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arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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A Low-Cost Service Node Selection Method in Crowdsensing Based on Region-Characteristics 13th

A Low-Cost Service Node Selection Method in Crowdsensing Bas...

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13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018

作者： Peng, Zhenlong An, Jian Gui, Xiaolin Liao, Dong Gui, RuoWei School of Electronics and Information Engineering Xi’an Jiaotong University No. 28 Xianning West Road Xi’an710049 China Xi’an Jiaotong University Shenzhen Research School High-Tech Zone Shenzhen518057 China TSL Business School Quanzhou Normal University Donghai Streat Quanzhou362000 China Shaanxi Province Key Laboratory of Computer Network No. 28. Xianning West Road Xi’an710049 China

ISBN: (纸本)9783030150921

Crowdsensing is a human-centred perception model. Through the cooperation of multiple nodes, an entire sensing task is completed. To improve the efficiency of accomplishing sensing missions, a proper and cost-effective set of service nodes is needed to perform tasks. In this paper, we propose a low-cost service node selection method based on region features, which builds on relationships between task requirements and geographical locations. The method uses DBSCAN to cluster service nodes and calculate the centre point of each cluster. The region then is divided into regions according to rules of Voronoi diagram. Local feature vectors are constructed according to the historical records in each divided region. When a particular perception task arrives, Analytic Hierarchy Process (AHP) is used to match the feature vector of each region to mission requirements to get a certain number of service nodes satisfying the characteristics. To get a lower cost output, a revised Greedy Algorithm is designed to filter the exported service nodes to get the required low-cost service nodes. Experimental results suggest that the proposed method shows promise in improving service node selection accuracy and the timeliness of finishing tasks. © 2019, Springer Nature Switzerland AG.

关键词： Analytic hierarchy process

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An Improved Non-Geometrical Stochastic Model for Non-WSSUS Vehicle-to-Vehicle Channels

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ZTE Communications 2019年第4期17卷 62-71页

作者： HUANG Ziwei CHENG Xiang ZHANG Nan State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Key Laboratory of Wireless Sensor Network&Communication Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Wireless Algorithm Department Product Research and Development SystemZTE CorporationShanghai 201203China

A novel non-geometrical stochastic model(NGSM)for non-wide sense station ary uncorrelated scattering(non-WSSUS)vehicle-to-vehicle(V2V)channels is *** model is based on a conventional NGSM and employs a more accurate method to reproduce the realistic characteristics of V2V channels,which successfully extends the existing NGSM to include the line-of-sight(LoS)***,the statistical properties of the proposed model in different scenarios,including Doppler power spectral density(PSD),power delay profile(PDP),and the tap correlation coefficient matrix are simulated and compared with those of the existing ***,the simulation results dem onstrate not only the utility of the proposed model,but also the correctness of our theoreti cal derivations.

关键词： vehicle-to-vehicle non-WSSUS channels non-geometrical stochastic model LoS component statistical properties

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Slice-Based Window Detection from Scene Point Clouds

Slice-Based Window Detection from Scene Point Clouds

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International Conference on Virtual Reality and Visualization (ICVRV)

作者： Wen Hao Yinghui Wang Wei Liang Xiaojuan Ning Ye Li Institute of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781538684986;9781538684979

In this paper, we propose a comprehensive strategy to detect the windows from scene point clouds. First, the planar points are extracted after computing the dimensionality structure of each point. The planar points are then grouped into clusters based on the normal direction and distance between points. Next, the building walls are extracted from the scene point clouds according to a collection of characteristics such as surface area, normal direction, and topological relationship. Finally, the building facade is sliced both horizontally and vertically. A slice-based algorithm based on the point number is proposed to locate the windows. The experimental results demonstrate that our method can detect the window location quickly and effectively.

关键词： Windows Three-dimensional displays Microsoft Windows Two dimensional displays Clustering algorithms Feature extraction

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A novel convolution neural network for background segmentation recognition 4th

A novel convolution neural network for background segmentati...

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4th International Conference on Cloud Computing and security, ICCCS 2018

作者： Fang, Wei Ding, Yewen Zhang, Feihong School of Computer and Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9783030000059

The convolution neural network for image classification is an application of deep learning on image processing. Convolutional neural networks have the advantage of being able to convolve directly with image pixels and extract image features from image pixels. This approach is closer to the treatment of the human brain’s visual system. However, up to now, it is impossible to achieve 100% classification accuracy regardless of any kind of convolutional neural network models. At the same time, we also found that sometimes the picture background will also affect the recognition effect of the neural network on the picture, and after removing the picture background, it can correctly recognize the same picture. Therefore, we design a model based on the convolutional neural network to remove the image background for some specific images (such as selfies, animals, flowers, etc.) here, and then the image after processing will be identified and classified. Experiments show that the proposed method can maintain a high level of integrity of the target to be detected in the image after removing the background (here the indicator is Intersection Over Union, IOU);moreover, through multiple classification models verify that the classification accuracy of some background-removed pictures is significantly higher than that of pictures without any treatment. © Springer Nature Switzerland AG 2018.

关键词： Image classification

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Evolutionary Cryptography Theory-Based Generating Method for Secure ECs

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Tsinghua Science and Technology 2017年第5期22卷 499-510页

作者： Chao Wang Feng Hu Huanguo Zhang Jie Wu the Key Laboratory of Specialty Fiber Optics and Optical Access Networks School of Communication and Information EngineeringShanghai University the Department of Computer and Information Sciences Temple University the Key Lab of Aerospace Information Security and Trusted Computing Wuhan University

Ant Colony Optimization （AGO） has the character of positive feedback, distributed searching, and greedy searching. It is applicable to optimization grouping problems. Traditional cryptographic research is mainly based on pure mathematical methods which have complicated theories and algorithm. It seems that there is no relationship between cryptography and ACO. Actually, some problems in cryptography are due to optimization grouping problems that could be improved using an evolutionary algorithm. Therefore, this paper presents a new method of solving secure curve selection problems using ACO. We improved Complex Multiplication （CM） by combining Evolutionary Cryptography Theory with Weber polynomial solutions. We found that ACO makes full use of valid information generated from factorization and allocates computing resource reasonably. It greatly increases the performance of Weber polynomial solutions. Compared with traditional CM, which can only search one root once time, our new method searches all roots of the polynomial once, and the average time needed to search for one root reduces rapidly. The more roots are searched, the more ECs are obtained.

关键词： ECs Weber polynomial evolutionary algorithm ant algorithm complex multiplication

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Simple Method for Realizing Weil Theorem in Secure ECC Generation

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Tsinghua Science and Technology 2017年第5期22卷 511-519页

作者： Feng Hu Chao Wang Huanguo Zhang Jie Wu the Key Laboratory of Specialty Fiber Optics and Optical Access Networks School of Communication and Information EngineeringShanghai University the Department of Computer and Information Sciences Temple University the Key Lab of Aerospace Information Security and Trusted Computing Wuhan University

How to quickly compute the number of points on an Elliptic Curve （EC） has been a longstanding challenge. The computational complexity of the algorithm usually employed makes it highly inefficient. Unlike the general EC, a simple method called the Weil theorem can be used to compute the order of an EC characterized by a small prime number, such as the Kobltiz EC characterized by two. The fifteen secure ECs recommended by the National Institute of Standards and Technology （NIST） Digital Signature Standard contain five Koblitz ECs whose maximum base domain reaches 571 bits. Experimental results show that the computation speed decreases for base domains exceeding 600 bits. In this paper, we propose a simple method that combines the Weil theorem with Pascals triangle, which greatly reduces the computational complexity. We have validated the performance of this method for base fields ranging from 2l^100 to 2^1000. Furthermore, this new method can be generalized to any ECs characterized by any small prime number.

关键词： Elliptic Curves （ECs） Pascal＇s triangle Weil theorem

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Phishing detection research based on LSTM recurrent neural network 4th

Phishing detection research based on LSTM recurrent neural n...

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4th International Conference of Pioneer computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Chen, Wenwu Zhang, Wei Su, Yang Key Laboratory for Network and Information Security of Chinese Armed Police Force Engineering University of Chinese Armed Police Force Xi’anShaanxi China Department of Electronic Technology Engineering University of the Chinese Armed Police Force Xi’anShaanxi China

ISBN: (纸本)9789811322020

In order to effectively detect phishing attacks, this paper designed a new detection system for phishing websites using LSTM Recurrent neural networks. LSTM has the advantage of capturing data timing and long-term dependencies. LSTM has strong learning ability, has strong potential in the face of complex high-dimensional massive data. Experimental results show that this model approach the accuracy of 99.1%, is higher than that of other neural network algorithms. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Long short-term memory

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Corrigendum to "Generation of a local lung respiratory motion model using a weighted sparse algorithm and motion prior-based registration" [Comput. Biol. Med. 123 (2020) 103913]

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computers in biology and medicine 2021年 140卷 104271页

作者： Dong Chen Hongzhi Xie Lixu Gu Jing Liu Liang Tian College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Shijiazhuang China Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China. Department of Cardiothoracic Surgery Peking Union Medical College Hospital Beijing China. School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China. Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China. Electronic address: liujing01@***. Key Laboratory of Augmented Reality College of Mathematics and Information Science Hebei Normal University Shijiazhuang China. Electronic address: tianliang@***.

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Spatial Pyramid Pooling based Convolutional Autoencoder network for Loop Closure Detection

Spatial Pyramid Pooling based Convolutional Autoencoder Netw...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Rong Xiang Yuan Liu Qieshi Zhang Jun Cheng School of Computer Science and Information Security Guilin University of Electronic Technology GuangXi China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Loop closure detection is a crucial module in simultaneous localization and mapping (SLAM), which reduces accumulative error in building environment map. Traditional appearance-based methods for loop closure detection are vulnerable to environmental variations as they mainly rely on hand-crafted features. The convolutional neural networks (ConvNets) can automatically learn feature representation from original image, and it is more robust to illumination changes. However, the ConvNets methods may fail when the viewpoint changes significantly due to it extract global features. In order to solve the problem mentioned above, in this paper, we design an unsupervised network which combines the advantage of the traditional and ConvNets methods, and propose a new module named spatial pyramid pooling based convolution autoencoder (SPP-CAE). We evaluate the performance of the proposed method on several open datasets using precision-recall metric. The results show that our method is feasible for detecting loops and is more robust than state-of-the-art methods.

关键词： Feature extraction Convolution Lighting Visualization Training Simultaneous localization and mapping network architecture

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