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2017 Asia-Pacific Computational Intelligence and Information Technology Conference

作者： Tien-Wen Sung Chia-Jung Lee Pei-Wei Tsai Sheng-Hui Meng Fu-Tian Lin Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and EngineeringFujian University of Technology Department of Electrical Engineering Tung Fang Design University

The popularity of Internet and growing B2 C electronic commerce nowadays make product or service information easy to be ***,making an optimal choice from the various alternative products becomes a laborious *** this paper,an ontology-based Decision Support System(DSS) with Analytic Hierarchy Process(AHP) was proposed for the specific application of tour package *** system is composed of two subsystems,the product gatherer and the decision maker,which are used to find out right products and make an expected choice *** the product gatherer subsystem,an ontology-based web service architecture with Web Ontology Language(OWL) was established for the semantic content processing of product *** Simple Object Access Protocol(SOAP) is utilized to establish the communication interface and gather XML-based contents through Remote Procedure Calls(RPC) between the system and the database servers of travel *** the decision maker subsystem,the Analytic Hierarchy Process is utilized to make an optimal decision for satisfying the requirement given by the *** system aims to help consumers to avoid falling into decision-making hesitation and get an expected choice from various and similar products.

关键词： Protocol Internet OWL Ontology-based decision support system with analytic hierarchy process for tour package selection Web

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Ontology-based decision support system with analytic hierarchy process for tour package selection

Ontology-based decision support system with analytic hierarc...

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2017第二届亚太计算智能与信息技术学术会议 (APCIIT 2017)

The popularity of Internet and growing B2 C electronic commerce nowadays make product or service information easy to be acquired. However,making an optimal choice from the various alternative products becomes a laborious process. In this paper,an ontology-based Decision Support System(DSS) with Analytic Hierarchy Process(AHP) was proposed for the specific application of tour package selection. The system is composed of two subsystems,the product gatherer and the decision maker,which are used to find out right products and make an expected choice respectively. In the product gatherer subsystem,an ontology-based web service architecture with Web Ontology Language(OWL) was established for the semantic content processing of product information. The Simple Object Access Protocol(SOAP) is utilized to establish the communication interface and gather XML-based contents through Remote Procedure Calls(RPC) between the system and the database servers of travel agencies. In the decision maker subsystem,the Analytic Hierarchy Process is utilized to make an optimal decision for satisfying the requirement given by the consumer. The system aims to help consumers to avoid falling into decision-making hesitation and get an expected choice from various and similar products.

关键词： Protocol Internet Ontology-based decision support system with analytic hierarchy process for tour package selection Web

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Utility-based anonymisation for dataset with multiple sensitive attributes

Utility-based anonymisation for dataset with multiple sensit...

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作者： Wang, Lixia Zhu, Qing Key Laboratory for Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing100872 China

Privacy-preserving data publication problem has attracted more and more attentions in recent years. A lot of related research works have been done towards dataset with single sensitive attribute. However, usually, original dataset contains more than one sensitive attribute. In this paper, we apply k-anonymity principle to solve the data publication problem for dataset with multiple sensitive attributes. We first cluster sensitive values based on a utility matrix. Then, we use a greedy strategy to partition tuples into equivalence classes. Our method can guarantee that the size of equivalence class is k except the last one, which reduces information loss. Also, we can guarantee the diversity of sensitive value in an equivalence class, which can protect privacy against the homogeneity attack. Experiments on a real dataset show that our method performs well on information loss, which indicates that we can guarantee data utility while protecting personal privacy. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Equivalence classes

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Investigation on Automatic data Acquisition of Well Logging Curve based on Color Recognition

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Journal of Physics: Conference Series 2019年第2期1237卷

作者： Han Wang Zhihui Ye Dong Chen Dandan Zhu Shouding Li College of Safety and Ocean Engineering China University of PetroleumBeijing Beijing 102249 China State Key Laboratory of Petroleum Resources and Prospecting College of Petroleum Engineering China University of PetroleumBeijing Beijing 102249 China Beijing Key Lab of Petroleum Data Mining Department of Computer Science and Technology Beijing 102249 China Institute of Geology and Geophysics Chinese Academy of Sciences Key Laboratory of Shale Gas and Geoengineering Institute of Geology and Geophysics Chinese Academy of Sciences Beijing 100029 China University of Chinese Academy of Sciences Beijing 100083 China Institutions of Earth Science Chinese Academy of Sciences Beijing 100029 China

Well logging data are important to evaluate rock and fluid properties underground. Many well logging data, as functions of well depth, are conserved as figures in the literature. Digitizing technics should be applied when utilizing the well logging data. In order to address this problem, a method was proposed to automatically identify and acquire the well logging data from well logging curves. Based on the color recognition, this algorithm is able to extract curve features based on pixels. Comparing to other data digitizing methods, the background grid lines could be easily filtered and removed. The algorithm is verified by digitizing some typical well logging curves. This main contribution of this work is to provide a new and efficient way for data digitizing from well logging curves.

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Accuracy estimation of link-based similarity measures and its application

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Frontiers of Computer Science 2016年第1期10卷 113-123页

作者： Yinglong ZHANG Cuiping LI Chengwang XIE Hong CHEN School of Software East China Jiaotong University Nanchang 330045 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and Department of Computer Science Renmin University of China Beijing 100872 China Intelligent Optimization and Information Processing Laboratory East China Jiaotong University Nanchang 330013 China

Link-based similarity measures play a significant role in many graph based applications. Consequently, mea- suring node similarity in a graph is a fundamental problem of graph data mining. Personalized PageRank （PPR） and Sim- Rank （SR） have emerged as the most popular and influen- tial link-based similarity measures. Recently, a novel link- based similarity measure, penetrating rank （P-Rank）, which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calcula- tion. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guaran- tee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition： the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similar- ity scores becomes. Furthermore, we demonstrate the effec- tiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of exper- iments on real world data sets to verify the effectiveness and efficiency of our upper bounds.

关键词： personalized PageRank SimRank P-Rank up-per bound

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Efficient ontology meta-matching using alignment prescreening approach and gaussian random field model assisted NSGA-II

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Journal of Information Hiding and Multimedia Signal Processing 2017年第5期8卷 1062-1068页

作者： Xue, Xingsi Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Minhou FuzhouFujian350118 China

Multi-Objective Evolutionary Algorithm (MOEA) is emerging as a new methodology to tackle the ontology meta-matching problem. However, for dynamic applications, besides the alignment’s quality, runtime and memory consumption in the matching process are also of great importance. In this paper, we propose an efficient NSGA-II based ontology meta-matching technology to improve the efficiency of NSGA-II based ontology meta-matching technology. In particular, our approach can automatically prescreen the less promising ontology alignments to be combined, which can reduce the search space of NSGA-II and improve its runtime, and reduce the number of exact individual evaluations by using Gaussian Random Field Model (GRFM), which can decrease the memory consumption of NSGA-II. The experimental results show that the utilization of alignment prescreening approach and GRFM is able to significantly improve the efficiency without sacrificing the alignment’s quality. © 2017, Ubiquitous International. All rights reserved.

关键词： Ontology

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Using artificial bee colony algorithm for optimizing ontology alignment

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Journal of Information Hiding and Multimedia Signal Processing 2017年第4期8卷 766-773页

作者： He, Yao Xue, Xingsi Zhang, Shunmiao College of Information Science and Engineering Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou City350118 China

Due to the high heterogeneity of ontologies, a combination of many methods is necessary to correctly discover the semantic correspondences between their elements. But how to determine the optimal combination way in order to obtain an ontology alignment with high quality is still a challenge. To this end, in this paper, we propose to use the Artificial Bee Colony Algorithm (ABC) to solve the ontology meta-matching problem. In particular, we construct a new optimal model for the ontology meta-matching problem and design a problem specific ABC to solve it. The benchmark of OAEI 2014 is utilized to test our approach, and the experimental results show that the mean recall and precision of our approach is generally high, and the mean f-measure of the alignments obtained by our approach outperform all other state of the art matching systems. © 2017, Ubiquitous International. All rights reserved.

关键词： Ontology

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Examining scientific writing styles from the perspective of linguistic complexity

arXiv

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arXiv 2018年

作者： Lu, Chao Bu, Yi Wang, Jie Ding, Ying Torvik, Vetle Schnaars, Matthew Zhang, Chengzhi School of Economics and Management Nanjing University of Science and Technology Nanjing Jiangsu China School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Center for Complex Networks and Systems Research School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Information Management Nanjing University Nanjing Jiangsu China School of Information Management Wuhan University Wuhan Hubei China School of Information Sciences University of Illinois UrbanaIL United States Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing Jiangsu China

Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. In order to uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (1) syntactic complexity, including measurements of sentence length and sentence complexity;and (2) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity. Copyright © 2018, The Authors. All rights reserved.

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Large-scale Nonparallel Support Vector Ordinal Regression Solver

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Procedia Computer Science 2017年 108卷 1261-1270页

作者： Huadong Wang Jianyu Miao Seyed Mojtaba Hosseini Bamakan Lingfeng Niu Yong Shi Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68118 USA

Large-scale linear classification is widely used in many areas. Although SVM-based models for ordinal regression problem are proven to be powerful techniques, the performance with nonlinear kernels are often suffering from time consuming. Recently, linear SVC not only is shown to obtain competitive performance in most of the cases, but also it is considerably fast during the process of training and testing. However, few studies focused on linear SVM-based ordinal regression models. In this paper, we propose a new approach, called linear Nonparallel Support Vector Ordinal Regression (NPSVOR), which can deal with large-scale problems. An efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the proposed model. Our experiments are performed on large document data sets to demonstrate the effectiveness of the proposed method.

关键词： Ordinal Regression Support Vector Machine ADMM

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An evolutionary algorithm based ontology matching system

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Journal of Information Hiding and Multimedia Signal Processing 2017年第3期8卷 551-556页

作者： Xue, Xingsi Tang, Zhengyi College of Information Science and Engineering Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China

Due to the high heterogeneity of ontologies, a combination of many methods is necessary to discover correctly the semantic correspondences between their elements. An ontology matching system can be seen as a collection of several matching components, which implements a specific method dealing with a specific heterogeneity type like the ter- minological, structural and semantic matchers. In this paper, a novel ontology matching system architecture is proposed and the details of its kernel model are presented, i.e. matching and mapping extraction module, where we construct a new optimal model for the ontology matching and mapping extraction problem and design a problem specific Evolutionary Algorithm (EA) for it. The experimental results show that the mean recall and precision of our approach is generally high, and the mean f-measure of the align- ments obtained by our approach outperform all other state of the art matching systems. © 2017.

关键词： Extraction

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