Highlights from the main track, poster/demo-session & the VISSW/UDISW/EGIHMI workshops. This is an informal compilation of personal notes from the conference & proceedings, twitter (#iui2010), Ian Ozsvald's blog (http://ianozsvald.com/), and other sources. Citations were not coherently possible, so I chose to stick with links instead. Please let me know if you'd like to see your work more thoroughly referenced.

1. IUI 2010 Informal Summary http://www.iuiconf.org/images/iui2010_banner.jpg Highlights from the main track, poster/demo-session & the VISSW/UDISW/EGIHMI workshops Jan Smeddinck & Hidir Aras jan83(at)tzi(dot)de | aras(at)tzi(dot)de Digital Media, FB 3, University of Bremen, Germany

2. About this Summary Compilation of personal notes from the conference & proceedings, twitter (#iui2010), Ian Ozsvald's blog (http://ianozsvald.com/), and more… Biased for the digital media workgroup … had to skip many interesting pieces of work  Sloppy references – lack of time – but all links! Will be on slideshare: http://www.slideshare.net/Sanook/presentations

3. IUI General Information IUI = Intelligent User Interfaces Single track conference with corporate and univ. participation Formerly workshop, yearly conference since 1997 ACM sponsored HCI meets AI and related fields… ~ 30 % paper acceptance rate Website: http://www.iuiconf.org/ Proceedings: http://portal.acm.org/toc.cfm?id=1719970&idx=SERIES823&type=proceeding&coll=ACM&dl=ACM&part=series&WantType=Journals&title=Proceeding%20of%20the%2014th%20international%20conference%20on%20Intelligent%20user%20interfaces&CFID=78317288&CFTOKEN=12971413

5. VISSW/UDISW Workshop Visual Interfaces to the Social and Semantic Web http://smart-ui.org/events/vissw2010/ User Data Interoperability in the Social Web http://www.wis.ewi.tudelft.nl/UDISW2010/

6. Ontology Based Queries – Investigating a Natural Language InterfaceIelka van der Sluis et al., Trinity College Dublin, Ireland Qualitative comparison study between the written interface semantic web browser "Longwell" and the natural language query interface "LIBER" Test was done with queries about US geograpy posed by untrained users Complex tasks (e.g. How many lakes are there in a certain state?) “From the experimental data, it is clear that subjects preferred Longwell over LIBER and they performed better with Longwell than with LIBER in almost all respects. It should be noted, however, that subjects felt that both interfaces were needlessly complicated.” http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Sluis.pdf

7. An Intelligent Query Interface Based on Ontology NavigationEnrico Franconi et al., Free University of Bozen-Bolzano, Italy Ontology based data access: How to formulate queries? Ontology navigation / queries: Queries as multi-labelled trees Alternative: Written language Lexicon derived from the ontology (engineers definition) Problematic 70% success rate http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Franconi.pdf

8. An Intelligent Query Interface Based on Ontology NavigationEnrico Franconi et al., Free University of Bozen-Bolzano, Italy http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Franconi.pdf

9. Semantic Cloud: An Enhanced Browsing Interface for Exploring Resources in Folksonomy SystemsHidir Aras, Sandra Siegel, Rainer Malaka, University of Bremen, Germany Innovative interface approach for browsing resources in folksonomy systems based on a hierarchical semantic representation of the folksonomy space using tag co-occurrence analysis Provides multiple topic clouds that can be explored hierarchically Allows for the composition of queries from the tag cloud, while consulting results and refining the query afterwards

10. http://semanticcloud.sandra-siegel.de/

11. Designing Social Mobile Interfaces: Experiences with MobiMood, a Mobile Mood Sharing ApplicationKaren Church et al., Telefonica Research, Spain http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Church.pdf

12. Designing Social Mobile Interfaces: Experiences with MobiMood, a Mobile Mood Sharing ApplicationKaren Church et al., Telefonica Research, Spain People were really interested in strangers moods The interface was often abused for status updates Concrete replies to moods not that frequent Most used: custom moods / positive presets Audience suggested to integrate the moods into the contact list … update frequency is a problem http://www.smart-ui.org/events/vissw2010/papers/VISSW2010_Church.pdf

13. EGIHMI Workshop International Workshop on Eye Gaze in Intelligent Human Machine Interaction http://links.cse.msu.edu:8000/iui/program.html Information from Ian Ozsvald‘s report: http://ianozsvald.com/2010/02/07/intelligent-user-interfaces-2010-conference/

14. The Text 2.0 Framework – Writing Web-Based Gaze-Controlled Realtime Applications Quickly and EasilyR. Biedert et al., DFKI, Germany http://www.youtube.com/watch?v=8QocWsWd7fc http://text20.net/ Browser Plugin with mark-up for OnGazeOver, OnPersual, OnRead, etc. Exciting Technology, but expensive tracking hardware!

15. Robust Pupil Detection for Gaze-based User InterfacesW.H. Liao 60 $, 40x40 pixel accuracy, IR based, 30fps on core2 http://www.youtube.com/watch?v=WvWdwB6nTkk

16. IUI 2010 Conference

17. Cortically-Coupled Computer VisionPaul Sajda et al., Columbia University, USA Image recognition at the blink of an eye… System harnesses brain'sability to recognize an image much faster than the person can identifyit Neural activity recording of visual cortex activity while observing flashing images to score "gist" of the images Standard signal + target + novel items neural activity test Normally done with averaging many iterations How to achieve single test precision? Decode EEG signal over time (~ 800 ms per sample) and space (electrodes spread over the skull) http://www.wired.com/medtech/health/news/2006/07/71364 http://newton.bme.columbia.edu/publications/triage_ieee.pdf http://www.wired.com/news/images/full/brain1_f.jpg

18. Cortically-Coupled Computer VisionPaul Sajda et al., Columbia University, USA Sample subset of a large image db > feature abstraction of entire image db based on sample subset results C3 vision search: E.g. help with labeling in maps: vision module recognizes possibly interesting regions in huge maps Chips of possibly interesting images shown rapidly to the actual labeller (person) Then lead the labeler to image(-sections) of interest

19. Personalized News Recommendation Based on Click BehaviorJiahui Liu, Peter Dolan, Elin Rønby Pedersen, Google Inc., USA Web provides access to news articles from millions of sources around the world Help users find the articles that are interesting to read Recommendationsystem builds profiles of users’ news interests based on their past click behavior large-scale analysis of anonymized Google News users click logs Bayesian framework for predicting users’ current news interests from the activities of that particular user and the news trends demonstrated in the activity of all users Deployed in Google News Improves quality of news recommendation and increases traffic http://portal.acm.org/ft_gateway.cfm?id=1719976&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

20. http://twitpic.com/1216bu

21. Personalized News Recommendation Based on Click BehaviorJiahui Liu, Peter Dolan, Elin Rønby Pedersen, Google Inc., USA http://portal.acm.org/ft_gateway.cfm?id=1719976&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

22. Aspect-Level News BrowsingS. Park, KAIST, Korea Media bias comparison Puts news snippets of different services side-to-side Splits articles in (1)first and mostly similar reports and (2) later, more and more diverse articles / comments Scans title, subtitle and lead for keywords and clusters common and uncommon keywords Uses uncommon keywords to make opinion opposites: http://newscube.kr/

23. Aspect-Level News BrowsingS. Park, KAIST, Korea Audience suggested a dynamic number of clusters… http://nclab.kaist.ac.kr/papers/Conference/NewsCube.pdf

24. Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA Offspring of RADAR project in DARPAs PAL program Turns inbox into action-list (task-based) by scanning emails for commonly mentioned tasks Statistically significantly helped users organize their email and tasks RADAR 2.0 system: task-centric workflow enabled by AI technologies helps users User performance varied significantly Hypothesis: Some users had difficulties finding a high-level strategy for completing the work (novice users lacked meta-knowledge about tasks such as task importance, expected task duration, and task ordering dependencies)

25. Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA http://portal.acm.org/ft_gateway.cfm?id=1719980&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

26. Agent-Assisted Task Management that Reduces Email OverloadA. Faulring et al., CMU, USA Simulated conference-planning scenario Scheduling, website, informational requests, vendors, briefing An evaluation score, designed by external program evaluators, summarized overall performance into a single objective score ( 0 – 1 ) http://portal.acm.org/ft_gateway.cfm?id=1719980&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

27. Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA Most existing approaches are vector-set analyses based on words, phrases or time User picks a news article to read (full article on a specific news website) and "more" information is shown along-side from various sources Paragraph analysis based on OpenCalais combined with WPED (checks if entities are present in wikipedia to normalize the results of OC) To tackle problems like senator Kennedy != ted Kennedy http://portal.acm.org/ft_gateway.cfm?id=1719982&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

28. Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA http://portal.acm.org/ft_gateway.cfm?id=1719982&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

29. Tell Me More, Not Just More of the SameF. Lacobelli et al. , Northwestern Univ., USA Evaluation suggested that users trust the new information presented 96% of participants read news online, 76% of them consult more than onesource Respondents said TellMeMore contains relevant details and background information They would like to see a similar interface in their news reading experience http://infolab.northwestern.edu/projects/news-at-seven/

30. Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan Analysis of business participants mimics, also tracking movement, activities of employees of companies and storing in huge db for analysis <-> Audience: Big Brother! Organizational-behavior db with about 100.000 data sets http://www.hitachi-hitec.com/global/business-microscope/solution/index.html

31. http://www.hitachi-hitec.com/global/business-microscope/solution/index.html

32. Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan Organizational network visualization with: each person one node, connected to other persons that they are in contact with by springs: more dense interaction equals stronger springs ~ most important persons at the center Raises questions about the general applicability of laws of physics on social / organizational science Allows for “biofeedback effects” if organization is seen as an organism http://www.hitachi-hitec.com/global/business-microscope/solution/index.html

33. Business Microscope: Interfacing with Organizational NetworksKazuo Yano, Hitachi, Japan http://www.youtube.com/watch?v=mlGFzevfftk&feature=related http://www.youtube.com/watch?v=cGW15V9Lt80&feature=related

34. Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany Interesting approach for recommendation + interaction as opposed to limited recommendation systems Most motivating introduction of IUI 2010: http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488 Next 9 slides are a direct rip-off / re-enactment!

35. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488

36. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488

37. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488

38. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488

39. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488

40. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488

41. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488

42. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488

43. http://www.slideshare.net/dominikus/rush-repeated-recommendations-on-mobile-devices-iui10-3119488

44. Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany http://portal.acm.org/ft_gateway.cfm?id=1719984&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

45. Rush: Repeated Recommendations on Mobile DevicesD. Baur et. al., Univ. of Munich, Germany http://www.youtube.com/watch?v=2nGopSdD-hA

46. Social Search BrowserK. Chruch et. al. Telefonica Research, Spain Questions of mobile phone users placed on map locations, so people close-by can help Exploratory Search “In standard Web search, users submit a query via a searchbox and view a textual list of results. More recently, a newclass of search has emerged, called exploratory search…” Only SMS notifications really encouraged interaction http://portal.acm.org/ft_gateway.cfm?id=1719985&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

47. Social Search BrowserK. Chruch et. al. Telefonica Research, Spain http://portal.acm.org/ft_gateway.cfm?id=1719985&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

48. Estimating User's Acute Engagement from Eye-gaze Behaviors in Human-Agent ConversationsY. Nakano & R. Yukiko, Seikei Uni., Japan Eye-movement has large impact on dialogues (turn-taking, grounding, etc.) Tracking dialogues with a mobile phone sales agent Survey showed statistically significant increase in "natural feel" of the conversation as well as avoiding distraction http://portal.acm.org/ft_gateway.cfm?id=1719990&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

49. Estimating User's Acute Engagement from Eye-gaze Behaviors in Human-Agent ConversationsY. Nakano & R. Yukiko, Seikei Uni., Japan http://portal.acm.org/ft_gateway.cfm?id=1719990&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

50. Embedded Media Markers: Marks on Paper that Signify Associated MediaQ. Liu et al., FXPAL, USA Markers both human and machine readable http://www.youtube.com/watch?v=K-Qdap6h9TQ http://www.fxpal.com/?p=abstract&abstractID=551 http://www.fxpal.com/publications/FXPAL-PR-10-551.pdf

51. Lowering the Barriers to Website Testing with CoScripterM. Jalal & T. Lau, IBM Research, USA Nice FF plugin to record and share java-script macros Lots of automation features and smart inter-exchangeable variables http://coscripter.researchlabs.ibm.com/

52. Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA Background knowledge of people in the same culture tends to have shared structures using similar vocabularies and their corresponding meanings users of the same social tagging system may also share similar semantic representations of words and concepts For simple information retrieval expert networks serve better purpose For exploratory search a match of internal knowledge and external folksonomies is important (better for expert - expert and novice - novice) Results have significant implications on how social information systems should be designed to facilitate knowledge exchange among users with different background knowledge Social tags are more important in exploratory search http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

53. Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

54. Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

55. Facilitating Exploratory Search by Model-Based Navigational CuesW. Fu et al., UIUC, USA http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

57. A Code Reuse Interface for Non-Programmer Middle School StudentsP. Gross et al., Washington Univ., USA Based on „Looking Glass Storytelling“ (programming) learning-tool for middleschoolers Correlates function calls to screenshots from storytelling action view Animations propagated through different working and presentation groups http://portal.acm.org/ft_gateway.cfm?id=1720001&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

58. Speeding Pointing in Tiled WidgetsJ. Ruiz & E. Lank, Univ. of Waterloo, Canada Based on using Fitt's Law to predict motion targets and then resizing them to allow for better performance Results not easily adaptable to different use cases http://portal.acm.org/ft_gateway.cfm?id=1720002&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413

59. QuickWoZ: A Multi-purpose Wizard-of-Oz Framework for Experiments with Embodied Conversational Agents Jan Smeddinck, Kamila Wajda, et. al. Digital Media, FB 3, University of Bremen, Germany

60. Using Sketch Recognition to Teach DrawingTracy Hammond, Texas A&M Univ., USA Sketch recognition Tool uses an off-the-shelf face recognizer to help sketching students learn to draw better faces. Tracy is also the creator of the tech behind all the sketch-a-car-and-watch-it-move physics demos that appeared in the last year or so, see a video of her original approach here. http://faculty.cs.tamu.edu/hammond/ Via: http://ianozsvald.com/2010/02/07/intelligent-user-interfaces-2010-conference/ http://www.flickr.com/photos/54145418@N00/4343172889/

62. Other Notable Posters / Demos Avara: a system to improve user experience in web and virtual world Important for online games An intuitive texture picker Similarities to HSBs Sound Torch Automatic configuration of spatially consistent mouse pointer navigation in multi-display environments Basic work towards future shared systems Understanding web documents using semantic overlays Relevance to CEI development NAO Demo: http://www.youtube.com/watch?v=VdhGYn32ACg&feature=youtu.be&a

63. Thanks! Questions? http://www.youtube.com/watch?v=VdhGYn32ACg&feature=youtu.be&a