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2011 International Conference on Frontiers in Computer education(ICFCE 2011)

作者： Wei Zhang Feng Cen Fanglai Zhu The Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Control Science & Engineering Tongji University

Arithmetic coding is a widely applied compression tool with superior coding efficiency to other entropy coding methodsHowever, it suffers from the error resilience and complexityIn this paper, the integer implementation of binary arithmetic coding with forbidden symbol for error resilience is studiedCoding redundancies for employing different quantization coefficients in probability representation and cost effective backtracking distance in bits for maximum a posteriori(MAP) decoding are studied in depthWe observe that the optimal quantization coefficients are independent of forbidden symbol and the probabilities of source and the cost effective backtracking distance is related to the source entropy and the given forbidden symbol probabilitiesSuch observations are also demonstrated by extensive experiments.

关键词： Arithmetic coding Forbidden symbol Error resilience MAP

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Automatic Web service composition based on Horn clauses and Petri nets

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Expert systems with Applications 2011年第10期38卷 13024-13031页

作者： Tang, Xianfei Jiang, Changjun Zhou, Mengchu Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States

This paper introduces an automatic Web service composition method based on logical inference of Horn clauses and Petri nets. The Web service composition problem is transformed into the logical inference problem of Horn clauses by exploring the dependency relations among services. An algorithm is presented, to determine whether the composite service exists in time that is linear in the size of the knowledge base. It can also return the rule set necessary for the composition. Petri nets are chosen to model the rule set and its structural analysis techniques are used to obtain the composite service. © 2010 Elsevier Ltd. All rights reserved.

关键词： Automatic reasoning Knowledge base Petri nets service composition Web service

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Semantic based file retrieval on resource limited devices with ontology alignment support

Semantic based file retrieval on resource limited devices wi...

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2011 8th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Jan, Sadaqat Li, Maozhen Al-Sultany, Ghaidaa Al-Raweshidy, Hamed School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781612841816

In this paper, we present a file search mechanism that can utilize the ontology alignments to facilitate file retrieval on resource-limited devices by exploiting the knowledge of more than one ontologies. This enables the device users to take advantage of all ontologies, which might have been developed in the same domain. A semantic-based file retrieval framework was previously presented for resource-limited devices, which annotates files with its basic attributes and provides file retrieval capabilities. A generic ontology was developed to define the most commonly used keywords which can be used as metadata of the stored files. The ontology was employed to exploit the semantic based file retrieval capability. The search mechanism was evaluated by formulating two test cases. In first test case the framework make use of single ontology while in the second test case, the ontology alignments were utilized by aligning two ontologies. The implications of ontology alignment support were analyzed through probabilistic evaluation, which shows the progress in file retrieval efficiency. © 2011 IEEE.

关键词： Ontology

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Ontology alignment using Rough Sets

Ontology alignment using Rough Sets

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2011 8th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

ISBN: (纸本)9781612841816

Ontology alignment has been one of the most significant research issues to bridge the semantic gap between heterogeneous systems toward Semantic Web. Many proposed systems in recent years, use multiple similarity measures and variety of aggregating algorithms to map entities from two different ontologies. However, the real issue arises when alignment systems turn out to be uncertain about entities which are neither completely similar nor dissimilar. This paper presents an ontology alignment system which uses a combination of various similarity measures in order to map entities from two different ontologies. Rough Sets have been used to deal with uncertainties during the mapping process by defining the similarity measures as the attributes of entities. The proposed alignment system employs the combination of string based, linguistic based and structural matchers. The structural matcher compares the super-classes, sub-classes and properties of two class entities. We use WordNet to compare the linguistic similarity between entities in the mapping process. The performance of proposed system is evaluated in terms of precision and recall and compared with existing state of the art alignment systems. For this purpose, the Ontology Alignment Evaluation Initiative (OAEI) benchmark group-3 test ontologies have been used. Experimental tests show that the performance our proposed alignment system is comparable with existing alignment systems. © 2011 IEEE.

关键词： Ontology

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Parallelizing multiclass Support Vector Machines for scalable image annotation

Parallelizing multiclass Support Vector Machines for scalabl...

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2011 8th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Alham, Nasullah Khalid Li, Maozhen Liu, Yang Hammoud, Suhel School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781612841816

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. SVM was initially designed for binary classifications. However, most classification problems arising in domains such as image annotation usually involve more than two classes. Notably SVM training is a computationally intensive process especially when the training dataset is large. This paper presents MRMSVM, a distributed multiclass SVM algorithm for large scale image annotation which partitions the training dataset into smaller binary chunks and train SVM in parallel using a cluster of computers. MRMSVM is evaluated in an experimental environment showing that the distributed Multiclass SVM algorithm reduces the training time significantly while maintaining a high level of accuracy in classifications. © 2011 IEEE.

关键词： MapReduce

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Intelligent mobile messaging

Intelligent mobile messaging

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2011 8th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Al-Sultany, Ghaidaa Li, Maozhen Jan, Sadaqat Al-Raweshidy, Hamed School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781612841816

Pervasive computing paradigm's goal is to visualize an ambient smart world via integration of devices into the users in their environment intelligently. Specifically, context aware computing is emerging as one of trails to achieve this paradigm in which services are seamlessly available at right time, right place and with right situation. This paper presents SOIM framework, which facilitates users to publish, annotate and retrieve service queries that are published to cloud. SOIM framework is built on semantic web technologies in support of query action retrieval automatically on low-end mobile devices. A generic ontology is developed which defines a number of keywords, and properties based on semantic reasoning. © 2011 IEEE.

关键词： Ontology

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Designing and analysis of a new book-lending system based on logic Petri nets

Designing and analysis of a new book-lending system based on...

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2011 International Conference on Electric Information and Control Engineering, ICEICE 2011

作者： Xia, Jiang Luan, Wenjing Department of Computer Laiwu Vocational and Technical College Laiwu 271100 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China College of Information Science and Engineering Shandong University of Science and Technology Qingdao 266510 China

A new book-lending system is designed and analyzed based on logic Petri nets in this paper. The batch processing function and indeterminacy of readers are included in the system. Its logic Petri net model is establish... 详细信息

ISBN: (纸本)9781424480395

关键词： Petri nets

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Performance evaluation of Latent Dirichlet Allocation in text mining

Performance evaluation of Latent Dirichlet Allocation in tex...

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2011 8th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Liu, Zelong Li, Maozhen Liu, Yang Ponraj, Mahesh School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781612841816

This paper introduces three classic models of statistical topic models: Latent Semantic Indexing (LSI), Probabilistic Latent Semantic Indexing (PLSI) and Latent Dirichlet Allocation (LDA). Then a method of text classification based on LDA model is briefly described, which uses LDA model as a text representation method. Each document means a probability distribution of fixed latent topic sets. Next, Support Vector Machine (SVM) is chose as classification algorithm. Finally, the evaluation parameters in classification system of LDA with SVM are higher than other two methods which are LSI with SVM and VSM with SVM, showing a better classification performance. © 2011 IEEE.

关键词： Support vector machines

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A distributed SVM for scalable image annotation

A distributed SVM for scalable image annotation

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2011 8th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Alham, Nasullah Khalid Li, Maozhen Liu, Yang Hammoud, Suhel Ponraj, Mahesh School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781612841816

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. This paper presents MRSVM, a distributed SVM algorithm for large scale image annotation which partitions the training data set into smaller subsets and train SVM in parallel using a cluster of computing nodes. MRSVM is evaluated in an experimental environment showing that the distributed SVM algorithm reduces the training time significantly while maintaining a high level of accuracy in classifications. © 2011 IEEE.

关键词： MapReduce

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Load balancing in MapReduce environments for data intensive applications

Load balancing in MapReduce environments for data intensive ...

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2011 8th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Liu, Yang Li, Maozhen Alham, Nasullah Khalid Hammoud, Suhel Ponraj, Mahesh School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781612841816

The distributed computations are widely used in the modern world for processing large scale jobs. Hadoop framework which is based on Google MapReduce model becomes popular due to its great processing power and ease to use. However, due to lack of load management, especially in a heterogeneous computing environment, the performance of Hadoop framework may be deteriorated. Therefore this paper presents a load balancing algorithm which aims to balance the load among heterogeneous nodes. And also, the Hadoop simulator HSim is involved to evaluate the performance of the load balancing algorithm. The results indicate that the performance of the cluster has been significantly enhanced. © 2011 IEEE.

关键词： MapReduce

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