1. 普及運算報告 Ubiquitous System Software Managing Uncertainty:Modeling Users in Location-Tracking Applications Present ：研一 張永昌 Chang Yung-Chang

2. Outline Ubiquitous System Software Managing Uncertainty:Modeling Users in Location-Tracking Applications

3. 普及運算報告 Ubiquitous System Software Present ：研一 張永昌 Chang Yung-Chang

4. Outline Introduction THE MOST SENSED CAMPUS MONITORING EARTHQUAKEINDUCED LOADING WITH CAMERA NETWORKS MIN: MIDDLEWARE FOR NETWORK- CENTRIC UBIQUITOUS SYSTEMS DESIGNING UBIQUITOUS SYSTEMS THROUGH ARCHITECTURAL REFLECTION INTERACTION METAPHORS APPLICATION MODELING FOR CONTEXT AWARENESS

5. Introduction THE MOST SENSED CAMPUS MONITORING EARTHQUAKEINDUCED LOADING WITH CAMERA NETWORKS MIN: MIDDLEWARE FOR NETWORK- CENTRIC UBIQUITOUS SYSTEMS DESIGNING UBIQUITOUS SYSTEMS THROUGH ARCHITECTURAL REFLECTION INTERACTION METAPHORS APPLICATION MODELING FOR CONTEXT AWARENESS

6. THE MOST SENSED CAMPUS Michael W. Bigrigg and H. Scott Matthews, Carnegie Mellon University most wired→most wireless→most sensed

7. MONITORING EARTHQUAKEINDUCED LOADING WITH CAMERA NETWORKS Tara C. Hutchinson and Falko Kuester, University of California, Irvine

8. MONITORING EARTHQUAKEINDUCED LOADING WITH CAMERA NETWORKS The investigation has two primary objectives ：  Characterize the seismic response of an important class of equipment and building contents  Study the applicability of tracking this response using arrays of image-based monitoring systems

9. MONITORING EARTHQUAKEINDUCED LOADING WITH CAMERA NETWORKS Exploits several issues in designing networked sensing systems for field applications:  Viability of high-speed networks of sensors under adverse conditions (in this case, earthquake loads)  Communication with a variety of different sensor types  Interpretation capacity of the sensed information (by a remote user)  Network latency and failure tolerance under high- demand conditions (high rates of acquisition, through adverse conditions)

10. MIN: MIDDLEWARE FOR NETWORK- CENTRIC UBIQUITOUS SYSTEMS Lu Yan, Turku Centre for Computer Science and Åbo Akademi University MIN=Formal Methods in Peer-to-Peer Network

11. MIN: MIDDLEWARE FOR NETWORK- CENTRIC UBIQUITOUS SYSTEMS systems require  A self-organizing infrastructure  Dynamic topology  A hop connection  Decentralized service  Integrated routing  Context awareness

12. DESIGNING UBIQUITOUS SYSTEMS THROUGH ARCHITECTURAL REFLECTION Francesca Arcelli, Claudia Raibulet, Francesco Tisato, and Marzia Adorni, Università degli Studi di Milano-Bicocca

13. DESIGNING UBIQUITOUS SYSTEMS THROUGH ARCHITECTURAL REFLECTION Several relevant features ：  complex multimedia  multichannel  mobile distributed systems features ：  context awareness  Location awareness  self adaptation  service orientation  quality-of-service support  awareness  negotiation capability(to solve conflict resolution)

14. DESIGNING UBIQUITOUS SYSTEMS THROUGH ARCHITECTURAL REFLECTION We’ve designed a reflective architecture for multichannel adaptive information systems (the MAIS project).

15. INTERACTION METAPHORS Christoph Endres, German Research Center for Artificial Intelligence Andreas Butz, Munich University, Germany

16. INTERACTION METAPHORS The FLUIDUM project =Flexible User Interfaces for Distributed Ubiquitous Machinery WIMP=Windows 、 Icon 、 Menus 、 Pointing devices FLUIDUM addresses instrumented environments at three different scales— the desk, room, and building levels 。

17. APPLICATION MODELING FOR CONTEXT AWARENESS Maja Vukovic and Peter Robinson, University of Cambridge

18. 普及運算報告 Managing Uncertainty:Modeling Users in Location-Tracking Applications Present ：研一 張永昌 Chang Yung-Chang

19. Outline Introduction Modeling users Collecting user data Building the user model Using Bayesian networks Performance issues Experimental results Discussion

20. Introduction Applications ：  Track elderly people  Provide targeted advertising to mobile users  track moving objects

21. Modeling users main variable types ：  Temporal variables represent when events occur, including the time of year, day of the week, and time of day.  Spatial variables represent possible RU locations, such as a building, town, certain part of town, certain road or highway, and so forth.  Environmental variables represent things such as weather conditions, road conditions, and special events.  Behavioral variables represent things such as typical speeds, resting patterns, preferred work areas, and common reactions in certain situations.

22. Collecting user data Divide the data into two categories ：  User-specific data consists of personal information or trip-related information  Environment-specific data describes the different artifacts of the environments （ weather conditions 、 traffic conditions 、 special events taking place ）

23. Building the user model Common ways to build user models ：  Machine learning  Predicate logic  First-order logic

24. Building the user model

25. Using Bayesian networks Values ：  Event  Time of day  Source  Destination  Weather conditions  Route  Speed

26. Using Bayesian networks

27. Performance issues We would maintain the BN, which involves updating the probabilities associated with each node based on new observations, and we’d perform inference given some observation.

28. Experimental results simulation ： used these typical speeds to create the user speeds during a trip as follows:  45 percent of the time, the speed should be within 10 of the RU’s typical speed under the current circumstances.  25 percent of the time, the speed should be within 20 of the RU’s typical speed under the current circumstances.  20 percent of the time, the speed should be within 50 of the RU’s typical speed under the current circumstances.  10 percent of the time, the speed should be within 100 of the RU’s typical speed under the current circumstances.

29. Experimental results Experimental design  100,000 trips and performed 10 queries on each trip.  the experiment for RIs 5, 10, 15, 20, 30, 50, 100, 150, and 200 time units.

30. Experimental results The figure shows that LSR performed better when the RI was less than 12 time units, at which point the two techniques performed equally well. We can see how our approach outperforms LSR, especially with high RIs. Our technique at RI 100 and 200 performs better than LSR at RI 50 and 100, respectively.

31. Experimental results