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IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces
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IUI 2010: An Informal Summary of the International Conference on Intelligent User Interfaces

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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 …

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.

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  • Requested QuickWoZ info!!!
  • Folksonomy: User generated vocabularies (e.g. tags)A combined initial tag + related tags does not typically help to filter results <- too general and depends on how "relation" is determined!Semi-automatic clustering
  • The brain emits a signal as soon as it sees something interesting, and that "aha" signal can be detected by an electroencephalogram, or EEG cap. While users sift through streaming images or video footage, the technology tags the images that elicit a signal, and ranks them in order of the strength of the neural signatures. Afterwards, the user can examine only the information that their brains identified as important, instead of wading through thousands of images.Pasted from <http://www.wired.com/medtech/health/news/2006/07/71364>
  • Large workgroup including psychologists, economists, comp. sc., etc.WHAT IF WE APPLY OTHER FORCES TO THE NETWORK : PRESSURE / REMOVE NODES / OBSERVE SPREADMORE TIES SPRINGS -> MORE RIGID COMPANY NETWORKCONTRADICTION OF PHYS MODEL WITH PRESENTED CORRELATION OF PLASTICITY AND STABILITY OF COMPANY NETWORKS
  • The image on this slide is adapted for a hierarchical display … not the original springs model.
  • Andreas Butz‘s workgroup…
  • Such a lot of media to choose from that we could really use some good recommendations
  • Sometimes a lot of great different ingredients…
  • … just don‘t mix that well !
  • So the goal is to find a right combination and order.
  • Some systems out there try to achieve just that.
  • But more often than not they can‘t dynamically adjust to ever-changing human moods.
  • … and most-likely just recommend more of the same.
  • So how to adjust for the right order and the needs of the many?
  • [29],whichsupportstheexplorationanddiscoveryofinforma-tionthroughbothqueryingandbrowsingstrategies.Inthatregard,Marchionini[21]identifiedthreetypesofsearchac-tivities:(1)lookup,(2)learnand(3)investigate.Lookupsearchescanbethoughtofastraditionalsearch,whilelearnandinvestigatesearchesrelatetodiscovery-orientedtasks.GOOD SOURCE FOR CEION A MAP: WORLD LOCATIONS /// WHAT ABOUT OTHER TOPICSINTERFACE PRIMES FOR LOCATION QUERIES … WHAT ABOUT ALTERNATIVE INTERFACES?
  • Interesting for digital media confetti project…
  • works by fingerprinting the print pattern in a marked area … needs online db … not currently possible on purely white bgThey are investigating further codes to allow less distinct patterns
  • Interesting to CEI group…
  • KINDA IMPORTANT TO THE CEI!!! As described earlier, by changing the spread of prior distributions of words over all the available words, different knowledge representations of the user could be created. The smaller spread (i.e., lower s.d.) in the probability distribution of words within each topic implied that the words were more accurate in predicting the concepts in the document, such that the simulated user would be better able to interpret a tag and infer the topic as well as to assign a tag to represent the topic. We assumed that this reflected the performance of domain experts.  The most generally accepted property of social tagging systems is that the proportion of tags assigned to a document converges over time [12]. So, as the total number of tags increase in a system, the ratio of the frequency of a tag to the total number of tags remains fairly constant. This emergent property of tags, called convergence, was attributed to the social nature of the tagging process. In our previous simulations [7], we showed that the semantic imitation model produced not only the convergence, but also predicted how experts and novices could lead to different rates of convergence. http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413
  • faster convergence in the expert network can be explained: tags assigned by experts were more predictive of the topics in the document and experts could extract these topics better than novices. Additionally, other experts tagging the same resource tended to choose the same higher quality tags.In contrast, novices were less knowledgeable about the contents of the document and consequently less effective in extracting the appropriate topics (and therefore tags) from the documents. Novices therefore selected tags that were more diverse than experts and hence the slower convergence. http://portal.acm.org/ft_gateway.cfm?id=1719998&type=pdf&coll=ACM&dl=ACM&CFID=78317288&CFTOKEN=12971413Exploratory information search by domain experts and novices POSTER ALSO INTERESTING
  • Nice video!
  • Hello, my name is Jan Smeddinck from the Digital Media workgroup at the University of Bremen, Often times we would like to test certain aspects of an embodied conversational agent before natural language processing is solved. Thus we do WoZ experiments where the user is tricked to believe to be interacting with a real functioning artificial agent. These experiments are slow and complicated to setup, especially when researching 3D agents. That’s why we developed the QuickWoZ framework where scenes with agents are constructed using traditional 3d modeling software including animations, foliage, etc. and then exported to our system that allows a wizard operator to easily steer the interaction with experiment participants.
  • Professor Tracy HammondThe system performs facial recognition on the user’s sketch and compares it to the target image so it can give feedback on areas that are wrong.
  • Henry Lieberman’s poster
  • Transcript

    • 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
    • 4.
    • 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
    • 56. Towards a Reputation-based Model of Social Web SearchK. McNally, et al., Univ. College Dublin, Ireland
      HeyStacks: Support collaboration on search
      Recommendations based on user experiences
      • Automatically calculates reputation scores for users to value their contributions (i.e. points per follow-up by other users)
      http://www.heystaks.com/
    • 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/
    • 61. Why UI: Using Goal Networks to Improve User InterfacesD. A. Smith & H. Lieberman, MIT, USA
      • Matching Users Problems / Questions with typical solutions and embedding the results in a map-based interface
      Mapping how people solve tasks by performing natural language processing at 43Things to build networks of goals
      Automatically extract the steps required to solve goals by analyzing existing stories
      http://farm5.static.flickr.com/4055/4343923626_9c77b9e617_o.jpg
    • 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

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