Shifting ICT to ICA - Towards I nformation and C ommunication A ctivity N avigation -

1. Shifting ICT to ICA- Towards Information and Communication Activity Navigation - Hideaki Takeda National Institute of Informatics & The Graduate University for Advanced Studies takeda@nii.ac.jp http://www-kasm.nii.ac.jp/~takeda/

2. Table of Contents Introduction: Shifting ICT to ICA Our Research Topics Kmedia: Finding relationship via WWW bookmarks NMM:Re-configuration of personal human network Community Navigator: Collaborative Scheduling Support System for Conferences Social Scheduler: Collaborative Scheduler for Personal Resources TelMeA: Expressive Media for Online Communities TelMeA for e-Kyoshitsu: Application to Distance Learning Summary

3. Shifting ICT to ICA- Towards Information and Communication Activity Navigation - H. Takeda

4. From “old computing” to “new computing” “The old computing was about what computers could do; the new computing is about what users can do. Successful technologies are those that are in harmony with users’ needs. They must support relationships and activities that enrich the users’ experiences.” Ben Shneiderman, Leonardo's Laptop: Human Needs and the New Computing Technologies, MIT Press, 2002 • Paradigm shift is needed • Technology-centered approach • Human-centered approach

5. ART(Activity-Relationship-Table) Ben Shneiderman, Leonardo's Laptop: Human Needs and the New Computing Technologies, MIT Press, 2002

6. Information layeronly concerns explicitly represented and processed information. • Communication layer concerns potential information. Potential information can be revealed through communication among people. Communication Layer Collaborate Relate Present Information Layer Create Collect Donate Information and Communication Activities • Two layers for our activities

7. Information Layer Collect Donate Information Activities • A cycle of information exploitation • Collect • Find and retrieve information • Create • Process (classify, extract, combine, mix, …) information • Generate new information • Donate • Publish and distribute information Create

8. Collaborate Relate Present Communication Activities • A cycle of human relationship exploitation • Relate • Find and contact people • Collaborate • Work with other people (organized work, teamwork, cooperation, …) • Present • Identify and contribute ourselves to communities Communication Layer

9. Collaborate Relate Present Create Collect Donate Information and Communication Activity Navigation (ICAN) • Support by computers for six categories of ICA Community Navigator: Collaborative Scheduling Support System for Conferences Social Scheduler: Collaborative Scheduler for Personal Resources NMM:Re-configuration of personal human network TelMeA: Expressive Media for Online Communities Kmedia: Finding relationship via WWW bookmarks TelMeA for e-kyoshitsu

10. Discovery of Shared Topics Networks among PeopleA Simple Approach to Find Community Knowledge from WWW Bookmarks H. Takeda, M. Hamasaki, T. Matsuzuka, Y. Taniguchi

11. Purpose • Generation of human network guiding individual information activities • An example • I want to watch sports programs on TV. What your recommendation? • Who and What • Shared Topics Network among Users (STN)

12. Folder (topic) WWW page (information on the topic) Shared Topics Network g 5 Bookmarks as Knowledge • A bookmark folder = A topic interested by the user • URLs in a bookmark folder = Examples of the topic User x 2 1 b a c f e d 3 4 y z Bookmark (person’s interest)

13. Discovery of topic relations • Common relations • (search, IR), (academia, research-related) • similar but words themselves are different • Un-common relations …(Unix, academia) • Speciality of thecommunity computer-related research-related B search A Information retrieval academia C

14. B research research A C Discovery of relationship among people • What are common topics with others? • Who is good at this topic? computer-related research-related search Information retrieval unix academia

15. Nfij × Rfij Cij = Npij Category Resemblance (1)Categorization Is Human Relation? • Human relation can be measured by resemblance of folder structure Cij : Category resemblance Nfij : No. of recommended folders Rfij : Folder relevance Npij : No. of recommended pages Folder structure is similar Not similar

16. Effects of Category Resemblance (4) for Page Recommendation • Better page recommendation results for new group made from category resemblance (CR) Page Recommendation Score New groups All In-community Cross-community

17. Summary • Proposal of shared topic network to enhance user’s communication • Proposal of algorithm of discovery of shared topic networks with WWW bookmark files • Validity of our approach by an experiment • Proposal of category resemblance as measurement for community effects

18. Re-configuration of personal networks by the neighborhood matchmaker method M. Hamasaki, H. Takeda

19. Purpose • Personal network is usually “ad hoc” • We may miss better friends nearby • We need better network • One Solution: • Collect data for all people, then generate the “best” network • Disadvantage: • Scalability • Privacy • Our approach: • Neighborhood Matchmaker Method (NMM)

20. Neighborhood Matchmaker Method (NMM) • A iterative approach to optimize the network • Every node works as a matchmaker for neighborhood nodes to improve the network • The basic idea • In our real life, introducing new friends by the current friends is a practical way to optimize personal networks • We can know persons who you have not known before • Your friend can filter people for you • Advantages • No need for central servers • Applicable to any size of community • Less computational cost

21. !? Good !! matchmaker matchmaker matchmaker OK OK Introducing each nodes Calculating connection values Adding a new path Algorithm • 1. A node calculates connection values between its neighbor nodes • We call that node “matchmaker” • 2. If the matchmaker finds a pair of nodes which has a good enough connection value, it selects this pair for recommendation. The matchmaker introduces both nodes of recommended pair to each other • 3. The node that receives recommendation decides whether it accepts or not. If it accepts, it adds a path to the recommended node

22. Results: Cover-Rate w.r.t. Nodes • The path size is fixed as three times as the node size • All cases were converged • The average of cover-rate and the turn of convergence vary with the node size 20node 40node 60node 80node 100node Cover-Rate Turn

23. Results: Average of Convergence Turn • The number of convergence turn is linearly increased with the node size • Computational cost • NMM: O(N) • Central Server Model: O(N2) 5path/node 4path/node 3path/node Convergence Turn 2path/node 1path/node The node size

24. Conclusion • Proposal of optimization of “ad hoc” network • Good news for the Internet communities • No need for central servers • Applicable to any size of community • Anytime Algorithm

25. Community Navigator:Collaborative Scheduling Support System for Conferences H. Takeda, M. Hamasaki In cooperation with Yutaka Matsuo and Takuichi Nishimura

26. Purpose • System Aim: Support people to find their friends in a specific group • Research Theme: Investigate different human networks in the same group • Three human networks • Human network in the activity: I worked with him • Human network by communication: I know him • Human network by behavior: I meet him • Scheduling on conferences • Plan and Action Planning action “I worked with him” “I meet him” “I know him”

27. System Functions • Easy-to-use scheduling system for the conference • Just add presentations what you want to watch • Can refer schedules of other people • Manually collaborative scheduling • Can only see schedules of who know you • Can recommend schedules • collaborative scheduling • On-site support of schedules • Small communication device with sensors Cobit Takuichi Nishimura, Hideo Itoh, Yoshinobu Yamamoto and Hideyuki Nakashima. ``A compact battery-less information terminal (CoBIT) for location-based support systems," In Proceeding of SPIE, number 4863B-12, 2002.

28. Outlook

29. Recommendation • Recommendation by pattern similarity Naïve collaborative filtering • Paper recommendation • Person recommendation • Recommendation by personal network Reply on selection by friends • Paper recommendation • Person recommendation

30. Recommendation

31. Social Scheduler: Collaborative Personal Task Scheduler Ikki Ohmukai, H. Takeda

32. Social Scheduler: Collaborative Personal Task Scheduler • Mobile Task Scheduler for Daily Life • Everyone belongs several groups and communities • Some groups were built emergently and have ambiguous boundaries • No one cannot manage “my” schedule • Collaborative Model of Personal Scheduling • Based on information sharing with one’s friends • Access control and filtering according to each group • Cell-phone application

33. Social SchedulerCollaborative Personal Task Scheduler • Create friends network by authorization • Task conditions become viewable and updatable by each other • Server merges all friends network into alliance network • The system considers partial complete graphs as emerging groups (unit of information sharing/filtering)

34. Social Scheduler: Collaborative Personal Task Scheduler • Experiment 12 subjects from various communities 3 weeks in use 41.9 tasks/person 76 collaborative tasks • Social Network The system can find multiple communities around the user Distance of each friend can be measured by the number of groups they share

35. TelMeAShow Me What You Mean -Expressive Media for Online Communities Toru Takahashi, Yasuhiro Katagiri, H. Takeda

36. TelMeA2002

37. 1. Edit a message in terms of a script language Editor Personified Media Other Participants of the Community Screen Shot of TelMeA2003 4. Request for seeing the message from others 2. Submit the message to the community server 5. The required message is downloaded Conversation Log of the TelMeA Community Conversation Process in TelMeA2002 Hello! 6. The message is asynchronously enacted Hello! Participant of a TelMeA Community 3. The massage is accumulated in the conversation log

38. Our Goal • Find pragmatic rules of social and nonverbal interactions • Supporting social and nonverbal interactions • Archiving the logs of long-term community activities • Analyzing usages and effects of nonverbal expressivity • Make a model of multimodal social interaction • Calculate social evaluations for involved information • Summarize or make reutilize the involved information

39. e-kyoshitu(e-classroom) Project Toru Takahashi, Yasuhiro Katagiri, H. Takeda

40. Trial Use: e-教室(e-classroom) Project • e-教室(e-classroom) Project: • Run by NPO • Distance learning for children (mainly junior-high school, 12-15yrs) • Several classrooms (math, economics, CG, etc) • TelMeA for e-教室 • Experimental use of TelMeA • Classroom for • Leaning “agent” as new technologies by using • Communicating to each other (“BBS” for participants) • (demo)

41. TelMeA for e-教室

42. The current status of “TelMeA for e-教室” • Period: c.a. 4 month (2003.1.16-) • Login users: 64 • Posted users: 24 • Post No.: 297, Post thread No.: 22

43. Summary • Information technologies, in particular AI can offer new opportunities for communities • Reducing constraints of the real world • Time, space, etc • new communication ways • Knowing new related people, communication via agents etc • They will change meaning or roles of communities • e.g, • Very weak communities • Quick life cycle of communities • Belonging so many communities

44. Summary • Challenges • Support of life cycle of communities • Create, maintain, diverse, merge, disappear • Trust • Trust is very difficult • Trust may be more complicated than the real world…