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arXiv 2020年

作者： Xu, Zichuan Zhou, Pan Wu, Jiangkai Ren, Jiankang Xia, Qiufen Liang, Huizhi Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Software Dalian University of Technology Dalian China School of Computer Science Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science University of Reading Reading United Kingdom

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

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Cost-efficient and skew-aware data scheduling for incremental learning in 5G network

arXiv

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arXiv 2020年

作者： Pu, Lingjun Yuan, Xinjing Xu, Xiaohang Chen, Xu Zhou, Pan Xu, Jingdong The College of Computer Science Nankai University Tianjin300071 China The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710126 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Pazhou Lab. Guangzhou510335 China The Hubei Engineering Research Center on Big Data Security The School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

To facilitate the emerging applications in 5G networks, mobile network operators will provide many network functions in terms of control and prediction. Recently, they have recognized the power of machine learning (ML) and started to explore its potential to facilitate those network functions. Nevertheless, the current ML models for network functions are often derived in an offline manner, which is inefficient due to the excessive overhead for transmitting a huge volume of dataset to remote ML training clouds and failing to provide the incremental learning capability for the continuous model updating. As an alternative solution, we propose Cocktail, an incremental learning framework within a reference 5G network architecture. To achieve cost efficiency while increasing trained model accuracy, an efficient online data scheduling policy is essential. To this end, we formulate an online data scheduling problem to optimize the framework cost while alleviating the data skew issue caused by the capacity heterogeneity of training workers from the long-term perspective. We exploit the stochastic gradient descent to devise an online asymptotically optimal algorithm, including two optimal policies based on novel graph constructions for skew-aware data collection and data training. Small-scale testbed and large-scale simulations validate the superior performance of our proposed framework. © 2020, CC BY.

关键词： 5G mobile communication systems

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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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Poisoning attack against estimating from pairwise comparisons

arXiv

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arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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Corrigendum to “Wetland classification using parcel-level ensemble algorithm based on Gaofen-6 multispectral imagery and Sentinel-1 dataset” [J. Hydrol. 606 (2022) 127462]

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Journal of Hydrology 2022年 613卷

作者： Meng Zhang Hui Lin Research Center of Forestry Remote Sensing & Information Engineering Central South University of Forestry & Technology Changsha 410004 China Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province Changsha 410004 China Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area Changsha 410004 China College of Forestry Central South University of Forestry & Technology Changsha 410004 China

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Deep Learning for Password Guessing and Password Strength Evaluation, A Survey

Deep Learning for Password Guessing and Password Strength Ev...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Tao Zhang Zelei Cheng Yi Qin Qiang Li Lin Shi Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China Department of Computer and Information Technology Purdue University West Lafayette USA PLA Information Engineering University Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.

关键词： Deep learning Training Transfer learning security Gallium nitride Password Text processing

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What Is Required to Prevent a Second Major Outbreak of SARS-CoV-2 upon Lifting Quarantine in Wuhan City,China

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The Innovation 2020年第1期1卷 152-157页

作者： Lei Zhang Mingwang Shen Xiaomeng Ma Shu Su Wenfeng Gong Jing Wang Yusha Tao Zhuoru Zou Rui Zhao Joseph T.F.Lau Wei Li Feng Liu Kai Ye Youfa Wang Guihua Zhuang Christopher K.Fairley China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’anShaanxi 710061PR China Melbourne Sexual Health Centre Alfred HealthMelbourneAustralia Central Clinical School Faculty of MedicineNursing and Health SciencesMonash UniversityMelbourneVICAustralia Department of Epidemiology and Biostatistics College of Public HealthZhengzhou UniversityZhengzhou 450001 HenanChina Institute of Health Policy Management and Evaluation University of TorontoTorontoONCanada School of Public Health and Preventive Medicine Faculty of MedicineMonash UniversityMelbourneAustralia Centre for Research Behaviors Centre for Health Behaviours ResearchSchool of Public Health and Primary CareThe Chinese University of Hong KongHong KongHong Kong Bill and Melinda Gates Foundation SeattleWAUSA Department of Epidemiology and Health Statistics School of Public HealthSoutheast UniversityNanjing210009 JiangsuChina Shaanxi Provincial Center for Disease Control and Prevention Xi’an710054 ShaanxiChina MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’an 710049 China School of Automation Science and Engineering Faculty of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’an 710049 China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061 China The School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049 China Global Health Institute Department of Epidemiology and BiostatisticsSchool of Public HealthXi’an Jiaotong University Health Science CenterXi’anShaanxi 710061China

BACKGROUND The Chinese government implemented a metropolitan-wide quarantine of Wuhan city on 23rd January 2020 to curb the epidemic of the coronavirus *** of this quarantine is *** modelled the effects of two key hea... 详细信息

关键词： Wuhan break Major

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DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

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Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu Huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been *** optimal performance,they require the fragment ion spectra of targeted peptides as prior *** this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue *** build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on *** then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer *** extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)***,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was *** a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control *** expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by *** data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker *** is freely accessible at https://***/page/***?id=IPX0001400000.

关键词： data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

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The characteristic ofcorvus pectoralis’s complete mitochondrial genome and phylogeny analysis

引用

Mitochondrial DNA Part B 2019年第2期4卷 3513-3514页

作者： Tian Huang Libo Zhou Zhenggang Xu College of Information and Electronic Engineering Hunan City University YiYang China Hunan Engineering Research Center for Internet of Animals Changsha China Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province Central South University of Forestry and Technology Changsha China

The Collared Crow (Corvus pectoralis), in the order Passeriformes, it widely distributed in large areas encompassing China and northern Vietnam. It is a vulnerable bird that is of international concern. In this study, we first sequenced and described the complete mitochondrial genome and phylogeny ofC. pectoralis. The results showed that the whole genome ofC. pectoraliswas 16,857 bp long and contains 13 PCGs, 2 ribosomal RNA genes, 23 transfer RNA genes, and 1 loop region. The overall base composition of the mitochondrial DNA was 31.13% for A, 29.52% for C, 24.46% for T, and 14.89% for G, with a GC content of 44.41%. The phylogenetic tree showed thatC. pectoraliswas clustered withC. brachyrhynchosand then together with other two crows in family Passeriformes. This information will be useful in the current understanding of the phylogeny and evolution of Passeriformes.

关键词： Corvus pectoralis Passeriformes mitochondrial genome phylogeny

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Complete mitochondrial genome ofPelecanus crispusand its phylogeny

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Mitochondrial DNA Part B 2019年第2期4卷 3075-3076页

作者： Tian Huang Yonghong Wang Libo Zhou Zhenggang Xu College of Information and Electronic Engineering Hunan City University Yiyang China Hunan Engineering Research Center for Internet of Animals Changsha China Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province Central South University of Forestry and Technology Changsha China

The Dalmatian Pelican (Pelecanus crispus), in the order Pelecaniformes, its habitat is distributed in Europe, Asia, and Africa. It is a large waterfowl that is of international concern. In this study, we first sequenced and described the complete mitochondrial genome and phylogeny ofP. crispus. The results showed that the whole genome ofP. crispuswas 16,131 bp in length, and contained 13 protein-coding genes, 22 transfer RNA genes, two ribosome RNA genes, and one non-coding control region. The overall base composition of the mitochondrial DNA was 30.48% for A, 23.15% for T, 31.68% for C, and 14.69% for G, with a GC content of 46.37%. A phylogenetic tree ofP. crispusin Pelecaniformes confirmed thatP. crispuswas sister toC. occidentalis. This information will be useful in the current understanding of the phylogeny and evolution of Pelecaniformes.

关键词： Pelecanus crispus Pelecaniformes mitochondrial genome phylogeny

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