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2017 IEEE 2nd International Conference on Big data Analysis（ICBDA 2017）

作者： Guohui Li Kun Guo YuZhong Chen Ling Wu Danhong Zhu College of Mathematics and Computer Sciences Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing(Fuzhou University) Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education

ISBN: (纸本)9781509036202

One of the traditional ways for detecting dynamic communities is to find the communities at each interval through the static community detection ***,it usually leads to high computation *** this paper,a novel algorithm based on the Map Reduce model and the label propagation progress with the strategy of incremental related vertices is proposed,which is called PLPIRV(Parallel Label Propagation and Incremental Related Vertices).Based on the communities found at the previous interval,the new algorithm adjusts the communities the incremental related vertices belong *** clustering of the whole network can be avoided by incrementally analyzing the variation of the networks,so that the time cost can be greatly *** on artificial and real datasets show that the proposed algorithm performs well on dynamic community detection.

关键词： dynamic community detection parallel computation incremental algorithm label propagation

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Parallel Multi-Label Propagation Based on Influence Model and Its Application to Overlapping Community Discovery

Parallel Multi-Label Propagation Based on Influence Model an...

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作者： Qiu, Qirong Guo, Wenzhong Chen, Yuzhong Guo, Kun Li, Rongrong School of Economics and Management Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China College of Mathematics and Computer Science Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350116 China

Finding communities in networks is one of the challenging issues in complex network research. We have to deal with very large networks that contain billions of vertices, which makes community discovery a computationally intensive work. Moreover, communities usually overlap each other, which greatly increases the difficulty of identifying the boundaries of communities. In this paper, we propose a parallel multi-label propagation algorithm (PMLPA) that enhances traditional multi-label propagation algorithm (MLPA) in two ways. First, the critical steps of MLPA are parallelized based on the MapReduce model to get higher scalability. Second, new label updating strategy is used to automatically determine the most valuable labels of each vertex. Furthermore, we study the improvement of PMLPA through considering the influence of vertices and labels on label updating. In this way, the importance of each label can be described with higher precision. Experiments on artificial and real networks prove that the proposed algorithms can achieve both high discovering accuracy and high scalability. © 2017 World Scientific Publishing Company.

关键词： Complex networks

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Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

TechRxiv

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TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

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Tri-level combination for image representation 17th

Tri-level combination for image representation

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Ruiying Zhang, Chunjie Huang, Qingming School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319488899

The context of objects can provide auxiliary discrimination beyond objects. However, this effective information has not been fully explored. In this paper, we propose Tri-level Combination for Image Representation (TriCoIR) to solve the problem at three different levels: object intrinsic, strongly-related context and weakly-related context. Object intrinsic excludes external disturbances and more focuses on the objects themselves. Strongly-related context is cropped from the input image with a more loose bound to contain surrounding context. Weaklyrelated one is recovered from the image other than object for global context. First, strongly and weakly-related context are constructed from input images. Second, we make cascade transformations for more intrinsical object information, which depends on the consistency between generated global context and input images in the regions other than object. Finally, a joint representation is acquired based on these three level features. The experiments on two benchmark datasets prove the effectiveness of TriCoIR. © Springer International Publishing AG 2016.

关键词： Image representation

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Encoding-Aware data Placement for Efficient Degraded Reads in XOR-Coded Storage Systems

Encoding-Aware Data Placement for Efficient Degraded Reads i...

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Reliable Distributed Systems

作者： Zhirong Shen Patrick P.C. Lee Jiwu Shu Wenzhong Guo College of Mathematics and Computer Science Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Computer Science and Technology Tsinghua University Key Laboratory of Spatial Data Mining and Information Sharing Minstry of Education Fuzhou China

ISBN: (纸本)9781509035144

Erasure coding has been increasingly used by distributed storage systems to maintain fault tolerance with low storage redundancy. However, how to enhance the performance of degraded reads in erasure-coded storage has been a critical issue. We revisit this problem from two different perspectives that are neglected by existing studies: data placement and encoding rules. To this end, we propose an encoding-aware data placement (EDP) approach that aims to reduce the number of I/Os in degraded reads during a single failure for general XOR-based erasure codes. EDP carefully places sequential data based on the encoding rules of the given erasure code. Trace-driven evaluation results show that compared to two baseline data placement methods, EDP reduces up to 37.4% of read data on the most loaded disk and shortens up to 15.4% of read time.

关键词： Encoding Distributed databases Layout Fault tolerant systems Redundancy Computer science

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No reference video quality assessment based on 3D convolutional neural network

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Ruan Jian Xue Bao/Journal of Software 2016年 27卷 103-112页

作者： Wang, Chun-Feng Su, Li Zhang, Wei-Gang Huang, Qing-Ming Key Laboratory on Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology The Chinese Academy of Sciences Beijing100190 China Weihai264209 China

No reference video quality assessment (NR-VQA) measures distorted videos quantitatively without the reference of original high quality videos. Conventional NR-VQA methods are generally designed for specific types of distortions, or not consistent with human's perception. This paper innovatively introduces 3D deep convolutional neural network (3D-CNN) into VQA and proposes a 3D-CNN based NR-VQA method, which is universal for non-specific types of distortions. First, the proposed method utilizes 3D patches to learn spatio-temporal features that represent video content effectively. Second, the original 3D-CNN model is modified which is used to classify videos to make it adapt to VQA task. Experiments demonstrate that the proposed method is highly consistent with human's perception across numerous distortions and metrics. Compared with other state-of-the-art no-reference VQA methods, the proposed method runs much faster while keeping the similar performance. As a no-reference VQA method, it is even comparable with many of the state-of-the-art full-reference VQA methods, which provides the proposed method with better application prospects. © Copyright 2016, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Convolution

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Coal mine safety evaluation method based on incomplete labeled data stream classification

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Open Cybernetics and Systemics Journal 2014年第1期8卷 918-923页

作者： Gang, Sun Huaping, Zhou Kelei, Sun Data Mining and Intelligent Computing Laboratory Hefei University of Technology Hefei China School of Computer and Information Fuyang Teachers College Fuyang China School of Computer Science and Engineering Anhui University of Science and Technology Huainan China

Monitoring data in coal mine is essentially data stream, and missing coal mine monitoring data is caused by harsh coal mine environment, therefore coal mine safety evaluation can be seen as incomplete labeled data stream classification. The method is proposed for unlabeled data and concept drift in incomplete labeled data stream in this paper that uses semi-supervised learning method based on k-Modes algorithm and incremental decision tree model and concept drift detection mechanism based on clustering concept-cluster. Experimental results show the method can better label unlabeled data and detect concept drift in incomplete labeled data stream, and it has better classification accuracy for incomplete labeled data stream, and it provides a new practical approach for coal mine safety evaluation. © Gang et al.

关键词： Coal mines

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Sentiment classification in Persian: Introducing a mutual information-based method for feature selection

Sentiment classification in Persian: Introducing a mutual in...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Ayoub Bagheri Mohamad Saraee Franciska de Jong Intelligent Database Data Mining and Bioinformatics Laboratory Electrical and Computer Engineering Department Isfahan University of Technology Isfahan Iran School of Computing Science and Engineering University of Salford Manchester UK Human Media Interaction University of Twente Enschede Netherlands

With the enormous growth of online reviews in Internet, sentiment analysis has received more and more attention in information retrieval and natural language processing community. Up to now there are very few researches conducted on sentiment analysis for Persian documents. This paper considers the problem of sentiment classification for online customer reviews in Persian language. One of the challenges of Persian language is using of a wide variety of declensional suffixes. Another common problem of Persian text is word spacing. In Persian in addition to white space as interwords space, an intra-word space called pseudo-space separates word's part. One more noticeable challenge in customer reviews in Persian language is that of utilizing many informal or colloquial words in text. In this paper we study these challenges by proposing a model for sentiment classification of Persian review documents. The proposed model is based on a lemmatization approach for Persian language and is employed Naive Bayes learning algorithm for classification. Additionally we present a new feature selection method based on the mutual information method to extract the best feature collection from the initial extracted features. Finally we evaluate the performance of the model on a manually gathered collection of cellphone reviews, where the results show the effectiveness of the proposed model.

关键词： Feature extraction Mathematical model Classification algorithms Equations Mutual information Training data mining

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Learning heterogeneus cooperative linguistic fuzzy rules using local search: Enhancing the COR search space

Learning heterogeneus cooperative linguistic fuzzy rules usi...

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International Conference on intelligent Systems Design and Applications (ISDA)

作者： Javier Cózar Luis delaOssa Jose A. Gámez Department of Computing Systems Intelligent Systems and Data Mining Laboratory Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain

The COR methodology allows the learning of Linguistic Fuzzy Rule-Based Systems by considering cooperation among rules. In order to do that, COR firstly finds the set of candidate fuzzy rules that can be fired by the examples in the training set, and then uses a search algorithm to find the final set of rules. In the algorithms proposed so far, all candidate rules have the same number of antecedents, which is the number of input variables. However, these rules could be too specific, and rules more generic are not considered. In this paper we study the effect of considering all possible rules, regardless of their number of antecedents. Experiments show that the rule bases obtained use simpler rules, and the results for the error of prediction improve upon those obtained by using classical COR methods.

关键词： Pragmatics Training intelligent systems Cities and towns Input variables Genetic algorithms Cybernetics

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Efficient and sound evaluation when learning Bayesian networks in the space of orderings based on local methods

Efficient and sound evaluation when learning Bayesian networ...

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International Conference on intelligent Systems Design and Applications (ISDA)

作者： Juan I. Alonso-Barba Luis delaOssa Jose M. Puerta Department of Computing Systems Intelligent Systems and Data Mining Laboratory Albacete Research Institute of Informatics. University of Castilla-La Mancha Albacete Spain

There are methods that allow building a Bayesian network when an ordering among variables is provided. Based on them, structural learning of Bayesian networks can be carried out by searching for the ordering which generates the best network, instead of directly searching in the space of Directed Acyclic Graphs. In a previous work, a simple Hill-Climbing algorithm defined over the space of orderings was proposed by the authors. Although it achieves competitive results when comparing with the state of the art algorithms, such as GES, it is not very efficient when the number of variables is high. In this work, we propose a more efficient version of the method that improves its scalability in high dimensional domains and is equivalent to the original. We also propose an asymptotically equivalent algorithm based not only on the orderings, but also on the properties of the underlying network. Besides, we prove the correctness of the operations used in both versions, as well as the correctness of the improvements proposed. The algorithms have been tested over a set of different domains, showing that changes introduced lead to significant reductions on execution time, which become more important as the number of variables grows.

关键词： Bayesian methods Algorithm design and analysis intelligent systems Complexity theory Measurement Buildings Heuristic algorithms

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