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Correction to: Path-based estimation for link prediction

International Journal of Machine Learning and Cybernetics 2021年第9期12卷 2459-2459页

作者： Ma, Guoshuai Yan, Hongren Qian, Yuhua Wang, Lingfeng Dang, Chuangyin Zhao, Zhongying Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee Milwaukee USA Department of Manufacture Engineering and Engineering Management City University of Hong Kong Kowloon Tong Hong Kong The School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

A correction to this paper has been published: https://***/10.1007/s13042-021-01350-4

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Adaptive Structure-Constrained Robust Latent Low-Rank Coding for Image Recovery

Adaptive Structure-Constrained Robust Latent Low-Rank Coding...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Lei Wang Sheng Li Yang Wang Zheng Zhang Zhengjun Zha Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Georgia Athens GA Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2, 1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery.

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Privacy Preserving Scheme for Location Based Services Using Cryptographic Approach 2

Privacy Preserving Scheme for Location Based Services Using ...

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2nd IEEE Symposium on Privacy-Aware Computing, PAC 2018

作者： Pu, Yuwen Luo, Jin Wang, Ying Hu, Chunqiang Huo, Yan Zhang, Jiong School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Ningbo Branch of Agricultural Bank of China Ningbo China

ISBN: (纸本)9781538684429

Location based services pose strong security and privacy requirements but achieving privacy protection at the client side is a non-trial problem. In this paper, we propose an efficient and reliable location privacy preserving scheme using cryptographic approach. It can preserve users' privacy data by making their identity be anonymity for LBS provider and fog server. For communication, both AES with One-Time-Pad keys and IBE are employed to guarantee confidentiality and integrity of request and response data. Besides, we also provide security analysis to demonstrate that our scheme can resist internal attack, external attack and colluding attack. © 2018 IEEE.

关键词： Location based services

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Application and development of elastic fibers focusing on composition and structure

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Journal of Physics: Conference Series 2021年第1期2021卷

作者： Jin Zhang Qinyue Sun Yumin Wang Xiongwei Dong Yi Xiong Wei Zeng Department of Pharmacy Zhongnan Hospital Wuhan University Wuhan 430071 People's Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application School of Electronics and Information Engineering Anhui University Hefei 230601 Anhui People's Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro-and Nano-Structures Wuhan University Wuhan 430072 People's Republic of China Science and Technology Institute Key Laboratory of Textile Fiber and Products Ministry of Education Laboratory for Electron Microscopy Wuhan Textile University Wuhan 430073 Hubei People's Republic of China

Elastic fabric is comfortable to wear, and has been a consumer textile due to its good shape retention and wrinkle resistance after washing. This paper briefly introduces the types and performance characteristics of several common elastic fibers, and analyzes the relationship between the structure and elasticity of different elastic fibers. Finally, the development trend of elastic fibers is proposed.

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Load balancing for ultra-dense networks: A deep reinforcement learning based approach

arXiv

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arXiv 2019年

作者： Xu, Yue Xu, Wenjun Wang, Zhi Lin, Jiaru Cui, Shuguang Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Shenzhen Research Institute of Big Data School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering University of California DavisCA95616 United States

—In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intra-cluster load balancing afterwards. Second, for the intra-cluster load balancing, this paper proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this work proposes an offline-evaluation based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity

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Genomics, Proteomics & Bioinformatics 2019年第4期17卷 441-452页

作者： Zhenyu Liu Jiangang Liu Huijuan Yuan Taiyuan Liu Xingwei Cui Zhenchao Tang Yang Du Meiyun Wang Yusong Lin Jie Tian CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Department of Endocrinology and Metabolism Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Department of Radiology Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Cooperative Innovation Center for Internet Healthcare&School of Software Zhengzhou UniversityZhengzhou 450003China School of Mechanical Electrical&Information EngineeringShandong University(Weihai)Weihai 264209China University of Chinese Academy of Sciences Beijing 100080China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’an 710126China

Majority of type 2 diabetes mellitus(T2DM)patients are highly susceptible to several forms of cognitive impairments,particularly ***,the underlying neural mechanism of these cognitive impairments remains *** aimed to investigate the correlation between whole brain resting state functional connections(RSFCs)and the cognitive status in 95 patients with *** constructed an elastic net model to estimate the Montreal Cognitive Assessment(MoCA)scores,which served as an index of the cognitive status of the patients,and to select the RSFCs for further ***,we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected *** estimated and chronological MoCA scores were significantly correlated with R=0.81 and the mean absolute error(MAE)=***,cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54%and the area under the receiver operating characteristic(ROC)curve(AUC)of *** connectivity pattern not only included the connections between regions within the default mode network(DMN),but also the functional connectivity between the task-positive networks and the DMN,as well as those within the task-positive *** results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.

关键词： Type 2 diabetes mellitus Resting state functional connectivity Elastic net Support vector machines MoCA

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Gestational Diabetes Mellitus Prediction Based on Two Classification Algorithms

Gestational Diabetes Mellitus Prediction Based on Two Classi...

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Weiyang Zhong Shanxi Jiang Wei Wu Danhong Peng Tian Xu Jun Wang Gongdao Wang Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing PR China China Tower Corporation Limited Nantong Branch PR China Key Laboratory of Environment Medicine and Engineering Ministry of Education School of Public Health Southeast University Nanjing PR China Zhongda Hospital Southeast University NanjingSchool of Science China Pharmaceutical University Nanjing PR China

Gestational diabetes mellitus (GDM) is a disease with normal glucose tolerance before pregnancy and only diabetes during pregnancy. The discovery of GDM has a long history. The specific causes and mechanisms of its occurrence are still unclear. There is a lack of research on intelligent diagnosis of GDM. GDM has many adverse effects on pregnant women and fetuses, which is of great significance for the early diagnosis of GDM. Based on the measured data of the hospital, this paper realizes the intelligent diagnosis of GDM by using a improved KNN algorithm and a improved BP neural network.

关键词： Classification algorithms Training Neural networks Diabetes Pregnancy Genetic algorithms Sociology

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Scene Classification via Learning a Multi-Branch Convolutional Network

Scene Classification via Learning a Multi-Branch Convolution...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoyong Bian Chunfang Chen Yang Chen Xiongjun Fei Jingshan Tang Intelligent Information Processing and Real-time Industrial Systems Hubei Province Key Laboratory School of Computer Science and Technology Wuhan University of Science and Technology and Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering and Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China School of Technology Michigan Technological University Michigan USA

Efficient and accurate classification of high-resolution scene remains a challenge of within-class diversity and between-class similarity due to rich image variations in viewpoint, object pose, spatial resolution and background. To address these issues, we propose a multi-branch convolutional neural network (MB-CNN), which focuses on tackling the problem of learning the appropriate representation of a high-resolution scene that is rich enough to discriminate between different semantic classes. First, pyramid scene parsing network (PSPNet) with minor modification is introduced to gather global object information. Then, an attention net is proposed to highlight transformation invariance and key regions for attention feature extraction. Finally, above two branches are fused with original input branch to learn consistently semantic class information and generate powerful predictions. Our approach achieves better performance favorably against state-of-the-arts on two publicly available scene datasets.

关键词： Conferences Cybernetics

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Impact of Prior knowledge and data Correlation on Privacy Leakage: A Unified Analysis

arXiv

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arXiv 2019年

作者： Li, Yanan Ren, Xuebin Yang, Shusen Yang, Xinyu National Engineering Laboratory for Big Data Analytics [NEL-BDA Xi’an Jiaotong University Xi’an Shaanxi710049 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an Shaanxi710049 China School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi710049 China Ministry of Education Key Lab for Intelligent Networks and Network Security [MOE KLINNS Lab Xi’an Jiaotong University Xi’an Shaanxi710049 China

It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named prior differential privacy (PDP), is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph (WHG) and the multivariate Gaussian model to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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An efficient quantum circuits optimizing scheme compared with QISKit

arXiv

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arXiv 2018年

作者： Zhang, Xin Xiang, Hong Xiang, Tao Fu, Li Sang, Jun School of Big Data and Software Engineering Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education School of Computer Science Chongqing University

Recently, the development of quantum chips has made great progress-the number of qubits is increasing and the fidelity is getting higher. However, qubits of these chips are not always fully connected, which sets additional barriers for implementing quantum algorithms and pro-gramming quantum programs. In this paper, we introduce a general circuit optimizing scheme, which can efficiently adjust and optimize quantum circuits according to arbitrary given qubits' layout by adding additional quantum gates, exchanging qubits and merging single-qubit gates. Compared with the optimizing algorithm of IBM's QISKit, the quantum gates consumed by our scheme is 74.7%, and the execution time is only 12.9% on average. Copyright © 2018, The Authors. All rights reserved.

关键词： Timing circuits

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