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A Team Workbench for Scholarly Investigation

A Team Workbench for Scholarly Investigation.

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A Team Workbench for Scholarly Investigation

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  1. A Team Workbench for Scholarly Investigation Scott Poole, UIUC; Noshir Contractor, Northwestern; Mark Hasegawa-Johnson, UIUC; Feniosky Pena-Mora, Columbia; David Forsyth, UIUC; Kenton McHenry, UIUC; Dorothy Espelage, UIUC; Margaret Fleck, UIUC; Alex Yahja, National Center for Supercomputing Apps

  2. The Story behind Cultural Artifacts

  3. Challenges • Socio-cultural consequences of group decisions • Inability to collect, analyze, and manage • High resolution, • High quality, • High volume interaction network data • Effective computer-aided collaboration among • Scholars • Scientists • Students • Volunteers • Stakeholders

  4. Scientific Challenges • We understand small teams co-located (1-6 persons) and we think we understand large aggregations of 1000s • We don’t understand large teams: 8-25, 25-70, 50-300, 350-500, 400-1000—the sweet spot of scholarly collaborations and conferences • Current studies are surveys and case studies, not direct observation, the gold standard • No tech to study these even though we coalesce in natural groups of size 2, 5, 15,… • Spatial dispersion and movement make big difference

  5. Importance of the Problem • Many critical groups are of this size: • Design Teams • Scholarly Collaborations • Cultural Studies • Legislative Bodies • Disaster Response Teams • Archaeology Teams • Medical Teams • Military Units

  6. “Swarming” Disaster Response

  7. Supported By • Cyber-enabled Discovery and Innovation (CDI) program, National Science Foundation • Two Million Dollars Grant • National Center for Supercomputing Apps • Office of the Vice Chancellor for Research, University of Illinois • Year 2 of Five Year Project • Project “GroupScope”

  8. Approach • End-to-end system from data capture to analysis to user and team engagement • Video cameras to capture video and audio, of • Study subjects such as children on playground • Scholars and researchers executing the study—in team and individually • Synchronization of video and audio data • Annotation of video and audio • Coding of video and audio • Management of video and audio data • Analysis of video and audio; scenario simulation and machine learning • Community involvement

  9. Circle of Continuous Improvement Data Management (Medici content management, ELAN transcription) Data Acquisition (cameras, Kinect, audio recorders, GPS, iPhones, iPads) First-order Data (audios, photos, videos, sensor data) Second-order Data (visual, audio and text annotations, coding and metadata) Network analysis, Group identification, Interaction categorization What-if Scenario Simulation and Machine Learning

  10. 2D Face Tracking (Kalal TLD)

  11. Depth-image “Kinect” Skeleton Tracking

  12. Human Movement Recognition

  13. Social Interaction Recognition

  14. Community Engagement • Professors and graduate students as primary research participants • Students help annotate videos and audios of • study objects and artifacts • research activities of professors and research assistants • Interested folks help transcribe, translate, and annotate videos and annotate • Multi-lingual collaboration enabled • Scenario “what-if” analyses of interactions and events • Annotated videos will “live” across time and place • Insights, inspirations, and moments are recorded and not lost to time and place

  15. In Closing • “GroupScope” tool is designed to provide • Computer-assisted collaboration among human teams • Natural and native human and professional social-networking—synergistic human machine effort • Scholarly collaboration tool with native domain-specific design and interfaces • Natural collaboration space • By your consent, putting up video cameras to get PNC 2017 networking? • Will put up video cameras for NSF Radical Innovation Summit 2013

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