CSI 9851 지식기반 시스템 응용 (상황인지 시스템 및 서비스) 2011년도 제 1학기

강의진 소개 담당 교수 조성배 (공대 C515;  2123-2720; sbcho@cs.yonsei.ac.kr) 웹 페이지 : http://sclab.yonsei.ac.kr/courses/11contexts 강의 시간 월 6, 7, 수 2 (공대A646) 면담 시간 수 9, 10 담당 조교 이영설, 윤종원

Mobile Device (Digital Device with Mobility) SenseWear DejaView Microsoft SenseCAM

Improvement of Mobile Devices (Advent of Smartphones) iPhone Windows Mobile Android

Logging Your Life with Mobile Devices Physical Sensors Location (GPS, Indoor GPS, Active Badge, RFID) Temperature, Humidity, Acceleration, Touch, Illumination Body sensors Vision (Camera) / Sound Bluetooth (Proximity Sensor) Device Usage Application usage, User profile, PIMS data, Scheduler, E-mail, Address book Device Status Battery/CPU/Memory Level UCC (User-Created Contents) Photo, Video, Audio Clip

Personal Databases Personal Databases

Mobile Intelligence: Overview GPS Call SMS 사진 Device MP3 마이닝 모델링 해석 추론 학습 특이성 추출 시맨틱 표현 의미 이해 에피소딕 메모리 정보 생성 정보추천 정보검색 정보관리 Context-aware service Query by memory fraction 로그수집/가공 도구 랜드마크 추출엔진 모바일 모델링 라이브러리 모바일 추론학습 엔진 모바일 해석엔진 대화 인터페이스 모바일 검색엔진 아바타 생성/제어도구 로그 수집

Related Works Helsinki University, ContextPhone Microsoft Research, MyLifeBits Microsoft Research, MemoryLens (PhotoViewer, LifeBrowser) Microsoft Research, JamBayes Carnegie Mellon University, Context-aware Phone MIT Ambient Intelligence Group, PhotoWhere MIT Reality Mining Group, Serendipity Service MIT Reality Mining Group, Interactive Automatically Generated Diary ATR, ComicDiary NOKIA, LifeBlog SK Telecom, 1mm Service …

Relevant Research using Mobile Device (LifeLogging)

Relevant Research using Mobile Device (User Adaptive Service)

과목의 최종 목표 모바일 장비로부터 얻어진 로그정보의 해석과 서비스제공을 위한 지식기반 시스템 기술 학습 및 상황인지 시스템 구현실습을 통한 실기 능력습득 지식 기반 시스템 기술 학습 지식 표현, 습득, 관리, 공유 Data Mining, Probabilistic Modeling, Ontology 실제 상황인지 시스템의 구현실습 로그 정보의 획득, 전처리, 해석, 모델링, 시각화, 서비스 개발

강의계획 1. 3/2 : 과목소개 2. 3/7, 9 : 상황인지 개요 (상황인지 기술 동향) 3. 3/14, 16 : 상황인지 기반 감성시스템 동향 (보강) 4. 3/21, 23 : 상황인지 기반 감성서비스 동향 5. 3/28, 30 : Mobile context-aware framework 6. 4/4, 6 : Location awareness 7. 4/11, 13 : Activity awareness 8. 4/18, 20 : 제안서 발표, 중간시험기간 9. 4/25, 27 : 중간시험기간, Social contexts 10. 5/2, 4 : Recommendation 11. 5/9, 11 : Battery awareness-1 12. 5/16, 18 : Battery awareness-2 13. 5/23, 25 : Battery awareness-3 (보강) 14. 5/30, 6/1 : Battery awareness-4 15. 6/8, 13 : 최종발표 16. 6/15, 20 : 기말시험기간

Mobile context aware framework Course Schedule 강의 관련 자료 1주 과목소개 - 2주 상황인지 개요 3주 상황인지기반 감성시스템 동향 4주 감성서비스 동향 5주 Mobile context aware framework [1] A. C. Santos et al., “Providing user context for mobile and social networking applications,” Pervasive and Mobile Computing, vol. 6, no. 3, pp. 324-341, 2010. [2] P. Korpipaa et al., “Managing context information in mobile devices,” IEEE Pervasive Computing, vol. 2, no. 3, pp. 42-51, 2003. 6주 Location awareness [3] C. Zhou et al., “Discovering personally meaningful places: an interactive clustering approach,” ACM Trans. on Information Systems, vol. 25, no. 3, pp. 1-30, 2007. [4] A. Mulloni et al., “Indoor positioning and navigation with camera phones,” IEEE Pervasive Computing, vol. 8, no. 2, pp. 22-31, 2009. [5] C.-M. Chen, “Intelligent location-based mobile news service system with automatic news summarization,” Expert Systems with Applications, vo. 37, no. 9, pp. 6651-6662, 2010. 7주 Activity awareness [6] S. Reddy et al., “Using mobile phones to determine transportation modes,” ACM Trans. on Sensor Networks, vol. 6, no. 2, pp. 1-27, 2010. [7] D. Choujaa and N. Dulay, “Predicting human behaviour from selected mobile phone data points,” In Proc. of the 12th ACM Int’l Conf. on Ubiquitous Computing (Ubicomp ’10), pp. 105-108, 2010. [8] V. Bellotti et al., “Activity-based serendipitous recommendations with the Magitti mobile leisure guide,” In Proc. of the 26th Annual SIGCHI Conf. on Human Factors in Computing Systems (CHI ’08), pp. 1157-1166, 2008.

Course Schedule - 강의 관련 자료 8주 제안서 발표, 중간고사 9주 Social contexts 10주 [9] B. Adams et al., “Sensing and using social context,” ACM Trans. on Multimedia Computing, Communications, and Applications, vol. 5, no. 2, pp. 1-27, 2008. [10] R.-H. Hwang et al., “UbiPhone: human-centered ubiquitous phone system,” IEEE Pervasive Computing, vol. 8, no. 2, pp. 40-47, 2009. 10주 Recommendation [11] S.-K. Lee et al., “Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations,” Information Sciences, vol. 180, no. 11, pp. 2142-2155, 2010. [12] M.-H. Kuo et al., “Building and evaluating a location-based service recommendation system with a preference adjustment mechanism,” Expert Systems with Applications, vol. 36, no. 2, pp. 3543-3554, 2009. [13] S.-T. Yuan and Y. W. Tsao, “A recommendation mechanism for contextualized mobile advertising,” Expert Systems with Applications, vol. 24, no. 4, pp. 399-414, 2003. 11주 Battery awareness 1 (에너지 관리를 위한 프레임워크) [14] D. Sathan et al., "Context aware lightweight energy efficient framework," World Academy of Science, Engineering and Technology, vol. 52, pp. 64-70, 2009. [15] Y. Fei et al., "An Energy-Aware Framework for Dynamic Software Management in Mobile Computing Systems," ACM Transactions on Embedded Computing Systems (TECS) , vol. 7, no. 3, pp. 27:1-27:31, 2008. [16] S. Kang et al., "SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments," Proc. of the 6th international conference on Mobile systems, applications, and services, pp. 267-280, 2008.

Course Schedule - 강의 관련 자료 12주 Battery awareness 2 (배터리 관리를 위한 상황 인식) [17] C. Harris et al., "Exploiting user behavior for context-aware power management," IEEE Int. Conf. on Wireless And Mobile Computing, Networking And Communications, pp. 122-130, 2005. [18] K. Murao et al., "A context-aware system that changes sensor combinations considering energy consumption," Proc. of the 6th Int. Conf. on Pervasive Computing, pp. 197-212, 2008. [19] N. Ravi et al., "Context-aware battery management for mobile phones," Proc. of the 6th IEEE Int. Conf. on Pervasive Computing and Communications, pp. 1-10, 2008. 13주 Battery awareness 3 (상황 인식을 위한 배터리 관리) [20] J. Ryder et al., Ambulation: a tool for monitoring mobility patterns over time using mobile phones,” Int’l Conf. on Computational Science and Engineering, pp. 927-931, 2009. [21] S. Reddy et al., “Using mobile phones to determine transportation modes,” ACM Transactions on Sensor Networks (TOSN), vol. 6, no. 2, pp. 13:1-13:27, 2010. [22] Y. Wang et al., “A framework of energy efficient mobile sensing for automatic user state recognition,” Proceedings of the 7th international conference on Mobile systems, applications, and services, pp. 179-192, 2009. 14주 Battery awareness 4 (위치 인식을 위한 배터리 관리) [23] J. Paek et al., “Energy-efficient rate-adaptive GPS-based positioning for smartphones,” Proc. of the 8th international conference on Mobile systems, applications, and services, pp. 299-314, 2010. [24] Z. Zhuang et al., “Improving energy efficiency of location sensing on smartphones,” Proceedings of the 8th international conference on Mobile systems, applications, and services, pp. 315-330, 2010. [25] I. Shafer et al., “Movement detection for power-efficient smartphone WLAN localization,” Proc. of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems, pp. 81-90, 2010. 15주 최종 발표 - 16주 기말 시험 기간

Evaluation Criteria Evaluation Criteria Term Project (written report & oral presentation) : 60% Course Report : 10% Course Presentation : 30% Term Project (Oral presentation is required) : Theoretical Issue (analysis, experiment, simulation) : Originality Interesting Programming (Game, Demo, etc) : Performance Survey : Completeness

Mobile Context Applications Context Sharing (Photo, Location) Mobile Visualization Mobile Context-Aware System Place Annotation

Possible Project List Battery-awareness for energy efficiency Swapping Wi-Fi, GPS, and Cell for energy efficient location awareness Battery aware sensor / service selection Predicting battery recharge depending on battery usage and context Development of context middleware for energy efficiency User state recognition considering sensor on/off Context-aware recommendation Application recommendation depending on mobile context Recommendation based on local contexts Predicting & recommending user activities Social context-based recommendation Using combinations of two or more contexts for recommendation