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A New Anti-Spam Model Based on E-mail Address Concealment Technique

Wuhan University Journal of Natural Sciences 2018年第1期23卷 79-83页

作者： ZHANG Yuqiang HE Jingsha XU Jing Beijing Institute of Aerospace Control Devices Beijing100039 China College of Computer Science and Technology Beijing University of Technology Beijing 100124 China School of Software Engineering Beijing University ofTechnology Beijing 100124 China Department of Automation Tsinghua University Beijing100084 China

To deal with the junk e-mail problem caused by the e-mail address leakage for a majority of Internet users, this paper presents a new privacy protection model in which the e-mail address of the user is treated as a piece of privacy information concealed. Through an interaction pattern that involves three parties and uses an e-mail address code in the place of an e-mail address, the proposed model can prevent the e-mail address from being leaked, thus effectively resolving the junk e-mail problem. We compare the proposed anti-spam method with the filtering technology based on machine learning. The result shows that 100% spams can be filtered out in our scheme, indicating the effectiveness of the proposed anti-spam method.

关键词： spare e-mail address protection model e-mail address code

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Road Detection for autonomous truck in mine environment

Road Detection for autonomous truck in mine environment

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International Conference on Intelligent Transportation

作者： Jianlin Tang Xiaowei Lu Yunfeng Ai Bin Tian Long Chen Jiangsu XCMG Construction Machinery Research Institute LTD Xuzhou China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China School of Data and Computer Science Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9781538670255

Road boundary line detection provides information of safe driving range and ensures the safety of the vehicles, which is an important part in the sensing system. Due to the mine roads are complicated and the road boundary lines are blurred, the existing road detection methods are not *** this paper, a real-time road boundary detection algorithm is proposed based on Lidar sensors. For the uneven ground of mine roads, this paper proposes a ground detection algorithm called Double Meshing. The elevated point is divided into beam areas after filtering the detected ground. Finally, the road boundary points are extracted in the beam area and the road boundary line is fitted. The performance of the proposed method is verified by experiments, which highlights its accuracy and real-time.

关键词： Roads Three-dimensional displays Laser radar Sensors Data mining Real-time systems Autonomous vehicles

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A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Polash Kumar Das Bin Hu Chang Liu Kaixin Cui Prabhat Ranjan Gang Xiong School of Electronic and Information Engineering South China University of Technology Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Science Dongguan China

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature.

关键词： Face Videos Feature extraction Databases Face recognition Password

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Network Resilience

arXiv

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arXiv 2020年

作者： Liu, Xueming Li, Daqing Ma, Manqing Szymanski, Boleslaw K. Eugene Stanley, H. Gao, Jianxi Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China School of Reliability and Systems Engineering Beihang University Beijing100191 China College of Safety Science and Engineering Civil Aviation University of China Tianjin 300300 China Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Network Science and Technology Center Rensselaer Polytechnic Institute TroyNY12180 United States Center for Polymer Studies Department of Physics Boston University BostonMA02215 United States

Many systems on our planet shift abruptly and irreversibly from the desired state to an undesired state when forced across a "tipping point". Some examples are mass extinctions within ecosystems, cascading failures in infrastructure systems, and changes in human and animal social networks. The ability to avoid such regime shifts or to recover quickly from such a non-resilient state demonstrates a system's resilience;system resilience is a quality that enables a system to adjust its activities to retain its basic functionality when errors and failures occur. In the past 50 years, attention has been paid almost exclusively to low-dimensional systems;scholars have focused on the calibration of the resilience functions of such systems and the identification of indicators of early warning signals based on two to three connected components. In recent years, taking advantage of network theory and the availability of lavish real datasets, network scientists have begun to explore real-world complex networked multidimensional systems, as well as their resilience functions and early warning indicators. This report presents a comprehensive review of resilience functions and regime shifts in complex systems in domains such as ecology, biology, society, and infrastructure. The research approach includes empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also review the definitions of some ambiguous terms, including robustness, resilience, and stability. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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The research of the smart office availability model considering patches on the router firewall software 9

The research of the smart office availability model consider...

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9th IEEE International Conference on Dependable Systems, Services and Technologies, DESSERT 2018

作者： Kharchenko, Vyacheslav Kolisnyk, Maryna Piskachova, Iryna Department of Computer Systems and Networks National Aerospace University 'KhAI' 17 Chkalova Str. Kharkiv61000 Ukraine Automation and Control in Technical Systems Department National Technical University 'Kharkiv Politechnical Institute' 2 Kirpichova Str. Kharkiv61002 Ukraine Department of Computer Science and Control System Ukrainian State University of Railway Transport 7 Feierbakh Square Kharkiv61050 Ukraine

ISBN: (纸本)9781538659038

The emergence of the Internet of things (IoT), web of things, when heterogeneous devices need to have access to the Internet, leads to the need to solve serious problems to ensure the reliable and security operation of such devices. Developing a model of the IoT system states in conditions of growing attacks on the IoT infrastructure, taking into account the energy spent on round-the-clock maintenance of the IoT system, and the timely installation of patches for the router's firewall software, will allow investigating the IoT systems availability from the point of view of providing their security and energy efficiency. The purpose of this paper is to improve the availability model of Smart Office (SO) and to investigate it in terms of server and router power modes, router and server faults and failures, successful cyber-attacks and timely installation of patches on possible router firewall vulnerabilities. © 2018 IEEE.

关键词： Energy efficiency

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Scheduling for Re-entrant Hybrid Flowshop Based on Wolf Pack Algorithm

Scheduling for Re-entrant Hybrid Flowshop Based on Wolf Pack...

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2018 International Conference on Advanced Materials, Intelligent Manufacturing and automation, AMIMA 2018

作者： Han, Zhonghua Tian, Xutian Ma, Xiaofu Lin, Shuo Faculty of Information and Control Engineering Shenyang Jianzhu University Liaoning110168 China Digital Factory Department Shenyang Institute of Automation CAS Shenyang110016 China Information and Control Engineering Faculty Shenyang Jianzhu University Liaoning110168 China Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States

In order to solve the scheduling problems of Re-entrant Hybrid Flowshop (RHFS), this paper investigates the mathematical programming model of RHFS, and proposes the wolf pack based algorithm (WPA) as a global optimization method. Multi-Sticking Crape Masking procedure scheduling problems in painting workshop of a bus manufacturing enterprise are of typical features of RHFS. We regard it as an application objective of the proposed algorithm, applying WPA and multiple evolutionary algorithms to solve the problems. The results show that the algorithm can solve re-entrant scheduling problems of hybrid flowshop more effectively compared with other conventional methods. © Published under licence by IOP Publishing Ltd.

关键词： Global optimization

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An Effective On-line Surface Particles Inspection Instrument for Large Aperture Optical Element

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International Journal of automation and computing 2017年第4期14卷 420-431页

作者： Wen-Dong Ding Zheng-Tao Zhang Da-Peng Zhang De Xu Hai-Bing Lv Xin-Xiang Miao Guo-Rui Zhou Hao Liu Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences Laser Fusion Research Center China Academy of Engineering Physics

Surface particles growing in large aperture optical element （LAOE） have significant impact on LAOE＇s stable operation. It is a challenge for the online system to inspect the particles with long working distance, enough precision and high efficiency because of the system constraints. In this paper, an effective and portable inspection instrument is designed based on dark-field imaging principle. A Nikon lens and an industrial high definition （HD） camera are selected to construct the vision system to inspect particles of microns size spreading over hundreds of millimeters. Using two motors and other mechanical structure, the system can realize auto-focus and image rectification functions. The line light sources are installed on both sides of the LAOE in a sealed box while the vision system is portable and working outside the box. An adaptive binarization method is proposed to process the captured dark-field image. The distribution of particles on the LAOE＇s surface is investigated. Because of the high resolution of the captured image, the SSE2 instructions optimization method is used to reduce the time cost of the algorithm. Experiments show that the instrument can inspect LAOE effectively and accurately.

关键词： Dark-field imaging image rectification adaptive binarization particle inspection large aperture optical elements（LAOE）.

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PDP: Parallel Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 1-5页

作者： Fei-Yue Wang Jie Zhang Qinglai Wei Xinhu Zheng Li Li IEEE State Key Laboratory of Management and Control for Complex Systems(SKL-MCCS) Institute of AutomationChinese Academy of Sciences(CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Department of Computer Science and Engineering University of Minnesota Department of Automation Tsinghua University

Deep reinforcement learning is a focus research area in artificial intelligence. The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods. The principle of adaptive dynamic programming ADP is first presented instead of direct dynamic programming DP , and the inherent relationship between ADP and deep reinforcement learning is developed. Next, analytics intelligence, as the necessary requirement, for the real reinforcement learning, is discussed. Finally, the principle of the parallel dynamic programming, which integrates dynamic programming and analytics intelligence, is presented as the future computational intelligence. © 2014 Chinese Association of automation.

关键词： Artificial intelligence Neural networks Reinforcement learning

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Deep learning for object detection and grasping: A survey

Deep learning for object detection and grasping: A survey

引用

2018 IEEE International Conference on Information and automation, ICIA 2018

作者： Jia, Qun Cai, Jun Cao, Zhiqiang Wu, Yelan Zhao, Xionglei Yu, Junzhi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9781538680698

Detecting and grasping objects in unstructured environments is an important yet difficult task. Fortunately, the breakthroughs from deep convolutional networks stimulate the development of object detection and grasping. The survey aims to serve as a comparison for region-based and region-free detection framework based on deep learning, and supplies the latest research results of object grasping with deep learning. Firstly, we briefly analyze the object detection and grasping. Then, the representative object detection methods based on deep learning are overviewed. Thirdly, we introduce the application of convolutional neural networks in object grasping. Finally, the potential trends in object detection and grasping based on deep learning are discussed. © 2018 IEEE.

关键词： Object detection

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Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation 15

Integrating semi-supervised label propagation and random for...

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15th IEEE International Symposium on Biomedical Imaging, ISBI 2018

作者： Zheng, Qiang Fan, Yong Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States School of Computer and Control Engineering Yantai University Yantai264005 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538636367

A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of the images to be segmented. The regression model is used to obtain reliable segmentation results to guide the label propagation for the segmentation. The proposed method has been compared with state-of-the-art multi-atlas based image segmentation methods for segmenting the hippocampus in MR images. The experiment results have demonstrated that our method obtained superior segmentation performance. © 2018 IEEE.

关键词： Image segmentation

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