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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8786卷 79-94页

作者： Zhong, Mingjie Ding, Zhijun Sun, Haichun Wang, Pengwei The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Department of Computer Science University of Pisa Pisa56127 Italy

This paper presents a novel clustering algorithm based on clustering coefficient. It includes two steps: First, k-nearest-neighbor method and correlation convergence are employed for a preliminary clustering. Then, the results are further split and merged according to intra-class and inter-class concentration degree based on clustering coefficient. The proposed method takes correlation between each other in a cluster into account, thereby improving the weakness existed in previous methods that consider only the correlation with center or core data element. Experiments show that our algorithm performs better in clustering compact data elements as well as forming some irregular shape clusters. It is more suitable for applications with little prior knowledge, e.g. hotspots discovery. © Springer International Publishing Switzerland 2014.

关键词： Clustering algorithms

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A parallel computing model for container terminal logistics

A parallel computing model for container terminal logistics

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Bin Li Weiming Shen School of Transportation Fujian University of Technology Fuzhou China The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Existing planning and scheduling solutions for container terminal logistics systems (CTLS) are not sufficient today due to the highly complexity and uncertain environments. This paper reviews the advantages and shortcomings of the existing solutions and proposes a container terminal conceptual parallel computing model for scheduling and execution based on multi-processor systems. It is built on the computational architecture and fundamental principles of distributed, cooperative, parallel, heterogeneous, and reconfigurable computation in essence. The proposed approach is demonstrated and validated by investigating the stress testing, tailor-made processor affinity, load migration and load balancing of a typical container terminal logistics service case with comprehensive computational experiments.

关键词： Containers Computational modeling Processor scheduling Computer architecture Logistics Marine vehicles

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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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Image captioning with deep LSTM based on sequential residual

Image captioning with deep LSTM based on sequential residual

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Kaisheng Xu Hanli Wang Pengjie Tang Tongji University Shanghai Shanghai CN Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai CN Key Laboratory of Embedded System and Service Computing Tongji University Shanghai P. R. China Tongji University Shanghai P. R. China

ISBN: (纸本)9781509060689

Image captioning is a fundamental task which requires semantic understanding of images and the ability of generating description sentences with proper and correct structure. In consideration of the problem that language models are always shallow in modern image caption frameworks, a deep residual recurrent neural network is proposed in this work with the following two contributions. First, an easy-to-train deep stacked Long Short Term Memory (LSTM) language model is designed to learn the residual function of output distributions by adding identity mappings to multi-layer LSTMs. Second, in order to overcome the over-fitting problem caused by larger-scale parameters in deeper LSTM networks, a novel temporal Dropout method is proposed into LSTM. The experimental results on the benchmark MSCOCO and Flickr30K datasets demonstrate that the proposed model achieves the state-of-the-art performances with 101.1 in CIDEr on MSCOCO and 22.9 in B-4 on Flickr30K, respectively.

关键词： Computational modeling Feature extraction Semantics Training Logic gates Recurrent neural networks

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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 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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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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An Accuracy-and-Diversity-based Ensemble Method for Concept Drift and Its application in Fraud Detection

An Accuracy-and-Diversity-based Ensemble Method for Concept ...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Shujie Yin Guanjun Liu Zhenchuan Li Chungang Yan Changjun Jiang Key Laboratory of the Ministry of Education for Embedded System and Service Computing Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China

ISBN: (数字)9781728190129

ISBN: (纸本)9781728190136

Concept Drift is one of the most challenging issues in various applications such as fraud detection, spam filtering and sensor networks, causing the performance degradation of learning algorithms which are dedicated to processing static data such as Naive Bayes, due to their poor adaptability to changes. Several techniques and algorithms have been proposed to address the concept drift problem, e.g. the ensemble methods that have already achieved great success. However, most of the ensemble methods consider either accuracy or diversity of a trained model when adapting to new concepts, and thus they can hardly deal with different types of drifts. This paper proposes a novel ensemble method that considers both accuracy and diversity. We linearly combine accuracy and diversity with different coefficients, and propose an entropy-based policy to compute these coefficients. Experiments on 10 synthetic datasets and 4 public datasets demonstrate that our method outperforms the state-of-the-art ones on dealing with different concept drifts. Our method is also applied to the electronic transaction fraud detection and achieves an excellent performance.

关键词： Degradation Adaptation models Filtering Design methodology Conferences Computational modeling Data mining

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