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Quotient space granularity selection based Affinity Propagation clustering algorithm

Journal of Computational information Systems 2014年第6期10卷 2425-2433页

作者： Ding, Shifei Li, Hui School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Jiangsu Normal University Xuzhou 221116 China

The aim of this paper is to develop an improved AP clustering algorithm based on the quotient space granularity selection. Firstly, we give the characteristics of the quotient space granularity and Affinity Propagation (AP) clustering. Secondly, we analyze the interlinking of clustering and the quotient space granularity. And then we introduce the quotient space concept to the AP clustering analysis, which can find an optimal granularity from all possible granularities. Simulation results show that our proposed method here, quotient space granularity selection based AP clustering algorithm, is effective and feasible and has a better performance. Copyright © 2014 Binary information Press.

关键词： Clustering algorithms

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A decoupled hybrid structure for active noise control with uncorrelatednarrowband disturbances 43

A decoupled hybrid structure for active noise control with u...

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43rd International Congress on Noise Control Engineering: Improving the World Through Noise Control, INTERNOISE 2014

作者： Wu, Lifu Qiu, Xiaojun Burnett, Ian S. Cheng, Eva Guo, Yecai Royal Melbourne Institute of Technology School of Electrical and Computer Engineering Australia Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing University of Information Science and Technology 210044 China

ISBN: (纸本)9780909882037

In real active noise control (ANC)applications,the following situations frequently occur, one isthat disturbances only present at the error sensor and havelowcorrelation with reference signal, the other is thatthere is no enough space or ideal position for locating the reference sensor to satisfy causality condition. Thusthe residual noise after feedforward control can be seen as uncorrelated narrowband disturbancesin these situationsand ahybrid adaptive feedforward and feedback structure is often utilized to cope with this *** efforts have been paid to improve the performance of the hybrid ANC system, nevertheless, few interests are concerned about the combination method between the feedforward and feedback structure. After investigating the conventional combination method of hybrid feedforward and feedback system, this paper introduces analternate combination method for hybrid ANC systemwhich featuresthat itavoidsthe coupling between the feedforward and feedback structures and both structures are concatenated to attenuate the ambient noise. Simulations are carried out to validatethe effectiveness of the introduced methodfor ANCwith uncorrelated narrowband disturbances.

关键词： Hybrid systems

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An Improved DV-Hop Positioning Algorithm for Wireless Sensor Network

An Improved DV-Hop Positioning Algorithm for Wireless Sensor...

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2015 IEEE International Conference on Progress in Informatics and Computing(PIC 2015)

作者： Jiuhu Zheng Huanyan Qian Lie Wang School of Computer Science and Engineering Nanjing University of Science and Technology School of Information Technology Jiangsu Institute of Commerce Key Lab of Broadband Wireless Communication and Sensor Network Technology ( Nanjing University of Posts and Telecommunications) Ministry of Education National Mobile Communications esearch Laboratory Southeast University

In the view of the DV-Hop positioning algorithm which relies on the distance vector and beacon nodes, several aspects are improved: the average hop distance and hop count are corrected;a method to detect the abnormal distribution of beacon nodes is used to avoid the influence of the abnormal distribution on the positioning performance. The proposed algorithm is simple and effective. Simulation results show that compared with the traditional DV-Hop algorithm, the performance of the proposed algorithm is greatly *** performance is even even superior to that of the algorithm mentioned in literature [10]

关键词： Wireless sensor network DV-Hop algorithm node positioning positioning accuracy

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Estimation of distribution algorithms for knapsack problem

Journal of Software

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Journal of Software 2014年第1期9卷 104-110页

作者： Gao, Shang Qiu, Ling Cao, Cungen School of Computer Science and Technology Jiangsu University of Science and Technology Zhenjiang 212003 China Artificial Intelligence of Key Laboratory of Sichuan Province Sichuan University of Science and Engineering Zigong 643000 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

Estimation of distribution algorithms (EDAs) is a new kind of evolution algorithm. In EDAs, through the statistics of the information of selected individuals in current group, the probability of the individual distribution in next generation is given and the next generation of group is formed by random sampling. A wide range of mathematical model of the knapsack problem are proposed. In this paper, the EDAs is applied to solve the knapsack problem. The influence of several strategies, such as numbers of population and better population selection proportions are analyzed. Simulation results show that the EDAs is reliable and effective for solving the knapsack problem. The Maltab code is given also. It can easily be modified for any combinatorial problem for which we have no good specialized algorithm. © 2014 ACADEMY PUBLISHER.

关键词： Genetic algorithms

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Ranking based activity trajectory search

Ranking based activity trajectory search

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15th International Conference on Web information Systems Engineering, WISE 2014

作者： Chen, Wei Zhao, Lei Jiajie, Xu Zheng, Kai Zhou, Xiaofang School of Computer Science and Technology Soochow University China Jiangsu Provincial Key Laboratory for Computer Information Processing Technology Soochow University China School of ITEE The University of Queensland Brisbane Australia

ISBN: (纸本)9783319117485

With the proliferation of the GPS-enabled devices and mobile techniques, there has been a lot of work on trajectory search in the last decade. Previous trajectory search has focused on spatio-temporal features and text descriptions. Different from them, we study a novel problem of searching trajectories with activities and corresponding ranking information. Given a query q, which is attached with a set of activities and a threshold of distance, the results of ranking based activity trajectory search (RTS) are k trajectories such that the given activities are performed with the highest ranking within the threshold of distance. In addition, we also extend the query with an order, i.e., order-sensitive ranking based activity trajectory search (ORTS), which takes both the order of activities in a query q and the order of trajectories into account. It is challenging to answer RTS and ORTS efficiently due to the structural complexity of trajectory data with ranking information. In this paper, a hybrid index AC-tree and its optimized variant RAC-tree are proposed to achieve higher efficiency. Extensive experiments verify the high efficiency and scalability of the proposed algorithms. © Springer International Publishing Switzerland 2014.

关键词： Trajectories

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Research and application analysis of feature binding mechanism 8

Research and application analysis of feature binding mechani...

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8th IFIP International Conference on Intelligent information processing, IIP 2014

作者： Han, Youzhen Ding, Shifei School of Computer Science and Technology China University of Mining and Technology XuzhouJiangsu221116 China Key Laboratory of Intelligence Information Processing Institute of Computer Technology Academy of Sciences Beijing City100190 China

ISBN: (纸本)9783662449790

In order to recognize the object of the external world, brain need to integrate the information of different cortical areas, then form a complete world. And binding problem study on a process to percept a complete object by integrating information, which scattering on different cortical areas. As a central problem of cognitive science and neuroscience, the concept of feature binding is becoming to the focus of consciousness argument. At the beginning of this paper, we introduced the concept, characteristics and theory source of feature binding. And according to the main theoretical research methods of this mechanism, combining the latest advance at home and abroad, we made a systematical review of the research situation of feature binding problem applying in perceptual learning. At last, we pointed out the key point of the further study, which may be refined research of bundled brain mechanism on different cognition process and systematical study of general bundled brain mechanism. © IFIP International Federation for information processing 2014.

关键词： Brain

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Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition

Shape driven kernel adaptation in Convolutional Neural Netwo...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Shaoxin Li Junliang Xing Zhiheng Niu Shiguang Shan Shuicheng Yan Department of Electrical and Computer Engineering National University of Singapore Singapore Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China

ISBN: (纸本)9781467369657

One key challenge of facial trait recognition is the large non-rigid appearance variations due to some irrelevant real world factors, such as viewpoint and expression changes. In this paper, we explore how the shape information, i.e. facial landmark positions, can be explicitly deployed into the popular Convolutional Neural Network (CNN) architecture to disentangle such irrelevant non-rigid appearance variations. First, instead of using fixed kernels, we propose a kernel adaptation method to dynamically determine the convolutional kernels according to the spatial distribution of facial landmarks, which helps learning more robust features. Second, motivated by the intuition that different local facial regions may demand different adaptation functions, we further propose a tree-structured convolutional architecture to hierarchically fuse multiple local adaptive CNN subnetworks. Comprehensive experiments on WebFace, Morph II and MultiPIE databases well validate the effectiveness of the proposed kernel adaptation method and tree-structured convolutional architecture for facial trait recognition tasks, including identity, age and gender recognition. For all the tasks, the proposed architecture consistently achieves the state-of-the-art performances.

关键词： Kernel Shape Face Face recognition Robustness Neural networks computer architecture

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Orientational Spatial Part Modeling for Fine-Grained Visual Categorization

Orientational Spatial Part Modeling for Fine-Grained Visual ...

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IEEE International Conference on Mobile Services (MS)

作者： Hantao Yao Shiliang Zhang Fei Xie Yongdong Zhang Dongming Zhang Yu Su Qi Tian CAS Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China NEC Labs America USA Department of Computer Science University of Texas at San Antonio San Antonio USA Xin Hua News Agency China

Although significant success has been achieved in fine-grained visual categorization, most of existing methods require bounding boxes or part annotations for training and test, resulting in limited usability and flexibility. To conquer these limitations, we aim to automatically detect the bounding box and parts for fine-grained object classification. The bounding boxes are acquired by a transferring strategy which infers the locations of objects from a set of annotated training images. Based on the generated bounding box, we propose a multiple-layer Orientational Spatial Part (OSP) model to generate a refined description for the object. Finally, we employ the output of deep Convolutional Neural Network (dCNN) as the feature and train a linear SVM as object classifier. Extensive experiments on public benchmark datasets manifest the impressive performance of our method, i.e., Classification accuracy achieves 63.9% on CUB-200-2011 and 75.6% on Aircraft, which are actually higher than many existing methods using manual annotations.

关键词： Feature extraction Training Visualization Testing Aircraft Histograms Computational modeling

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3D Representation of Chinese Event Factuality

3D Representation of Chinese Event Factuality

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第十五届汉语词汇语义学国际研讨会

作者： CAO Yuan ZHU Qiaoming LI Peifeng School of Computer Science and Technology of Soochow University Key Lab of Computer Information Processing Technology of Jiangsu Province

In general,Chinese event factuality is determined by the specific vocabularies and syntactic structures of *** Chinese event factuality corpus is to annotate these specific vocabularies and syntactic *** these information is raw and complex,it will result in high computation complexity and low correct rate if we use these information to compute factuality *** paper proposes a 3D representation of Chinese event factuality based on the annotated factual information in a Chinese event factuality *** also presents the transformation rules between the factual information and 3D representation and those between the 3D representation and event *** experimental results demonstrate the effectiveness of our 3D representation.

关键词： Representation of Chinese Event Factuality

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Hierarchical Extreme Learning Machine for Unsupervised Representation Learning

Hierarchical Extreme Learning Machine for Unsupervised Repre...

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International Joint Conference on Neural Networks

作者： Wentao Zhu Jun Miao Laiyun Qing Guang-Bin Huang Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China School of Electrical & Electronic Engineering Nanyang Technological University Nanyang Avenue Singapore 639798

ISBN: (纸本)9781479919611

Learning representations from massive unlabeled data is a hot topic for high-level tasks in many applications. The recent great improvements on benchmark data sets, which are achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters, usually require many tedious tricks and much expertise to tune. However, filters learned by these complex architectures are quite similar to standard hand-crafted features visually, and training the deep models costs quite long time to fine-tune their weights. In this paper, Extreme Learning Machine-Autoencoder (ELM-AE) is employed as the learning unit to learn local receptive fields at each layer, and the lower layer responses are transferred to the last layer (trans-layer) to form a more complete representation to retain more information. In addition, some beneficial methods in deep learning architectures such as local contrast normalization and whitening are added to the proposed hierarchical Extreme Learning Machine networks to further boost the performance. The obtained trans-layer representations are followed by block histograms with binary hashing to learn translation and rotation invariant representations, which are utilized to do high-level tasks such as recognition and detection. Compared to traditional deep learning methods, the proposed trans-layer representation method with ELM-AE based learning of local receptive filters has much faster learning speed and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101. State-of-the-art performances are achieved on both Caltech 101 15 samples per class task and 4 of 6 MNIST variations data sets, and highly impressive results are obtained on MNIST data set and other tasks.

关键词： Hierarchical Extreme Learning Machine Unsupervised Representation Learning deep learning models Learning machines Dataset representation method Recognition (Psychology) Receptive field Learning representations Religious Missions Hierarchical

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