Hari Sundaram, Ph.D. - US grants

This project develops novel quality-adaptive real-time media-flow architecture to support sensory/reactive environments. Arts, Media, and Engineering (AME) Center at Arizona State University houses an advanced performance stage, known as the Intelligent Stage that allows performers to have novel interactions with their environment through various sensors and actuators. Within this framework, we develop an adaptive and programmable media-flow ARchitecture for Interactive Arts (ARIA) that enables design, simulation, and execution of interactive performances. ARIA interfaces with real-time sensing components and it streams various types of audio, video, and motion data. It extracts various features from streamed data, and fuses and maps media streams onto output devices as constrained by the Quality of Service (QoS) requirements. ARIA provides the choreographer with a visual design tool to describe the structure of the media-flow network, where the nodes correspond to sensors, quality-adaptive computing elements, and actuators. We develop novel media-flow management techniques including real-time and QoS driven media sensing, feature extraction, and data and media fusion algorithms. The impact of the project includes a better understanding of QoS-based data and media-stream management. ARIA offers a new medium that allows artists to integrate novel sensing, interaction, content, and response mechanisms in staged performances. ARIA also acts as a research-/test-/development-bed where engineering and arts students learn and experiment with various aspects of multimedia stream management. The results obtained by this project will lead to a better understanding of quality-based data and stream management issues and will therefore influence the development of novel sensor data management technologies. The project web site is http://aria.asu.edu.
ARIA is being disseminated to the public through the Arts, Media, and Engineering Center at ASU.

This IGERT award at the Arts, Media and Engineering Program at Arizona State University will develop research and training mechanisms for the creation of a new class of media scientists. These scientists will produce new approaches for the integration of computational elements and digital media in the physical human experience. Their work will result in experiential media systems - hybrid physical-digital environments that address significant challenges in key areas of the human condition such as health, education and everyday living.

The knowledge required to create experiential media systems is currently fragmented across engineering, sciences and arts. This IGERT award will train a new generation of hybrid media engineers-scientists-artists who are equipped to transcend this fragmentation. The training will be realized through a large interdisciplinary network combining expertise from twelve contributing disciplines. This network will allow integrated advanced research in sensing, modeling, feedback, experiential construction and learning. The research will result in new knowledge in media systems as well as within each contributing area. It will also result in the development of large-scale applications of societal significance. The graduate training mechanisms are implemented through formally approved concentrations within the graduate degree programs of participating disciplines. They combine discipline specific education in one of the IGERT research areas with interdisciplinary training in media development. The framework of this IGERT allows for methodology found in the sciences to be combined with creativity found in the arts. It will bridge the gap between computation and the physical experience, advance human-centric technologies and produce major advances in education, rehabilitation, communication, and everyday living. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

Integrative, Hybrid and Complex Systems
Hari Sundaram, Arizona State University
Vikram Jandhyala, University of Washington
Collabortive Research: Design of Dense RFID Systems for Indexing in the Physical World across Space, Time, and Human Experience


Intellectual Merit: Radio frequency identification (RFID) technology embeds small, inexpensive, and passively-powered radio tags in physical objects. Many potential innovative applications of RFID are not yet supported by current technology. This research pursues a multidisciplinary, integrative approach to investigate fundamental technical challenges in RFID, both at the physical and data management layers, to realize innovative data-centric applications such as attribute-based object search. The research focuses on two areas, distributed data management, and (ii) physical layer limitations. The research will investigate data structures that can resolve queries for remote objects by only accessing local tags within a specified radio range; efficient encoding of symbolic and numeric logical attributes to address the cost of associative search and random access to a large data structure from a single tag; and redundancy-based algorithms for overcoming data access reliability limitations and access failures in dense tag environments. At the physical layer, the research will address issues due to tag density, the need for extended memory storage, and the absence of on-chip power.

Broader Impact: This research has the potential to aid individuals in living in increasingly complex environments. The research will aid in the development of new course modules on large-scale dense RFID simulation at the University of Washington and robust information dissemination at Arizona State University. The project will also allow students at both universities to experiment with various aspects of RFID-based distributed information management, thus increasing student awareness of mechanisms for addressing information-overload and privacy issues that arise due to increasingly widespread, yet loosely controlled, digital "footprints."

Increased participation by the blind and sight impaired individuals in NSDL is resulting from the development of Middleware for Accessible Information Spaces on NSDL (MAISON). The software is enhancing the accessibility of NSDL, its internal and external resources, and existing services such as stand maps and community tools like blogs, wiki, and RSS newsfeeds by supporting user interaction through screen readers such as Window-Eyes, Dolphin and JAWS. MAISON is providing task oriented, individualized information exploration including information filtering, ranking and summarization. When accessing strand maps the user is listening and navigating through and across strand maps via hotkeys.

Services are being developed to aid teachers, librarians, and learners in sharing resources and promoting further access to NSDL resources. The Middleware for Network- and Context-aware Recommendations (MiNC) being developed provides online integrated services for a) understanding the personal activity context through access patterns and analysis of user documents, b) context-aware resource discovery, including search, presentation, and exploration support within the knowledge structure (e.g. Strand Maps) provided by NSDL, and c) peer discovery, peer-network management, and peer driven resource and knowledge sharing and collaborative recommendations.

To support these services, several specific tasks, including representation of the personal activity context, context-aware ranking, filtering, and previews, and integration of context and collaborative filtering are being accomplished. As an integrated service, MiNC operates on existing NSDL digital collection resources as well as on materials from other sites and repositories through available standard interfaces. For accessing NSDL resources, it leverages existing Open Archives Initiative (OAI) based protocols, available web service interfaces based on the Concept Space Interchange Protocol (CSIP), CSIP-Adaptable (CSIP-A) services as well as available NSDL Data Repository APIs. MiNC also integrates with other services, such as IntegraL. The middleware provides API based services to enable future projects and other NSDL contributors to leverage MiNC components, technologies, and information spaces.

The potential exists for MiNC to have a broad educational and social impact. The open interfaces enable future service developers to develop better interfaces targeting general user populations. In addition to the direct educational impact through NSDL, the MiNC system is being integrated into both undergraduate and graduate courses as a project platform. This introduces computer science students to information extraction, information management, recommendation, visualization, and privacy issues as well as to familiarize the students with the important theories related to e-learning.

Collaborative Projects: EAGER: A virtual eXchange to support networks of creativity and innovation amongst Science, Engineering, Arts and Design (XSEAD)

Intellectual Merit
One of the greatest challenges facing the United States in research and education is how to fundamentally encourage innovation across all sectors and spawn new solutions to address global challenges. Increasing research evidence and industrial innovations (i.e. mobile computing, social media) confirm that broad interdisciplinary collaborations that include both science and art fields have great potential for spawning creativity and innovation in computer science, engineering and the sciences. An emerging hybrid community of scientists, engineers, artists and designers is producing innovative and entrepreneurial research that advances new knowledge and proposes holistic solutions to societal challenges including health, education and environmental change. Yet, this burgeoning interdisciplinary community continues to face problems in its efforts to self-organize among constraints imposed by academic systems and historical biases; it continues to seek a dynamic and synergizing research and outreach exchange.

Building upon lessons-learned, a new Virtual eXchange to support networks of creativity and innovation amongst Science, Engineering, Art and Design (XSEAD) will be developed. The XSEAD project will address the following urgent needs of the interdisciplinary science-art community: establish a cohesive view of the field and provide a mechanism to attract entrepreneurs and industry; create a venue for multimodal documentation of research outcomes; provide extensive databases of prior and current research; allow rapid dissemination of research outcomes; facilitate forming of collaborations and specialized sub-communities; document and help evolve science-art curricula efforts and evaluation approaches; provide context and support mechanisms for science-arts careers; establish evidence of the societal impact of interdisciplinary science-art integration. The software engineering development components of XSEAD will contribute further knowledge in three technical areas: Content organization (improve the effectiveness of algorithms for dynamic, usage based, organization of large multimedia databases); Recommendation algorithms (promote the use of multi-relational structures for providing effective recommendations); Community dynamics (develop novel algorithms to extract structures that encode meaningful interactions in online social networks).

Broader Impact
XSEAD will expose general non-expert audiences to the evolution and potential of collaborative research across science and arts. It will attract the interest of young people searching for careers that combine the rigor of science and engineering with the creativity and reflection of arts and design. It will serve teachers and informal learning communities seeking exemplars for curricular development, active practitioners looking for further institutional opportunities to present and support their ongoing work, academics developing related interdisciplinary efforts and commercial companies seeking cross-trained expertise. XSEAD will enable rapid research exchange and in-depth peer-reviewed scholarship between the worlds of science and art and provide a unique and deeply engaging inroad to a vast and creative repository. XSEAD will help promote new paradigms for developing human centric solutions to complex societal problems (i.e. cost effective health and wellness, globalization and conflict, adaptive K-12 learning, electronic communication and security). These paradigms will combine knowledge across broad and diverse areas of human knowledge.

Today, multimedia data are produced and consumed in massive quantities in a broad range of applications with significant economic and societal benefit, including e-commerce, surveillance, education, web services, and social media. Hence, there is an urgent need for systems to provide highly scalable processing and efficient analysis of large media data collections. The RanKloud prototype system, developed in this research project, focuses on the needs and requirements of applications that deal with large quantities of multimedia data in a cloud-based scalable environment.

Most multimedia applications share a few core operations, including integration/fusion, classification, clustering, graph analysis, near-neighbor search, and similarity search. When performed naively, however, these core operations are often very costly, because the number of objects and object features that need to be considered can be prohibitive. Avoiding this cost requires that redundant work is avoided. This research focuses on the next generation cloud-based massive media processing and analysis systems where the fundamental principles that govern their design include an awareness of the utilities of data and features to a particular analysis task. Incorporating data and feature utilities for performing a particular utility task is expected to significantly reduce the overall cost of the analysis task. The RanKloud project research plan includes: (1) data model and query language to specify multimedia data processing workflows; (2) adaptable, rank-aware parallel multimedia data processing primitives; (3) run-time data sampling strategies to support adaptation to data and resource; and (4) waste- and unbalance-avoidance strategies for utility-aware data partitioning, resource allocation, and for incremental batched processing.

RanKloud bridges an important gap in our understanding of cloud-based computing in general, and efficient processing of multimedia data in particular. The results are expected to enable new tools and systems supporting scalability in a large class of problems in content-aware multimedia and social media analysis with impact in web intelligence, business intelligence, and scientific and sensor applications all of which need to handle imprecise multimedia data for more effective decision making. This project provides research experience opportunities for graduate and undergraduate students and includes research results and challenges in courses, including Capstone projects. Arizona State University (ASU) recruits top-quality undergraduates through a nationally recognized residential Honors College and the Minority Access to Research Careers program. The national and international dissemination of the project results includes premier conference and journal publications, as well as open source software licenses at the RanKloud Web site (http://aria.asu.edu/rankloud).

There is growing interest in the use of information and communication technologies for community engagement and for crowd-sourcing solutions to difficult problems through challenges and prizes. Governmental and nongovernmental organizations are being encouraged to design, deploy, manage and support appropriate online platforms to address both goals and improve economic competitiveness. These governance challenge platforms can create novel pathways for citizen participation, increase openness of governance activities, and increase both the effectiveness and legitimacy of the governing organization. Arizona State University is developing a new University-wide challenge platform to enhance community engagement and to solicit ideas from its 50,000-member University community to solve eight broad challenges, but little is known about the design, use and effects of such platforms.

The research team will engage the platform design team to incorporate the affordances required to improve the overall user experience and to test applicable theories of team composition, governance structures, legitimacy, and team capacity and commitment. Once these features have been developed, they will be used in a series of field studies designed to identify theoretical extensions and potential boundary conditions in online community engagement. The studies will initially map community participation, trace how participation spreads through the community, and test the effects of real-time feedback on the community?s participation patterns. The next phase will explore the impact of voting mechanisms on community dynamics, on perceptions of governance accountability, and on more sophisticated forms of community involvement. Finally, relationships between team formation, structure, diversity and effectiveness will be investigated focusing on the quality of the solutions generated.

Many of the challenges facing contemporary society, such as emission reductions or vaccination for infectious diseases, are collective action problems. To address these challenges, new approaches are needed to understand, stimulate and sustain collective action in large heterogeneous populations. To promote cooperative behavior at large scales, this project will develop computational tools to facilitate the context for cooperation - homogeneity, effective communication - observed in smaller scale case studies and field experiments. The investigators will test new ways to increase collective action using mobile applications and social media.

The project will use controlled decision-making experiments to test whether contributions to collective action can be increased by providing the right messages to the right people, such that cooperative behavior can cascade through a social network. Empirical research has shown that individuals are more likely to participate in collective action if they expect others like them to participate. Experiments will be performed in online social networks of students at Arizona State University to test the proposed approach to actualizing collective action within the university community. Data from the experiments will be used to develop mathematical models of collective action in social networks.

Broader impacts: The research will lead to new theoretical frameworks for understanding collective action and provide concrete tools to apply these insights. Insights about what kind of feedback to whom is most effective for increasing collective action may make social media a potential effective policy tool for organizations. Potentially, the project will have societal impact by promoting collective action in important areas such as healthcare, voter participation, and energy conservation.

To be practical and sustainable tools for innovation, the coming generation of community-engagement and crowd-sourcing platforms need to improve the user experience of participants, while simultaneously providing a reliable mechanism for synthesizing participants' contributions into usable problem-solving outputs. In the present project, a multi-disciplinary research team will explore how community participation spreads, the effects of feedback on participation, and the changes in community and collaboration structure over time. Empirically, the project lays out three research questions: 1) characteristics of participatory government platforms, 2) behavioral and system challenges over time, and 3) the impact of managerial and design interventions on individual behaviors and network structure.

The specific platform for this research is "10,000 Solutions", a many-to-many system managed by Arizona State University that empowers both individuals and organizations to host and participate in solutions, challenges, and collective actions. The research team will study participation in "10,000 Solutions" across online, physical, and hybrid environments, particularly focusing on participant community building, trajectories of participation, and output usability. A diverse slate of experiments, with the application of the application of agent-based modeling and network analysis, will provide useful insights for theory development on community engagement and participation, as well as generating best-practice guidelines for participatory design, operation, assessment and implementation.

Future advances in economic growth and national security require new technologies for harnessing the wisdom of crowds and the power of public innovation. However, these technologies are still in their infancy, and there is a growing need for robust and flexible platforms that can move beyond exploratory efforts, toward real-world deployment. The project will develop evidence-based policies and practices to improve collaboration while increasing perceptions of accountability, legitimacy and individual satisfaction, the effectiveness of the work outputs and the adoption and use of the products developed by the community and through the platform. Knowing the conditions that increase and sustain collective action will help in devising policies and practices for building platforms to enhance participation in government and nongovernment organizations.

The same types of social networks that transmit the COVID-19 disease may be leveraged to spread healthy norms and positive behaviors. This research gathers important, time-sensitive data to understand the conditions under which digital social networks can influence health behaviors relevant to the COVID-19 pandemic and how to reduce negative social influences in digital environments. At a time when people spend unprecedented amounts of time on digital networks, public health strategies deployed in these networks may shape the health and social outcomes of Americans in the next 12 months. This research advances understanding of these public health strategies.

The project?s theory is that discussing either general or specific issues (e.g., curbing COVID-19 disease or wearing a mask) can have important consequences on the spread of risky attitudes and behaviors through a network. The research entails (a) an ecological study of Twitter and Instagram networks and (b) experiments manipulating the mix of healthy and risky behaviors promoted in the network and the focus of the discussion on either general or specific issues. The project generates public health recommendations and algorithms to improve health discussions on social media. The investigators use a dynamic panel data model to predict individual behavior from the individual?s own attitudes and own past behaviors as well as the behaviors of other members of their network. The research team uses graph convolutional networks both to capture richer network aspects and to model sparse networks.

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.