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Web information Systems and Applications Conference (WISA)

作者： Yue Liu Nan Yang Gang Yang The school of Information Renmin University of China Bejing China The school of Information Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Bejing China

Recently, social tagging systems become more and more popular in many Web 2.0 applications. In such systems, Users are allowed to annotate a particular resource with a freely chosen a set of tags. These user-generated tags can represent users' interests more concise and closer to human understanding. Interests will change over time. Thus, how to describe users' interests and interests transfer path become a big challenge for personalized recommendation systems. In this approach, we propose a variable-length time interval division algorithm and user interest model based on time interval. Then, in order to draw users' interests transfer path over a specific time period, we suggest interest transfer model. After that, we apply a classical community partition algorithm in our approach to separate users into communities. Finally, we raise a novel method to measure users' similarities based on interest transfer model and provide personalized tag recommendation according to similar users' interests in their next time intervals. Experimental results demonstrate the higher precision and recall with our approach than classical user-based collaborative filtering methods.

关键词： Mathematical model Computational modeling Collaboration Educational institutions Tagging Filtering Heuristic algorithms

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Generalized Unique Game Problem

Generalized Unique Game Problem

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2012年全国理论计算机科学学术年会(NCTCS2012)

作者： Peng Cui Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Resource Management Renmin University of China Beijing 100872 P.R.China

this paper,the author defines Generalized Unique Game Problem (GUGP),where weights of the edges are allowed to be *** special types of GUGP are illuminated,GUGP-NWA,where the weights of all edges are negative,and GUGP-PWT(ρ),where the total weight of all edges are positive and the negative-positive ratio is at most ρ.

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Journal of Convergence information Technology 2012年第16期7卷 87-96页

作者： Minjuan, Zhong School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China

With the increasing of XML data over the Internet, managing and analyzing huge amount of XML documents has played an important role for information management. Clustering as an intelligent technique has been utilized as an excellent way of grouping the documents by their content or structure. However, the key problem is how to measure similarity between XML documents. In this paper, we propose an extended vector space model and on this basis put forward an effective semantic similarity measurement method combining content and structure semantics, in which a variety of XML document features impacting similarity measurement, such as term element frequency, term inverse element frequency, semantic weight of tag and level information of the term, are analyzed. In addition, information gain, for clustering quality evaluation are introduced motivated by the fact that collection has no classification information in advance. Experiment results show that proposed similarity method (EVSM_SS) outperforms the content and structure integration measurement based on structure path (VSM_SP) as well as traditional document clustering measurement (CO) in information gain and produce better clustering quality.

关键词： XML

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Combining term semantics with content and structure semantics for XML element search results clustering

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Journal of Convergence information Technology 2012年第15期7卷 26-35页

The biggest characteristic of the XML retrieval is able to return the element node results. This paper studies XML element search results clustering and proposes one similarity measurement method based on term semantics, in which the "core" concept between terms is got through latent semantic indexing technology(LSI) and the same time the XML element node content and semantic structure properties(CASS) are combined. In addition, two new performance evaluation methodologies, namely R_ClusterRatio and R_DocuRatio are introduced to evaluate clustering quality. It is motivated by the observations of relevant documents distribution and the fact that the experiment data collection, IEEE CS corpus, do not provide classification information. Experiment results show that proposed similarity method combining term semantics with content and structure semantics integration(LSI-CASS) is feasible, and it produces better clustering quality than LSI-CAS and CASS.

关键词： Semantics

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A Framework of Multi-Stage Classifier for Identifying Criminal Law Sentences

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Procedia Computer Science 2012年 13卷 53-59页

作者： Sotarat Thammaboosadee Bunthit Watanapa Nipon Charoenkitkarn Data and Knowledge Engineering Laboratory (D-Lab) School of Information Technology King Mongkut's University of Technology Thonburi 126 Pracha Utit Rd. Bangmod Thungkru Bangkok 10140 Thailand

This paper proposes a framework to identify the relevant law articles consisting of sentences and range of punishments, given facts discovered in the criminal case of interest. The model is formulated as a two-stage classifier according to the concept of machine learning. The first stage is to determine a set of case diagnostic issues, using a modular Artificial Neural Network (mANN), and the second stage is to determine the relevant legal elements which lead to legal charges identification, using SVM-equipped C4.5. The integrated multi-stage model aims at achieving high accuracy of classification while reserving “arguability”. Hypothetically, mANN handles well for digesting complexity in case-level issues analysis with acceptable explanatory power and C4.5 addresses the lesser extent of contingency and provides human-interpretable logic concerning the high-level context of legal codes.

关键词： Criminal law data mining Neural network Decision tree Legal reasoning

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Reducing Uncertainty of Low-Sampling-Rate Trajectories

Reducing Uncertainty of Low-Sampling-Rate Trajectories

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International Conference on data engineering

作者： Kai Zheng Yu Zheng Xing Xie Xiaofang Zhou School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia Microsoft Research Asia Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China

ISBN: (纸本)9781467300421

The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.

关键词： Trajectory Roads Global Positioning System Inference algorithms Uncertainty Artificial neural networks Educational institutions

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Optimizing parameters of algorithm trading strategies using MapReduce

Optimizing parameters of algorithm trading strategies using ...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Xiongpai Qin Huiju Wang Furong Li Jidong Chen Xuan Zhou Xiaoyong Du Shan Wang Key Laboratory of Data Engineering and Knowledge Engineering (RUC) Ministry of Education Beijing China School of Information Renmin University of China Beijing China EMC-Greenplum Research China China

In algorithm trading, computer algorithms are used to make the decision on the time, quantity, and direction of operations (buy, sell, or hold) automatically. To create a useful algorithm, the parameters of the algorithm should be optimized based on historical data. However, Parameter optimization is a time consuming task, due to the large search space. We propose to search the parameter combination space using the MapReduce framework, with the expectation that runtime of optimization be cut down by leveraging the parallel processing capability of MapReduce. This paper presents the details of our method and some experiment results to demonstrate its efficiency. We also show that a rule based strategy after being optimized performs better in terms of stability than the one whose parameters are arbitrarily preset, while making a comparable profit.

关键词： Optimization Measurement Computers Algorithm design and analysis Runtime Servers data processing

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Single attribute Join Queries within latest sampling periods in sensor networks

Single attribute Join Queries within latest sampling periods...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Shangfeng Mo Hong Chen Yinglong Li School of Math and Computing Science Hunan University of Science and Technology Xiangtan China Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing China

Join processing in wireless sensor networks is a challenging problem. Current solutions are not involved in the join operation among tuples of the latest sampling periods. In this article, we proposed a continuous Single attribute Join Queries within latest sampling Periods (SJQP) for wireless sensor networks. The main idea of our filter-based framework is to discard non-matching tuples, and our scheme can guarantee the result is correct independent of the filters. Experiments based on real-world sensor data show that our method performs close to a theoretical optimum and consistently outperforms the centralized join algorithm.

关键词： Base stations Wireless sensor networks Probes Filtering algorithms Temperature sensors information filters

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Drivers for strategic choice of cloud computing as online service in SMEs

Drivers for strategic choice of cloud computing as online se...

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International Conference on information Systems, ICIS 2012

作者： Li, Min Yu, Yan Zhao, J. Leon Li, Xin University of Science and Technology China-City University of Hong Kong Joint Advanced Research Center 166 Renai Road Dushu Lake Higher Education Town Suzhou China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China 59 Zhongguancun Street Haidian District Beijing China Department of Information Systems City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong

ISBN: (纸本)9781627486040

Cloud Computing Service (CCS) paradigm is changing IT strategy of organizations in the digital world. CCS that requires few upfront investments and uses lease-based pricing is especially relevant to the Small and Medium Enterprises (SMEs), which have limited resources and may not know their true valuation for the IT prior to adoption. Thus, this research aims to investigate the influential factors of SMEs' strategic choice of CCS as online service. Relying upon Technology-Organization-Environment (TOE) paradigm, we identify both generic and context-specific factors from the three aspects and explain how the identified factors affect SMEs' CCS strategic choices. We hope this research can make contributions to innovation diffusion theory and IT strategy literature. We also hope the research with progress going on can generate insights for the CCS vendors who care about the sector of SME as well as the government administrators to make appropriate policies or supports for SMEs.

关键词： Cloud computing

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

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) 2012年 7472 LNAI卷 V-VII页

作者： Atzmueller, Martin Chin, Alvin Helic, Denis Hotho, Andreas University of Kassel Knowledge and Data Engineering Group Wilhelmshöher Allee 73 34121 Kassel Germany Nokia Research Center Mobile Social Networking Group Beijing 100176 China Graz University of Technology Faculty of Computer Science Inffeldgasse 21a 8010 Graz Austria University of Würzburg Data Mining and Information Retrieval Group Am Hubland 97074 Würzburg Germany

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