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作者： Zhu, Jian-Qi Fu, Feng Yin, Ke-Xin Luo, Jia-Qian Wei, Da College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Xi'An Satellite Control Center Xi'an China College of Software Changchun University of Technology Changchun China

In this paper, we investigate the multiple attribute decision making (MADM) problems with hesitant interval-valued fuzzy information. Then, we further develop some new Einstein aggregation operators with hesitant interval-valued fuzzy information, such as the induced hesitant interval-valued fuzzy Einstein correlated averaging (I-HIVFECA) operator and induced hesitant interval-valued fuzzy Einstein correlated geometric (I-HIVFECG) operator. Then, we apply the induced hesitant interval-valued fuzzy Einstein correlated averaging (I-HIVFECA) operator and induced hesitant interval-valued fuzzy Einstein correlated geometric (I-HIVFECG) operator to deal with multiple attribute decision making under the hesitant interval-valued fuzzy environments. Finally, an illustrative example for evaluating software quality is given to verify the developed approach and to demonstrate its practicality and effectiveness. © 2014-IOS Press and the authors. All rights reserved.

关键词： Geometry

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Kernel Ridge Regression Classification

Kernel Ridge Regression Classification

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International Joint Conference on Neural Networks

作者： Jinrong He Lixin Ding Lei Jiang Ling Ma School of Computer State Key Laboratory of Software Engineering Wuhan University State Key Laboratory of Software Engineering Wuhan University Key Laboratory of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology Second Artillery Equipment Academy

ISBN: (纸本)9781479914821

We present a nearest nonlinear subspace classifier that extends ridge regression classification method to kernel version which is called Kernel Ridge Regression Classification (KRRC). Kernel method is usually considered effective in discovering the nonlinear structure of the data manifold. The basic idea of KRRC is to implicitly map the observed data into potentially much higher dimensional feature space by using kernel trick and perform ridge regression classification in feature space. In this new feature space, samples from a single-object class may lie on a linear subspace, such that a new test sample can be represented as a linear combination of class-specific galleries, then the minimum distance between the new test sample and class specific subspace is used for classification. Our experimental studies on synthetic data sets and some UCI benchmark datasets confirm the effectiveness of the proposed method.

关键词： Kernel Classification algorithms Educational institutions Linear regression Training Face recognition Robustness

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A Flexible and Efficient Algorithm for Regularized Marginal Fisher Analysis

A Flexible and Efficient Algorithm for Regularized Marginal ...

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International Joint Conference on Neural Networks

作者： Jinrong He Lixin Ding Lei Jiang Li Huang The School of Computer State Key Laboratory of Software Engineering Wuhan University Wuhan China State Key Laboratory of Software Engineering Wuhan University Wuhan China Key Laboratory of Knowledge Processing and Networked Manufacture Hunan University of Science and Technology Xiangtan China The School of Computer & Information Technology Shanxi University Shanxi China

Marginal Fisher analysis (MFA) is a well-known linear dimensionality reduction method. However, MFA does not utilize the local diversity information of the training data, which will degrade its performance. In order to enhance the discriminant power of MFA, this paper considers introducing local variation quantity to enlarge the distances between local neighborhood embeddings and proposes a flexible and efficient implementation of MFA (F-MFA) within the regularization framework. Therefore, the discriminant structure and diversity of data are preserved in low-dimensional subspace. Computationally, F-MFA is formulated as a trace differential optimization problem which can completely avoids the singularity problem as it exists in MFA. Further, an efficient algorithm is developed for implementing F-MFA via QR-decomposition. Experimental results on four face data sets demonstrate the effectiveness of our approach.

关键词： Algorithm design and analysis Symmetric matrices Linear programming Manifolds Eigenvalues and eigenfunctions Educational institutions TV

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CHC-TSCM:A Trustworthy Service Composition Method Based on an Improved CHC Genetic Algorithm

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China Communications 2013年第12期10卷 77-91页

作者：曹步清刘建勋 Xiaoqing(Frank) LIU 李兵周栋康国胜 Key Laboratory of Knowledge Processing and Networked Manufacture School of Computer Science and EngineeringHunan University of Science and Technology Department of Computer Science Missouri University of Science and Technology State Key Laboratory of Software Engineering School of ComputerWuhan University

Trustworthy service composition is an extremely important task when service composition becomes infeasible or even fails in an environment which is open,autonomic,uncertain and *** paper presents a trustworthy service composition method based on an improved Cross generation elitist selection,Heterogeneous recombination,Catacly-smic mutation(CHC) Trustworthy Service Composition Method(CHC-TSCM) genetic *** firstly obtains the total trust degree of the individual service using a trust degree measurement and evaluation model proposed in previous *** combination and computation then are performed according to the structural relation of the composite ***,the optimal trustworthy service composition is acquired by the improved CHC genetic *** results show that CHC-TSCM can effectively solve the trustworthy service composition *** with GODSS and TOCSS,this new method has several advantages:1) a higher service composition successrate;2) a smaller decline trend of the service composition success-rate,and 3) enhanced stability.

关键词： QoS service trust model CHC gen-etic algorithm trustworthy service composition

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TL-PLSA: Transfer Learning between Domains with Different Classes

TL-PLSA: Transfer Learning between Domains with Different Cl...

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IEEE International Conference on Data Mining (ICDM)

作者： Anastasia Krithara Georgios Paliouras Software and Knowledge Engineering Laboratory National Center for Scientific Research (NCSR) “Demokritosℍ Athens Greece

ISBN: (纸本)9781479913282

A new transfer learning method is presented in this paper, addressing a particularly hard transfer learning problem: the case where the target domain shares only a subset of its classes with the source domain and only unlabeled data are provided for the target domain. This is a situation that occurs frequently in real-world applications, such as the multiclass document classification problems that motivated our work. The proposed approach is a transfer learning variant of the Probabilistic Latent Semantic Analysis (PLSA) model that we name TL-PLSA. Unlike most approaches in the literature, TL-PLSA captures both the difference of the domains and the commonalities of the class sets, given no labelled data from the target domain. We perform experiments over three different datasets and show the difficulty of the task, as well as the promising results that we obtained with the new method.

关键词： Mathematical model Probabilistic logic Data models Training Graphical models Semantics Training data

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An algorithm on fairness verification of mobile sink routing in wireless sensor network

An algorithm on fairness verification of mobile sink routing...

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作者： Xu, Guangquan Li, Weisheng Xu, Rui Xiao, Yingyuan Gao, Honghao Li, Xiaohong Feng, Zhiyong Mei, Jia Institute of Knowledge Science and Engineering School of Computer Science and Technology Tianjin University 92 Weijin Road Nankai District Tianjin 300072 China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai University Shanghai 201112 China Guangxi Economic Management Cadre College Guangxi China

Congestion and starvation will occur among some nodes due to the emerging serious unfairness, which is derived from the limited communication capabilities of all nodes and sink or in the case of a mobile sink moving to a new place. The problem to be solved is to balance the network and keep the fairness for all nodes. For this purpose, this paper focuses on verifying the fairness of mobile sink routing based on both state and action, which is realized mainly by composing Labeled Kripke Transition Systems (LKTS). First, an approach is presented by LKTS to model node behaviors. Second, a notion of Fair Computational Tree Logic (CTL) is introduced to describe the fairness formulae in branching time transitions, and four kinds of fairness assumptions are defined for fairness verification. Moreover, in order to avoid the problem of state-space explosion, Bounded model Checking to explore states and transitions on-the-fly until a witness is found, while Strong Connected Components algorithm is used to pick up fair paths under fairness constraints of Fair CTL. The experimental results show the superiority of our method by the savings in memory and time consumptions during the mobile sink routing process. © 2012 Springer-Verlag London Limited.

关键词： Model checking

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Classification rules for requirements observation

Classification rules for requirements observation

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IADIS International Conference on Information Systems 2013, IS 2013

作者： Nakatani, Takako Sano, Toshikazu Okano, Michitaro Kondo, Narihito Fujiwara, Yukiko Nakazato, Ryu Inoki, Mari Hori, Shozo Tsumaki, Toshihiko Tsuda, Michio Katamine, Keiichi Univ. of Tsukuba 3-29-1 Otsuka Bunkyo Tokyo112-0012 Japan Nagoya Management Junior College Japan Knowledge Discovery Research Laboratories NEC Japan Software Engineering Center TOSHIBA Corporation Japan Advanced IT Laboratory Toshiba Solutions Corporation Japan YASKAWA Information Systems Corporation Japan National Institute of Informatics Japan Osaka University Japan Kyushu Institute of Technology Japan

ISBN: (纸本)9789728939830

The purpose of our research is to enable the management of the requirements elicitation process. To manage something, we fist have to observe it quantitatively. Therefore, to manage the requirements elicitation process, we must observe the process quantitatively. We are currently trying to observe requirements quantitatively in accordance with their categories. In general, requirements are classified into two categories: functional and non-functional. The requirements categories can be refined to more adequately observe them. We defined the taxonomy of requirements. In this paper, the classification rules of words in requirements are proposed. A prototype tool was developed, and named the "requirements counting tool (RCT)," with classification rules and morphological analysis. The rules were evaluated by comparing the results of the RCT and manual classification with the logrank test. In the paper, the results are shown, and the adequacy of the rules is confirmed. © 2013 IADIS.

关键词： Requirements engineering

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Detecting human features in summaries - Symbol sequence statistical regularity

Detecting human features in summaries - Symbol sequence stat...

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7th Hellenic Conference on Artificial Intelligence, SETN 2012

作者： Giannakopoulos, George Karkaletsis, Vangelis Vouros, George A. Software and Knowledge Engineering Laboratory National Center of Scientific Research Demokritos Greece Department of Digital Systems University of Pireaus Greece

ISBN: (纸本)9783642304477

The presented work studies textual summaries, aiming to detect the qualities of human multi-document summaries, in contrast to automatically extracted ones. The measured features are based on a generic statistical regularity measure, named Symbol Sequence Statistical Regularity (SSSR). The measure is calculated over both character and word n-grams of various ranks, given a set of human and automatically extracted multi-document summaries from two different corpora. The results of the experiments indicate that the proposed measure provides enough distinctive power to discriminate between the human and non-human summaries. The results hint on the qualities a human summary holds, increasing intuition related to how a good summary should be generated. © 2012 Springer-Verlag.

关键词： Computers

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Automatic extraction of foreground objects from Mars images

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Geo-Spatial Information Science 2012年第1期15卷 17-25页

作者： WANG Shuliang LIU Chang WU Shangru NIE Qianqian WANG Yongtao ZENG Shi ZHU Haifeng International School of Software Wuhan University129 Luoyu RoadWuhan 430079China Key Laboratory of Data Mining and Knowledge Engineering Inner Mongolia Autonomous RegionInner Mongolia UniversityHohhot 010021China Department of Geography East Michigan UniversityYpsilantiMI 48197USA

A novel method is proposed to automatically extract foreground objects from Martian surface *** characteristics of Mars images are distinct,*** illumination,low contrast between foreground and background,much noise in the background,and foreground objects with irregular *** the context of these characteristics,an image is divided into foreground objects and background *** filtering is first applied to rectify ***,wavelet transformation enhances contrast and denoises the ***,edge detection and active contour are combined to extract contours regardless of the shape of the *** results show that the method can extract foreground objects from Mars images automatically and accurately,and has many potential applications.

关键词： automatic object extraction Mars images homomorphic filtering wavelet transformation active contour edge detection

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Towards enriching linked open data via open information extraction

Towards enriching linked open data via open information extr...

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1st International Workshop on knowledge Discovery and Data Mining Meets Linked Open Data, KNOW@LOD 2012

作者： Koukourikos, Antonis Karkaletsis, Vangelis Vouros, George University of Piraeus Department of Digital Systems 80 Karaoli and Dimitriou Str. Piraeus 18534 Greece Software and Knowledge Engineering Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos Agia Paraskevi Attikis P.O. Box 60228 15310 Athens Greece

The descriptions of various entities on Linked Data repositories are subject to constant renewals and modifications, with respect to both the descriptions of concepts and relations and entities realizing their instantiations. Thus, the underlying ontologies have to be updated accordingly in order to reflect these changes. This paper presents a system for examining the possibilities of discovering new relations and updating/verifying existing ones for entities described in Linked Data repositories by using Open Information Extraction techniques. These are applied to web content. The process aims to the enrichment of the examined datasets and the expansion of the ontologies with newly-discovered concepts and relations. Towards this target, the paper discusses the intricacies, pitfalls and challenges present.

关键词： Linked data

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