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The penetration of mobile devices equipped with various embedded sensors also make it possible to capture the physical and virtual context of the user and surrounding environment. Further, the modeling of human behaviors based on those data becomes very important due to the increasing popularity of context-aware computing and people-centric applications, which utilize users' behavior pattern to improve...
Applications over cellular networks now range from operator–consumer applications (e.g., mobile television, voice-over-ip, video conferencing), peer-to-peer applications (e.g., instant messaging), machine-to-machine applications (e.g., data telemetry and automotive applications), mobile web services (e.g., music and video streaming), and social networking applications. The current approach for developing...
Modeling mobile users' behavior can lead to crucial applications in accountable mobile computing such as casual authentication and anomaly detection. We introduced a language approach to model mobile users' behavior from heterogeneous sensor data. By converting temporal and spacial features generated from WiFi RSS trace into symbols and fusing them into a 1-dimension ``language'' representation, we...
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