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Robust, discriminative and comprehensive dictionary learning for face recognition

Pattern Recognition 2018年 81卷 341-356页

作者： Guojun Lin Meng Yang Jian Yang Linlin Shen Weicheng Xie School of Data and Computer Science Sun Yat-Sen University Guangzhou China School of Automation and Electric Information Sichuan University of Science and Engineer Zigong China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-Sen University Ministry of Education China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China School of Computer Science and Software Engineering Shenzhen University Shenzhen China

For sparse representation or sparse coding based image classification, the dictionary, which is required to faithfully and robustly represent query images, plays an important role on its success. Learning dictionaries from the training data for sparse coding has shown state-of-the-art results in image classification and face recognition. However, for face recognition, conventional dictionary learning methods cannot well learn a reliable and robust dictionary due to suffering from the small-sample-size problem. The other significant issue is that current dictionary learning do not completely cover the important components of signal representation (e.g., commonality, particularity, and disturbance), which limit their performance. In order to solve the above issues, in this paper, we propose a novel robust, discriminative and comprehensive dictionary learning (RDCDL) method, in which a robust dictionary is learned from comprehensive training sample diversities generated by extracting and generating facial variations. Especially, to completely represent the commonality, particularity and disturbance, class-shared, class-specific and disturbance dictionary atoms are learned to represent the data from different classes. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discriminative information, which effectively improves the classification capability of the dictionary. The proposed RDCDL method is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art dictionary learning methods for face recognition.

关键词： Dictionary learning Face recognition Sparse representation

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A novel data quality assessment framework for vehicular network testbeds 12

A novel data quality assessment framework for vehicular netw...

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12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TRIDENTCOM 2017

作者： Tian, Daxin Zhu, Yukai Zhou, Jianshan Duan, Xuting Wang, Yunpeng Song, Jeungeun Rong, Hui Guo, Peng Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing XueYuan Road No.37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No.68 Tianjin300300 China Beihang University Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering XueYuan Road No.37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781631901577

Big data technique is considered as a powerful tool to exploit all the potential of the Internet of Things and the smart cities. The development of internet of Vehicles (IoV) and wireless communication technologies have boosted diverse applications related to smart cities and Cyber-Physical Systems, but the data quality of vehicular sensors is an important issue due to the high-speed mobile wireless communication environment and physical sensor noise. This paper presents our experiences for big data analytics based on a vehicular network testbed, in terms of sensors data management, multi-dimension data fusion and data quality assessment for the vehicular sensor data. The proposed data quality assessment framework consist of feature extraction based on multi-sensor data fusion and multi-level wavelet transform, as well as a semi-supervised learning based classification algorithm. The comparison experiment shows that the proposed framework and approaches can extract feasible features and solve the unbalanced label problems, which achieve a better assessment effect. Copyright © 2018 EAI.

关键词： Smart city

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Cyber-physical security assessment and simulation based on graph database

Cyber-physical security assessment and simulation based on g...

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2017 IEEE Conference on Energy Internet and Energy System Integration, EI2 2017

作者： Wang, Di Guo, Qinglai Song, Yan Gao, Kunlun Zhou, Aihua State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company Shanghai200437 China Advanced Computing and Big Data Laboratory of SGCC Global Energy Interconnection Research Institute Beijing102209 China

ISBN: (纸本)9781538614273

With the development of information technology, the operation of power grid increasingly depends upon the support of strong communication network. The power system consisting of grids infrastructure and communication equipment is a typical cyber-physical system(CPS). In recent years, power accidents that information-side failure impacts on physical operation happen at times. In order to simulate the impacts of information failure on grid operation, we develop a cyber-physical simulation tool for the secondary-voltage control of power system to implement the reasonable analysis and evaluation of the influence of different types of information failure on voltage control effect. In this paper, the graph database is introduced to construct the basic cyber-physical data model as an important part of the simulation tool, whose graphical modeling and management is friendly for users. In addition, the importance of different types of information is analyzed with the tool. It can be seen that the voltage information is more fragile and critical than the non-independent generator reactive information. © 2017 IEEE.

关键词： Cyber Physical System

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Private model compression via knowledge distillation

arXiv

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arXiv 2018年

作者： Wang, Ji Bao, Weidong Sun, Lichao Zhu, Xiaomin Cao, Bokai Yu, Philip S. College of Systems Engineering National University of Defense Technology Changsha China Department of Computer Science University of Illinois at Chicago Chicago United States State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Facebook Inc. Menlo Park United States Institute for Data Science Tsinghua University Beijing China

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices' capacity. What is worse, app service providers need to collect and utilize a large volume of users' data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful (9:83;10-6)-differential privacy is guaranteed, the compact model trained by RONA can obtain 20× compression ratio and 19× speed-up with merely 0.97% accuracy loss. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Examining scientific writing styles from the perspective of linguistic complexity

arXiv

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arXiv 2018年

作者： Lu, Chao Bu, Yi Wang, Jie Ding, Ying Torvik, Vetle Schnaars, Matthew Zhang, Chengzhi School of Economics and Management Nanjing University of Science and Technology Nanjing Jiangsu China School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Center for Complex Networks and Systems Research School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Information Management Nanjing University Nanjing Jiangsu China School of Information Management Wuhan University Wuhan Hubei China School of Information Sciences University of Illinois UrbanaIL United States Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing Jiangsu China

Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. In order to uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (1) syntactic complexity, including measurements of sentence length and sentence complexity;and (2) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity. Copyright © 2018, The Authors. All rights reserved.

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Recommendation system based on trusted relation transmission

Recommendation system based on trusted relation transmission

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Yixiong Bian Huakang Li Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

With the rapid development of the internet, applications of recommendation systems for online shops and entertainment platforms become more and more popular. In order to improve the effectiveness of recommendation, external information has been incorporated into various algorithms, such as location and social relationship. However, most algorithms only focus on the introduction of external information without depth analysis of the intrinsic mechanism in the external information. This paper proposed a transfer model of social trusted relationship, and optimized the reliability of the transfer model using pruning algorithm based on original trust recommendation. A credible social relationship macro-transfer model based on iterations of new credible relationships is defined by the similarity of social relationships. With a certain interest topic as a source of information, a micro-transfer model achieves the theme of interest and credibility of the expansion using social information dissemination algorithm. To demonstrate the effectiveness of the macro and micro credible transfer models, we used the Mantra search tree pruning algorithm and the optimization algorithm of similar category replacing similar products. The experimental results show that the proposed method based on the macroscopic and microscopic transfer models of the trusted relationship enhances the success rate and stability of the recommended system.

关键词： Collaboration Reliability Social network services Heuristic algorithms Optimization System performance Convergence

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Not just privacy: Improving performance of private deep learning in mobile cloud

arXiv

引用

arXiv 2018年

作者： Wang, Ji Zhang, Jianguo Bao, Weidong Zhu, Xiaomin Cao, Bokai Yu, Philip S. College of Systems Engineering National University of Defense Technology Changsha China Department of Computer Science University of Illinois at Chicago Chicago United States College of Systems Engineering State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Facebook Inc. Menlo Park United States Department of Computer Science University of Illinois at Chicago Chicago United States Institute for Data Science Tsinghua University Beijing China

The increasing demand for on-device deep learning services calls for a highly efficient manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity. The cloud-based solution is a promising approach to enabling deep learning applications on mobile devices where the large portions of a DNN are offloaded to the cloud. However, revealing data to the cloud leads to potential privacy risk. To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework Arden which partitions the DNN across mobile devices and cloud data centers. A simple data transformation is performed on the mobile device, while the resource-hungry training and the complex inference rely on the cloud data center. To protect the sensitive information, a lightweight privacy-preserving mechanism consisting of arbitrary data nullification and random noise addition is introduced, which provides strong privacy guarantee. A rigorous privacy budget analysis is given. Nonetheless, the private perturbation to the original data inevitably has a negative impact on the performance of further inference on the cloud side. To mitigate this influence, we propose a noisy training method to enhance the cloud-side network robustness to perturbed data. Through the sophisticated design, Arden can not only preserve privacy but also improve the inference performance. To validate the proposed Arden, a series of experiments based on three image datasets and a real mobile application are conducted. The experimental results demonstrate the effectiveness of Arden. Finally, we implement Arden on a demo system to verify its practicality. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Nonlinearly-Activated Noise-Tolerant Zeroing Neural Network for Distributed Motion Planning of Multiple Robot Arms

Nonlinearly-Activated Noise-Tolerant Zeroing Neural Network ...

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International Joint Conference on Neural Networks

作者： Long Jin Shuai Li Xin Luo Ming-sheng Shang School of Information Science and Engineering Lanzhou University Department of Computing The Hong Kong Polytechnic University Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences

ISBN: (纸本)9781509061839

This paper investigates the distributed motion planning of multiple robot arms with limited communications in the presence of noises. To do this, a nonlinearly-activated noise-tolerant zeroing neural network (NANTZNN) is designed and presented for the first time for solving the presented distributed scheme online. Theoretical analyses and simulation results show the effectiveness and accuracy of the presented distributed scheme with the aid of NANTZNN model.

关键词： robotic arm motion planning Distribute Noise Neural network Automatic incident detection Simulation results

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Random asynchronous iterations in distributed coordination algorithms

arXiv

引用

arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Improving stock market prediction via heterogeneous information fusion

arXiv

引用

arXiv 2018年

作者： Zhang, Xi Zhang, Yunjia Wang, Senzhang Yao, Yuntao Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Computer Science University of Illinois at ChicagoIL60607 United States Institute for Data Science Tsinghua University Beijing100084 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan Guangdong523808 China

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people’s sentiments towards the market and stocks, have been proved to play important roles in the stocks’ volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users’ sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsicrelations among the events and the investors’ sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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