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    题名: Inferring user activities from spatial-temporal data in mobile phones
    作者: 徐國偉
    Njoo, G.S.
    Ruan, X.W.
    Hsu, Kuo-Wei
    Peng, W.-C.
    贡献者: 資訊科學系
    关键词: 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
    日期: 2015-09
    上传时间: 2017-08-10 15:14:39 (UTC+8)
    摘要: 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.
    關聯: 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
    数据类型: conference
    DOI 連結: http://dx.doi.org/10.1145/2800835.2800868
    DOI: 10.1145/2800835.2800868
    显示于类别:[資訊科學系] 會議論文

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