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arXiv 2020年

作者： Zhu, Shijie Li, Jianxin Peng, Hao Wang, Senzhang He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

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IndexIt: Enhancing data locating services for parallel file systems 21

IndexIt: Enhancing data locating services for parallel file ...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data science and Systems, HPCC/SmartCity/DSS 2019

作者： Cheng, Peng Wang, Yong Lu, Yutong Du, Yunfei Chen, Zhiguang College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Changsha China National Supercomputer Center in Guangzhou School of Data and Computer Science Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9781728120584

While the ability to access a small fraction of data records from a large volume of scientific datasets is vital to accelerate scientific discovery, existing parallel file systems face serious challenges in managing scientific big data since data services have traditionally been decoupled from file systems. In this paper, we present IndexIt, an in-situ index and query middleware that aims to enhance record locating services for parallel file systems. When applications are writing data to parallel file systems, IndexIt allows users to index data while they are still in memory and bypass the performance bottleneck between memory and disks. By applying lightweight Bitmap-Range index and organizing the index as key-value pairs, IndexIt accelerates index building efficiently. Moreover, we propose a two-level query processing framework to process query requests. We build a prototype and evaluate its performance with real scientific datasets. Compared with existing data management tools, the index building time can be reduced dramatically. The proposed two-level query processing achieves up to 3 orders of magnitude speed-up than scanning the entire dataset and shows up to 1.35x speed-up than an existing tool. © 2019 IEEE.

关键词： Information management

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A personalized privacy protection data uploading scheme for mobile crowdsensing 2

A personalized privacy protection data uploading scheme for ...

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2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Ma, Rong Chen, Lei Xiong, Jinbo Tian, Youliang Li, Qi Liu, Ximeng Fujian Provincial Key Laboratory of Network Security and Cryptology College of Mathematics and Informatics Fujian Normal University Fuzhou350117 China College of Engineering and Computing Georgia Southern University GA30458 United States Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Fujian Provincial Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China

ISBN: (纸本)9781728129778

Mobile crowdsensing is a typical application of the Industrial Internet of hings and brings a promising opportunity to construct powerful industrial systems and services. The existing privacy protection schemes usually adopt a unified privacy protection policy for all sensing data due to lack of effective privacy metrics, thereby resulting in excessive or insufficient protection of privacy information and low accuracy of sensing data. To tackle these issues, in this paper, we propose a personalized privacy protection data uploading (PPDU) scheme based on game theory and sensing user's historical spatio-temporal data. In PPDU, a personalized privacy measurement algorithm is designed to obtain the personalized privacy degree of user sensing data. Based on the privacy degree, a user uploading behavior game is designed by employing an incomplete information game, and a game-based spatio-temporal sensing data uploading method is proposed for user privacy protection. Simulation experiments with real trajectory data show that the PPDU scheme makes a reasonable balance between retaining high quality of crowdsensing service and protecting user's privacy. © 2019 IEEE.

关键词： Game theory

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A deep belief network-based method to identify proteomic risk markers for Alzheimer’s disease

arXiv

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arXiv 2020年

作者： An, Ning Jin, Liuqi Ding, Huitong Yang, Jiaoyun Yuan, Jing Key Laboratory of Knowledge Engineering Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Neurology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing China

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer’s disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer’s-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer’s disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer’s disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer’s disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Energy Efficiency Recognition and Diagnosis of Complex Industrial Processes using Multivariate Nonlinear Regression Method

Energy Efficiency Recognition and Diagnosis of Complex Indus...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： ZhiQiang Geng Minjie Cheng YongMing Han Qin Wei Zhi Ouyang College of Information Science & Technology Beijing University of Chemical Technology Beijing China Guizhou Provincial Key Laboratory of Public Big Data Guiyang China

ISBN: (数字)9781728106816

ISBN: (纸本)9781728106823

Due to the characteristics of multi-dimensional, strong coupling and noise of complex industrial data, this paper establishes an energy efficiency recognition and diagnosis model using the multivariate nonlinear regression method to analyze the objective mathematical relationship between raw material consumption and the corresponding output in complex industrial processes. The curve estimation between raw materials and production of complex industrial processes are carried out. Then the raw materials are replaced by the calculation results of the equation with higher fitting degree. Finally, the linear regression and energy efficiency diagnosis of the substituted raw materials are carried out. The proposed model is tested and analyzed using actual industrial data of ethylene production plants. The results show the validity and applicability of the proposed model. Meanwhile, the objective mathematical relationship between the ethylene production capacity and the energy consumption of various raw materials can be recognized to adjust the proportion of raw materials in ethylene production plants, which can improve the energy efficiency and reduce raw materials waste.

关键词：

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User Identity Authentication and Identification Based on Multi-Factor Behavior Features

User Identity Authentication and Identification Based on Mul...

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IEEE Globecom Workshops

作者： Xing Ding Changgen Peng Hongfa Ding Maoni Wang Hui Yang Qinyong Yu College of Computer Science & Technology State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Information Guizhou University of Financial and Economics Guiyang China National Engineering Laboratory Big Data Application on Improving Government Governance Capabilities Guiyang China CETC Big Data Research Institute Co. Ltd Guiyang China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Behavior-based identity authentication has been of research interests due to its low cost and the fact that such authentication factors cannot easily copied nor stolen. However, single behavior feature authentication usuall cannot gaurantee sufficient accuracy. In this paper, a multi-factor behavior features identity authentication method is proposed by combining factors of mouse, keystroke and address. We apply support vector machine by using the information divergence of features as the weight of the linear kernel function to solve quantification's problem of features influence degree on authentication. The tailored support vector machine is applied to multi-factor behavior features to identity authentication. Our experimental results show that the improved support vector machine is better than both the linear and Gaussian kernel support vector machines for identity authentication in accuracy. The proppsed method can be applied to large volume users' identity authentication cases where their behavior features are likely to be similar.

关键词： Authentication Mice IP networks Feature extraction Support vector machines Hidden Markov models Presses

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A Game Theoretical Analysis of Risk Adaptive Access Control for Privacy Preserving

A Game Theoretical Analysis of Risk Adaptive Access Control ...

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International Conference on Networking and Network Applications (NaNA)

作者： Hongfa Ding Changgen Peng Youliang Tian Shuwen Xiang College of Mathematics and statistics State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Information Guizhou University of Finance and Economics Guiyang China College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9781728126296

ISBN: (纸本)9781728126302

More and more security and privacy issues are arising as new technologies, such as big data and cloud computing, are widely applied in nowadays. For decreasing the privacy breaches in access control system under opening and cross-domain environment. In this paper, we suggest a game and risk based access model for privacy preserving by employing Shannon information and game theory. After defining the notions of Privacy Risk and Privacy Violation Access, a high-level framework of game theoretical risk based access control is proposed. Further, we present formulas for estimating the risk value of access request and user, construct and analyze the game model of the proposed access control by using a multi-stage two player game. There exists sub-game perfect Nash equilibrium each stage in the risk based access control and it's suitable to protect the privacy by limiting the privacy violation access requests.

关键词： Privacy Access control Games Game theory Adaptation models Estimation

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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AI-GENERATED CONTENT ENHANCED computer-AIDED DIAGNOSIS MODEL FOR THYROID NODULES: A CHATGPT-STYLE ASSISTANT

arXiv

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arXiv 2024年

作者： Yao, Jincao Wang, Yunpeng Lei, Zhikai Wang, Kai Li, Xiaoxian Zhou, Jianhua Hao, Xiang Shen, Jiafei Wang, Zhenping Ru, Rongrong Chen, Yaqing Zhou, Yahan Chen, Chen Zhang, Yanming Liang, Ping Xu, Dong Department of Radiology Zhejiang Cancer Hospital Hangzhou310022 China Chinese Academy of Sciences Hangzhou310000 China Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province Hangzhou310022 China Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology Hangzhou310000 China Wenling Medical Big Data and Artificial Intelligence Research Institute 24th Floor Machang Road Taizhou310061 China Taizhou317502 China College of Optical Science and Engineering Zhejiang University No.38 of Zheda Road Zhejiang Province Hangzhou China Zhejiang Provincial Hospital of Chinese Medicine 54 Youdian Road Hangzhou310003 China Department of Ultrasound The Affiliated Dongyang Hospital of Wenzhou Medical University Dongyang322100 China Department of Ultrasound Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou510060 China Affiliated Xiaoshan Hospital Hangzhou Normal University No.728 North Yucai Road Hangzhou311202 China Zhejiang Provincial People's Hospital Affiliated People's Hospital Hangzhou Medical College Hangzhou314408 China Key Laboratory of Endocrine Gland Diseases of Zhejiang Province Hangzhou314408 China Department of Ultrasound Chinese PLA General Hospital Chinese PLA Medical School Beijing100853 China

An artificial intelligence-generated content-enhanced computer-aided diagnosis (AIGC-CAD) model, designated as ThyGPT, has been developed. This model, inspired by the architecture of ChatGPT, could assist radiologists in assessing the risk of thyroid nodules through semantic-level human-machine interaction. A dataset comprising 19,165 thyroid nodule ultrasound cases from Zhejiang Cancer Hospital was assembled to facilitate the training and validation of the model. After training, ThyGPT could automatically evaluate thyroid nodule and engage in effective communication with physicians through human-computer interaction. The performance of ThyGPT was rigorously quantified using established metrics such as the receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, and specificity. The empirical findings revealed that radiologists, when supplemented with ThyGPT, markedly surpassed the diagnostic acumen of their peers utilizing traditional methods as well as the performance of the model in isolation. These findings suggest that AIGC-CAD systems, exemplified by ThyGPT, hold the promise to fundamentally transform the diagnostic workflows of radiologists in forthcoming years. Copyright © 2024, The Authors. All rights reserved.

关键词： computer aided diagnosis

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Total Factor Productivity Analysis of Industrial Processes Based on Malmquist Model

Total Factor Productivity Analysis of Industrial Processes B...

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作者： Yongming Han Mingxing Zhang Zhiqiang Geng Kai Chen Yajie Wang Qin Wei Zhi Ouyang Guizhou Provincial Key Laboratory of Public Big Data College of Information Science& Technology Beijing University of Chemical Technology College of Information Science & Technology Beijing University of Chemical Technology Guizhou Academy of Science

The production of industrial process is the key factor of evaluating the national industry production ***,this paper proposes total factor productivity analysis method based on Malmquist model to analyze the production efficiency statically and *** on the input and output data of ethylene production plants in China,the total factor production index of ethylene production plants in industrial processes is decomposed into technical efficiency,technical progress,pure technical efficiency and scale efficiency through the Malmquist model based on the data envelopment analysis(DEA).Moreover,the energy efficiency of ethylene production plants can be improved.

关键词： DEA Malmquist index Ethylene production Total factor productivity Energy efficiency

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