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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Antonic, A. Zarko, I.P. Jakobovic, D. |
| Copyright Year | 2013 |
| Description | Author affiliation: Fac. of Electr. Eng. & Comput., Univ. of Zagreb, Zagreb, Croatia (Antonic, A.; Zarko, I.P.; Jakobovic, D.) |
| Abstract | In the context of communication services, presence is defined as the willingness and ability of a user to communicate across a set of devices with other users, and thus an up-to-date user presence status represents an essential prerequisite for real-time communications. Smartphones are a rich source of presence-related contex information, however; this information is currently not applied by the prevailing over-the-top communication systems to implicitly change user presence status in accordance with his/her context and typical daily behavior. Smartphone battery limitations and the abundance of context data generated from built-in sensors and mobile applications are the major factors limiting the adoption of rich presence solutions in state-of-the-art communication solutions. This paper presents an approach to learning and inferring user presence status on smartphones using the available context data with a goal to enable non intrusive and energy-efficient maintenance of presence status without user intervention. We apply the Mobile Data Challenge (MDC) data set collected during the Lausanne Data Collection Campaign from October 2009 until March 2011 in our evaluations. |
| Starting Page | 000600 |
| Ending Page | 000605 |
| File Size | 283692 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479937554 |
| DOI | 10.1109/ISCC.2013.6755013 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-07-07 |
| Publisher Place | Croatia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Smart phones Calendars Accuracy Context Servers Vectors Sensors Logistic Regression presence service rich presence supervised learning |
| Content Type | Text |
| Resource Type | Article |
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