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International Conference on Control engineering and Communication Technology (ICCECT)

作者： Minjuan Zhong Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

In this paper, we study how to perform XML query expansion effectively from the high quality pseudo-relevance documents. A solution for selecting good expansion information is presented, in which various features impacting weight, such as term element frequency, term inverse element frequency, semantic weight of tag and level information, are analyzed and those term with high weigh value are selected as expansion term. Experiment results show that proposed expansion method is feasible. Compared to original query and traditional expansion method with no structure features considered, our method achieves better retrieval performance.

关键词： XML Semantics Analytical models Vectors Information retrieval Computational modeling Educational institutions

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Radical features for Chinese text classification

Radical features for Chinese text classification

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： He Hu Xiaoyong Du Key Laboratories of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China

Chinese radicals play important roles in forming Chinese character's semantic meaning. The semantic properties of radicals make them a promising source of information to be analyzed in text mining and content extraction. However, until recently there is little research work concentrating on using the radical set in text mining related tasks. We investigate the roles of radicals in Chinese text classification tasks. In the task, texts are transformed into vectors of radicals, characters and words. Radicals are further pruned by their semantic strengths and network traits. We carry out experiments with real data from Open Directory Project. The experiments results justify Chinese radicals as important features for semantic processing in Chinese text mining tasks.

关键词： Europe Positron emission tomography Education Vectors

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Combining N-gram retrieval with weights propagation on massive RDF graphs

Combining N-gram retrieval with weights propagation on massi...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： He Hu Xiaoyong Du School of Information Renmin University of China Beijing China Key Laboratories of Data Engineering and Knowledge Engineering Ministry of Education China

N-gram approach takes the position information into account additionally and thus can offer higher accuracy in query answering than keyword based approaches and is widely used in IR and NLP. However, in large-scale RDF graphs, URIs instead of documents are the ranking and querying units; URIs are usually much shorter than documents, and different URIs are interlinked into a massive network. One shot n-gram querying is usually not good for the RDF data in many cases. In this paper, we present a hybrid framework which combines the n-gram retrieval with link analysis based weight propagation. The idea is to exploit the link structures in the RDF data graphs and propagate the one shot n-gram score weights along with these links. Large scale experiments using MapReduce on Billion Triples Challenge dataset show the hybrid framework achieves an 80.3% improvement in relevance scores over mere n-gram retrieval.

关键词： Resource description framework Semantics Conferences Educational institutions Legged locomotion Indexes USA Councils

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Endless and Scalable knowledge Table Extraction from Semi-structured Websites

Endless and Scalable Knowledge Table Extraction from Semi-st...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Yingqin Gu Lei Ji Ziheng Jiang Jun He Key Labs of Data Engineering and Knowledge Engineering University of China Microsoft Research Asia Beijing P.R. China School of Computer Science Beijing Institute of Technology P. R. China

ISBN: (纸本)9781467351645

The problem of scalable knowledge extraction from the Web has attracted much attention in the past decade. However, it is under explored how to extract the structured knowledge from semi-structured Websites in a fully automatic and scalable way. In this work, we define the table-formatted structured data with clear schema as knowledge Tables and propose a scalable learning system, which is named as Kable to extract knowledge from semi-structured Websites automatically in a never ending and scalable way. Kable consists of two major components, which are auto wrapper induction and schema matching respectively. In contrast to the state of the art auto wrappers for semi-structured Web sites, our adopted approach can run around 1'000 times faster, which makes the Web scale knowledge extraction possible. On the other hand, we propose a novel schema matching solution which can work effectively on the auto-extracted structured data. With 3 months' continuous run using ten Web servers, we successfully extracted 427,105,009 knowledge facts. The manual labeling over sampled knowledge extracted show the up to 87% precision for supporting various Web applications.

关键词： data mining knowledge engineering Motion pictures Clustering algorithms knowledge based systems Algorithm design and analysis Manganese

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Interval valued fuzzy rough classifier and its application on privacy protection

Interval valued fuzzy rough classifier and its application o...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Su-Yun Zhao Si Lin Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China College of Science Beijing Forestry University Beijing China

Currently, most works on interval valued problems mainly focus on attribute reduction (i.e., feature selection) by using rough set technologies. However, less research work on classifier building on interval-valued problems has been conducted. It is promising to propose an approach to build classifier for interval-valued problems. In this paper, we propose a classification approach based on interval valued fuzzy rough sets. First, the concept of interval valued fuzzy granules are proposed, which is the crucial notion to build the reduction framework for the interval-valued databases. Second, the idea to keep the critical value invariant before and after reduction is selected. Third, the structure of reduction rule is completely studied by using the discernibility vector approach. After the description of rule inference system, a set of rules covering all the objects can be obtained, which is used as a rule based classifier for future classification. Finally, numerical examples are presented to illustrate feasibility and affectivity of the proposed method in the application of privacy protection.

关键词： Abstracts Diabetes Sonar Iris Accuracy

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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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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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An Approach for Personalized Tag Recommendation Based on Interest Transfer Model

An Approach for Personalized Tag Recommendation Based on Int...

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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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LotusX: A Position-Aware XML Graphical Search System with Auto-Completion

LotusX: A Position-Aware XML Graphical Search System with Au...

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International Conference on data engineering

作者： Chunbin Lin Jiaheng Lu Tok Wang Ling Bogdan Cautis Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China China School of Computing National University of Singapore Singapore Télécom Paris Technology France

ISBN: (纸本)9781467300421

The existing query languages for XML (e.g., XQuery) require professional programming skills to be formulated, however, such complex query languages burden the query processing. In addition, when issuing an XML query, users are required to be familiar with the content (including the structural and textual information) of the hierarchical XML, which is diffcult for common users. The need for designing user friendly interfaces to reduce the burden of query formulation is fundamental to the spreading of XML community. We present a twig-based XML graphical search system, called LotusX, that provides a graphical interface to simplify the query processing without the need of learning query language and data schemas and the knowledge of the content of the XML document. The basic idea is that LotusX proposes "position-aware" and "auto-completion" features to help users to create tree-modeled queries (twig pattern) by providing the possible candidates on-the-fly. In addition, complex twig queries (including order sensitive queries) are supported in LotusX. Furthermore, a new ranking strategy and a query rewriting solution are implemented to rank and rewrite the query effectively. We provide an online demo for LotusX system: http://***:8080/LotusX.

关键词： XML database languages Indexes Books Query processing Programming keyword search

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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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