Indentification of informative gene subsets responsible for discerning between available samples of gene expression data is an important task in bioinformatics. Reducts, from rough sets theory, corresponding to a mini...
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Skyline query processing has recently received a lot of attention in database *** a set of multi-dimensional objects,the skyline query finds the objects that are not dominated by *** the best of our knowledge,the exis...
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Skyline query processing has recently received a lot of attention in database *** a set of multi-dimensional objects,the skyline query finds the objects that are not dominated by *** the best of our knowledge,the existing researches mainly focus on how to efficiently return the whole skyline ***,as the cardinality and dimensionality of input dataset increase,the number of skylines grows exponentially,and hence this "huge" skyline set is completely useless to *** by the above fact,in this paper,we present a novel type of l-SkyDiv query,which only returns l skylines having maximum diversity,to improve the usefulness of skyline ***,we prove that the l-SkyDiv query belongs to the NP-Hard problem theoretically,and propose three efficient heuristic algorithms whose time complexities are polynomial to fast implement the proposed ***,we present detailed theoretical analyses and extensive experiments,demonstrating that our algorithms are both efficient and effective.
Any mistaken maintenance for the complicated and distributed grid can bring unpredictable disaster. Here we focus on the system availability issues caused by service dependencies during the maintenance in grid. A nove...
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Any mistaken maintenance for the complicated and distributed grid can bring unpredictable disaster. Here we focus on the system availability issues caused by service dependencies during the maintenance in grid. A novel mechanism, called Cobweb Guardian, is proposed in this paper. It provides multiple granularities (service-, container-, and node-level) maintenance for service components in grid. By using the Cobweb Guardian, grid administrators can execute the maintaining task safely in runtime with high availability. The evaluation results show that our proposed dependency-aware maintenance can make the grid management more automatic and available.
Gene selection, a key procedure of the discriminant analysis of microarray data, is to select the most informative genes from the whole gene set. Rough set theory is a mathematical tool for further reducing redundancy...
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Information-centric networking (ICN) technology is becoming a popular research topic in vehicular networks due to the connectionless and lightweight characteristics of this networking paradigm. Caching plays an essent...
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Network embedding, mapping nodes in a network to a low-dimensional space, achieves powerful performance. An increasing number of works focus on static network embedding, however, seldom attention has been paid to temp...
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The clustering method based on one-class support vector machine has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some ra...
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A remote debugging system for OpenMP parallel program is presented in this paper. The system consists of two parts, namely, an integrated debugging environment running on the clent-side and a background daemon running...
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A semi-supervised subtractive clustering has been proposed recently. However, it performance depends greatly on the choice of the parameters of the mountain function and only proper parameters enable the clustering me...
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Heterogeneous fraud detection is an important means of credit card security assurance, which can utilize historical transaction records in a source and target domain to build an effective fraud detection model. Nevert...
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Heterogeneous fraud detection is an important means of credit card security assurance, which can utilize historical transaction records in a source and target domain to build an effective fraud detection model. Nevertheless, large feature distribution differences between source and target transaction instances and the complex intrinsic structure hidden behind transaction data make it difficult for existing credit card fraud detection (CCFD) models to capture and transfer the most informative feature representations and seriously hinder detection performance. In this work, we propose a novel adaptive heterogeneous CCFD model named adaptive heterogeneous credit card fraud detection model based on deep reinforcement training subset selection (RTAHC) based on deep reinforcement training subset selection, which mainly contains two components: selection distribution generator (SDG) and transaction fraud detector (TFD, including feature extractor with an attention mechanism and classifier). The SDG can generate the selection probability distribution vector via the reinforcement reward mechanism, and then transaction instances in the source domain relevant to the target domain are selected. The feature extractor with an attention mechanism can learn the abstract deep semantic feature representations of selected source transaction instances and the target domain. The joint training of SDG and TFD can provide more real-time and accurate transaction feature representations to reduce the distribution discrepancy between the two domains. We verify the detection performance of RTAHC across a large real-world credit card transaction dataset and four public datasets. Experimental results demonstrate that the RTAHC model can exhibit competitive CCFD performance. Impact Statement—With the rise of artificial intelligence (AI)generated models, credit card fraud has become increasingly rampant, which also causes tens of billions of U.S. dollars in credit card losses worldwide every year
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