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An algorithm for generating home states of Petri nets

Journal of Computational Information systems 2011年第12期7卷 4225-4232页

作者： Wang, Pengwei Ding, Zhijun Chai, Hua Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai 200092 China Department of Computer Science and Technology Tongji University Shanghai 201804 China

Home states are ground markings which can be reached from any other reachable markings of a Petri net. The existence of home states is a strong property, and it is not only of important theoretical research meaning, but also of great practical value. This paper presents an algorithm for generating home states. For any bounded Petri net, the algorithm can judge whether it contains home states or not, and obtain the set of all its home states. The correctness of the algorithm is proved, and its complexity is analyzed, the result shows that it is a polynomial-time algorithm, so it is effective. Meanwhile, we implemented the algorithm in the popular Petri net tool PIPE2, which can be used for experimental purposes. Then a number of conclusions about the relationships between home state and deadlock are given, which bring great convenience to judge deadlock and exert control pointedly. Copyright © 2011 Binary Information Press.

关键词： Petri nets

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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

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

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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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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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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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

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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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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Ontology based file retrieval framework for resource limited mobile devices

Ontology based file retrieval framework for resource limited...

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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

Recent advances in mobile technologies have emerged new challenges for researchers. File management and retrieval on resource-limited devices has become a challenging problem as these devices are now equipped with larger storage capacities. This paper presents an overview about the possibilities of implementing Semantic Web Technologies in order to facilitate the file retrieval on resource-limited devices. We have proposed a semantic based file retrieval framework which utilizes a generic ontology in file search process. The search module is implemented in Java ME platforms as a case study. Performance of the proposed search module is evaluated through experimental tests along with probabilistic evaluations. The implemented module not only automates file retrieval process but also shows significant improvement in file retrieval efficiency by utilizing the knowledge of employed ontology in the search module. © 2011 IEEE.

关键词： Ontology

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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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Extracting all minimal siphons from maximal unmarked siphons in manufacturing-oriented Petri nets

Extracting all minimal siphons from maximal unmarked siphons...

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2011 7th IEEE International Conference on Automation Science and Engineering, CASE 2011

作者： Wang, ShouGuang Zhou, MengChu Wang, ChengYing College of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou 310018 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102-1982 United States MoE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 200092 China

ISBN: (纸本)9781457717307

Deadlock control is an important research issue in automated manufacturing systems that have a high degree of resource sharing and concurrency. Since minimal siphons are closely tied with deadlocks in Petri net models, their efficient extraction is fundamentally important. The existing methods can rapidly extract one minimal siphon given a maximal unmarked siphon that is obtained by using a mixed integer programming approach. This paper for the first time presents an extraction algorithm that can efficiently extract all minimal ones. The idea is based on the generation and use of a subnet tree structure given the places in a maximal unmarked siphon. Several Petri net models of automated manufacturing systems are used to illustrate the proposed concepts and methods. © 2011 IEEE.

关键词： Petri nets

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