in this paper, we analyse the data access characteristics of a typical XML information retrieval system and propose a new query aware buffer replacement algorithm based on prediction of Minimum Reuse Distance (MRD for...
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Micro Expression (ME) is the subtle facial expressions that people show when they express their inner feelings. To address the problem that micro-expression recognition is difficult and less accurate due to the small ...
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The traveling salesman problem(TSP), a typical non-deterministic polynomial(NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimizat...
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The traveling salesman problem(TSP), a typical non-deterministic polynomial(NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimization algorithm(FOA) is used to solve TSP, since it has the advantages of being easy to understand and having a simple implementation. However, it has problems, including a slow convergence rate for the algorithm, easily falling into the local optimum, and an insufficient optimization precision. To address TSP effectively, three improvements are proposed in this paper to improve FOA. First, the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA. Second, an elimination mechanism is added to FOA to increase the diversity. Third, a reverse operator and a multiplication operator are proposed. They are performed on the solution sequence in the fruit fly's smell search and vision search processes, respectively. In the experiment, 10 benchmarks selected from TSPLIB are tested. The results show that the improved FOA outperforms other alternatives in terms of the convergence rate and precision.
With the increasing proliferation of the Mobile Social Networks (MSN) and the Location Based Service (LBS), location privacy has attracted broad attention in recent years. Most researches have been done with the assum...
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An incremental method of feature selection based on mutual information, called incremental Max-Relevance, and Min-Redundancy (I-mRMR), is presented. I-mRMR is an incremental version of Max-Relevance, and Min-Redundanc...
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In this paper, we present a new regularization classification method based on extreme learning machine for within network node classification problem. In particular, we define a new objective function, which contains ...
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Collective classification in networked data has become an important and active research topic, it has a wide variety of real world applications, such as hyperlinked document classification, protein interaction and gen...
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Genealogical knowledge graphs depict the relationships of family networks and the development of family histories. They can help researchers to analyze and understand genealogical data, search for genealogical descend...
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With the development of high-throughput microarray chip technology, there are a large number of microarray expression data, which have few samples compared to the genes of high dimensions. And in recent years, more an...
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In this letter, we suggest a novel Object Shrunken (OS) algorithm to handle the image classification task. Unlike the prior art, this letter considers the foreground or the object location in the image for more discri...
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In this letter, we suggest a novel Object Shrunken (OS) algorithm to handle the image classification task. Unlike the prior art, this letter considers the foreground or the object location in the image for more discriminative image-level representation. The OS algorithm suggests a straightforward procedure to box the object location. It first proposes a Weighted Local Outlier Factor (WLOF) to remove all the interest point outliers, and then positions the object location in terms of the distribution of the rest interest points. We evaluate the proposed algorithm on the well-known dataset Caltech-101. The resulting OS algorithm outperforms the state-of-art approaches in the image classification task.
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