Timothy W. Finin - US grants

With the advent of third and fourth generation wireless infrastructure, and the simultaneous emergency of pervasive connectivity for all devices based on bluetooth like systems and ad-hoc networks, a new vista is open for research in the area. We propose ideas for a research program aimed at realizing ubiquitous computing systems based on the cooperation of autonomous, dynamic and adaptive components (hardware as well as software) which are located in vicinity of one another. These systems will be composed of a collection of independently designed components that automatically become aware of each other, establish basic (wireless) communication, exchange information about their capabilities and requirements, discover and exchange APIs, and learn to cooperate effectively to accomplish their individual and collective goals. The proposed work will enable a new class of applications that effectively use mobility and pervasive computing. We address several research problems that span the fields of distributed computing, data management, and dynamic collaboration between components. The team of researchers is located at UMBC and UI-Chicago, and plans to interact closely with collaborators at industrial labs (IBM, Hughes, Sun).

EIA-0116101
Yun Peng
University of Maryland-Baltimore County

NGS: Workshop on Manufacturing Software Integration Research: Status and Future Directions

This workshop is to support travel and registration expenses for invited speakers, and students to attend The Workshop on Manufacturing Software Integration Research. It will be held at NIST and will bring together researchers to discuss systems software issues on supporting the integration of heterogeneous software components, a very important issue in enabling modern manufacturing planning and control support systems.

The constant enhancements in capabilities of palmtop, embedded and
wearable devices, together with the advent of pervasive connectivity,
represents a new paradigm for interaction among devices. A new vista
is open for research in the area of mobile data management where
mobile devices gather and exchange information from not just wired
sources, but also their environment and one another. Each device is
both a data source and a data consumer pursuing its individual and
collective tasks. New ideas are proposed for a research program aimed
at realizing ubiquitous computing systems based on the cooperation of
autonomous, dynamic and adaptive components (hardware as well as
software) which are located in vicinity of one another. This is
significantly different from ``infrastructure-based'' mobile
client/server computing between PDAs and network services. The
proposed research will enable a new class of applications that
effectively exploit mobility and pervasive computing. New prototypes
and applications will be built in the context of mobile information
management within dynamic communities of ad-hoc services and
devices. The research team is part of the eBiquity research group
located at The University of Maryland, Baltimore County (UMBC) and
will closely interact with collaborators from industry including IBM,
Fujitsu, and HP.

EIA-0203958 Anupam Joshi University of Maryland-Baltimore County Agent Oriented Approaches to a Ubiquitous Grid

At the level of computing and networking hardware we will see dramatic changes in the next few years. Computing will become pervasive. These developments will lead to wireless networks that will scale all the way from ad hoc body area networks to satellite WANs, and link together supercomputers, "palmstations" and embededded sensors & controllers.

Given this scenario, our proposed research will seek to extend the computational grid by making it ubiquitous and pervasive. In particular, we will develop agent based runtime systems where each component is autonomous, articulate, social and adaptive. Such a system will seamlessly partition computation across elements of the grid ranging from palmtops to supercomputers. Issues that we will investigate include (i) Component/Service Discovery, (ii) Dynamic Composition of components, and (iii) computation partition across highly asymmetric and heterogeneous systems.

Research in search techniques was a critical component of the first generation of the Web, and has gone from academe to mainstream. A second generation "Semantic Web" is being built by adding semantic annotations that machines can understand and from which humans can benefit. Modeling, discovering and reasoning about complex relationships on the Semantic Web will enable this vision and transform the hunt for documents into a more automated analysis enabled by semantic technology. The beginnings of this shift from search to analysis can be observed in research and industry as users look beyond finding relevant documents based on keywords to finding actionable information leading to decision making and insights. Large scale semantic annotation of data (both domain-independent and domain-specific) is now possible because of an accumulation of advances in entity identification, automatic classification, taxonomy and ontology development, and metadata extraction. The next frontier, which fundamentally changes the way we acquire and use knowledge, is to automatically identify complex relationships between entities in this semantically annotated data. Instead of a search engine that returns documents containing terms of interest, there will be a system that returns actionable information (with the associated sources and supporting evidence) to a user or application. The user interacts with information universe through a hypothesis driven approach that combines search and inferencing, enabling more complex analysis and deeper insight. The research will focus on the design, prototyping and evaluation of a system, called SemDIS (Semantic Discovery) that supports indexing and querying of complex semantic relationships and is driven by notions of information trust and provenance and models of hypotheses and arguments under investigation. Such a capability greatly enhances the capacity of intelligence analysts to obtain (in time) information leading to a more secure homeland and world. Corresponding to the breadth and depth of the topics involved in the challenge undertaken, this is a collaborative project involving researchers at UGA's LSDIS lab and UMBC.

SemDIS will have broader impacts beyond the education and training of graduate students, and the publication of research findings. Results from the research will be integrated with courses, both existing and new. Institutional mechanisms in place will seek participation of students from underrepresented groups. The work will also gain from several academic-industry collaborations of the investigators. There will be an opportunity to leverage commercial infrastructure and raw metadata provided by Semagix. The researchers will collaborate with industry, and the students will be encouraged to intern at collaborating industrial labs. Within a broader social context, emerging knowledge-centric technologies raise legitimate privacy and civil liberties concerns. Building upon past policy making experience, the investigators will comment on potential implications of their scientific progress.

More information can be found at http://http://lsdis.cs.uga.edu/SemDIS/ and at http://www.cs.umbc.edu/SemDIS/

This research investigates distributed trust management as an
alternative to traditional authentication and access control schemes
in dynamic and pen computing environments such as multiagent systems,
web services and pervasive computing. Distributed trust management
handles security with techniques used in human societies, where people
are judged on their abilities, assets, relationships and reputations.
Authorization decisions are made through the application of relevant
security and trust policies, expressed in a high-level declarative
language. The policies define rules and constraints on agents (human
or software) and the actions they can take on objects in terms of
their credentials and properties. This work extends the principles of
trust management with deontic notions of rights, obligations, and
prohibitions. An ontology grounded in a semantic language (e.g., RDF,
DAML or OWL) is used to represent security information constituting
credentials, policies, beliefs, and proofs as well as relevant
domain-specific properties to characterize the agents, actions and
objects. An important results will be a better understanding of how
the semantic web can address the critical issues of security, trust
and privacy in distributed open environments. The new concepts and
techniques will be evaluated and demonstrated through the
implementation of prototype tools and applications

This medium ITR project will develop a framework to facilitate science
research and education on the semantic web, and will implement and
evaluate prototype tools and applications for use in the biocomplexity
and biodiversity domains. These capabilities include the ability to
collaborate and convey meaning through the automatic and
semi-automatic semantic annotation of web documents; to improve
information retrieval using background knowledge and inference; and to
extract and fuse information from multiple, heterogeneous sources in
response to a query. A testbed for prototyping these capabilities will
be the web portal of the National Biological Information
Infrastructure (http://www.nbii.org/). The framework will include
specifications for ontologies, protocols, agents, and tools for
authoring, automated ingest, and annotation. These tools will leverage
collaboratively constructed ontologies to bring diverse communities
together and enable community construction of scientific
knowledge. Additional domain-independent, general purpose ontologies
will be developed to enable metadata about the contents and structure
of databases and other knowledge repositories to be expressed in
emerging knowledge markup languages such as RDF and OWL. This will
enable agents to both access and index the hidden web, and will also
support the data mining of diverse and distributed databases.

We live in the information age, a time when data and knowledge is plentiful and easily moved, processed and mined by machines. This makes it easier to discover knowledge and more efficiently manage our affairs but also increases concerns about information confidentiality, privacy and trust. Balancing these will be a defining challenge in the coming decades and is particularly urgent today in organizations responsible for national defense, law enforcement, emergency services, and public health and safety. The 9/11 Commission addressed this in their report and called for "a paradigm change from Need to Know to Need to Share". This project will explore one concrete aspect of this shift -- how executable policies can help organizations enhance their ability to share information and access while still maintaining appropriate levels of security, confidentiality and privacy.

The University of Maryland, Baltimore County, the University of Texas at San Antonio and the University of Texas at Dallas will build on existing work at our three institutions to develop and refine a a conceptual framework for computational policies to support information sharing in a need to share environment. Our framework will integrate and extend our work on access control (RBAC), usage control (UCON) and deontic policies (REI), grounding them in ontologies expressed in the Semantic Web language OWL. We will use it to design a policy specification language and enumerate required software artifacts and tools. Finally, we will study the framework applicability to realistic applications such as the management of healthcare records and homeland security related data.

Travel Support for Students from U.S. Universities to
Attend ISWC 2008

The ISWC 2008 Travel Fellowships provide funding for students at U.S.
Universities to attend the 2008 International Semantic Web Conference
(ISWC) which will be held October 26-30, 2008 in Karlsruhe, Germany. Our
goal is to encourage students who want to become part of the Semantic Web
research community, and we expect that participation in ISWC 2008 will be
a significant event in the graduate careers of the selected students.

The doctoral consortium, in particular, 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.

In selecting applications for 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.

Details, including application instructions, can be found at
http://ebiquity.umbc.edu/ISWC08Travel

This project will support participant costs for two workshops on Distributed Infrastructure for Security Monitoring and Intelligence Extraction. The first is a five-day workshop held in India involving researchers invited from the United States and India held 9-13 January 2010 on the campus of Indian Institute of Science in Bangalore, India. The goal is to establish new collaborations between the United States and India in cutting-edge research and technology development and knowledge transfer focused on areas of real-time security surveillance and extraction of actionable intelligence for both critical physical infrastructure and their associated computing and communication components. The research is expected to be of direct benefit to both the United States and India and to result in techniques for better management of public safety and security resulting from natural disasters and terrorist events. The workshops will also help strengthen existing partnerships between the United States and India in
related fields as well as promote new ones. A second, two day follow-up workshop will be held in Spring 2010 in the United States, probably in the Washington DC area. The net result of these workshops will be a large scale research proposal for which funding will be requested from appropriate agencies from both US and Indian sides.

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).

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.

This project focuses on Land Change Science (LCS).

Land Change Science is an emerging field of study, aimed at understanding interactions among human systems and the terrestrial biosphere, atmosphere and other Earth systems as mediated through human use of land. Advances in LCS are needed to better quantify, predict, mediate, and adapt to global climate change, biodiversity loss, and other consequences of land use and land cover change.

Despite vigorous efforts by a broad array of social and natural scientists, the cross-scale synthesis of multidisciplinary observations, models and theories on coupled human and natural systems (CHANS) that are required to advance LCS has yet to emerge. A major obstacle is the tremendous challenge in global integration and synthesis of local and regional CHANS case studies. This project will accelerate the emergence of new global workflows in land change science through GLOBE: an online collaboration environment combining quantitative real-time global relevance assessment, geovisualization, social-computational structures and machine learning algorithms. This will be accomplished in collaboration with international LCS institutions and experts, enabling researchers and institutions to rapidly share, compare, and synthesize local and regional studies by combining these with global datasets for human and environmental variables using a combination of machine learning, advanced visualization, semantic analysis and social networking.

The project has four core objectives that will be achieved through three integrated activities, as follows:

Objective 1: Create an online collaboration environment leveraging real-time global relevance analysis, geovisualization and social-computational knowledge generation towards the generation and sharing of new global workflows for land change science.
Objective 2: Understand how to build effective social media tools organized around structured and informal scientific workflows.
Objective 3: Develop evaluation methods and metrics and use them to demonstrate the utility of workflow-based social media tools in the context of scientists testing LCS hypotheses.
Objective 4: Leverage GLOBE to characterize and optimize global knowledge generation in LCS.

To achieve these goals, this team will engage in the following activities:

Activity 1: Develop the social-computational infrastructure for GLOBE.
Activity 2: Establish GLOBE as a means for social-computational knowledge generation. Characterize, share and optimize knowledge generation workflows for global synthesis and collaboration across CHANS studies and data collections.
Activity 3: Test hypotheses and identify new research opportunities.

To understand anthropogenic global changes in the Earth system, scientists must generalize globally from observations made locally and regionally. This project will make fundamental hypotheses on the nature of human interactions with earth systems more readily testable by scientific methods, enabling major advances in land-change science and theory. Moreover, this project will engage the computing and social sciences in developing interactive online tools for scientific collaboration and data synthesis that will help identify knowledge gaps in LCS science. The tools will result in new ways of visualizing, communicating, connecting, comparing and synthesizing observations and models of land change processes at global, regional and local scales. Empirical investigation of GLOBE in use will advance our understanding of scientific collaboration more generally.

Broader impacts
This project will develop, enhance and support long-term research collaborations across a broad set of scientific disciplines. It will support education and skill building for interdisciplinary collaboration by seasoned faculty, postdoctoral researchers, graduate students and undergraduate students. The project will design, host and disseminate advanced tools for cross-scale data and knowledge sharing, synthesis, and design of globally representative observing systems. By creating a new environment for sharing and integrating local knowledge, data and ideas across the social, biological and geophysical sciences, land change science will have greater potential to inform the sustainable stewardship of earth systems.

The Web has made humans smarter, providing ready access to vast amounts of knowledge and facts. The Semantic Web has the capacity to similarly enhance computer programs and devices by giving them access to enormous volumes of data, facts and knowledge. This project is exploring the feasibility of automatically extracting new knowledge directly from data found in spreadsheets, database relations, and document tables and representing it as highly interoperable linked open data (LOD) in the Semantic Web language RDF. The extraction is guided by probabilistic graphical models that use statistical information mined from current LOD knowledge resources. To demonstrate the potential payoff of the research, the system is used to extract knowledge from tables collected from medical journals and tables from web sites like data.gov.

While the W3C semantic web languages RDF and OWL are used to represent the knowledge, the results are applicable to other semantic data frameworks such as Microdata (Search Consortium), Freebase (Google), Probase (Microsoft) and the Open Graph (Facebook). The open sourced prototype software allows other researchers to experiment with automatically producing semantically enriched data from tables for their domains.

If successful, such software extraction systems are expected to become part of a new online knowledge ecology -- both consuming existing LOD knowledge to understand the intended meaning implicit in a table and producing new facts and knowledge that will become part of Web. This represents a dramatic increase in the breadth and depth of public semantic data that can make "big data" analytics more effective.

The ubiquity of computing technology and the Internet have created an age of big data that has the potential to greatly enhance the efficiency of our societies and the well-being of all people. The trend comes with problems that threaten to prevent or undermine the benefits. An immediate concern is how to fuse, integrate and analyze data while respecting privacy, security and usage concerns. A second issue is allowing data to remain distributed, enabling its owners to maintain and control quality as well as to enforce security and privacy policies. A final underlying challenge is helping to produce sound and useful results by assuring that systems understand the meaning of the data being integrated and analyzing access and usage policies. For some domains, like health informatics and clinical research, solving these problems will have a significant impact on society.

This project explores an approach to solving these problems by developing a policy-compliant integration system for linked healthcare data. The system models data, schemas and policies using open Web standards such as Semantic Web languages, federates queries to independent Linked Data stores based on content, provides policy enforcement by modifying incompliant queries, and uses formal methods to guarantee correctness of key components.

This project provides new approaches to solving one of the most significant problems our society faces in the 21st century: benefiting from the integration of distributed linked data while respecting security, privacy, and usage requirements. The prototype tools and systems are incorporated into our educational activities and made available to others via appropriate open source licenses.

This innovative project carries out exploratory research applying semantic technologies to support data representation, discovery, sharing, and integration between disparate geoscience data types and structures. It is a risky, high pay-off activity that, if successful, has the potential to transform our ability to discover, access, and use geoscience data in ways not possible at present. The goal of this research is to develop a prototype involving the data collections of some major NSF-funded Data Management Centers: IEDA and R2R at the Lamont Doherty Earth Observatory at Columbia University and BCO-DMO at the Woods Hole Oceanographic Institution. The effort is focused on making NSF-collected data for the ocean sciences and other associated datasets more easily and widely accessible and available to researchers and the public. Linked Open Data methodologies will be employed. Essential elements of this approach include the use of unique data identifiers to mark, link-to, and dereference specific data and details. Once the intial relationships are aligned, the new system can automatically infer new relationships between data and data locations. Goals will be to semantically integrate the data already available from the initially targeted data reposotiries in such a way that the approach can be scaled up to the whole of EarthCube, a new NSF initiative to develop a geoscience knowledge and data mangement system for the 21st Century. This project is a collaboration between ocean science resaerchers, computer scientists, and ocean data management centers from Maryland, Ohio, New York, and Massachusetts. Braoder impacts of the work include building infrastructure for science and improving public accessiblity to NSF-funded data collections.

One of the most critical security challenges of the 21st century is protecting the cyber-physical systems that manage and control our infrastructure, vehicles, homes, and personal devices as well as the information that they store, use and exchange. Artificial intelligence (AI) and machine learning-based tools can help human analysts sort through large volumes of data to determine if an attack on these systems has happened. Yet, AI components are also vulnerable to attacks, and require development of techniques to make them more robust. This collaborative project between the University of Maryland Baltimore County (UMBC) and the University of Illinois addresses the research and educational aspects of combining AI and cybersecurity. Educational and training materials will be developed for use by college and university instructors and students and by cybersecurity and AI professionals. These materials will address how AI can improve security systems and how cybersecurity analytics can protect AI systems. In addition, the project will recruit students from groups that have been traditionally underrepresented in computing.

This project has three interrelated topics. The first focuses on education and extends the project team’s existing cybersecurity concept inventory to include relevant AI-related concepts. Student knowledge and understanding of cybersecurity and AI relatedness will be assessed before and after taking AI or cybersecurity courses. Educational materials and projects will also be created to demonstrate how AI can be applied to cybersecurity problems and how cybersecurity tools can protect AI systems from attack. The second topic explores how the latest AI tools can support cybersecurity tasks. The creation and maintenance of semantic knowledge graphs of cyberthreat information will be studied and used to support reinforcement learning systems that are better at detecting the presence of malware in a host. The third topic focuses on finding new ways that cybersecurity tools can protect AI systems from becoming compromised by attacks such as data poisoning. Cyberthreat knowledge graphs and neural networks will be used to detect and eliminate likely disinformation from data used to train AI-based cybersecurity systems. This aspect of the project has applications beyond cybersecurity, such as countering disinformation.

This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case specifically cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.