English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 90756/120810 (75%)
Visitors : 25020611      Online Users : 300
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/111898
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/111898

    Title: Inferring user activities from spatial-temporal data in mobile phones
    Authors: 徐國偉
    Njoo, G.S.
    Ruan, X.W.
    Hsu, Kuo-Wei
    Peng, W.-C.
    Contributors: 資訊科學系
    Keywords: Cellular telephones;Classification (of information);Mobile phones;Semantics;Telephone sets;Wearable computers;Wearable technology;Wi-Fi;Activity inference;Computing applications;Geographical features;Location-based social networks;Semantic features;Spatial temporals;Spatial-temporal data;Temporal features;Ubiquitous computing
    Date: 2015-09
    Issue Date: 2017-08-10 15:14:39 (UTC+8)
    Abstract: Activity inference is a key to the development of various ubiquitous computing applications. Here, we observe that users perform several actions in their mobile phone: take photos, perform check-in, and access Wi-Fi networks. These behaviors generate spatial-temporal data that could be utilized to capture user activities. Hence, three features are extracted for activities inference: 1) geographical feature: indicating where user performs activities; 2) temporal feature: indicating when user performs activities; and 3) semantic feature: showing semantic concept of a place from location-based social networks. Here, we propose Spatial-Temporal Activity Inference Model (STAIM) to infer users' activities from aforementioned features. Experimental results show that STAIM is able to effectively infer users' activities, achieving 75% accuracy on average. Copyright 2015 © ACM.
    Relation: UbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers, 65-68
    ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015; Osaka; Japan; 7 September 2015 到 11 September 2015; 代碼 118356
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1145/2800835.2800868
    DOI: 10.1145/2800835.2800868
    Appears in Collections:[資訊科學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    p65-njoo.pdf427KbAdobe PDF159View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback