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Neighborhood-based Collaborative Filtering (CF) is a kind of techniques in the field of recommendation algorithms and has been widely used in lots of personalized recommender systems. In the big data era, the increasing data amounts make these CF recommendation algorithms become time-consuming and energy-wasted. At present, Cloud computing and Graphic Processing Unit (GPU) are the two major platforms...
In recent years, many clustering algorithms have been proposed to solve the problem of routing. However, many of these algorithms have the problem of uneven load of cluster heads. In this paper, we present an algorithm - the maximum balance cluster head energy consumes of routing (MCR) - to solve the above problem. In our proposed MCR algorithm, when ordinary nodes receive broadcast information from...
Ad Hoc network is a newly developed network without fixed infrastructure and a changing topology. Its vulnerability makes it prone to attacks, which brings greater challenges for intrusion detection for Ad Hoc. Through analyzing the existing intrusion detection techniques as well as the characteristics of Ad Hoc network, this paper has proposed an intrusion detection technique based on class association...
Motion trajectory is one of the most important cues for extracting semantic information from video data. Numerous studies have focused on the analysis and comparison of the similarity among motion trajectories. Most of the previous methods rely on a global measure that does not account for partial or lost information. In particular, existing techniques fail to capture the salient features of local...
High utility itemsets mining is another hot topic besides association rule in data mining field. It considers not only item's support values but also the quantities and profit. In current research, all algorithms mining high utility itemsets are based on candidate set generation-and-test category where too many candidates will be generated and tested. In this paper, an algorithm-DHUI based on PHUI-Tree...
Context-awareness is a key issue in pervasive computing. Context-aware applications are prone to the context consistency problem, where applications are confronted with conflicting contexts and cannot decide how to adapt themselves. In pervasive computing environments, users are often willing to accept certain degree of context inconsistency, as long as it can reduce the consistency maintenance cost,...
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