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International Workshop on Computational Transportation Science

作者： Xu, Wei Wang, Shengnan Zhang, Dailing Yang, Bo Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769543352

In this paper, a random rough subspace based neural network ensemble method is proposed for insurance fraud detection. In this method, rough set reduction is firstly employed to generate a set of reductions which can keep the consistency of data information. Secondly, the reductions are randomly selected to construct a subset of reductions. Thirdly, each of the selected reductions is used to train a neural network classifier based on the insurance data. Finally, the trained neural network classifiers are combined using ensemble strategies. For validation, a real automobile insurance case is used to test the effectiveness and efficiency of our proposed method with two popular evaluation criteria including the percentage correctly classified (PCC) and the receive operating characteristic (ROC) curve. The experimental results show that our proposed model outperforms single classifier and other models used in comparison. The findings of this study reseal that the random rough subspace based neural network ensemble method can provide a faster and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. © 2011 IEEE.

关键词： Insurance

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Using New Fusion Operations to Improve Trust Expressiveness of Subjective Logic

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Wuhan University Journal of Natural Sciences 2011年第5期16卷 376-382页

作者： ZHOU Hongwei1,2,3,SHI Wenchang1,2,LIANG Zhaohui1,2,LIANG Bin1,2 1.key laboratory of data engineering and knowledge engineering,Ministry of Education,Beijing 100872,China 2.school of Information,Renmin University of China,Beijing 100872,China 3.Institute of Electronic Technology,Information engineering University of PLA,Zhengzhou 450004,Henan,China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Institute of Electronic Technology Information Engineering University of PLA Zhengzhou Henan China

Subjective logic provides a means to describe the trust relationship of the realworld. However, existing fusion operations it offers Weal fused opiniotts equally, which makes it impossible to deal with the weighted opinions effectively. A. Jcsang presents a solution, which combines the discounting operator and the fusion operator to produce the consensus to the problem. In this paper, we prove that this approach is unsuitable to deal with the weighted opinions because it increases the uncertainty of the consensus. To address the problem, we propose two novel fusion operators that are capable of fusing opinions according to the weight of opinion in a fair way, and one of the strengths of them is improving the trust expressiveness of subjective logic. Furthermore, we present the justification on their definitions with the mapping between the evidence space and the opinion space. Comparisons between existing operators and the ones we proposed show the effectiveness of our new fusion operations.

关键词： trust subjective logic opinion fusion weizht

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Detecting stealthy malware with inter-structure and imported signatures 11

Detecting stealthy malware with inter-structure and imported...

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6th International Symposium on Information, Computer and Communications Security, ASIACCS 2011

作者： Liang, Bin You, Wei Shi, Wenchang Liang, Zhaohui Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781450305648

Recent years have witnessed an increasing threat from kernel rootkits. A common feature of such attack is hiding malicious objects to conceal their presence, including processes, sockets, and kernel modules. Scanning memory with object signatures to detect the stealthy rootkit has been proven to be a powerful approach only when it is hard for adversaries to evade. However, it is difficult, if not impossible, to select fields from a single data structure as robust signatures with traditional techniques. In this paper, we propose the concepts of inter-structure signature and imported signature, and present techniques to detect stealthy malware based on these concepts. The key idea is to use cross-reference relationships of multiple data structures as signatures to detect stealthy malware, and to import some extra information into regions attached to target data structures as signatures. We have inferred four invariants as signatures to detect hidden processes, sockets, and kernel modules in Linux respectively and implemented a prototype detection system called DeepScanner. Meanwhile, we have also developed a hypervisor-based monitor to protect imported signatures. Our experimental result shows that our DeepScanner can effectively and efficiently detect stealthy objects hidden by seven real-world rootkits without any false positives and false negatives, and an adversary can hardly evade DeepScanner if he/she does not break the normal functions of target objects and the system. Copyright 2011 ACM.

关键词： Malware

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A neural network based forecasting method for the unemployment rate prediction using the search engine query data

A neural network based forecasting method for the unemployme...

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2011 8th IEEE International Conference on e-Business engineering, ICEBE 2011

作者： Xu, Wei Zheng, Tingting Li, Ziang Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769545189

Unemployment rate prediction has become critically important, because it can help government to make decision and design policies. In recent years, forecast of unemployment rate attracts much attention from governments, organizations, and research institutes, and researchers. Recently, a novel method using search engine query data to forecast unemployment was proposed by scholars. In this paper, a data mining based framework using web information is introduced for unemployment rate prediction. Under the framework, a neural network method, as one of the most effective data mining tools, is developed to forecast unemployment trend using search engine query data. In the proposed method, search engine query data related with employment activities is firstly found. Secondly, feature selection models including correlation coefficient method and genetic algorithm are constructed to reduce the dimension of the query data. Thirdly, various neural networks are employed to model the relationship between unemployment rate data and query data. Fourthly, an optimal neural network is selected as the selective predictor by using the cross-validation method. Finally, the selective neural network predictor with the best feature subset is used to forecast unemployment trend. The empirical results show that the proposed method clearly outperforms the classical forecasting approaches for the unemployment rate prediction. These findings imply that data mining method, such as neural networks, together with web information, can be used as an alternative tool to forecast social/economic hotspot. © 2011 IEEE.

关键词： Forecasting

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A RFID mixed coding scheme based on huffman algorithm

A RFID mixed coding scheme based on huffman algorithm

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2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Jian, Jiang Guoqiong, Liao Hao, Wang Qizhi, Wan School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang Nanchang China

ISBN: (纸本)9780769545448

In RFID application systems with multiple packaging layers, labeling packaging relationship of objects in different packaging layers by encoding methods is a important technology field. Prefix-based labeling scheme is a method by which the packaging relationship of objects amounts to testing whether one object is an prefix of the other, whereas the region numbering scheme is a method by which an ancestor query amount to an interval containment test on the labels. This paper first presents a mixed-encoding scheme based on Huffman algorithm, then propose a variant of this encoding scheme in which new "virtual nodes" are involved in order to improve the stability of encoding. Our experiments prove that the mixed-based encoding scheme is effective due to the enhancement of update performance and query efficiency. © 2011 IEEE.

关键词： Encoding (symbols)

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Spatial data mining under Smart Earth

Spatial data mining under Smart Earth

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2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Wang, Shuliang International School of Software of Wuhan University Wuhan University Wuhan Hubei 430072 China Key Laboratory of Data Mining and Knowledge Engineering Hohhot China

ISBN: (纸本)9781457703713

Complex spatial data is the fundamental content of Smart Earth. Spatial data mining plays an important role in Smart Earth. In this paper, it is proposed that "Smart earth equals to Digital Earth plus the Internet of Things", which brings a new challenge for spatial data mining. First, the background of spatial data is summarized. Second, Smart Earth is put forward to be an integration of Digital Earth and the Internet of Things. Third, by analyzing the spatial data sources, types and structures, it uncovers that spatial data is the resource of Smart Earth. Fourth, spatial knowledge to discover in Smart Earth are analyzed on its connotations, extensions and categories. It indicates that spatial knowledge is the intelligent sources of Smart Earth. Fifth, based on spatial data characteristics and possible methodologies, the framework of spatial data mining is given under Smart Earth. Finally, the perspective of applicable fields is prospected for spatial data mining under Smart Earth. It is concluded that spatial data mining is one of the fundamental tools to achieve Smart Earth. © 2011 IEEE.

关键词： Internet of things

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Visual group identification method of technical competitors using LinLog graph clustering algorithm

Visual group identification method of technical competitors ...

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IEEE International Conference on Computer Science and Automation engineering

作者： Han, Hong-Qi An, Xiaomi Zhu, Donghua Wang, Xuefeng Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Management and Economics Beijing Institute of Technology Beijing 100872 China

ISBN: (纸本)9781424487257

Visualization technique is a powerful method used by science and technology intelligence analysis experts to identify technical competitor groups. Common visualization methods tend to create graphs meeting the aesthetic criteria instead of finding better clusters, and their analysis results may provide misleading information. A process model of technical group identification method was presented using LinLog graph clustering algorithm to find better competitor groups. In the model, technical similarity value of each pair of competitors is measured based on their R&D output in sub-fields, and two competitors have a link when they have high similarity value;LinLog algorithm, which is aimed at producing better clusters, was employed to layout graph with competitors as nodes, their links as edges and technology similarity values as weights of edges. Experiment results show the efficiency of presented method. © 2011 IEEE.

关键词： Visualization

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Formal analysis of trusted computing: One case study

Formal analysis of trusted computing: One case study

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International Conference on Communications and Mobile Computing

作者： Zhou, HongWei Yuan, JinHui Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Institute of Electronic Technology Zhengzhou Information Engineering University of China Zhengzhou China

ISBN: (纸本)9780769543574

LS2 is the logic to reason about the property of trusted computing. However, it lacks the capability of modeling the isolation provided by virtualization which is often involved in previous trusted computing system. With the support of changed LS2, we model three types of isolation. Moreover, we formally analyze the integrity measurement property of Trust Visor proposed recently which provides the isolated execution environment for security-sensitive code. © 2011 IEEE.

关键词： Virtualization

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B-IRP: An improving integrity report protocol for remote attestation

B-IRP: An improving integrity report protocol for remote att...

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International Conference on Communications and Mobile Computing

ISBN: (纸本)9780769543574

Trusted platform module (TPM) has little computation capability, and it is the performance bottleneck of remote attestation. In the scenario where the server is the attestation-busy entity which answers attestation requirement frequently, the massive delay is inevitable. Without the modification to TPM, we propose Batch Integrity Report Protocol (BIRP) to overcome the performance bottleneck. B-IRP bundles these requirements in interval as a batch requirement, and creates the response messages for these requirements with one expensive TPM operation. Discussion shows that B-IRP has improved the performance without loss of security. © 2011 IEEE.

关键词： Trusted computing

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Clustering XML search results based on content and structure similarity

Clustering XML search results based on content and structure...

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2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Min-Juan, Zhong Chang-Xuan, Wan De-Xi, Liu Xian-Pei, Jiao School of Information and Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9780769545448

Clustering XML search results is an effective way to improve performance. However, the key problem is how to measure similarity between XML documents. In this paper, we propose a semantic similarity measure method combining content with structure, in which a variety of XML document features, including term element frequency, term inverse element frequency, semantic weight of tag label and level information of the term, are analyzed and applied for computing the similarity between XML documents. In addition, two new performance evaluation methodology, namely Cluster Ratio Relevant and Docu Ratio Relevant, for clustering quality are introduced motivated by the observations of relevant documents distribution and the fact that collection has no classification information. Experiment results show that proposed similarity method(CAS measure)outperforms traditional document clustering(CO measure) in Cluster Ratio Relevant and Docu Ratio Relevant and produces better clustering quality. © 2011 IEEE.

关键词： XML

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