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Journal of Software 2012年第12期7卷 2823-2831页

作者： Xie, Linquan Wang, Ying Yu, Fei Xu, Chen Yue, Guangxue School of Science Jiangxi University of Science and Technology 341000 Ganzhou China School of Science Jiangxi University of Science and Technology 341000 Ganzhou China Jiangsu Provincial Key Laboratory for Computer Information Processing Technology Soochow University 215000 Soochow China School of Information Science and Engineering Hunan University 416000 Changsha China Department of Computer Science and Technology Huaihua University Huaihua China

A fuzzy clustering algorithm for intrusion detection based on heterogeneous attributes is proposed in this paper. Firstly, the algorithm modifies the comparability measurement for the categorical attributes according to the formula of Hemingway;then, for the shortages of fuzzy C-means clustering algorithm: initialize sensitively and easy to get into the local optimum, the presented new algorithm is optimized by GuoTao approach. We simulate our algorithm with the KDDCUP99 data set, and the results show that the convergence rate of the new algorithm is faster than the original fuzzy C-means clustering algorithm and the performance of our algorithm is more stable. © 2012 ACADEMY PUBLISHER.

关键词： Intrusion detection

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Erratum to: Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at s = 8 TeV with the ATLAS detector

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The European Physical Journal C 2015年第9期75卷 1-14页

作者： G. Aad B. Abbott J. Abdallah S. Abdel Khalek O. Abdinov R. Aben B. Abi M. Abolins O. S. AbouZeid H. Abramowicz H. Abreu R. Abreu Y. Abulaiti B. S. Acharya L. Adamczyk D. L. Adams J. Adelman S. Adomeit T. Adye T. Agatonovic-Jovin J. A. Aguilar-Saavedra M. Agustoni S. P. Ahlen F. Ahmadov G. Aielli H. Akerstedt T. P. A. Åkesson G. Akimoto A. V. Akimov G. L. Alberghi J. Albert S. Albrand M. J. Alconada Verzini M. Aleksa I. N. Aleksandrov C. Alexa G. Alexander G. Alexandre T. Alexopoulos M. Alhroob G. Alimonti L. Alio J. Alison B. M. M. Allbrooke L. J. Allison P. P. Allport A. Aloisio A. Alonso F. Alonso C. Alpigiani A. Altheimer B. Alvarez Gonzalez M. G. Alviggi K. Amako Y. Amaral Coutinho C. Amelung D. Amidei S. P. Amor Dos Santos A. Amorim S. Amoroso N. Amram G. Amundsen C. Anastopoulos L. S. Ancu N. Andari T. Andeen C. F. Anders G. Anders K. J. Anderson A. Andreazza V. Andrei X. S. Anduaga S. Angelidakis I. Angelozzi P. Anger A. Angerami F. Anghinolfi A. V. Anisenkov N. Anjos A. Annovi M. Antonelli A. Antonov J. Antos F. Anulli M. Aoki L. Aperio Bella G. Arabidze Y. Arai J. P. Araque A. T. H. Arce F. A Arduh J-F. Arguin S. Argyropoulos M. Arik A. J. Armbruster O. Arnaez V. Arnal H. Arnold M. Arratia O. Arslan A. Artamonov G. Artoni S. Asai N. Asbah A. Ashkenazi B. Åsman L. Asquith K. Assamagan R. Astalos M. Atkinson N. B. Atlay B. Auerbach K. Augsten M. Aurousseau G. Avolio B. Axen M. K. Ayoub G. Azuelos M. A. Baak A. E. Baas C. Bacci H. Bachacou K. Bachas M. Backes M. Backhaus P. Bagiacchi P. Bagnaia Y. Bai T. Bain J. T. Baines O. K. Baker P. Balek T. Balestri F. Balli E. Banas Sw. Banerjee A. A. E. Bannoura H. S. Bansil L. Barak S. P. Baranov E. L. Barberio D. Barberis M. Barbero T. Barillari M. Barisonzi T. Barklow N. Barlow S. L. Barnes B. M. Barnett R. M. Barnett Z. Barnovska A. Baroncelli G. Barone A. J. Barr F. Barreiro J. Barreiro Guimarães da Costa R. Bartoldus A. E. Barton P. Bartos A. Bassalat A. Basye R. L. Bates S. J. Batista J. R. Batley M. Battaglia M. Bauce F. Bauer H. S. Bawa J. B. Beacham M. D. B CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman USA Institute of Physics Academia Sinica Taipei Taiwan LAL Université Paris-Sud and CNRS/IN2P3 Orsay France Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam The Netherlands Department of Physics Oklahoma State University Stillwater USA Department of Physics and Astronomy Michigan State University East Lansing USA Department of Physics University of Toronto Toronto Canada Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel CERN Geneva Switzerland Department of Physics Stockholm University Stockholm Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Dipartimento di Chimica Fisica e Ambiente Università di Udine Udine Italy Faculty of Physics and Applied Computer Science AGH University of Science and Technology Kraków Poland Marian Smoluchowski Institute of Physics Jagiellonian University Kraków Poland Physics Department Brookhaven National Laboratory Upton USA Department of Physics Northern Illinois University De Kalb USA Fakultät für Physik Ludwig-Maximilians-Universität München Munich Germany Particle Physics Department Rutherford Appleton Laboratory Didcot UK Institute of Physics University of Belgrade Belgrade Serbia Laboratorio de Instrumentacao e Fisica Experimental de Particulas-LIP Lisbon Portugal Faculdade de Ciências Universidade de Lisboa Lisbon Portugal Department of Physics University of Coimbra Coimbra Portugal Centro de Física Nuclear da Universidade de Lisboa Lisbon Portugal Departamento de Fisica Universidade do Minho Braga Portugal Departamento de Fisica Teorica y del Cosmos and CA

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Process Calculus with Data Structure and its Model Checking Algorithm

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Physics Procedia 2012年 33卷 782-789页

作者： Qing Zheng Zi-Ning Cao Department of Computer Science and Technology Nanjing University of Aero. & Astro. Nanjing 210016 P.R. China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 P.R. China National Key Laboratory of Science and Technology on Avionics System Integration Shanghai 200233 P.R. China

Model checking is one of the most important technology for automatically verification. So this paper generally proposed a method of combining CCS and Z language, to perform model checking. It combines the advantage of current CCS and Z systems, where CCS is good at describing concurrent systems, also Z is a good tool for data structure. As a result, form the new system CCS-Z. Then we give the syntax and semantics of it, at last give the model checking *** advantage of this paper is combined data stucture on the famous system CCS, and increase the capacity of system description.

关键词： Model Checking CTL Z Language CCS

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Sentiment clustering of product object based on feature reduction

Sentiment clustering of product object based on feature redu...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Suge Wang Xueqian Yin Ru Li Jie Zhang Yunyun Lv School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China School of Mathematics Science Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China

Face to car domain, the content of reviews is very scattered. In order to evaluate rate of each product, we extract the sentences from reviews. Then, those sentences are merged which describe the same product together and summarizing them according to product performances. On this basis, we extract features from these sentences, and calculate their value. Owing to existing missing feature values, established information systems are called incomplete information systems. For the problems of high dimension and missing data, we adopt the feature reduction algorithm based on discernibility matrix to reduce the feature dimension. Lastly, we aggregate each product by K-means clustering algorithm. Our experimental results indicate that the proposed method is effective.

关键词： Feature extraction information systems Clustering algorithms Educational institutions Algorithm design and analysis Semantics Ontologies

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Left-to-Right Tree-to-String Decoding with Prediction 12

Left-to-Right Tree-to-String Decoding with Prediction

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Conference on Empirical Methods in Natural Language processing

作者： Yang Feng Yang Liu Qun Liu Trevor Cohn Department of Computer Science The University of Sheffield Sheffield UK State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Sci. and Tech. Tsinghua University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781622765034

Decoding algorithms for syntax based machine translation suffer from high computational complexity, a consequence of intersecting a language model with a context free grammar. Left-to-right decoding, which generates the target string in order, can improve decoding efficiency by simplifying the language model evaluation. This paper presents a novel left to right decoding algorithm for tree-to-string translation, using a bottom-up parsing strategy and dynamic future cost estimation for each partial translation. Our method outperforms previously published tree-to-string decoders, including a competing left-to-right method.

关键词： modelling languages DECODING decoding algorithm machine translation syntax context-free grammars

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Search for Higgs and Z Boson Decays to J/ψγ and ϒ(nS)γ with the ATLAS Detector

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Physical Review Letters 2015年第12期114卷 121801-121801页

作者： G. Aad B. Abbott J. Abdallah S. Abdel Khalek O. Abdinov R. Aben B. Abi M. Abolins O. S. AbouZeid H. Abramowicz H. Abreu R. Abreu Y. Abulaiti B. S. Acharya L. Adamczyk D. L. Adams J. Adelman S. Adomeit T. Adye T. Agatonovic-Jovin J. A. Aguilar-Saavedra M. Agustoni S. P. Ahlen F. Ahmadov G. Aielli H. Akerstedt T. P. A. Åkesson G. Akimoto A. V. Akimov G. L. Alberghi J. Albert S. Albrand M. J. Alconada Verzini M. Aleksa I. N. Aleksandrov C. Alexa G. Alexander G. Alexandre T. Alexopoulos M. Alhroob G. Alimonti L. Alio J. Alison B. M. M. Allbrooke L. J. Allison P. P. Allport A. Aloisio A. Alonso F. Alonso C. Alpigiani A. Altheimer B. Alvarez Gonzalez M. G. Alviggi K. Amako Y. Amaral Coutinho C. Amelung D. Amidei S. P. Amor Dos Santos A. Amorim S. Amoroso N. Amram G. Amundsen C. Anastopoulos L. S. Ancu N. Andari T. Andeen C. F. Anders G. Anders K. J. Anderson A. Andreazza V. Andrei X. S. Anduaga S. Angelidakis I. Angelozzi P. Anger A. Angerami F. Anghinolfi A. V. Anisenkov N. Anjos A. Annovi M. Antonelli A. Antonov J. Antos F. Anulli M. Aoki L. Aperio Bella G. Arabidze Y. Arai J. P. Araque A. T. H. Arce F. A. Arduh J-F. Arguin S. Argyropoulos M. Arik A. J. Armbruster O. Arnaez V. Arnal H. Arnold M. Arratia O. Arslan A. Artamonov G. Artoni S. Asai N. Asbah A. Ashkenazi B. Åsman L. Asquith K. Assamagan R. Astalos M. Atkinson N. B. Atlay B. Auerbach K. Augsten M. Aurousseau G. Avolio B. Axen M. K. Ayoub G. Azuelos M. A. Baak A. E. Baas C. Bacci H. Bachacou K. Bachas M. Backes M. Backhaus P. Bagiacchi P. Bagnaia Y. Bai T. Bain J. T. Baines O. K. Baker P. Balek T. Balestri F. Balli E. Banas Sw. Banerjee A. A. E. Bannoura H. S. Bansil L. Barak S. P. Baranov E. L. Barberio D. Barberis M. Barbero T. Barillari M. Barisonzi T. Barklow N. Barlow S. L. Barnes B. M. Barnett R. M. Barnett Z. Barnovska A. Baroncelli G. Barone A. J. Barr F. Barreiro J. Barreiro Guimarães da Costa R. Bartoldus A. E. Barton P. Bartos A. Bassalat A. Basye R. L. Bates S. J. Batista J. R. Batley M. Battaglia M. Bauce F. Bauer H. S. Bawa J. B. Beacham M. D. CPPM Aix-Marseille Université and CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman OK United States of America Institute of Physics Academia Sinica Taipei Taiwan LAL Université Paris-Sud and CNRS/IN2P3 Orsay France Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam Netherlands Department of Physics Oklahoma State University Stillwater OK United States of America Department of Physics and Astronomy Michigan State University East Lansing MI United States of America Department of Physics University of Toronto Toronto ON Canada Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion: Israel Institute of Technology Haifa Israel CERN Geneva Switzerland Department of Physics Stockholm University Sweden The Oskar Klein Centre Stockholm Sweden INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Physics Department Brookhaven National Laboratory Upton NY United States of America Department of Physics Northern Illinois University DeKalb IL United States of America Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Institute of Physics University of Belgrade Belgrade Serbia Laboratorio de Instrumentacao e Fisica Experimental de Particulas - LIP Lisboa Portugal Departamento de Fisica Teorica y del Cosmos and CAFPE Universidad de Granada Granada (Spain) Portugal Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics University of Bern Bern Switzerland Department of Physics Boston University Boston MA United States of America Joint Institute for Nuclear Resea

A search for the decays of the Higgs and Z bosons to J/ψγ and ϒ(nS)γ (n=1,2,3) is performed with pp collision data samples corresponding to integrated luminosities of up to 20.3 fb−1 collected at s=8 TeV with the ATLAS detector at the CERN Large Hadron Collider. No significant excess of events is observed above expected backgrounds and 95% C.L. upper limits are placed on the branching fractions. In the J/ψγ final state the limits are 1.5×10−3 and 2.6×10−6 for the Higgs and Z boson decays, respectively, while in the ϒ(1S,2S,3S)γ final states the limits are (1.3,1.9,1.3)×10−3 and (3.4,6.5,5.4)×10−6, respectively.

关键词： Bosons

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Challenges and Opportunities for Trust Management in Crowdsourcing

Challenges and Opportunities for Trust Management in Crowdso...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Han Yu Zhiqi Shen Chunyan Miao Bo An School of Computer Engineering Nanyang Technological University Singapore The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Crowd sourcing (CS) systems offer a new way for businesses and individuals to leverage on the power of mass collaboration to accomplish complex tasks in a divide-and-conquer manner. In existing CS systems, no facility has been provided for analyzing the trustworthiness of workers and providing decision support for allocating tasks to workers, which leads to high dependency of the quality of work on the behavior of workers in CS systems as shown in this paper. To address this problem, trust management mechanisms are urgently needed. Traditional trust management techniques are focused on identifying the most trustworthy service providers (SPs) as accurately as possible. Little thoughts were given to the question of how to utilize these SPs due to two common assumptions: 1) an SP can serve an unlimited number of requests in one time unit, and 2) a service consumer (SC) only needs to select one SP for interaction to complete a task. However, in CS systems, these two assumptions are no longer valid. Thus, existing models cannot be directly used for trust management in CS systems. This paper takes the first step towards a systematic investigation of trust management in CS systems by extending existing trust management models for CS trust management and conducting extensive experiments to study and analyze the performance of various trust management models in crowd sourcing. In this paper, the following key contributions are made. We 1) propose extensions to existing trust management approaches to enable them to operate in CS systems, 2) design a simulation test-bed based on the system characteristics of Amazon's Mechanical Turk (AMT) to make evaluation close to practical CS systems, 3) discuss the effect of incorporating trust management into CS system on the overall social welfare, and 4) identify the challenges and opportunities for future trust management research in CS systems.

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Properties and axiomatization of fuzzy rough sets based on fuzzy covering

Properties and axiomatization of fuzzy rough sets based on f...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Ming-Fen Wu Hao-Han Han Yan-Fei Si School of Computer Science Wuyi University Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Some expanded fuzzy rough sets models have been investigated to handle fuzzy databases with uncertain, imprecise and incomplete real-valued information. In this paper, we make further research on fuzzy rough sets models in fuzzy environment, and we generalize rough fuzzy sets model based on a covering to fuzzy rough sets model based on a fuzzy covering. The lower and upper approximations of fuzzy subsets are defined based on a fuzzy covering, and basic properties are investigated. Then, the axiom definition of the lower approximation operator is given. It is shown that the rough fuzzy sets model based on a covering is a special instance of the fuzzy rough sets model based on a fuzzy covering.

关键词： Abstracts

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Formal concept analysis in hybrid relational databases

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International Review on computers and Software 2012年第6期7卷 2904-2910页

作者： Lei, Yuxia Sui, Yuefei Cao, Baoxiang School of Computer Science Qufu Normal University Rizhao 276826 Shandong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

Relational Database Model (RDM) has been proven to be a very useful data-storage technique. As information is stored as data in relational databases, the induction of concepts from data is a pivotal topic in the data mining field. Formal Concept Analysis (FCA) turns out to be a perfect instrument for a meaningful and conceptual exploration of the stored data. However, many databases in practice are often hybrid ones. This paper proposes an approach towards extracting concepts from hybrid relations, which preserve the RDM and allow complex values such as relations in columns for relations. The method can be described as follows: the sub-tuples/subattributes pairs are regard as new attributes and inserted into a main relation, and we further obtain a new complex relation called a target relation from which concepts can be extracted. The insertion operation is semantics-preserving and extent-preserving. In addition, this paper analyzed the structural connections among concept lattices in detail, which can be used to construct a complex concept lattice from simple ones. The method may reduce time complexity of constructing the concept lattice of a complex relation. © 2012 Praise Worthy Prize S.r.l. - All rights reserved.

关键词： Formal concept analysis

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Improving CCA via Spectral Components Selection for Facial Expression Recognition

Improving CCA via Spectral Components Selection for Facial E...

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IEEE International Symposium on Circuits and Systems

作者： Xiaoyan Zhou Wenming Zheng Minghai Xin Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science & Technology Nanjing 210044 China Key Laboratory of Child Development and Learning Science (Ministry of Education) Research Center for Learning Science Southeast University Nanjing 210096 China

ISBN: (纸本)9781467302180

In this paper, we propose a novel canonical correlation analysis (CCA) algorithm for facial expression recognition. In contrast to the traditional CCA algorithm, the proposed method is capable of selecting the optimal spectral components of the training data matrix in modelling the linear correlation between the facial feature vectors and the corresponding expression class membership vectors. We formulate this spectral selection problem as a sparse optimization problem, where the l_1-norm penalty is adopted to this goal. To recognize the emotion category of each facial image, we present a linear regression formula to predict the emotion class membership for each facial image. The experiments on the JAFFE facial expression database confirm the better recognition performance of the proposed method.

关键词： Facial Recognition Spectral composition expression recognition component selection Emotions Linear Regression spectral selectivity Face Face Facial Features

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