Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/111898
DC Field | Value | Language |
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dc.contributor | 資訊科學系 | zh_Tw |
dc.creator | 徐國偉 | zh_TW |
dc.creator | Njoo, G.S. | en_US |
dc.creator | Ruan, X.W. | en_US |
dc.creator | Hsu, Kuo-Wei | en_US |
dc.creator | Peng, W.-C. | en_US |
dc.date | 2015-09 | en_US |
dc.date.accessioned | 2017-08-10T07:14:39Z | - |
dc.date.available | 2017-08-10T07:14:39Z | - |
dc.date.issued | 2017-08-10T07:14:39Z | - |
dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/111898 | - |
dc.description.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. | en_US |
dc.format.extent | 437359 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.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 | en_US |
dc.relation | 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 | en_US |
dc.subject | 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 | en_US |
dc.title | Inferring user activities from spatial-temporal data in mobile phones | en_US |
dc.type | conference | |
dc.identifier.doi | 10.1145/2800835.2800868 | |
dc.doi.uri | http://dx.doi.org/10.1145/2800835.2800868 | |
item.grantfulltext | restricted | - |
item.cerifentitytype | Publications | - |
item.openairetype | conference | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | 會議論文 |
Files in This Item:
File | Description | Size | Format | |
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p65-njoo.pdf | 427.11 kB | Adobe PDF2 | View/Open |
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