The ribonucleic acid (RNA) secondary structure prediction has designed to characterize the ensemble of structures, instead of only computing the minimum free energy structure. Clustering methods are brought to aid in ...
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Hadoop is a reasonable tool for cloud computing in big data era and MapReduce paradigm may be a highly successful programming model for large-scale data-intensive computing application, but the conventional MapReduce ...
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Aiming at relieving the shortage problem of wireless frequency resource, sec- ondary users in cognitive radio networks occupy the spectrum hole opportunistically. In cognitive radio networks, primary users have preemp...
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In order to solve the overload problem of root ONS in the EPC network, a load balancing algorithm based on multi-root ONS is proposed. Based on the proposed load balancing ONS (LB ONS) architecture, the ONS Root is de...
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The existing model simplification algorithm in simplified speed and quality can't reach a better compromise, so we present an improved quadric error metrics edge collapse mesh simplification algorithm. This algori...
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On the basis of the introduction to basic theory of Minkowski sum, we analyze the procedure of constructing the boundary of Minkowski sum and present a new method that can compute Minkowski sum of the polyhedra based ...
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In software execution network, PageRank and betweenness methods are used to determine the importance of nodes. The experiment results show that the differences between nodes are not strong and cannot reflect the softw...
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Mining important patterns in complex software executing network plays an important role in analyzing software security. The general sequential pattern mining algorithms may lead to poor performance due to the lack of ...
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With the growing popularity of API-driven multiservice application (mashup) development, the burgeoning web APIs have left developers drowning in the sea of web API selections. Matching developers with the most approp...
With the growing popularity of API-driven multiservice application (mashup) development, the burgeoning web APIs have left developers drowning in the sea of web API selections. Matching developers with the most appropriate APIs is the key to improving user satisfaction and promoting more popular web applications. As a result, more and more researchers pay attention to web API recommender systems based on collaborative filtering. However, employing collaborative filtering to recommend APIs is challenging due to the severe sparsity of mashup and API interactions. To address this problem, we propose a probabilistic generative model, called the Binary-API Topic model (BAT), to parameterize mashups and APIs. Technically, BAT is equipped with a mechanism to extract binary-APIs and predict unknown pairwise interactions. To improve generality and capture more relevance from a limited number of interactions, we learn binary-API topics by directly modeling the generation of API co-occurrence patterns across the repository (all mashup collections from ***). The main advantage of BAT is that it preserves API co-occurrence patterns in model learning and exploits the rich global relevance. Finally, through extensive experiments, we demonstrate that BAT can achieve the highest performance on the sparse real-world data set.
In the peer-to-peer (P2P) live streaming systems, a single stream is decomposed into multiple sub-streams. For the participating nodes, data blocks of different sub-streams are stored in different sub-buffers for sync...
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