPCC 9216 Standard Grant 9215 Stephen Griffin 9251 network 9237 VA Virginia Polytechnic Institute and State University Fox, Edward networking INFO INTEGRATION & INFORMATICS 7364 7364 83000 0908532 September 1, 2009 RI: Large:Collaborative Research: Richer Representations for Machine Translation (REPS) Research in machine translation of human languages has made substantial progress recently, and surface patterns gleaned automatically from online bilingual texts work remarkably well for some language pairs. However, for many language pairs, the output of even the best systems is garbled, ungrammatical, and difficult to interpret. Chinese-to-English systems need particular improvement, despite the importance of this language pair, while English-to-Chinese translation, equally important for communication between individuals, is rarely studied. This project develops methods for automatically learning correspondences between Chinese and English at a semantic rather than surface level, allowing machine translation to benefit from recent work in semantic analysis of text and natural language generation. One part of this work determines what types of semantic analysis of source language sentences can best inform a translation system, focusing on analyzing dropped arguments, co-reference links, and discourse relations between clauses. These linguistic phenomena must generally be made more explicit when translating from Chinese to English. A second part of the work integrates natural language generation into statistical machine translation, leveraging generation technology to determine sentence boundaries, ordering of constituents, and production of function words that translation systems tend to get wrong. A third part develops and compares algorithms for training and decoding machine translation models defined on semantic representations. All of this research exploits newly-developed linguistic resources for semantic analysis of both Chinese and English. The ultimate benefits of improved machine translation technology are easier access to information and easier communication between individuals. This in turn leads to increased opportunities for trade, as well as better understanding between cultures. This project's systems for both Chinese-to-English and English-to-Chinese are developed with the expectation that the approaches will be applied to other language pairs in the future. IIS CSE HPCC Standard Grant 9215 CA algorithms INFORMATION TECHNOLOGY RESEARC 1640 University of Southern California Tatiana D. Korelsky 580000 7495 Knight, Kevin 7925 0908506 January 15, 2009 NSF Workshop Sponsorship for the Second International Workshop on Social Computing, Behavioral Modeling, and Prediction The Workshop on Social Computing, Behavioral Modeling, and Prediction provides an interdisciplinary platform to encourage researchers of traditionally disjoint fields such as sociology, psychology, behavioral science, cognitive science, mathematics, computer science, religious studies, and engineering to exchange ideas and findings, enhance mutual understanding of state‐of‐the art development in individual fields, develop cross‐discipline awareness, promote collaborative research opportunities, and offer a conducive environment for graduate students. In the last few years, the emergence of the social web has had a profound influence on computing and on society. A pertinent example stems from social networks where individuals from various cultural and social backgrounds interact and exchange information. In such a scenario, it will be useful to understand the development of such social networks and the behavior of individuals who are part of such networks to gain insight into different cultures and patterns of social behavior that can form the basis for predictive models. The focus of this workshop is not only on applications pf social computing but also on the encompassing research from varied areas that can provide insight. Computer scientists are key members of this community of researchers but social scientists are integral as well. This workshop provides a training ground for budding researchers in the area. This workshop will create awareness among the community about the uses of such research to various areas. The workshop will be webcast to include people that are unable to attend, and will result in a proceedings published as a book by Springer. IIS CSE HPCC Standard Grant 9215 William Bainbridge Arizona State University AZ cognitive science HUMAN-CENTERED COMPUTING 7367 Liu, Huan 2000 0908146 October 1, 2008 ITR : Tutoring scientific explanations via natural language dialogue It is widely acknowledged, both in academic studies and the marketplace, that the most effective form of education is the professional human tutor. A major difference between human tutors and computer tutors is that only human tutors understand unconstrained natural language input. Recently, a few tutoring systems have been developed that carry on a natural language (NL) dialogue with students. Our research problem is to find ways to make NL-based tutoring systems more effective. Our basic approach is to derive new dialogue strategies from studies of human tutorial dialogues, incorporate them in an NL-based tutoring system, and determine if they make the tutoring system more effective. For instance, some studies are determining if learning increases when human tutors are constrained to follow certain strategies. In order to incorporate the new dialogue strategies into our existing text and spoken NL-based tutoring systems, two completely new modules are being developed. One new module will interpret student utterances using a large directed graph of propositions called an explanation network, which is halfway between the shallow and deep representations of knowledge that are currently used. The second new module uses machine learning to improve the selection of dialogue management strategies. The research is thus a multidisciplinary effort whose intellectual merit lies in new results in the cognitive psychology of human tutoring, in the technology of NL processing, and in the design of effective tutoring systems. Improved NL-based tutoring systems could have a broad impact on education and society. IIS CSE Continuing grant HPCC 9218 network machine learning education Arizona State University AZ Kenneth C. Whang 1653 ITR MEDIUM (GROUP) GRANTS 1687 VanLehn, Kurt 139488 0907889 April 1, 2009 Two-Day Workshop: Studying Visual and Spatial Reasoning in Design Creativity Proposal Number: IIS - 0907889 PI: John Gero Institution: George Mason University The Problem: Visual and spatial reasoning often play pivotal roles in design creativity, most noticeably through sketching, diagrams, visualization and visual imagery. There is research on visual and spatial reasoning in multiple disciplines but very little is focused on design creativity. There is a need to bring this research to bear onto design creativity to improve it. The Proposal: This Workshop brings together the leading researchers from the disciplines that carry out research into visual and spatial reasoning of relevance to design creativity: design science, computer science, cognitive science, and neuroscience in order to define the state-of-the-art and to produce the opportunity for transdisciplinary activity related to design creativity. The workshop format allows a focus on discussion and groups work on research agendas and grand challenges. Presentations are catalysts for discussion rather than the primary focus. To improve international collaboration, the workshop is held in France. One doctoral student from each area is also invited. Intellectual Merit: Each of the four sciences studies visual and spatial reasoning in isolation without a focus on design creativity. Bringing together researchers from these different communities provides opportunities for transdisciplinary research. Making designing the focus is expected to result in knowledge about processes for studying visual and spatial reasoning that will produce new knowledge about design creativity. One enduring output of the workshop will be archival proceedings, expected to be published by Springer. Broader Impact: Design creativity holds the key to transformational products and processes. The outcomes of this workshop will form the basis of the development of a research agenda for visual and spatial reasoning in design creativity and provide the basis for an education agenda to improve the creativity of designers. The aim is to develop international research collaborations. Studying visual and spatial reasoning produces knowledge of design creativity. The knowledge derived from studying design creativity has the potential to form the foundations of new kinds of support tools for creative designing. Attention to a diverse and inclusive group of discussants, attention to design for social impact, and plans for a larger conference are also elements of the meeting. IIS CSE HPCC Standard Grant 9215 visualization education VA INFORMATION TECHNOLOGY RESEARC 1640 cognitive science George Mason University 7788 Joan M. Peckham Gero, John 88778 0907847 May 1, 2009 Student Research Workshop in Computational Linguistics at the North American Association for Computational Linguistics and Human Language Technologies 2009 Conference The NAACL HLT conference is the major international conference in North America in the field of natural language processing. This project is to subsidize travel, conference, and housing expenses of students selected to participate in the NAACL HLT Student Research Workshop which will be held during the conference May 31-June 5, 2009 in Boulder, Colorado. The workshop consists of two tracks: full paper presentations and poster presentations. All selected work has only student primary authors. The full paper sessions are composed of paper presentations followed by a discussion panel led by respected researchers in the field. The workshop is organized and run by students. The Student Research Workshop provides a valuable opportunity for the next generation of natural language processing researchers to enter the computational linguistics community. It allows the best students in the field to take their first important step toward becoming professional computational linguists by receiving critical feedback on their work from external experts, and by making contacts with other students and senior researchers in their field. The students who are involved in running and selecting papers for the workshop also gain valuable opportunities for professional growth and interaction with the researchers on the organizing committee of the main conference. The workshop contributes to the maintenance and development of a skilled and diverse computational linguistics and natural language processing research community. IIS CSE HPCC Standard Grant 9215 Carnegie-Mellon University PA Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 9102 7495 Rose, Carolyn 20200 0907374 October 31, 2008 Group Travel Assistance Fund for 2008 IEEE/RSJ IROS This NSF project provides travel funds to assist approximately 25 US researchers to participate in the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), in a time where airfare costs are increasing. This meeting has served as a forum gathering international researchers together for the exchange of ideas on technical problems and their solutions. One distinguishing aspect of IROS, setting it apart from its sister conference ICRA, is the increased presence of participants from Asia. The conference is held in Nice, France, and the transatlantic flight will incur a significant portion of the whole cost of attendance for US participants. The purpose of this Group Travel Grant Proposal is two-fold: - To assist US participation in this key international conference, and - To stimulate collaborations between US researchers and their international colleagues. A strong participation and presence of US researchers at IROS 2008 is expected to facilitate the exposure of US scientists to ideas from around the world, promote their own work, and increase their opportunities for international collaborations and visits. The financial support provided by this NSF project stimulates interaction between US scientists with their colleagues abroad, and contributes to the professional development of the former. IIS CSE HPCC Standard Grant 9215 9150 Paul Yu Oh University of Delaware DE ROBUST INTELLIGENCE 7495 7495 Tanner, Herbert 22335 0907003 October 31, 2008 CAREER: Formal Cooperative Planning of Decentralized Robot Actions - Career Development Plan This project links for the first time high level planning and reconfiguration to low level execution in heterogeneous multi-agent systems. Planing decisions are automated and combine the diverse capabilities of the underlying systems towards a common objective. The derived plans have guaranteed refinement into timed switching sequences of novel, decentralized continuous controllers. The interconnected system can complete a variety of complex cooperative tasks without hardware reconfiguration. This is the first scalable methodology for complete planning and controlled execution in heterogeneous interconnected systems. The method builds on a hierarchical hybrid architecture in which decision making takes place in the higher purely discrete layers, while the execution of plans is supervised by continuous controllers at the lower layers. The link between the low continuous dynamics and the higher discrete models is established using abstraction. Different homogeneous robotic groups obtain asymptotic abstract discrete representations via a palette of cooperative controllers. Controller switching within a group, as well as concurrent and synchronized execution among several groups is captured in a product-timed automaton. The timed automaton is further abstracted into a purely discrete push down automaton that uses its stack to store timing information. From the push down automaton we extract an equivalent context free grammar, and express the cooperative tasks as words in this grammar. Automatic parsing yields derivations that correspond to cooperative plans, in the form of sequences of controller activations in the underlying groups. Being one of the three Hispanic Serving Institutions in Carnegie Doctoral/Research Universities-Extensive, UNM is uniquely positioned to recruit Hispanic and Native American students IIS CSE Continuing grant HPCC 9216 architecture ROBOTICS 6840 9150 1187 1045 multi-agent systems Paul Yu Oh University of Delaware DE ROBUST INTELLIGENCE 7495 Tanner, Herbert 225704 0906798 June 1, 2009 2009 SIGART/AAAI Doctoral Consortium This award supports participation of doctoral students in the Thirteenth SIGART/AAAI Doctoral Consortium to be held July 12-13, 2009 in Pasadena, CA in conjunction with the 2009 International Joint Conference on Artificial Intelligence, July 11-17, 2009. The Doctoral Consortium aims to: (1) provide a setting for feedback on participants' current research and guidance on future research directions; (2) develop a supportive community of scholars and a spirit of collaborative research; and (3) support a new generation of researchers. The Doctoral Consortium organizers strive to recruit and include students from underrepresented groups (e.g., women and underrepresented minorities) and smaller schools and schools with less established programs in artificial intelligence. Each student gives a 25-minute presentation to be followed by 20 minutes of discussion; there are one-on-one meeting with a faculty mentor. There will also be opportunities to discuss career issues in both academic and other career pathways. A report on the Consortium will be published in the AI Magazine. IIS CSE HPCC Standard Grant 9215 CA artificial intelligence Douglas H. Fisher ROBUST INTELLIGENCE 7495 American Association for Artificial Intelligence 7495 Wallace, Scott Christopher Brooks 16815 0906643 September 9, 2008 "IPS: Decision Theoretic Approaches to Measuring and Minimizing Customized Privacy Risk" The goal of this project is to provide a principled way of quantitatively characterizing the effect of disclosing private data. Based on statistical decision theory, the proposed framework incorporates user-defined sensitivity information and identification model into a personalized risk function. The risk is intuitive and interpretable as it is based only on a user-specified loss function and elementary laws of probability and statistics. The proposed framework leads to a more accurate measure of the consequences of popular disclosure policies such as k-anonymity as well as efficient search for novel optimal policies. Currently, private data is being disclosed according to general policies that do not necessarily reflect users preferences. The novel framework will let users obtain a quantitative grasp on the consequences of current data disclosure policies. Due to the simplicity and interpretability of the risk this will apply, in particular, to people lacking in technical or scientific education that otherwise remain uninformed about the use of their private data. Effective dissemination of the research results to industry and the popular press have the potential to transform current disclosure policies to become more focused on serving the needs of the community. The project also aims to enhance graduate and undergraduate education in the interdisciplinary area of statistical approaches to privacy preservation. Outreach efforts include mentoring of minority students in science and technology. The results of this project are disseminated via the web-page http://www.ecn.purdue.edu/~lebanon/privacyRisk. IIS CSE HPCC Standard Grant 9215 Sylvia J. Spengler education GA Tech Research Corporation - GA Institute of Technology GA privacy INFORMATION PRIVACY & SECURITY 7486 7486 Lebanon, Guy 371625 0906550 September 9, 2008 CAREER: Multiresolution Representations of Documents An effective document representation is a crucial text processing component and without it, even the most sophisticated methods and models perform poorly. Current document representations such as the bag of words or Markov n-gram models ignore nearly all sequential information and focus instead on the histogram of words or short phrases. The proposed work develops sequential representations for documents that go beyond bag of words and Markov models and effectively capture a wide range of sequential information. The main idea behind these representations is to use smoothing techniques to transform the word sequence into smooth curves representing sequential content through changes in the local word histogram. By varying the amount of smoothing, the proposed representations interpolate between different sequential resolutions, thus conveniently capturing sequential details at varying levels of granularity. The proposed work provides improved document analysis, including the classification, segmentation, and summarization of documents. Furthermore, it enables visualizing the sequential trends in documents thus leading to the emergence of computer-assisted document browsing technology. In addition to computer experiments validating improved modeling accuracy, the project involves a series of user studies thus demonstrating the wide applicability of the project. Broader impacts include the development of visualization tools that will assist users in reading and browsing documents thus potentially helping millions of people to quickly and effectively absorb textual information. Other education components include assisting foreign language learning, strengthening the computational aspects of the statistics program at Purdue and mentoring minority students. http://www.stat.purdue.edu/~lebanon/research/projects/multiResDocuments/ IIS CSE HPCC Standard Grant 9215 visualization Sylvia J. Spengler education 1045 GA Tech Research Corporation - GA Institute of Technology GA INFO INTEGRATION & INFORMATICS 7364 7364 Lebanon, Guy 405458 0906244 April 1, 2009 Workshop on Computational Approaches to Linguistic Creativity - Element 7495 The main purpose of the workshop on Computational Approaches to Linguistic Creativity (CALC-09) is to strengthen research in automatic detection, classification, understanding, and generation of neologisms, figurative language, indirect speech acts, poetry, fiction, and other phenomena illustrating linguistic creativity. The workshop also welcomes descriptions and discussions of computational tools that support people in using language creatively. These include computational and cognitive models, metrics and tools for evaluating the performance of creativity-aware systems, specific application scenarios, and the design and implementation of creativity-aware systems. The CALC-09 workshop is held in conjunction with the NAACL - Human Language Technologies (NAACL-HLT 2009) conference in Boulder, Colorado. This award subsidizes travel and accommodation expenses for students actively participating in the workshop. These travel grants support geographic and other minorities by giving them an opportunity to exchange ideas, get into contact with key persons in the field, and gain invaluable feedback from the senior participants. The award also subsidizes travel and accommodation for an invited keynote speaker from the U.S. The workshop brings together people working on different forms and modalities of linguistic creativity. It fosters collaboration between theoreticians and practitioners working in academic or industrial settings, across disciplines. Thereby, the workshop facilitates the integration of current punctual efforts on different aspects of linguistic creativity into more comprehensive approaches. IIS CSE HPCC Standard Grant 9215 NJ Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 HUMAN-CENTERED COMPUTING 7367 9102 7495 Montclair State University Feldman, Anna 20656 0905678 July 1, 2009 RI: Medium: Collaborative Research: Solving Stochastic Planning Problems Through Principled Determinization Probabilistic and decision-theoretic planning, which operates under conditions of uncertainty, has important applications in science and engineering, but such stochastic methods have been under-utilized because these planners do not scale to large, complicated problems. In contrast, the past decade has seen tremendous scale-up in deterministic planning techniques, which do not directly address the same challenges of uncertainty head on. This project provides a systematic framework for exploiting this progress in deterministic planning technology - be it classical, temporal or partial satisfaction planning problems - in stochastic planning as well, by novel adaptation of theory and/or methods of determinization, hindsight optimization, and machine learning. This project is helping to bridge the traditional divide between deterministic and stochastic planning communities, and it is bringing stochastic planning technology to real-world applications. This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). IIS CSE HPCC Standard Grant 9215 machine learning OR Oregon State University Douglas H. Fisher ROBUST INTELLIGENCE 7495 7495 Fern, Alan 6890 7924 278793 0905672 July 1, 2009 RI: Medium: Collaborative Research: Solving Stochastic Planning Problems Through Principled Determinization Probabilistic and decision-theoretic planning, which operates under conditions of uncertainty, has important applications in science and engineering, but such stochastic methods have been under-utilized because these planners do not scale to large, complicated problems. In contrast, the past decade has seen tremendous scale-up in deterministic planning techniques, which do not directly address the same challenges of uncertainty head on. This project provides a systematic framework for exploiting this progress in deterministic planning technology - be it classical, temporal or partial satisfaction planning problems - in stochastic planning as well, by novel adaptation of theory and/or methods of determinization, hindsight optimization, and machine learning. This project is helping to bridge the traditional divide between deterministic and stochastic planning communities, and it is bringing stochastic planning technology to real-world applications. This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). IIS CSE HPCC Standard Grant 9215 machine learning Arizona State University AZ Douglas H. Fisher ROBUST INTELLIGENCE 7495 Kambhampati, Subbarao 7495 6890 7924 328821 0905671 July 1, 2009 RI: Medium: Integrated Analysis and Synthesis for Data Mining in a Video Network The University of California-Riverside is awarded a grant to develop a new research para-digm in which both analysis-by-synthesis and synthesis-by-analysis are integrated in a closed-loop learning framework for developing robust, scalable systems for video mining in network of cameras. This paradigm allows a principled transition from limited domains to large-scale de-ployment. The collaborative project brings together three institutions (UCR, UCLA, and SUNY-SB) for aggregating and interpreting information and discovering patterns of human behavior from multiple video streams and evaluating them in realistic virtual and real-life scenarios such as video surveillance, traffic monitoring, and elderly care. The project introduces four novel elements. First, it develops methods for incremental model-ing and scaling of Bayesian nets for videos and glues together the analysis and synthesis. Sec-ond, it employs multiple strategies based on game theory and a multi-objective optimization framework for cooperative and distributed on-line control of active cameras. Third, it uses multi-ple representations in a framework of hierarchical Bayesian and Markov random fields and sta-tistical tensor models for learning long-term models of activities. Finally, it involves new models based on dynamical systems theory for seamless tracking and recognition in a video network. The project blends the theoretical and algorithmic contributions with the development of a prototype that integrates a network of video cameras with a virtual vision simulator which incorporates sophisticated artificial life models of humans. It builds increasingly sophisticated models of humans, vehicles, context, illumination, texture, shape, and motion over time. The software tools integrating the analysis and synthesis are widely disseminated. IIS CSE Continuing grant HPCC 9215 CA network data mining vision Bhanu, Bir University of California-Riverside Jie Yang 1200000 GRAPHICS & VISUALIZATION 7453 Amit Roy Chowdhury 7453 7924 Demetri Terzopoulos 0905661 August 1, 2009 HCC: Medium: Collaborative Research: Computational Analysis of Nonverbal Behavior in Adaptive Tutoring This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The goal of this project is to develop computational models of the nonverbal behavior and interactive strategies observed during face-to-face teaching. These computational models will serve as a foundation for a new generation of embodied teaching agents that approximate the benefits of face-to-face human tutoring. The project will help advance the science of learning and teaching by improving our understanding of the dynamics of nonverbal behavior in teaching at a computational level, across multiple time scales: From low-level micro-expressions in the timescale of tens of milliseconds, to cognitive and affective processes with time scales of seconds, to higher level strategic behaviors operating at longer time scales. In addition to its scientific and technological value, this project has a significant outreach component. The project would help grow links between a research oriented campus (UCSD) an undergraduate teaching university (SDSU). The robotics aspects of the project will be developed in collaboration with the Preuss School Robotics Club. The Preuss School is a charter school for low-income students in grades 6-12 and is currently ranked as one of the top high schools in the nation. IIS CSE HPCC Standard Grant 9215 CA 0116000 Human Subjects robotics HUMAN-CENTERED COMPUTING 7367 San Diego State University Foundation 7367 David W. McDonald 6890 7924 Reilly, Judy 399996 0905647 July 15, 2009 HCC: Medium: Collaborative Research: Computer Vision and Online Communities: A Symbiosis This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The project represents a new paradigm for research in computer vision related to content-based image management--?one that exploits a symbiosis between this technology and the online communities it serves. It is quite clear that communities stand to benefit from improved searching and browsing tools enabled by visual technology, but we argue that the converse is equally true. Visual information that is shared online is almost always embedded in a social network, and these networks provide rich contextual information that can be a boon to vision systems. Our goal is to build foundations for a new generation of computer vision systems that are 'socially aware'. These vision systems will exploit knowledge of the relationships between individuals and the social events that occur in the embedding online community, and in doing so they will exhibit significant performance gains over current technology. They will be trained discriminatively using the data embedded in existing online communities, and they will adapt in response to observations of how they are used by community members. Almost every image that is shared online is embedded in some form of online community, and any system that seeks to analyze and catalog such imagery will gain from the proposed research. We foresee benefits for communities that wish to share historical image archives, scientific image collections, forensic imagery, surveillance videos, and more. Also, while we focus on tools for searching and browsing imagery, our work will have the important effect of improving the quality and density of textual metadata. In this way, the research stands to benefit all members of an online community, including those with visual impairments. IIS CSE HPCC Standard Grant 9215 CA network vision computer vision International Computer Science Institute 400000 HUMAN-CENTERED COMPUTING 7367 7367 Darrell, Trevor David W. McDonald 6890 7924 0905633 July 1, 2009 RI: Medium: Collaborative Research: Explicit Articulatory Models of Spoken Language, with Application to Automatic Speech Recognition This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). One of the main challenges in automatic speech recognition is variability in speaking style, including speaking rate changes and coarticulation. Models of the articulators (such as the lips and tongue) can succinctly represent much of this variability. Most previous work on articulatory models has focused on the relationship between acoustics and articulation, but more significant improvements require models of the hidden articulatory state structure. This work has both a technological goal of improving recognition and a scientific goal of better understanding articulatory phenomena. The project considers larger model classes than previously studied. In particular, the project develops graphical models, including dynamic Bayesian networks and conditional random fields, designed to take advantage of articulatory knowledge. A new framework for hybrid directed and undirected graphical models is being developed, in recognition of the benefits of both directed and undirected models, and of both generative and discriminative training. The project activities include major extension of earlier articulatory models with context modeling, asynchrony structures, and specialized training; development of factored conditional random field models of articulatory variables; and discriminative training to alleviate word confusability. The scientific goal addresses questions about the ways in which articulatory trajectories vary in different contexts. Existing databases are used, and initial work in manual articulatory annotation is being extended. In addition, the project uses articulatory models to perform forced transcription of larger data sets, providing an additional resource for the research community. Other broad impacts include new models and techniques with applicability to other time-series modeling problems. Extending the applicability of speech recognition will help it fulfill its promise of enabling more efficient storage of and access to spoken information, and equalizing the technological playing field for those with hearing or motor disabilities. IIS CSE HPCC IL Standard Grant 9215 Tatiana D. Korelsky ROBUST INTELLIGENCE 7495 9102 7495 Toyota Technological Institute at Chicago 6890 7924 Livescu, Karen 438830 0905622 August 1, 2009 HCC: Medium: Collaborative Research: Computational Analysis of Nonverbal Behavior in Adaptive Tutoring This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The goal of this project is to develop computational models of the nonverbal behavior and interactive strategies observed during face-to-face teaching. These computational models will serve as a foundation for a new generation of embodied teaching agents that approximate the benefits of face-to-face human tutoring. The project will help advance the science of learning and teaching by improving our understanding of the dynamics of nonverbal behavior in teaching at a computational level, across multiple time scales: From low-level micro-expressions in the timescale of tens of milliseconds, to cognitive and affective processes with time scales of seconds, to higher level strategic behaviors operating at longer time scales. In addition to its scientific and technological value, this project has a significant outreach component. The project would help grow links between a research oriented campus (UCSD) an undergraduate teaching university (SDSU). The robotics aspects of the project will be developed in collaboration with the Preuss School Robotics Club. The Preuss School is a charter school for low-income students in grades 6-12 and is currently ranked as one of the top high schools in the nation. IIS CSE HPCC Standard Grant 9215 CA 0116000 Human Subjects robotics University of California-San Diego Javier Movellan HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald 6890 7924 Bartlett, Marian 799618 0905593 July 15, 2009 HCC: Medium: Collaborative Research: Effective Communication with Robotic Assistants for the Elderly: Integrating Speech, Vision and Haptics This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The purpose of this study is to develop a communication interface that will enable older people to effectively communicate with robotic assistants, allowing them in this way to remain living safely in their homes. The proposed communication interface will be: (1) multimodal, that is supporting spoken, gestural and physical interactions, as all these typically occur simultaneously when people communicate with one another, and (2) adaptive so that the robotic assistant will adjust to the older person rather than the older person to the robotic assistant. The combination of speech, gestures and physical interactions (haptics) has received only limited attention, but it will be critical for successful deployment of assistive robots for many elderly individuals. The transformative component of this research is to view haptics as one of the drivers of the dialogue between the user and the robot, and to study its relation to speech and gestures through language processing methods. To adapt to each user, the interpretation of the speech, gestures and haptic signals will be performed by means of RISq (Recognition by Indexing and Sequencing), a novel, adaptive and reliable recognition methodology. Finally, a formal and modular control design methodology will be developed, that guarantees that the robot responds safely and reliably to the interpretation of the user intent provided by dialogue processing. Robot assistive technology holds great promise for the future. Supporting the independent functioning of older people so that they can safely remain living in the community is of paramount importance, especially since the world's population is aging at an ever increasing pace. However, one of the main obstacles to the widespread use of robot assistants is the lack of interfaces that are easy to use for the elderly and allow the robot to be used in complex real-world environments such as a typical apartment. The proposed research aims to develop new tools that will enable robot developers to fill this void. The research could also have significant implications for the delivery of institutionally based health care. The deployment of robots to assist nursing personnel in hospitals and long-term care facilities, as well as at home, has enormous implications for improved health outcomes and quality of life for older patients while minimizing costs of care. Furthermore, the reduction of the nursing workload by such robot assistants promises to alleviate the critical shortage of nursing personnel in the USA that is only expected to worsen. IIS CSE Ephraim P. Glinert HPCC IL Standard Grant 9215 University of Illinois at Chicago 0116000 Human Subjects vision assistive technology Zefran, Milos HUMAN-CENTERED COMPUTING 7367 7367 6890 7924 Jezekiel Ben-Arie Barbara DiEugenio 775940 0905581 September 1, 2009 DC: Medium: Collaborative Research: ELLF: Extensible Language and Library Frameworks for Scalable and Efficient Data-Intensive Applications The growth of scientific data sets to petabyte sizes offers significant opportunities for important discoveries in fields such as combustion chemistry, nanoscience, astrophysics, climate prediction and biology as well as from data on the internet. However, the realization of new scientific insights from this data is limited by the difficulty of creating scalable applications due to the lack of easy-to-use programming models and tools. To address challenges in creating data intensive applications, the project will build an extensible language framework, backed by an expressive collection of high-performance libraries (I/O and analytic), to provide a development environment in which multiple domain-specific language extensions allow programmers and scientists to more easily and directly specify solutions to data-intensive problems as programs written in domain-adapted languages. The project will build on recent attribute grammar research to build an extensible specification of C to host domain-specific language extensions which will also address the inadequate performance in storage, I/O and analysis capabilities in low-level language such as C. The proposed extensible language and library framework has the potential to be a transformative problem solving environment for programmers and scientists since it allows scalable and efficient solutions to data-intensive problems to be specified at a high-level of abstraction. The resulting language framework and libraries will be freely available to researchers writing applications for climate and other applications involving spatio-temporal data. This includes many applications in the physical sciences and engineering and thus it is expected that the framework will find use in other scientific domains as well. IIS CSE HPCC 9216 Standard Grant University of Minnesota-Twin Cities MN James C. French Michael Steinbach 7752 7793 7924 DATA-INTENSIVE COMPUTING 7793 Van Wyk, Eric Vipin Kumar 730000 0905569 September 1, 2009 HCC: Medium: Collaborative Research: Generating Effective Dynamic Explanations in Augmented Reality To survive and flourish, people must interact with their environment in an organized fashion. To do so, they need to learn, imagine, and perform an assortment of transformations on and in the world. Primary among these are manipulation of objects and navigation in space. This project integrates research in computer science and cognitive science to develop and evaluate augmented reality tools to create effective dynamic explanations that enhance manipulation and navigation, in conjunction with identification and visualization. Augmented reality refers to user interfaces in which virtual material is integrated with and overlaid on the user?s experience of the real world; for example, by using tracked head-worn and hand-held displays. Dynamic explanations are task-appropriate sequences of actions, presented interactively, with appropriate added information. The tools will be created in collaboration with subject matter experts for exploratory use in indoor and outdoor real world domains: navigating and identifying landmarks in a wooded park area, assembling a piece of furniture, and navigating and visualizing for planning the site of a new urban campus. Cognitive science research will determine the best ways to convey explanations and information to people. Computer science research will address the design and implementation of systems that embody the best candidate approaches for identifying objects and locations, specifying actions, and adding non-visible information. In situ experiments will be used to assess and refine the systems. Manipulation, navigation, identification, and visualization are representative of important things that people do every day, ranging from fixing broken equipment to reaching a desired destination in an unfamiliar environment. The ways in which we perform these tasks could potentially be improved significantly through augmented reality systems designed using the principles to be developed by this project. Both the cognitive principles and the augmented reality tools will have broad applicability. The systems developed will inform the design of future systems that can aid the general public, for educational and recreational ends, as well as systems that can assist people with auditory, visual, or physical impairments. IIS CSE NY Continuing grant HPCC 9215 visualization 0116000 Human Subjects augmented reality cognitive science Feiner, Steven Columbia University HUMAN-CENTERED COMPUTING 7367 7367 David W. McDonald 7924 137853 0905553 August 15, 2009 III: Medium: Longview: Querying the Future Now This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Traditional database systems are designed to provide the user with a clear picture of past states of the world as is represented in the database. Recently, stream processing systems are introduced to produce near real-time answers for those applications applications that require up-to-date information. Thus, we see a trend toward shrinking the ?reality gap? to zero. But for some applications, even real-time is not good enough. There is often a desire to get out in front of the present by delivering predictions of future events to take advantage of opportunities or to avert calamity. Security applications are a good example since they are typically interested in preventing a breach rather than simply reporting that one has happened. There is currently no database system that can effectively serve as a generic platform to support such predictive applications. This project aims to fill this gap by designing and building a prototype database system called "Longview" to enable data-centric predictive analytics. Longview facilitates the use of statistical models to analyze historical and current data and make predictions about future data values and events. Users can plug new predictive models into the system along with a modest amount of meta-data, and the system uses those models to efficiently evaluate predictive queries. Longview treats predictive models as first-class citizens by intelligently managing them in the process of data management and query optimization. This involves automatically building models and determining when and which model(s) to apply to answer predictive queries. This also involves creating and using the proper physical data structures to facilitate efficient model building, selection, and execution. Longview handles both streaming and historical queries. In fact, many streaming queries need efficient access to an archive of past values, making it necessary to seamlessly combine both stream and historical processing. Finally, Longview investigates "white-box" model support, in which the database leverages the operational semantics and representation of models to improve performance. Longview's goal is to make it much easier to build predictive analytics applications in data intensive situations. Seamlessly combining data and model management is key to make the process of computing with predictions far easier to express and more efficient than the current ad-hoc application-level approaches. The resulting technology also allows for a better understanding and support for user-defined functions in database systems. Longview is initially used for a real-world sensor-based tracking application and a predictive web portal for easy experimentation with different models and data sets. Further information on the project can be found on the project web page: http://database.cs.brown.edu/projects/longview/ IIS CSE HPCC 9216 Standard Grant 9150 database security Brown University RI INFO INTEGRATION & INFORMATICS 7364 Frank Olken Stanley Zdonik 1200001 7364 Cetintemel, Ugur Eli Upfal 6890 7429 0905546 December 15, 2008 Rocky Mountain Regional Bioinformatics Conference Support This award funds travel fellowships to assist graduate student and postdoc attendees to the Sixth Rocky Mountain Regional Bioinformatics Conference to be held Dec. 5-7, 2008 at Snowmass, Colorado. The Rocky Mountain Regional Bioinformatics Conference encompasses a variety of topics in bioinformatics and computational biology including biological sequence comparison and analysis, metabolic and other biological networks, genetics, phylogeny, etc. For further information on the Sixth Rocky Mountain Regional Bioinformatics Conference see the URL: http://www.icsb.org/rocky08/ IIS CSE HPCC 9216 Standard Grant computational biology CO bioinformatics INFO INTEGRATION & INFORMATICS 7364 Frank Olken 7364 12000 Hunter, Lawrence University of Colorado at Denver and Health Sciences Center 0905523 August 1, 2009 RI: Medium Collaborative Research: Minimalist Mapping and Monitoring "This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)." This project addresses fundamental and challenging questions that are common to robotic systems that build their own maps and solve monitoring tasks. In particular, the work contributes to our general understanding of the interplay between sensing, control, and computation as people attempt to design systems that minimize costs and maximize robustness. Powerful new abstractions, planning algorithms, and control laws that accomplish basic mapping and monitoring operations are being developed in this effort. This is expected to lead to improved technologies in numerous settings where mapping and monotoring are basic components. Ample motivation is provided by technological challenges that involve searching, tracking, and monitoring the behavior of people, wildlife, and robots. Examples include search-and-rescue, security sweeps, mapping abandoned mines, scientific study of endangered species, assisted living, ground-based military operations, and even analysis of shopping habits. The work is particularly transformative because it lives outside of the traditional boundaries of algorithms, computational geometry, sensor networks, control theory, and robotics. Furthermore, national interest continues to grow in the direction of developing distributed robotic systems that combine sensing, actuation, and computation. By helping to break down traditional academic and scientific barriers, it is expected that the work will transform the way we think about robotics algorithms, the engineering design process, and the education of students across the robotics, computational geometry, and control disciplines. IIS CSE HPCC IL Standard Grant 9215 algorithms education University of Illinois at Urbana-Champaign security robotics computational geometry Paul Yu Oh LaValle, Steven ROBUST INTELLIGENCE 7495 7495 6890 7924 432000 0905516 August 1, 2009 HCC: Medium: The Accessible Aquarium Project: Access to Dynamic Informal Learning Environments via Advanced Bio-Tracking and Adaptive Sonification The goal of this project is to make dynamic exhibits in informal learning environments (ILEs), such as zoos, science centers, and aquaria, accessible and engaging for visitors with vision impairments by providing real-time audio interpretations. Our efforts will also enhance the exhibit experience for all visitors, regardless of visual ability. We focus on the aquarium domain, since virtually every exhibit is dynamic, and the aquatic nature of the facility provides unique and interesting challenges to bio-tracking. The principles and techniques we develop will be immediately applicable to zoos, museums, and other ILEs with dynamic exhibits, potentially leading to a dramatic increase in the educational and entertainment opportunities for people with vision impairments. The project will employ focus groups and innovative simulations to test prototype exhibits during the development stages. The tracking and auditory displays will then be evaluated through laboratory studies and field testing in exhibits in a large public aquarium. The ability to design exhibits and interpretation materials that are accessible to visitors with vision impairments is a growing concern. In addition to persons with specific vision impairments, the underserved population includes their family members, as well as the millions of older adults who have some vision loss that impacts their ability to read signage, see artifacts, and follow the activity in a dynamic exhibit. The results of our project will also enhance the experience for those with full vision, but for whom mobility, height, or other problems make it hard to see traditional signage. Indeed, all visitors will benefit from an audio enhancement that allows them to learn about the exhibit, while keeping their visual attention on the exhibit itself. IIS CSE Continuing grant HPCC 9215 GA Tech Research Corporation - GA Institute of Technology GA 0116000 Human Subjects vision HUMAN-CENTERED COMPUTING 7367 7367 Walker, Bruce David W. McDonald Tucker Balch Gil Weinberg 7924 Aaron Bobick Carrie Bruce 287229 0905506 July 1, 2009 HCC: Medium: Sound Rendering for Physically Based Simulation Computational physics can help us animate crashing rigid and deformable bodies, or fracturing solids, or splashing water, but the results are silent movies. Virtually no practical algorithms exist for synthesizing synchronized sounds automatically. Instead, sound recordings are edited manually for pre-produced animations or triggered automatically in interactive settings. The former is labor intensive and inflexible, while the latter produces awkward, repetitive results. This situation is a serious obstacle to building realistic, interactive simulations (whether for entertainment, training, or other applications), which require sound to be compelling,. In this research the PIs will begin filling this broad void by pursuing fundamental advances in computational methods while solving several particularly challenging sound rendering problems. The goal is to produce some of the first viable methods in this area, upon which many more can be built. Successful implementation of this program will fundamentally transform our relationship with our increasingly convincing simulated realities, because for the first time we will be able to hear them as well as see them. To these ends, the PIs will develop fundamental algorithms that address the problems of simulating the vibrations that cause sound and computing the sound field produced by those vibrations. 1) Reduced-order vibration models. Simulating vibration in complex structures is expensive because of the need for both high model complexity and audio-rate temporal resolution. The PIs will develop dimensional model reduction methods to enable efficient sound rendering from complex, nonlinearly vibrating geometry, such as thin shells. 2) All-frequency sound radiation. Realistic sound requires computing the radiated sound field from a vibrating surface over the very broad range of audible frequencies. But existing methods are either inaccurate for low frequencies or impractical for high frequencies. The PIs will develop hybrid algorithms based on a broad toolbox and discover which methods are most successful for which problems. Complementing the algorithmic work, the PIs will pursue solutions to a series of difficult, unsolved sound rendering problems that are of value in applications: a) Harmonic fluid sounds. Few sounds are as distinctive as pouring a glass of water or the babbling of a brook, yet no algorithms exist to compute these sounds automatically. The PIs will investigate practical algorithms for harmonic bubble-based sound radiation characteristic of splashing fluids. b) Multi-object sound. Sounds made by collections of objects in contact (think of a bin of LEGOs or a basket of blocks) involve close-proximity effects that are often ignored. The PIs will develop sound rendering methods to approximate multi-object contact sounds with object-object interactions. c) Fracture. Brittle fracture creates distinctive sounds during destructive processes like breakage of glass. The PIs will research the efficient generation and excitation of vibrating fragments, and multi-object sound radiation from vibrating debris. In all aspects of this research, the PIs will ensure that they are solving problems accurately by comparing every approximation to a reference solution, and they will also ensure they are solving the right problems by testing perceptual equivalence between approximate solutions, reference solutions, and recorded sounds. Broader Impacts: Successful implementation of this program will lead to practical innovations of immediate relevance to computer graphics, and applications of acoustic simulation. In the future, the methods developed in this project or their successors will completely transform how sound is computed in interactive virtual environments. IIS CSE NY Ephraim P. Glinert Continuing grant HPCC 9215 algorithms simulation 0116000 Human Subjects computer graphics graphics Cornell University GRAPHICS & VISUALIZATION 7453 7453 7924 James, Doug Steve Marschner Kavita Bala 507972 0905505 July 1, 2009 HCC: Medium: Haptic Simulation Design for Motor Rehabilitation and Skill Training The PI seeks to design and investigate novel features and strategies for use of virtual reality based haptic simulations to retrain impaired motor functions and train new fine motor skills in veterans (medics) with traumatic brain injury (TBI). Previous approaches to haptic simulation design have typically focused on completeness of presentation relative to reality. However, simulators may be used for many different tasks, so completeness may not ensure usefulness. The PIs argue that the role of the simulation for a user and its required functionality are the more important design questions, and that consideration of critical applications like motor skill training in haptic simulation design will promote effective design from a human performance perspective. Thus, they propose a cognitive-oriented approach to haptic simulation design and evaluation. In this project, the PIs will test their ideas by designing and prototyping advanced VR haptic simulators for drawing and surgical tasks. The simulations will be defined in terms of the type and resolution of (medical) data sources used for virtual object modeling, the type of visual and force presentation for motor skill and brain function assessment, and the approach to graphic and haptic rendering of the simulation. Through human factors experimentation with the simulations, the PIs will also assess interventional strategies for motor development, including virtual haptic aids (e.g., force boundaries and potentials) and force graduation across rehabilitation trials. Finally, they will validate the effect of the haptic simulations on neurocognitive and motor performance using behavioral indices and advanced magnetic resonance imaging (fMRI). Cognitive tasks analyses will be conducted with expert neuropsychologists and surgeons on psychomotor task performance and surgical operations to inform the simulation design process. Simulation design manipulations will include source data reduction through approximation of point-cloud data using graphical methods, development of a haptic-based motor skill training workstation with an effective human-computer interface, and optimization of visual and haptic representation using GPU-based graphics and a smoothed particle hydrodynamics (SPH) model for reduction of CPU overhead in haptic object rendering with force feedback. The simulation design will also reflect results on baseline motor performance and VR drawing and surgical simulator practice. This testing will be followed by fMRI of subjects in motor tasks in order to examine activation of brain regions mediating motor control. Subsequently, a series of motor training sessions will be conducted using the haptic simulators. Subjects will be exposed to the various settings of the simulation design parameters along with the virtual haptic aids and gradual reduction of force feedback, relative to nominal forces, across sessions. Post-therapy motor and simulator tests, as well as follow-up fMRI scanning, will be conducted. Motor recovery and neuroimaging of brain regions mediating motor performance will provide evidence of the effectiveness of the simulation design and rehabilitation efficacy. The PIs' hypothesize that experience with a haptic simulation design based on motor skill training demands and human performance metrics will accelerate skill development relative to a fidelity-centered approach to design. They also expect that haptic-simulator experience will improve fine motor control and motor planning (praxis), that experience on the drawing-simulation device will generalize to improved performance on the surgical-simulation device, and that brain blood flow will increase in regions mediating motor control. Broader Impacts: This work will make contributions to the design of computer graphics and haptic rendering, in terms of better understanding of optimal computational modeling for VR haptic simulation. It will also advance the state of the art in computer-based therapeutic approaches to motor skill development with haptic simulation, and enhance our understanding of brain-behavior relationships governing motor output and the nature of neural recovery following motor rehabilitation. Improvements in existing VR-based rehabilitation strategies for motor and praxis impairment in individuals suffering from TBI will be identified; rehabilitation treatment regimens will be identified that may have implications for various populations suffering from brain injuries (e.g., stroke patients). IIS CSE Ephraim P. Glinert Continuing grant HPCC 9215 simulation NC 0116000 Human Subjects computer graphics graphics North Carolina State University HUMAN-CENTERED COMPUTING 7367 Kaber, David 7367 7924 Yuan-Shin Lee Larry Tupler 169128 0905502 July 15, 2009 HCC: Medium: Robust and Accurate Modeling with Multifield Geometry Computer tools for creating, analyzing and modifying geometry are the core component in computer-aided design systems, indispensable for engineering and product prototyping, surgical simulation, prosthesis design, dentistry, architecture, computer animation and games. There is a considerable demand for high-level modeling tools, expressing the user?s intent directly while being capable of handling complex models robustly. Surface optimization techniques unify traditional ab initio modeling of high-quality surfaces and manipulation of existing geometric data in a single natural framework by specifying suitable optimization functionals governing the surface behavior. This framework is conceptually representation-independent: the surface can be approximated by spline patches, subdivision surfaces, meshes or in implicit form. While this framework is highly appealing for practical applications, surface optimization rarely finds its way into industrial applications, as existing interactive techniques are not sufficiently robust, while the more precise and reliable methods are too slow to be used interactively. This research project develops fundamental mathematical and algorithmic techniques essential for bringing surface optimization methods to mainstream geometric modeling applications. The research is addressing the following problems: shape representation and its accuracy, such that the modeled surfaces depend solely on the user input and not the particular choice of sampling or approximation basis; robust and efficient modeling algorithms to ensure predictable and responsive behavior in interactive applications; diverse control to allow sufficient freedom for arbitrary shape manipulation. The investigated approach has two key components: multifield geometry descriptions, building on ideas from discrete geometry, high-order geometric modeling, and finite elements and novel multiscale numerical solvers that combine robust global algorithms at coarse scales with fast and accurate algorithms at fine scales to achieve the performance and robustness needed in applications. The new methods are explored in the context of high-level modeling tasks, including modeling from two-dimensional projections or drawings, feature-based and appearance-based editing and template fitting for dental CAD. IIS CSE NY Continuing grant HPCC architecture 9215 algorithms simulation animation New York University GRAPHICS & VISUALIZATION 7453 7453 Lawrence Rosenblum 7924 Sorkine, Olga Denis Zorin 319105 0905493 September 15, 2009 NetSE: Medium: Privacy-Preserving Information Network and Services for Healthcare Applications This research explores challenges in developing privacy-preserving information networks and services (PPNs). Next generation healthcare information systems and applications, such as personalized and predictive medicine, need PPNs for privacy-preserving information sharing and dissemination among independent healthcare providers, enabling information access over distributed access controlled content, while safeguarding personal health information and medical privacy of individuals from unauthorized disclosures. The intellectual merits of this research include the development of: (1) privacy-preserving search capabilities over distributed access controlled content, a critical functionality for PPNs; (2) a suite of utility-aware data anonymization services, preserving the privacy of personal medical information against unauthorized disclosure, at the same time maximizing the data utility for medical service providers; and (3) the PPN architecture and middleware optimized for high availability, scalability and failure recovery. The broad impact is two-fold. First, this research will create better and broader understanding of the challenges and functional requirements for building the next generation of privacy preserving networked information systems over distributed access controlled content. A domain-specific proof-of-concept prototype on top of the PPN core will be developed for discovering and analyzing risk factors for resistant bacterial infections. These real-world studies will be conducted in collaboration with Morehouse School of Medicine and Children's Healthcare of Atlanta, and be use as both a driver and a testbed for this research. Second, this research will demonstrate that the PPN is an enabling infrastructure for real-time, continuous and on demand data analysis over massively-distributed and privately-shared data repositories. IIS CSE Continuing grant HPCC architecture 9218 network GA Tech Research Corporation - GA Institute of Technology GA privacy middleware data analysis 0000912 Computer Science David W. McDonald Liu, Ling 7924 NETWORK SCIENCE & ENGINEERING Mustaque Ahamad Calton Pu Lilly Immergluck 275000 7794 7794 0905485 July 15, 2009 HCC: Medium: Collaborative Research: The Social Medium is the Message "This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)." In this project the PIs will investigate human-robot interaction in situations where a human is highly dependent upon a robot that serves as the medium for the outside world, such as emergency response, hostage negotiation, and healthcare. In these domains, the human "dependent" is typically connected to multiple other human "controllers" (medical specialists, structural engineers, rescue operations officials, etc.) via the robot proxy for long periods of time. The literature suggests that under these circumstances the dependent will respond to the robot socially, and will become distrustful as well as cognitively confused by a robot that presents a different affect for different controllers rather than a consistent communication strategy. The PIs believe that such a robot would occupy a novel "social medium" position within the Computers as Social Actors (CASA) model, and would be perceived as a loyal and helpful go-between who is an advocate for the dependent rather than as a device for accomplishing the goals of the controllers. To explore this hypothesis, the PIs will make use of the Survivor Buddy, a multimedia attachment for a robot which allows trapped victims to engage in two-way video conferencing, watch news, listen to music, etc. Formative experiments will be conducted at Stanford's CHIMe lab, followed by comprehensive, high fidelity experiments at Texas A&M's Disaster City using point-of-injury care scripts developed under prior work with medical doctors and rescue professionals. The paralinguistic aspects of the associated communication strategy will couple the ongoing work by Nass in voice characteristics and mannerisms with research in affective physical mannerisms in non-anthropomorphic robots under development by Murphy; the intellectual merit of the project thus stems from its multidisciplinary merging of communications and computer science. The research will introduce the CASA spectrum of relationships as a complement to robot-centric taxonomies, and will define a new relationship where a human is highly dependent upon a medium for long durations along with a new identity of social medium, which the PIs expect will have a greater impact on integrating robots into society than autonomous social actors and tele-operation. Project outcomes will include creation of a formal and comprehensive communication strategy for HRI, which combines verbal and nonverbal affect; this will unify the theory and practice of social robots, thereby breaking the pattern of ad hoc application of affect currently seen in the robotics literature and establishing the fundamental models and paradigms for continuing basic research in HRI. Broader Impacts: This project will ultimately help save the lives of victims of accidents, disasters, and terrorism, and will also generally improve the quality of life for "shut-ins." The concept of social medium is well-matched to the current capabilities of tele-operation and semi-autonomy in civilian and military robotics; thus, the communication strategies developed will be immediately applicable to domains such as law enforcement and emergency response, which currently use robots, as well as to healthcare, where robots have not yet found a strong economic niche but have huge economic potential. The educational outreach plan includes multi-disciplinary curriculum development, as well as outreach to K-12 teachers and museums. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 0116000 Human Subjects robotics Texas Engineering Experiment Station TX multimedia HUMAN-CENTERED COMPUTING 7367 7367 Murphy, Robin 6890 7924 841243 0905468 July 15, 2009 HCC: Medium: RUI: Control of a Robotic Manipulator via a Brain-Computer Interface A brain-computer interface (BCI) is a system that allows users, especially individuals with severe neuromuscular disorders, to communicate and control devices using their brain waves. There are over two million people in the United States afflicted by such disorders, many of whom could greatly benefit from assistive devices controlled by a BCI. Over the past two years, it has been demonstrated that a non-invasive, scalp-recorded electroencephalography (EEG) based BCI paradigm can be used by a disabled individual for long-term, reliable control of a personal computer. This BCI paradigm allows users to select from a set of symbols presented in a flashing visual matrix by classifying the resulting evoked brain responses. One of the goals of this project is to establish that the same BCI paradigm and techniques used for the aforementioned demonstration can be straightforwardly implemented to generate high-level commands for controlling a robotic manipulator in three dimensions according to user intent, and that such a BCI can provide superior dimensional control over alternative BCI techniques currently available, as well as a wider variety of practical functions for performing everyday tasks. Electrocorticography (ECoG), electrical activity recorded directly from the surface of the brain, has been demonstrated in recent preliminary work to be another potentially viable control for a BCI. ECoG has been shown to have superior signal-to-noise ratio, and spatial and spectral characteristics, compared to EEG. But the EEG signals used at present to operate BCIs have not been characterized in ECoG. The PI believes ECoG signals can be used to improve the speed and accuracy of BCI applications, including for example control of a robotic manipulator. Thus, additional goals of this project are to characterize evoked responses obtained from ECoG, to use them as control signals to operate a simulated robotic manipulator, and to assess the level of control (speed and accuracy) between the two recording modalities and compare the results to competitive BCI techniques. Because this is a collaborative effort with the Departments of Neurology and Neurosurgery at the Mayo Clinic in Jacksonville, the PI team will have access to a pool of ECoG grid patients from which to recruit participants for this study. Broader Impacts: This research will make a number of contributions in the emerging field of BCI and thus will serve as a step toward providing severely disabled individuals with a new level of autonomy for communicating with others and for performing everyday tasks, which will ultimately dramatically improve their quality of life. IIS CSE Ephraim P. Glinert HPCC Standard Grant 9215 0116000 Human Subjects FL HUMAN-CENTERED COMPUTING 7367 7367 7924 Krusienski, Dean Daniel Cox Jerry Shih University of North Florida 742685 0905467 September 1, 2009 III: Medium: Learning from Implicit Feedback Through Online Experimentation This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The goal of the project is to harness the information contained in users' interactions with information systems (e.g., query reformulations, clicks, dwell time) to train those systems to better serve their users' information needs. The key challenge lies in properly interpreting this implicit feedback and collecting it in a way that provides valid training data. Moving beyond existing passive data collection methods, the project draws on multi-armed bandit algorithms, experiment design, and machine learning to actively collect implicit feedback data. Developing these interactive experimentation methods goes hand-in-hand with developing machine learning algorithms that can use the resulting training data, and empirical evaluations that validate the models of user behavior assumed by the algorithms. This research will improve retrieval quality for important applications like intranet search and desktop search. Additionally, the project will provide an operational full-text search engine for the Physics E-Print ArXiv and potentially other digital libraries, thus forming a test-bed for the research while also providing a valuable service and dissemination tool to the academic community beyond computer science. The project provides interesting and motivating research opportunities to undergrads and international exchange students, and the PIs will include relevant material into the undergraduate and graduate curriculum. Finally, following their prior work on the Support Vector Machine, SVM-light (http://svmlight.joachims.org/) and an open-source search engine for learning ranked retrieval functions and evaluating the learned rankings, OSMOT (http://radlinski.org/osmot/), the PIs will continue to provide easy-to-use software that enables research and teaching, via the project website (http://www.cs.cornell.edu/People/tj/implicit/). IIS CSE NY HPCC 9216 Standard Grant Maria Zemankova algorithms machine learning 0116000 Human Subjects Cornell University INFO INTEGRATION & INFORMATICS 7364 7364 1000000 Joachims, Thorsten 6890 7924 Robert Kleinberg 0905460 September 1, 2009 HCC: Medium: Collaborative Configuration: Supporting End-User Control of Complex Computing This project will develop techniques that will allow users to help each other create and maintain configurations of complex, pervasive computing and communication environments. As personal computing environments become ever more complex, growing to include not just desktop and laptop computers, but also mobile phones, media devices, sensors, and more, configuration tasks grow in importance and difficulty. In particular, it becomes challenging for end-users to create, understand, and maintain the hardware and software configurations that allow them to carry out the activities that matter to them. Moreover, this problem will only get worse with time as computing environments grow to include the hundreds or even thousands of different devices and software services that have been forecast by computer scientists. The approach taken in this project derives from the observation that, even as each user may have specific devices, services, and preferences that make it difficult for her to find information relevant to their particular needs, there frequently exists some other user, somewhere, who has experienced and solved a similar problem. This other user's knowledge would doubtless be of great benefit to the first user, but existing tools for seeking help and modifying configurations do not make it easy for such information exchange to take place. An important goal, then, is to match each user with the knowledge she needs in order to accomplish the configuration tasks facing her, on the assumption that such knowledge resides with some other user with a similar system. This project will address a number of challenges, including: 1) How can "configuration knowledge" be identified and made available without placing undue burden on the individuals who possess it? 2) How can help-seekers be presented with information in a way that allows them to act on it with minimal effort and likelihood of error? 3) How can the complexity of large spaces of possible configurations be reduced to only the dimensions that matter for users' decision-making? In order to address these challenges, this project will develop the Collaborative Configuration Service (CCS) - a general service that collects configuration information from various users of a particular system and matches similar users with each other fo