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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Mingjia Fu Jin Wang Jingya Zhou Jianping Wang Kejie Lu Xiaobo Zhou School of Computer Science and Technology Soochow University P.R. China Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science Georgia Institue of Technology USA Department of Computer Science City University of Hong Kong Department of Computer Science and Engineering University of Puerto Rico at Mayagüez Department of Computer Science and Technology Tianjin University P. R. China

ISBN: (纸本)9781728125848

Edge computing is attracting more and more attention in recent years to fulfill the requirements of latency-critical and computation-intensive applications. By using the coding redundancy, coded edge computing has emerged to optimize the total computation latency. Compared with the servers in cloud computing, edge devices located at the edge of network may not be reliable and trustworthy. In coded edge computing, even one incorrect intermediate result will lead to the incorrect final result. Therefore, considering the low computation capabilities of edge devices and low latency requirements of user, we study the result verification problem for coded edge computing. Specifically, we propose an efficient Orthogonal Mark (OM) verification scheme by the properties of linear space. We also conduct solid theoretical analysis to show the successful verification probabilities under two kinds of attack models, respectively. Finally, we conduct extensive simulations to show the effectiveness of the proposed OM verification scheme when comparing with basic coded edge computing scheme and Decoding Comparison (DC) scheme.

关键词： Verifiable computation Null space Edge computing Linear coding Distributed computing Distributed computing Verification Edge Device Scheme Servers Edge of a figure or solid

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Adaptive Locality Preserving Regression

arXiv

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arXiv 2019年

作者： Wen, Jie Zhong, Zuofeng Zhang, Zheng Fei, Lunke Lai, Zhihui Chen, Runze School of Computer Science and Technology Guangdong University of Technology GuangzhouGuangdong510006 China Institute of Textiles and Clothing Hong Kong Polytechnic University Hong Kong School of Information Technology & Electrical Engineering University of Queensland BrisbaneQLD4072 Australia College of Computer Science and Software Engineering Shenzhen University ShenzhenGuangdong518055 China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen518055 China

This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional ‘Frobenius norm’ with the special l2,1 norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owning to the row-sparsity property of the l2,1 norm. Extensive experiments conducted on the synthetic database with manifold structure and many real-world databases prove the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Regression analysis

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A Game Theoretical Pricing Scheme for Vehicles in Vehicular Edge Computing

A Game Theoretical Pricing Scheme for Vehicles in Vehicular ...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Chaogang Tang Chunsheng Zhu Huaming Wu Xianglin Wei Qing Li Joel J. P. C. Rodrigues School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of the Ministry of Education China University of Mining and Technology Xuzhou China SUSTech Institute of Future Networks Southern University of Science and Technology Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Center for Applied Mathematics Tianjin University Tianjin China The 63rd Research Institute National University of Defense Technology Changsha China The Hong Kong Polytechnic University Hong Kong Federal University of Piauí Teresina - Pi Brazil Instituto de Telecomunicações Portugal

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Vehicular edge computing (VEC) brings the computing resources to the edge of the networks and thus provisions better computing services to the vehicles in terms of response latency. Meanwhile, the edge server can earn their revenues by leasing the computing resources. However, a higher price does not always bring forth more benefits for the edge server in VEC, since it may discourage vehicles from renting more computing resources from VEC. To the best of our knowledge, few of previous works have focused on the real-time pricing problem for VEC. We investigate in this paper the pricing problem from the viewpoints of both vehicles and the edge server, so as to optimize the utility values and revenues of vehicles and the edge server, respectively. We resort to the Stackelberg game for modeling the interactions between vehicles and edge server, and a distributed algorithm for this pricing problem is proposed in the paper. Experimental results have displayed the efficiency and effectiveness of the proposed algorithm.

关键词： Computational modeling Pricing Games Sensors Servers Distributed algorithms Edge computing

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Bilinear supervised hashing based on 2D image features

arXiv

引用

arXiv 2019年

作者： Ding, Yujuan Wong, Wai Kueng Lai, Zhihui Zhang, Zheng Institute of Textiles and Clothing Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Textiles and Clothing Hong Kong Polytechnic University Hong Kong School of Information Technology & Electrical Engineering University of Queensland BrisbaneQLD4072 Australia

Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost. Most of the existing hashing methods focus on learning the low-dimensional vectorized binary features based on the high-dimensional raw vectorized features. However, studies on how to obtain preferable binary codes from the original 2D image features for retrieval is very limited. This paper proposes a bilinear supervised discrete hashing (BSDH) method based on 2D image features which utilizes bilinear projections to binarize the image matrix features such that the intrinsic characteristics in the 2D image space are preserved in the learned binary codes. Meanwhile, the bilinear projection approximation and vectorization binary codes regression are seamlessly integrated together to formulate the final robust learning framework. Furthermore, a discrete optimization strategy is developed to alternatively update each variable for obtaining the high-quality binary codes. In addition, two 2D image features, traditional SURF-based FVLAD feature and CNN-based AlexConv5 feature are designed for further improving the performance of the proposed BSDH method. Results of extensive experiments conducted on four benchmark datasets show that the proposed BSDH method almost outperforms all competing hashing methods with different input features by different evaluation protocols. Copyright © 2019, The Authors. All rights reserved.

关键词： Binary codes

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Deep CNNs meet global covariance pooling: Better representation and generalization

arXiv

引用

arXiv 2019年

作者： Wang, Qilong Xie, Jiangtao Zuo, Wangmeng Zhang, Lei Li, Peihua School of Information and Communication Engineering Dalian University of Technology Liaoning116024 China College of Intelligence and Computing Tianjin University Tianjin300350 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Computing Hong Kong Polytechnic University Hung Hom Hong Kong

Compared with global average pooling in existing deep convolutional neural networks (CNNs), global covariance pooling can capture richer statistics of deep features, having potential for improving representation and generalization abilities of deep CNNs. However, integration of global covariance pooling into deep CNNs brings two challenges: (1) robust covariance estimation given deep features of high dimension and small sample;(2) appropriate use of geometry of covariances. To address these challenges, we propose a global Matrix Power Normalized COVariance (MPN-COV) Pooling. Our MPN-COV conforms to a robust covariance estimator, very suitable for scenario of high dimension and small sample. It can also be regarded as power-Euclidean metric between covariances, effectively exploiting their geometry. Furthermore, a global Gaussian embedding method is proposed to incorporate first-order statistics into MPN-COV. For fast training of MPN-COV networks, we propose an iterative matrix square root normalization, avoiding GPU unfriendly eigen-decomposition inherent in MPN-COV. Additionally, progressive 1 × 1 and group convolutions are introduced to compact covariance representations. The MPN-COV and its variants are highly modular, readily plugged into existing deep CNNs. Extensive experiments are conducted on large-scale object classification, scene categorization, fine-grained visual recognition and texture classification, showing our methods are superior to the counterparts and achieve state-of-the-art performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Assessing Mental Stress Based on Smartphone Sensing Data: An Empirical Study

Assessing Mental Stress Based on Smartphone Sensing Data: An...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Feng Wang Yasha Wang Jiangtao Wang Haoyi Xiong Junfeng Zhao Daqing Zhang Key Laboratory of High Confdence Software Technologies Ministry of Education Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Peking University Information Technology Institute Tianjin Binhai China National Engineering Research Center of Software Engineering Beijing China Big Data Lab Baidu Research Beijing

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Mental stress is a critical factor affecting one's physical and mental well-being. At the early stage, the effect of stress is often underestimated, while it usually leads to serious issue Lateran. Therefore, it is crucial to detect stress before it evolves into severe problems. Traditional stress detection methods are based on either questionnaires or professional devices, which are time-consuming, costly and intrusive. With the popularity of smartphones embedded with a rich set of sensors, which can capture people's context, such as movement, sound, location and so on, it is an alternative way to access people's behavior by smartphones. Through an empirical study, this paper proposes an automatic and non-intrusive stress detection framework based on smartphone sensing data. First, we construct various discriminative features from multi-modality phone sensing data, in which both absolute and relative features are considered to make the model more personalized. Then, to tackle the challenge of label insufficiency, we further develop a co-training based method for stress level classification. Finally, we evaluate our model based on an open dataset, and the experimental results verify its advantages over other baselines.

关键词： Sensors Stress Feature extraction Psychology Mobile handsets Data models Biomedical monitoring

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Towards cost effective privacy provision for typed resources in iot environment 30

Towards cost effective privacy provision for typed resources...

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30th International Conference on software engineering and Knowledge engineering, SEKE 2018

作者： Duan, Yucong Song, Zhengyang Yang, Xiaoxian Zou, Quan Sun, Xiaobing Zhang, Xinyue State Key Laboratory of Marine Resource Utilization in the South China Sea College of Information Science and Technology Hainan University Haikou570228 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China College of Computer Science Tianjin University China School of Information Engineering Yangzhou University China

ISBN: (纸本)1891706446

We present privacy resources in IoT as data, information, and knowledge. We construct a privacy protection architecture on our previously proposed DIKW graphs: Data Graph, Information Graph, and Knowledge Graph. On this architecture, we search privacy protection target resources both as they appear explicitly in their original types and as they appear implicitly which means that they are expressed not in their original types. For a single privacy protection target, it may have various concrete compositions in various layers of DIKW Graph. It becomes more complex since the implementation of a privacy target might also be intertwined with the implementation of other privacy targets. We propose to protect target resources according to their types by either isolating the elements comprising an implementation, or weakening relationships among elements comprising an *** optimize among several choices of implementing a protection in a business environment, we introduced the tradeoff between customers' expectations/investment and privacy providers' expectation. Thereafter we proposed to prioritize implementation according to their ratio of cost/benefit. © 2018 Universitat zu Koln. All rights reserved.

关键词： Internet of things

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Variational Autoencoder based Latent Factor Decoding of Multichannel EEG for Emotion Recognition

Variational Autoencoder based Latent Factor Decoding of Mult...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Li Zhigang Zhao Dawei Song Yazhou Zhang Chunyang Niu Junwei Zhang Jidong Huo Jing Li Key Laboratory of Medical Artificial Intelligence Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences China School of Computer Science and Technology Beijing Institute of Technology China Software Engineering College Zhengzhou University of Light Industry China School of Computer Science and Technology Tianjin University China Jiuquan Satellite Launch Center China

Robust cross-subject emotion recognition based on multichannel EEG has always been a hard work. In this work, we hypothesize there exists default brain variables across subjects in emotional processes. Hence, the states of the latent variables that related to emotional processing must contribute to building robust recognition models. We propose to utilize variational autoencoder (VAE) to determine the latent factors from the multichannel EEG. Through sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED), and compared with traditional matrix factorization based (ICA) and autoencoder based (AE) approaches. Experimental results demonstrate that neural network is suitable for unsupervised EEG modeling and our proposed emotion recognition framework achieves the state-of-the-art performance. As far as we know, it is the first work that introduces VAE into multichannel EEG decoding for emotion recognition.

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Strict pattern matching under non-overlapping condition

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science China(Information sciences) 2017年第1期60卷 5-20页

作者： Youxi WU Cong SHEN He JIANG Xindong WU School of Computer Science and Engineering Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation School of Computer Science and Technology Tianjin University School of Software Dalian University of Technology School of Computer Science and Information Engineering Hefei University of Technology School of Computing and Informatics University of Louisiana at Lafayette

Pattern matching(or string matching) is an essential task in computer science, especially in sequential pattern mining, since pattern matching methods can be used to calculate the support(or the number of occurrences) of a pattern and then to determine whether the pattern is frequent or not. A state-of-the-art sequential pattern mining with gap constraints(or flexible wildcards) uses the number of non-overlapping occurrences to denote the frequency of a pattern. Non-overlapping means that any two occurrences cannot use the same character of the sequence at the same position of the pattern. In this paper, we investigate strict pattern matching under the non-overlapping condition. We show that the problem is in P at first. Then we propose an algorithm, called NETLAP-Best, which uses Nettree structure. NETLAP-Best transforms the pattern matching problem into a Nettree and iterates to find the rightmost root-leaf path, to prune the useless nodes in the Nettree after removing the rightmost root-leaf path. We show that NETLAP-Best is a complete algorithm and analyse the time and space complexities of the algorithm. Extensive experimental results demonstrate the correctness and efficiency of NETLAP-Best.

关键词： pattern matching sequential pattern mining gap constraint flexible wildcard non-overlapping occurrence Nettree

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Effectiveness of exploring historical commits for developer recommendation： an empirical study

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Frontiers of computer science 2018年第3期12卷 528-544页

作者： Xiaobing SUN Hui YANG Hareton LEUNG Bin LI Hanchao (Jerry) LI Lingzhi LIAO School of Information Engineering Yangzhou University Yangzhou 225127 China Information Technology Research Base of Civil Aviation Administration of China Civil Aviation University of China Tianjin 300300 China Department of Computing The Hong Kong Polytechnic University Hong Kong China Coventry University Coventry CVI 5FB UK State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Nanjing University of Information Science and Technology Nanjing 210044 China

Developer recommendation is an essential task for resolving incoming issues in the evolution of software. Many developer recommendation techniques have been developed in the literature; among these studies, most techniques usually combined historical commits as supplementary information with bug repositories and/or source-code repositories to recommend developers. However, the question of whether the messages in historical commits are always useful has not yet been answered. This article aims at solving this problem by conducting an empirical study on four open-source projects. The results show that：（1） the number of meaningful words of the commit description has an impact on the quality of the commit, and a larger number of meaningful words in the description means that it can generally better reflect developers＇ expertise; （2） using commit description to recommend the relevant developers is better than that using relevant files that are recorded in historical commits; （3） developers tend to change the relevant files that they have changed many times before; （4） developers generally tend to change the files that they have changed recently.

关键词： developer recommendation historical commits empirical study

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