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IEEE International Conference on Big knowledge (ICBK)

作者： Gongqing Wu Shengjie Hu Yinghuan Wang Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

At present, the query and acquisition of the fragmented knowledge in Chinese court verdicts mainly adopt the class case retrieval method based on the search engine and the rough extraction method for a part of the data in court verdicts. These traditional methods cannot structurally extract fragmented knowledge in Chinese court verdicts and meet the needs of people for the follow-up analysis of court verdicts. Thus, in this paper, we present a structured subject event extraction method (SEE) for Chinese court verdict cases combining with techniques of event extraction (EE) and attribute-value pair extraction (AVPE). Specifically, we provide a subject event representation frame for organizing fragmented knowledge in Chinese court verdict cases. Then, we extract subject events from the unstructured cases based on the trained sequence labeling models and constructed heuristic rules, and fill them into the subject event representation frame in the form of attribute-value pairs (AVPs). The experimental results show that SEE can efficiently and automatically extract subject events from Chinese court verdict cases and visually display them via frame-filling, which promotes the efficiency of people in searching for legal materials and facilitates further research and analysis.

关键词： Feature extraction data mining Labeling Information retrieval Semantics Machine learning knowledge engineering

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Interference and power constrained Broadcasting and Multicasting in wireless ad hoc networks with directional antennas

Interference and power constrained Broadcasting and Multicas...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Zheng Li Deying Li Ming Liu Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China China School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9781424451142

Broadcasting/Multicasting problems have been well studied in wireless ad hoc networks. However, only a few approaches take into account the low interference and energy efficiency as the optimization objective simultaneously. In this paper, we study the interference and power constrained broadcast/multicast and the delay-bounded interference and power constrained broadcast/multicast routing problems in wireless ad hoc networks using directional antennas. We propose an approximation and a heuristic algorithm for the two problems, respectively. Importantly, motivated by the study of above optimization problems, we propose approximation schemes for two multi-constrained directed Steiner tree problems, respectively. Broadcast/Multicast message by using the trees found by our algorithms tend to have less channel collisions and higher network throughput. The theoretical results are corroborated by simulation studies.

关键词： Interference constraints Broadcasting Mobile ad hoc networks Directional antennas Multicast algorithms Energy efficiency Routing Approximation algorithms Heuristic algorithms Throughput

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A key-phrase Extraction Method Based on Multi-size Convolution Windows for Scientific Literatures

A Key-phrase Extraction Method Based on Multi-size Convoluti...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Yuhong Zhang Yuxin Xie Peipei Li Xuegang Hu Ministry of Education Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei Hefei University of Technology Hefei China

ISBN: (纸本)9781665441506

The key-phrase extraction is important for the downstream tasks in natural language process, and has attracted a lot of attention. Compared with other documents, scientific literatures contain many long phrases. Most existing methods perform poor on these literatures. To address this problem, a key-phrase extraction method based on multi-size convolution windows (KE-MCW) is proposed for scientific literatures in this paper. More specifically, in order to represent more contextual information, a convolutional neural network(CNN) with multi-size filters is introduced to map the documents into distributed feature vectors, then each vector can represent different size phrases. Next, in order to determine whether each word is a part of a keyphrase, a deep recurrent neural network is used to mark the role of each word. Finally, the attention mechanism is used to further judge the importance of each phrase. Experimental results show that our proposed method performs better than some competitive methods for technology literatures.

关键词： Cloud computing Recurrent neural networks Convolution Conferences Natural languages Information filters Feature extraction

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The Design of an Open Hybrid Recommendation System for Mobile Commerce

The Design of an Open Hybrid Recommendation System for Mobil...

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International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce

作者： Chengzhi Liu Caihong Sun Jia Yu Information School and Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing China School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia

To meet more and more complex recommendation needs, it is quite important to implement hybrid recommendations for mobile commerce. In this paper, we propose a design for open hybrid recommendation systems in mobile commerce, which could integrate multiple recommendation algorithms together to improve recommendation performance. First, three solutions for an open hybrid recommendation approach are discussed in detail, which are generic customer profile, weighted hybrid recommendation algorithm, and mobile device profile creation. After that, we give out a multi-agent architecture design to make the three solutions work together. Finally a prototype system based on our proposed architecture is implemented to demonstrate the feasibility of our design and evaluate the performance of the proposed open hybrid recommendation system.

关键词： Business Customer profiles Context awareness Laboratories data engineering knowledge engineering Prototypes XML Sun Australia

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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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Clustering XML retrieval results based on hybrid similarity

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Journal of Computational Information Systems 2008年第3期4卷 1323-1330页

作者： Wan, Changxuan Yu, Hong 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 unceasing growth of XML data in World Wide Web, XML document retrieval and clustering retrieval results are confronted with both challenges and opportunities. One of the challenges is how to improve the quality of XML retrieval results. Firstly, according to the features of XML documents, a method of modeling XML retrieval result documents is brought forward, which integrates both structural semantic features and content information of XML documents. Then, a measure method to compute similarity, including structural semantic similarity and keywords similarity, between retrieval result documents is suggested;and a strategy named Item Frequency in Cluster-Inverse Cluster Frequency to extract labels from result clusters is presented. Experiments indicate that the clustering quality for XML retrieval results based on hybrid similarity is obviously better than the one only based on content similarity.

关键词： XML

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Pseudo-relevance feedback driven for XML query expansion

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Journal of Convergence Information Technology 2010年第9期5卷 146-156页

作者： Minjuan, Zhong Changxuan, Wan 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

Pseudo-relevance feedback has been perceived as an effective solution for automatic query expansion. However, a recent study has shown that traditional pseudo-relevance feedback may bring into topic drift and hence be harmful to the retrieval performance. It is often crucial to identify those good feedback documents from which useful expansion terms can be added to the query. Compared with traditional query expansion, XML query expansion needs not only content expansion but also considering structural expansion. This paper presents a solution for both identifying related documents and selecting good expansion information with new content and path constrains. Combined with XML semantic feature, a naïve document similarity measurement is proposed in this paper. Based on this, k-median clustering algorithm is firstly implemented and some related documents are found. Secondly, query expansion is only performed by two steps in the set of related documents, which key phrase extraction algorithm is carried out to expand original query in the first step and the second step is structural expansion based on the expanded key phrases. Finally a full-edged content-structure query expression which can represent user's intention is formalized. Experimental results on IEEE CS collection show that the proposed method can reduce the topic drift effectively and obtain the better retrieval quality.

关键词： Clustering algorithms

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Configurable ranking for XML vague retrieval

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Journal of Computational Information Systems 2009年第2期5卷 683-692页

作者： Jiang, Tengjiao Wan, Changxuan Liu, Xiping 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

Ranking is one of the key factors for efficient and effective XML information retrieval. Compared with traditional IR, XML information retrieval has introduced many new challenges, one of which is that the traditional static document ranking method is no longer sufficient for ranking XML query results. As a result, we need a new ranking method. The paper studies the proper ranking model for XML vague retrieval. We first study the properties of vague retrieval over XML documents, and conclude the influencing factors of structure and content relaxation of XML vague retrieval. To improve recall and precision, we propose a new idea of confident structure query and vague structure query. Then, a configurable ranking model for twig pattern query results is proposed, in which users can configure query parameter. The model is designed to handle the new features of XML VCAS (vague content and structure). The architecture of corresponding XML search engine is also provided. © 2009 Binary Information Press March, 2009.

关键词： XML

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Detecting Compromised Kernel Hooks with Support of Hardware Debugging Features

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China Communications 2012年第10期9卷 78-90页

作者： Shi Wenchang Zhou HongWei Yuan JinHui Liang Bin School of Information Renmin University of China Beijing 100872 P. R. China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872 P. R. China Information Engineering University of China Zhengzhou 450004 P. R. China

Although there exist a few good schemes to protect the kernel hooks of operating systems, attackers are still able to circumvent existing defense mechanisms with spurious context infonmtion. To address this challenge, this paper proposes a framework, called HooklMA, to detect compromised kernel hooks by using hardware debugging features. The key contribution of the work is that context information is captured from hardware instead of from relatively vulnerable kernel data. Using commodity hardware, a proof-of-concept pro- totype system of HooklMA has been developed. This prototype handles 3 082 dynamic control-flow transfers with related hooks in the kernel space. Experiments show that HooklMA is capable of detecting compomised kernel hooks caused by kernel rootkits. Performance evaluations with UnixBench indicate that runtirre overhead introduced by HooklMA is about 21.5%.

关键词： operating system kernel hook integrity hardware control flow

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