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A Fabric Defect Detection system Based Improved YOLOv5 Detector

Journal of Physics: Conference Series 2021年第1期2010卷

作者： Ying Wang Zhengyang Hao Fang Zuo Shanshan Pan Institute of Intelligence Networks System Henan University Kaifeng Henan 475001 China Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Kaifeng Henan 475001 China Intelligent Data Processing Engineering Research Center of Henan Province Kaifeng Henan University Kaifeng Henan 475001 China

Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. For the problems of irregular shapes and many small objects, an improved YOLOv5 object detection algorithm for fabric defects is propose. In order to improve the detection accuracy of small objects, the ASFF(Adaptively Spatial Feature Fusion) feature fusion method is adopted to improve the PANet's bad effect on multi-scale feature fusion. The transformer mechanisms can enhance fused features, allowing the network to focus on useful information. Experimental results show that the mean average precision of the improved YOLOv5 object detection algorithm in fabric defect map detection is 71.70%. The improved algorithm can quickly and accurately improve the accuracy of fabric defect detection and the accuracy of defect localization.

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Fake Colorized Image Detection with Channel-wise Convolution based Deep-learning Framework

Fake Colorized Image Detection with Channel-wise Convolution...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Long Zhuo Shunquan Tan Jishen Zeng Bin Lit Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9781728102436;9789881476852

Colorization is one remarkable emerging image manipulating technique, which maybe potentially used for illegal purpose. In this paper, we introduce WISERNet (Wider Separate-then-reunion Network), a recently proposed deep-learning based data-driven color image steganalyzer in the field of fake colorized image detection. We believe that statistical inconsistencies introduced by different automatic colorization methods can be captured by advanced deep-learning based data-driven color-image steganalyzers such as WISERNet. Experimental evidences reported in this paper supports our claims: the detection performance of our proposed detector obviously outperforms FCID-HIST and FCID-FE, two state-of-the-art hand-crafted features specific to fake colorized image detection. Please note that in our approach we have never explicitly utilized information from the specific channels other than the ordinary red, green, and blue color channel, which is completely different from prior works in this field.

关键词： Detectors Training Image color analysis Error analysis Convolution Forensics

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Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

arXiv

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arXiv 2020年

作者： Xu, Jialong Ai, Bo Chen, Wei Yang, Ang Sun, Peng Rodrigues, Miguel The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing100044 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China vivo Communication Research Institute Beijing100015 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom

Recent research on joint source-channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch. © 2020, CC BY.

关键词： Signal to noise ratio

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Optimizing Location Quality in Privacy Preserving Crowdsensing

Optimizing Location Quality in Privacy Preserving Crowdsensi...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yuhui Zhang Ming Li Dejun Yang Jian Tang Guoliang Xue Colorado School of Mines Golden CO Colorado School of Mines Golden CO USA Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Syracuse University Syracuse NY Arizona State University Tempe AZ

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations. Protecting users' location privacy has been a central issue. The study of various location perturbation techniques for protecting users' location privacy has received widespread attention. Despite the huge promise and considerable attention, the location perturbation operation causes inevitable location errors, which can diminish the location quality of the crowdsensing results. Provable good algorithms that consider location quality in privacy preserving crowdsensing from optimization perspectives are still lacking in the literature. In this paper, we investigate the problem of location quality optimization in privacy preserving crowdsensing, which is to minimize the location quality desegregation, while protecting all users' location privacy. We present an optimal algorithm OLQDM for this problem. Extensive simulations demonstrate that OLQDM significantly outperforms an existing algorithm in terms of the location quality and SSE.

关键词： Privacy Degradation Sensors Perturbation methods Optimization Minimization Mobile handsets

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FIMI： A Constant Frugal Incentive Mechanism for Time WindowCoverage in Mobile Crowdsensing

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Journal of Computer Science & Technology 2017年第5期32卷 919-935页

作者： Jia Xu Jian-Ren Fu De-Jun Yang Li-Jie Xu Lei Wang Tao Li School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China Department of Computer Science Colorado School of Mines Golden 80401 U.S.A.

Mobile crowdsensing has become an efficient paradigm for performing large-scale sensing tasks. An incentive mechanism is important for a mobile crowdsensing system to stimulate participants and to achieve good service quality. In this paper, we explore truthful incentive mechanisms that focus on minimizing the total payment for a novel scenario, where the platform needs the complete sensing data in a requested time window (RTW). We model this scenario as a reverse auction and design FIMI, a constant frugal incentive mechanism for time window coverage. FIMI consists of two phases, the candidate selection phase and the winner selection phase. In the candidate selection phase, it selects two most competitive disjoint feasible user sets. Afterwards, in the winner selection phase, it finds all the interchangeable user sets through a graph-theoretic approach. For every pair of such user sets, FIMI chooses one of them by the weighted cost. Further, we extend FIMI to the scenario where the RTW needs to be covered more than once. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve the properties of RTW feasibility (or RTW multi-coverage), computation efficiency, individual rationality, truthfulness, and constant frugality.

关键词： crowdsensing incentive mechanism constant frugality

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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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A Malware Identification and Detection Method Using Mixture Correntropy-Based Deep Neural Network 3rd

A Malware Identification and Detection Method Using Mixture ...

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3rd International Conference on Cyberspace data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Luo, Xiong Li, Jianyuan Wang, Weiping Gao, Yang Zhao, Wenbing School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Beijing Intelligent Logistics System Collaborative Innovation Center Beijing101149 China Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States China Information Technology Security Evaluation Center Beijing100085 China Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH44115 United States

ISBN: (纸本)9789811519215

With the rapid development of CPS technology, the identification and detection of malware has become a matter of concern in the industrial application of CPS. Currently, advanced machine learning methods such as deep learning are popular in the research of malware identification and detection, and some progress has been made so far. However, there are also some problems. For example, considering the existing noise or outliers in the datasets of malware, some methods are not robust enough. Therefore, the accuracy of classification of malware still needs to be improved. Aiming at it, we propose a novel method thought the combination of correntropy and deep neural network (DNN). In our proposed method for malware identification and detection, given the success of mixture correntropy as an effective similarity measure in addressing complex dataset with noise, it is therefore incorporated into a popular DNN, i.e., convolutional neural network (CNN), to reconstruct its loss function, with the purpose of further detecting the features of outliers. We present the detailed design process of our proposed method. Furthermore, the proposed method is tested both on a popular benchmark dataset and a real-world malware classification dataset, to verify its learning performance. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Mixtures

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Secure Cryptography Infrastructures in the Cloud

Secure Cryptography Infrastructures in the Cloud

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IEEE Global Communications Conference

作者： Dawei Chu Kaijie Zhu Quanwei Cai Jingqiang Lin Fengjun Li Le Guan Lingchen Zhang State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences National Digital Switching System Engineering & Technological R&D Center CHINA Data Assurance and Communication Security Research Center Chinese Academy of Sciences Department of Electrical Engineering and Computer Science The University of Kansas USA Department of Computer Science University of Georgia USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Information systems are deployed in clouds as virtual machines (VMs) for better agility, elasticity and reliability. It is necessary to safekeep their cryptographic keys, e.g., the private keys used in TLS and SSH, against various attacks. However, existing virtualization solutions do not improve the cryptography facilities of in-cloud systems. This paper presents SECRIN, a secure cryptography infrastructure for VMs in the cloud. SECRIN is composed of a) virtual cryptographic devices implemented in VM monitors (VMMs), and b) a device management tool integrated in the virtualization management system. A virtual device receives requests from VMs, computes with cryptographic keys within the VMM and returns results. The keys appear only in the VMM's memory space, so that they are kept secret even if the VMs were compromised. With the management tool, the operator of virtualization management systems assigns virtual cryptographic devices to a VM as well as other resources, while the tenant (or owner) of a VM still holds proper controls on the keys. The virtual devices work compatibly with live migration, and the cryptographic computations are not interrupted when the VMs are moving from a host to another. We develop the SECRIN prototype with KVM-QEMU and oVirt. Experimental results show that, it works compatibly with existing virtualization solutions, provides reliable cryptographic computing services for applications, and is secure against attacks happening in VMs.

关键词： Cryptography Virtualization Cloud computing Virtual machine monitors Password Reliability Tools

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Publication recommendations of manuscripts using improved C4.5 decision tree algorithm 2nd

Publication recommendations of manuscripts using improved C4...

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2nd International Conference on intelligent and Interactive systems and Applications, IISA 2017

作者： Jia, Didi Xie, Wu Chen, Zhiyong Qiang, Baohua School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin541004 China

ISBN: (纸本)9783319690957

In view of the problem that the result accuracy of the research publication recommendations of manuscripts is often low. It is very difficult for many contributors to find the suitable journals from many recommendation results. To solve this problem, a novel method of publication recommendations for authors’ manuscripts is proposed using our improved C4.5 decision tree algorithm. For the shortcomings of too many values of data samples during dealing with the traditional C4.5 methods, the five-value logic ideas are adopted to improve this C4.5 algorithm to apply in the research fields of publication recommendations. The experimental results show that the related publications are easier to obtain for authors via the decision trees with the improved C4.5 method than before, and this method has higher accuracy than ever. It is of great significance to help the researchers, especially for those who have no enough experience to choose the most suitable journals from a lot of publications. © 2018, Springer International Publishing AG.

关键词： Decision trees

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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