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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Weifeng Zhang Tianhao Pan Yun Wang Ziyuan Wang Lei Xu School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

With the widespread of microblog, the microblog platform like Sina has become information provider to release mass information. Mining the valuable information from microblog and making personalized recommendations have become a hotspot of recent research. But most researches about microblog primarily focus on user's social circle. The recommendation is mainly based on the user's friends or the users they follow, and the study on how to find users with similar hobbies from unfamiliar social circles is rare. Therefore, we propose a microblog generation model UT-LDA (User Topic Latent Dirichlet Allocation) to compute users' similarities. This model takes one user's microblog as a document, and gets the different topic distributions on different categories in each microblog. According to the users' topic distributions and the profile information of each user, we can calculate the similarities between users. The experimental results show that our UT-LDA model works effectively in recommending similar users.

关键词： Conferences software quality software reliability Security

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Spectrum-Based Fault Localization Method with Test Case Reduction

Spectrum-Based Fault Localization Method with Test Case Redu...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Xiaohong Zhang Ziyuan Wang Weifeng Zhang Hui Ding Lin Chen School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

Spectrum-based fault localization aims to find the program statements that contain faults, according to the dynamic information of executing program. We propose a spectra-based fault localization method with test case reduction, which removes redundant test cases in fault localization. Experiments show that new method requires fewer test cases to obtain the better fault localization results.

关键词： software Schedules computers Conferences computer science Telecommunications Statistical analysis

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Feature selection method with proportionate fitness based binary particle swarm optimization

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8886卷 582-592页

作者： Zhou, Zhe Liu, Xing Li, Ping Shang, Lin State Key Laboratory of Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

Particle swarm optimization(PSO) has been applied on feature selection with improved results. Traditional PSO methods have some drawbacks when dealing with binary space, which may bring negative effects on the results. In this paper, an algorithm based on fitness proportionate selection binary particle swarm optimization(FPSBPSO) will be discussed in detail aiming to overcome the problems of traditional PSO methods. FPSBPSO will be utilized in the feature subset selection domain. The performance of feature selection will be compared in a benchmark dataset, and experimental results prove that the FPSBPSObased feature selection methods can avoid premature convergence and improve the classification accuracy at the same time. © Springer International Publishing Switzerland 2014.

关键词： Feature Selection

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A Monotonic Optimization based Optimal Discrete Charging Protocol for Electric Vehicles with Multilevel Charging Rates

A Monotonic Optimization based Optimal Discrete Charging Pro...

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第27届中国控制与决策会议

作者： Xiaoqi Tan Yuan Wu Danny H.K.Tsang Department of Electronic and Computer Engineering Hong Kong University of Science and Technology College of Information Engineering Zhejiang University of Technology State Key Laboratory for Novel Software Technology Nanjing University

ISBN: (纸本)9781479970186

In this paper,we propose ODCP,a novel optimal discrete charging protocol,to investigate the optimal charging strategy for Electric Vehicles(EVs) with multilevel charging *** the assumptions in most previous works that the charging rate is continuously adjustable below the power rating and the charging process can be intermittent,the proposed ODCP model explicitly takes into account the facts that the charging rate is discrete and the charging process should not be interrupted,which is a favorable yet conventional practice to prolong the battery ***,the ODCP is first formulated as a semi-infinite programming problem,which is proved to be reducible by presenting an equivalent discretized *** discretized model is combinatorial in the charging rate and is a nonconvex optimization problem which is generally difficult to ***,by exploiting the hidden monotonicity in this model,we transform the problem into a canonical monotonic optimization *** on the Polyblock outer approximation algorithm,we propose the Gor Pa algorithm,which efficiently solves the problem to global(η,)-optimal under the case when EVs' operational intervals do not *** demonstrate the applicability of the proposed algorithm and show its convergence by real data simulation.

关键词： Electric Vehicles,Multilevel Charging Rates,Monotonic Optimization

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Exploring review content for recommendation via latent factor model

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8862卷 668-679页

作者： Chen, Xiaoyu Yao, Yuan Xu, Feng Lu, Jian State Key Laboratory for Novel Software Technology Nanjing China Department of Computer Science and Technology Nanjing University China

Recommender systems have been widely studied and applied in many real applications such as e-commerce sites, product review sites, and mobile App stores. In these applications, users can provide their feedback towards the items in the form of ratings, and they usually accompany the feedback with a few words (i.e., review content) to justify their ratings. Such review content may contain rich information about user tastes and item characteristics. However, existing recommendation methods (e.g., collaborative filtering) mainly make use of the historical ratings while ignore the content information. In this paper, we propose to explore the review content for better recommendation via latent factor model. In particular, we propose two strategies to leverage the review content. The first strategy incorporates review content as a guidance term to guide the learnt latent factors of user preferences;the second strategy formulates a regularization term to constrain the preference differences between similar users. Experimental evaluations on two real data sets demonstrate the usefulness of review content and the effectiveness of the proposed method for recommendation. © Springer International Publishing Switzerland 2014.

关键词： Recommender systems

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A causal model for disease pathway discovery

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8834卷 350-357页

作者： Cai, Ruichu Yuan, Chang Hao, Zhifeng Wen, Wen Wang, Lijuan Chen, Weiqi Li, Zhihao School of Computer Science and Technology Guangdong University of Technology Guangzhou China State Key Lab for Novel Software Technology Nanjing University China

Pathway provides a deep insight into the mechanism of the biological process. With the increasing of high-throughout gene expression monitoring technology, a lot of data driven methods have been proposed to reconstruct the pathways from the observation data. Low reliability of the discovered results, especially the direction of the regulatory relation, is the main challenge of the existing methods. In this work, a level-wise causal search (LWCS) based disease pathway discovery method is proposed. The following three steps are conducted in each searching level of LWCS to locate the causal variables: firstly, in the parents and children (PC) discovery step, structure learning approach is employed to discover the candidate causal genes;then, in the casual direction learning step, additive noise models are explored to determine the direction of the edges, finally, the trivial causal candidates are pruned and not contained in the further level search. The proposed method is tested and verified on real life gene expression data sets. The success of the proposed method reflects that the causality is a proper model to present the regulatory relations among the genes and phenotypes. © Springer International Publishing Switzerland 2014.

关键词： Additive noise

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

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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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Identifying extract class refactoring opportunities for internetware

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science China(Information sciences) 2014年第7期57卷 37-54页

作者： CHEN Lin QIAN Ju ZHOU YuMing WANG Peng XU BaoWen State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Computer Science and Engineering Southeast University

The quality of internetware software is significantly associated with class *** software evolves,changes often introduce many unrelated responsibilities to the same classes or distribute tightly-related methods in different *** changes make the classes difficult to understand and *** class refactoring is an effective technique to improve the quality of software structure by decomposing unrelated methods in one class to create new classes or extracting tightly-related methods from different *** this paper,we propose a novel approach for class extraction from internetware source *** approach leverages a community structure detection technique to partition software into clusters and extracts classes from the resulting *** experimental results,which investigate the public well-known internetware PKUAS,indicate that:(1)the proposed approach is much faster than existing search-based clustering approaches(Hillclimbing and Genetic algorithm)and is thus applicable for large-scale internetware;(2)the proposed approach can identify meaningful class extractions for internetware;and(3)Extract Class refactoring candidates identified by the proposed approach significantly improve class cohesion of internetware.

关键词： refactoring extract class community structure software modularity internetware

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New texture-spatial features for keyword spotting in video images

New texture-spatial features for keyword spotting in video i...

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Asian Conference on Pattern Recognition (ACPR)

作者： Palaiahnakote Shivakumara Guozhu Liang Sangheeta Roy Umapada Pal Tong Lu Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479961016

keyword spotting in video document images is challenging due to low resolution and complex background of video images. We propose the combination of Texture-Spatial-Features (TSF) for keyword spotting in video images without recognizing them. First, a segmentation method extracts words from text lines in each video image. Then we propose the set of texture features for identifying text candidates in the word image with the help of k-means clustering. The proposed method finds proximity between text candidates to study the spatial arrangement of pixels that result in feature vectors for spotting words in the input frame. The proposed method is evaluated on word images of different fonts, contrasts, backgrounds and font sizes, which are chosen from standard databases such as ICDAR 2013 video and our video data. Experimental results show that the proposed method outperforms the existing method in terms of recall, precision and f-measure.

关键词： Image segmentation Semantics Video signal processing Indexing Pattern recognition Spatial resolution

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RFID cardinality estimation with blocker tags

RFID cardinality estimation with blocker tags

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiulong Liu Bin Xiao Keqiu Li Jie Wu Alex X. Liu Heng Qi Xin Xie Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Dalian University of Technology China Department of Computer and Information Sciences Temple University USA National Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Engineering Michigan State University USA

The widely used RFID tags impose serious privacy concerns as a tag responds to queries from readers no matter they are authorized or not. The common solution is to use a commercially available blocker tag which behaves as if a set of tags with known blocking IDs are present. The use of blocker tags makes RFID estimation much more challenging as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose REB, the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. The basic idea of REB is to conduct statistically inference from the two sets of responses and estimate the number of genuine tags. We conduct extensive simulations to evaluate the performance of REB, in terms of time-efficiency and estimation reliability. The experimental results reveal that our REB scheme runs tens of times faster than the fastest identification protocol with the same accuracy requirement.

关键词： Estimation Radiofrequency identification Protocols Accuracy computers Privacy Conferences

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