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Video-on-Demand (VoD) services are having more and more impact on people's daily life. The huge number of view records generated during the services reveal lots of information about video popularity and user behavior. In this work, we seek to understand user behavior from a network approach. Users who overlapped view records are defined as “familiar strangers” of each other. With network science concepts,...
Recently, Retrospective Event Detection attracts much attention. Most researches focus on detecting coarse-grained events or frequent events. However, they neglect to discover fine grained events or important rare events which are significant for human decision making. The important rare events are different from the frequent events revealing common patterns, their features are unnoticed and cannot...
In this paper, we have proposed a method to identify and track the drifted topics in the background of the social media through exploring the heated comments published, discussed, and voted by the participants of the social media. Based on this approach, we have further developed a way to optimize the recommendation of the relevant news to the readers of certain news by using the keywords generated...
Decision tree learning is one of the most widely used and practical methods for inductive inference. A fundamental issue in decision tree inductive learning is the attribute selection measure at each non-terminal node of the tree. However, existing literatures have not taken both classification ability and cost-sensitive into account well. In this paper, we present a new strategy for attributes selection,...
An analytical compact threshold voltage model is developed, which accounts for the narrow-channel effect, the short-channel effect, and the oxide-thickness effect in the nanoscale double-gate (DG) MOSFETs. The two-dimensional non-equilibrium Greenpsilas function (NEGF) approach coupled self-consistently with Poissonpsilas equation is applied to simulate the threshold voltage in comparison with the...
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