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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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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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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 RFID Mixed Coding Scheme Based on Huffman Algorithm

A RFID Mixed Coding Scheme Based on Huffman Algorithm

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International Conference on Management of e-Commerce and e-Government, ICMECG

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

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.

关键词： Encoding Labeling Radiofrequency identification XML Algorithm design and analysis Testing

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基于主题语义的非合作结构化Top-N深网数据源选择

基于主题语义的非合作结构化Top-N深网数据源选择

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第29届中国数据库学术会议

作者： Deng Song 邓松 Wan Changxuan 万常选 Liu Xiping 刘喜平 Jiang Tengjiao 江腾蛟 Lei Gang 雷刚 School of Information and Technology Jiangxi University of Finance and Economics Nanchang 330013 江西财经大学信息管理学院南昌 330013 Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economic 江西财经大学数据与知识工程江西省高校重点实验室南昌 330013

高效且准确地找出存在于深网中的与用户查询意图最相关的Top-N元组，是深网数据集成中的关键问题。针对数据源内容概括未见成果的现状，本文提出了一种能够有效概括非数字、非离散属性特征的非合作结构化深网数据源摘要构建方法。利用... 详细信息

高效且准确地找出存在于深网中的与用户查询意图最相关的Top-N元组，是深网数据集成中的关键问题。针对数据源内容概括未见成果的现状，本文提出了一种能够有效概括非数字、非离散属性特征的非合作结构化深网数据源摘要构建方法。利用主题词抽样，依据主题语义选择与主题词相关的特征词，通过主题词及特征词构建数据源语义摘要。实验结果表明与传统的基于词频的方法相比较，该方法在召回率及准确度方面均有较大幅度的提升。

关键词：深网数据集成检索系统优化设计信息提取主题语义

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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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K-nearest neighbors in uncertain graph

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2011年第10期48卷 1850-1858页

作者： Zhang, Yinglong Li, Cuiping Chen, Hong Du, Lingxia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Computer and Information Engineer Jiangxi Agriculture University Nanchang 330045 China

In many areas, a lot of data have been modeled by graphs which are subject to uncertainties, such as molecular compounds and protein interaction networks. While many real applications, for example, collaborative filtering, fraud detection, and link prediction in social networks etc, rely on efficiently answering k-nearest neighbor queries (kNN), which is the problem of computing the most "similar" k nodes to a given query node. To solve the problem, in this paper a novel method based on measurement of SimRank is proposed. However, because graphs evolve over time and are uncertainly, the computing cost can be very high in practice to solve the problem using the existing algorithms of SimRank. So the paper presents an optimization algorithm. Introducing path threshold, which is suitable in both determined graph and uncertain graph, the algorithm merely considers the local neighborhood of a given query node instead of whole graph to prune the search space. To further improving efficiency, the algorithm adopts sample technology in uncertain graph. At the same time, theory and experiments interpret and verify that the optimization algorithm is efficient and effective.

关键词： Collaborative filtering

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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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