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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Some Hybrid Geometric Aggregation Operators with 2-tuple Linguistic Information and Their Applications to Multi-attribute Group Decision Making

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2013年第4期6卷 750-763页

作者： Wan, Shu-Ping Jiangxi Univ Finance & Econ Coll Informat Technol Nanchang 330013 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Jiangxi Key Lab Data & Knowledge Engn Nanchang 330013 Peoples R China

A new method is developed to solve multi-attribute group decision making (MAGDM) problem in which the attribute values, attribute weights and expert weights are all in the form of 2-tuple linguistic information. First, the operation laws for 2-tuple linguistic information are defined and the related properties of the operation laws are studied. Then, some new hybrid geometric aggregation operators with 2-tuple linguistic information are developed, involving the 2-tuple hybrid weighted geometric average (THWAG) operator, the 2-tuple hybrid linguistic weighted geometric average (T-HLWG) operator and the extended 2-tuple hybrid linguistic weighted geometric average (ET-HLWG) operator. These hybrid geometric aggregation operators generalize the existing 2-tuple linguistic geometric aggregation operators and reflect the important degrees of both the given 2-tuples and the ordered positions of the 2-tuples. In the proposed decision method, using the ET-HLWG operators the individual overall preference values of the alternatives are integrated into the collective ones of the alternatives, which are used to rank the alternatives. The method can sufficiently consider the importance degrees of different experts and thus relieve the influence of those unfair arguments on the decision results. A real example of evaluating university faculty is given to illustrate the proposed method and the comparison analysis demonstrates the universality and flexibility of the proposed method in this paper.

关键词： Multi-attribute group decision making Linguistic preference 2-tuple linguistic information hybrid aggregation operator

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A Competitive Two-Agent Scheduling Problem on Parallel Machines with Release Dates and Preemption

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MATHEMATICAL PROBLEMS IN engineering 2013年第1期2013卷 1-5页

作者： Qi, Yawei Wan, Long Jiangxi Univ Finance & Econ Sch Informat Technol Jiangxi Key Lab Data & Knowledge Engn Nanchang 330013 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330013 Jiangxi Peoples R China

We consider a competitive two-agent scheduling problem on multiple identical machines with release dates and preemption. In the scheduling model, there are two agents a and b each having their own job sets J(a) = {J(1)(a),..., J(na)(a)} and J(b) = {J(1)(b),..., J(nb)(b)}, respectively. Each job J(j) epsilon J(a) boolean OR J(b) has a release date r(i) and the n = n(a) + n(b) jobs need to be preemptively scheduled on m identical machines. For m = 2, we show that the trade-off curve of all the Pareto optimal points can be characterized in polynomial time. When m is input, we show that P vertical bar r(j), pmtn vertical bar L-max(a) : L-max(b) < Q can be solved in strongly polynomial time.

关键词： PRODUCTION scheduling PARALLEL computers POLYNOMIAL time algorithms PROBLEM solving MACHINE theory COMPUTER scheduling

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The extended VIKOR method for multi-attribute group decision making with triangular intuitionistic fuzzy numbers

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knowledge-BASED SYSTEMS 2013年 52卷 65-77页

作者： Wan, Shu-Ping Wang, Qiang-Ying Dong, Jiu-Ying Jiangxi Univ Finance & Econ Coll Informat Technol Nanchang 330013 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Jiangxi Key Lab Data & Knowledge Engn Nanchang 330013 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Coll Stat Nanchang 330013 Jiangxi Peoples R China

Triangular intuitionistic fuzzy numbers (TIFNs) are a special intuitionistic fuzzy set (IFS) on a real number set, which are very useful for decision makers (DMs) to depict their fuzzy preference information. In this work, we investigate multiple attribute group decision-making (MAGDM) problems in which the ratings of alternatives are expressed with TIFNs, and the weights of the attributes and DMs are completely unknown. Firstly, the crisp weighted possibility mean of TIFNs is defined, and the Hamming distance and Euclidean distance for TIFNs are defined based on Hausdorff distance. The triangular intuitionistic fuzzy weighted average (TIF-WA) operator is developed. Then, the weights of attributes are calculated by applying Shannon entropy measure and the weights of DMs are determined objectively combining the evidence theory with Bayes approximation. The individual decision matrixes for all DMs are aggregated into the group decision matrix by using the TIF-WA operator. Thereby, the classic Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is extended for solving the MAGDM with TIFNs. Finally, the ranking order of alternative is given according to the closeness of alternative with respect to the ideal solution. The personnel selection example verifies the effectiveness of the proposed method. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Multi-attribute group decision making Triangular intuitionistic fuzzy numbers Shannon entropy measure Evidence theory VIKOR method

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Cluster labeling extraction and ranking feature selection for high quality XML pseudo relevance feedback fragments set

Cluster labeling extraction and ranking feature selection fo...

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9th International Conference on Advanced data Mining and Applications, ADMA 2013

作者： Zhong, Minjuan Wan, Changxuan Liu, Dexi Liao, Shumei Luo, Siwen School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783642539169

In traditional pseudo feedback, the main reason of the topic drift is the low quality of the feedback source. Clustering search results is an effective way to improve the quality of feedback set. For XML data, how to effectively perform clustering algorithm and then identify good xml fragments from the clustering results is a intricate problem. This paper mainly focus on the latter problem. Based on k-mediod clustering results, This work firstly proposes an cluster label extraction method to select candidate relevant clusters. Secondly, multiple ranking features are introduced to assist the related xml fragments identification from the candidate clusters. Top N fragments compose the high quality pseudo feedback set finally. Experimental results on standard INEX test data show that in one hand, the proposed cluster label extraction method could obtain proper cluster key terms and lead to appropriate candidate cluster selection. On the other hand, the presented ranking features are beneficial to the relevant xml fragments identification. The quality of feedback set is ensured. © 2013 Springer-Verlag.

关键词： Clustering algorithms

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Fast SimRank computation over disk-resident graphs

Fast SimRank computation over disk-resident graphs

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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Zhang, Yinglong Li, Cuiping Chen, Hong Sheng, Likun Key Lab of Data Engineering and Knowledge Engineering of Ministry of Education And Department of Computer Science Renmin University of China China JiangXi Agricultural University China

ISBN: (纸本)9783642374494

There are many real-world applications based on similarity between objects, such as clustering, similarity query processing, information retrieval and recommendation systems. SimRank is a promising measure of similarity based on random surfers model. However, the computational complexity of SimRank is high and several optimization techniques have been proposed. In the paper optimization issue of Sim-Rank computation in disk-resident graphs is our primary focus. First we suggest a new approach to compute *** we propose optimization techniques that improve the time cost of the new approach from O (kN2 D2) to O (kNL), where k is the number of iteration, N is the number of nodes, L is the number of edges, and D is the average degree of nodes. Meanwhile, a threshold sieving method is presented to reduce storage and computational cost. On this basis, an external algorithm computing SimRank in disk-resident graphs is introduced. In the experiments, our algorithm outperforms its opponent whose computation complexity also is O (kNL). © Springer-Verlag 2013.

关键词： Search engines

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An effective XML identifying feedback documents method based on two-stage ranking model for pseudo-relevance feedback

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2013 3rd International Symposium on Chemical engineering and Material Properties, ISCEMP 2013

作者： Zhong, Min Juan School of Information Technology Jiangxi University of Finance and Economics Nanchang China JiangXi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783037858363

Although pseudo relevant feedback is an effective query expansion method, query drift away from the topic has been occurred frequently. Therefore, the first important problem is how to identify relevant documents in the top retrieved set and form the good feedback source. In this paper, an effective XML identifying feedback documents method is proposed, in which a two-stage ranking model is presented and the relevant XML documents are found. The experiment results show that the proposed method is reasonable and the quality of feedback source is ensured. © (2013) Trans Tech Publications, Switzerland.

关键词： XML

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An Effective Search Results Semantic Optimization Clustering Method for XML Fragments

An Effective Search Results Semantic Optimization Clustering...

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International Conference on Computer Sciences and Applications (CSA)

作者： Zhong Minjuan Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang Peoples R China Jiangxi Univ Finance & Econ JiangXi Key Lab Data & Knowledge Engn Nanchang Peoples R China

ISBN: (纸本)9780769551258

With the emergence of more and more XML documents, the clustering of XML documents has become an active research area. However, it is more significance of XML element clustering than whole document due to the more focused and specific amount of information, especially to some application domain. Therefore, in this paper, we study the xml element clustering, in which the latent semantic indexing model is used to obtain the semantic relationship between terms firstly, and then a evaluation function for k-mediod is presented to automatic generate the optimal cluster number. In addition, information gain, for evaluating clustering quality is introduced. Experiment results show that the proposed semantic clustering optimization method outperforms the traditional method (No_optimization) in information gain and produces better clustering quality.

关键词： component XML Fragments Search Results Clustering Evaluation Function Optimal Cluster Number

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Weighting tags and paths in XML documents according to their topic generalization

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INFORMATION SCIENCES 2013年 249卷 48-66页

作者： Liu, Dexi Wan, Changxuan Chen, Lei Liu, Xiping Nie, Jian-Yun Jiangxi Univ Finance & Econ Nanchang 330013 Peoples R China Jiangxi Key Lab Data & Knowledge Engn Nanchang Peoples R China Hong Kong Univ Sci & Technol Hong Kong Hong Kong Peoples R China Univ Montreal Montreal PQ Canada

Text-centric (or document-centric) XML document retrieval aims to rank search results according to their relevance to a given query. To do this, most existing methods mainly rely on content terms and often ignore an important factor - the XML tags and paths, which are useful in determining the important contents of a document. In some previous studies, each unique tag/path is assigned a weight based on domain (expert) knowledge. However, such a manual assignment is both inefficient and subjective. In this paper, we propose an automatic method to infer the weights of tags/paths according to the topical relationship between the corresponding elements and the whole documents. The more the corresponding element can generalize the document's topic, the more the tag/path is considered to be important. We define a model based on Average Topic Generalization (ATG), which integrates several features used in previous studies. We evaluate the performance of the ATG-based model on two real data sets, the IEEECS collection and the Wikipedia collection, from two different perspectives: the correlation between the weights generated by ATG and those set by experts, and the performance of XML retrieval based on ATG. Experimental results show that the tag/path weights generated by ATG are highly correlated with the manually assigned weights, and the ATG model significantly improves XML retrieval effectiveness. (C) 2013 Elsevier Inc. All rights reserved.

关键词： XML retrieval Tag/path weighting model Topic generalization

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Improved factor analysis algorithm in factor spaces

Improved factor analysis algorithm in factor spaces

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2014 International Conference on Informatics, Networking and Intelligent Computing(INIC 2014)

作者： H.D.Wang Y.Shi P.Z.Wang H.T.Liu Key Research Lab on Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijingChina Research Center on Fictitious Economy & Data ScienceChinese Academy of SciencesBeijingChina School of Mathematical SciencesUniversity of Chinese Academy of Sciences Institute of Intelligent Engineering and Mathematics Liaoning Technical University

In order to further improve the velocity and the utilization of information contained in samples,an improved version of the Factor Analysis Algorithm(FAA) in factor spaces is presented in this *** primary algorithm is considered from the whole classes during the selection of the next classified factor,by which a smaller decision domain is generated than that generated by considering from each class,and the deletion of the decision domain is critical in decreasing calculation and increasing the velocity of the ***,based on inheriting the merits of the initial algorithm,the pushing way by each column is changed into that by each class during the selection of the next classified *** change not only decreases the calculation,but also improves the utilization of the sample *** testing results also indicate that the improvement is significant and the testing accuracy rate and velocity are both better than the primary algorithm.

关键词： Factor spaces factor analysis decision domain time complexity

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