Xiao Wen Ruan

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This paper investigates how digital traces of people's movements and activities in the physical world (e.g., at college campuses and commutes) may be used to detect local, short-lived events in various urban spaces. Past work that use occupancy-related features can only identify high-intensity events (those that cause large-scale disruption in visit(More)
In this paper, we propose a framework to infer different people's activity from the view of both the geographical habit and temporal habit of user. Such a personal activity inference framework is a crucial prerequisite for intelligent user experience, and power management of smart phones. By analyzing the real activity log data, we extract 3 kinds of(More)
In the recent years, several research works have been conducted on collecting context data from various sensors for activity inference. We observe that users perform several actions in their mobile phones: taking photos, performing check-ins, and accessing Wi-Fi networks. These actions generate spatial-temporal data that could be utilized to capture user(More)
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(More)
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