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Using New Fusion Operations to Improve Trust Expressiveness of Subjective Logic

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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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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Random rough subspace based neural network ensemble for insurance fraud detection

Random rough subspace based neural network ensemble for insu...

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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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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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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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A query-oriented summarization system for XML elements

A query-oriented summarization system for XML elements

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

作者： Liu, Dexi Wu, Shihan Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China Songjiang Branch Shanghai Rural Commercial Bank Shanghai 201600 China

ISBN: (纸本)9780769545448

In document-center XML dataset, an element may contain so many text that users have to spend enough time to judge the elements returned by XML search engine are valuable or not. Query-orient XML summarization system aim to provide users a brief and readable substitution of the original retrieved elements according to the user's query, which can relieve user's reading burden effectively. In this work, we extract sentences from the results of XML search engine, and combine them as a summary. Experiments on the IEEE-CS datasets used in INEX show that, the query-oriented XML summary generated by our method is reasonable. © 2011 IEEE.

关键词： XML

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Safe side effects commitment for OS-level virtualization 11

Safe side effects commitment for OS-level virtualization

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Proceedings of the 8th ACM international conference on Autonomic computing

作者： Shan, Zhiyong Wang, Xin Chiueh, Tzi-Cker Meng, Xiaofeng Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing China Stony Brook University Stony Brook NY United States Industrial Technology Research Institute Taiwan

ISBN: (纸本)9781450306072

A common application of virtual machines (VM) is to use and then throw away, basically treating a VM like a completely isolated and disposable entity. The disadvantage of this approach is that if there is no malicious activity, the user has to re-do all of the work in her actual workspace since there is no easy way to commit (i.e., merge) only the benign updates within the VM back to the host environment. In this work, we develop a VM commitment system called Secom to automatically eliminate malicious state changes when merging the contents of an OS-level VM to the host. Secom consists of three steps: grouping state changes into clusters, distinguishing between benign and malicious clusters, and committing benign clusters. Secom has three novel features. First, instead of relying on a huge volume of log data, it leverages OS-level information flow and malware behavior information to recognize malicious changes. As a result, the approach imposes a smaller performance overhead. Second, different from existing intrusion detection and recovery systems that detect compromised OS objects one by one, Secom classifies objects into clusters and then identifies malicious objects on a cluster by cluster basis. Third, to reduce the false positive rate when identifying malicious clusters, it simultaneously considers two malware behaviors that are of different types and the origin of the processes that exhibit these behaviors, rather than considers a single behavior alone as done by existing malware detection methods. We have successfully implemented Secom on the Feather-weight Virtual Machine (FVM) system, a Windows-based OS-level virtualization system. Experiments show that the prototype can effectively eliminate malicious state changes while committing a VM with small performance degradation. Moreover, compared with the commercial anti-malware tools, the Secom prototype has a smaller number of false negatives and thus can more thoroughly clean up malware side effects. In addit

关键词： Virtual machine

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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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Effect factors on secondary structure of protein sequence pattern

Effect factors on secondary structure of protein sequence pa...

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International Workshop on Intelligent Systems and Applications

作者： Liu, Tao Li, Minghui Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)9781424498574

Discovering the relationship between protein sequence pattern and protein secondary structure is important for accurately predicting secondary structure of protein sequence. A protein secondary structure pattern dictionary is constructed using protein sequence pattern and its corresponding secondary structure in this paper. Based on the constructed dictionary, we propose four effect factors on secondary structure of protein sequence pattern, including 1) the core pattern itself;2) patterns or amino acid residues that neighbor with the core pattern;3) patterns or amino acid residues that are far away from the core pattern;and 4) amino acid sequence segment that match the core pattern. Statistical measures are adopted to analyze these factors. The experimental result shows the reliability of these factors. The recognition of these effect factors presents new directions to predict protein secondary structure based on protein pattern dictionary. © 2011 IEEE.

关键词： Amino acids

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