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Proceedings of the 2019 8th International Conference on computing and Pattern Recognition

作者： Wei Li Yaochi Fan Qiaoyong Jiang Qingzheng Xu School of Computer Science and Engineering Xi'an University of Technology and Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China College of Information and Communication National University of Defense Technology Xi'an China

ISBN: (纸本)9781450376570

Particle swarm optimization (PSO) is an efficient nature-inspired optimization algorithm, which has been widely applied in many engineering fields. The performance of particle swarm optimization (PSO) has been significantly influenced by velocity update strategy. Traditionally, each particle updates its velocity based on its historical best experience and the global best experience, which may make the swarm lose its diversity and lead to premature convergence. To strengthen the performance of PSO, this paper proposes an improved PSO with the velocity-driven strategy (VD-PSO). In VD-PSO algorithm, the particles whose velocities are driven by better velocities focus on exploitation. The historical velocities, such as better velocities, may effectively characterize the landscape information of the optimization problems. In addition, the particles focus on exploration through information exchange amongst velocities. To verify the effectiveness of the proposed algorithm, experiments are conducted with CEC2014 test problems. The experimental results demonstrate the effectiveness of the proposed algorithm for solving the global optimization problems.

关键词： Evolutionary algorithms Velocity-driven strategy Particle swarm optimization Optimization problems

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A novel Image security Protection Method Based on DCT Compression Theory and Hyper-chaotic Mapping

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Journal of Physics: Conference Series 2021年第1期2066卷

作者： Xianglian Xue Haiyan Jin School of Computer Science and Engineering Xi'an University of Technology Xi'an 710077Shaanxi China Shaanxi University of Chinese Medicine Xianyang712046Shaanxi China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an710077Shaanxi China

This paper studies the current situation of image compression encryption and analyzes the influence of low frequency (DC coefficient) and high frequency (AC coefficient) on image structure in DCT transformation. Based on this, a novel image security protection method based on DCT compression theory and hyper-chaotic mapping is proposed. First, the position of the pixel of the original image is disturbed, and converts the image from spatial domain into frequency domain by the two-dimensional DCT transformation and quantization. Second, change the pixel values by modifying the values of the sign bit of AC coefficient and DC coefficient. At last, the encrypted image is obtained by carrying out inverse quantization, inverse transformation and reverse operation by bit.

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Velocity-driven particle swarm optimization 8

Velocity-driven particle swarm optimization

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8th International Conference on computing and Pattern Recognition, ICCPR 2019

作者： Li, Wei Fan, Yaochi Jiang, Qiaoyong Xu, Qingzheng School of Computer Science and Engineering Xi'An University of Technology China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an710048 China School of Computer Science and Engineering Xi'An University of Technology Xi'an710048 China College of Information and Communication National University of Defense Technology Xi'an710106 China

ISBN: (纸本)9781450376570

关键词： Velocity

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Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc networks 5

Revisiting Recent and Current Anomaly Detection based on Mac...

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5th Annual International Conference on network and Information Systems for Computers, ICNISC 2019

作者： Wang, Zhixiao Chen, Mingyu Yan, Wenyao Wang, Wendong Gao, Ang Nie, Gaoyang Wang, Feng Yang, Shaobo Xi'An University of Technology Xi'an710048 China Goettingen University Goettingen37077 Germany Xi'An Jiaotong University Xi'an710049 China Shaanxi Provincial Key Laboratory of Network Computing and Security Technology Xi'an710048 China College of xi'An Innovation Yan'An University Xi'an710100 China Yan'An University Yan'an716000 China National Satellite Meteorological Center China Meteorological Administration Beijing100081 China

Ad-Hoc network which is one kind of self-organized networks is much more vulnerable than the infrastructural network with the properties of highly changeable linkage, dynamic structure, and wireless connections so that the tradition intrusion detection system (IDS) should be improved to adapt in such network with limited computing resources and open channels. To ensure the security in Ad-Hoc network, the efficient anomaly detection methods should be probed. Over the past years, many studies have implemented anomaly detection methods (intrusion detection techniques) based on machine-learning methods in this field. This article analyzes the existing security problem in Ad-Hoc network, presents the basic theory of intrusion detection for Ad-Hoc network, and reviews the current and recent anomaly detection methods used machine learning techniques in the intrusion detection system. © Published under licence by IOP Publishing Ltd.

关键词： Intrusion detection

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Intelligent recommendation of related items based on naive bayes and collaborative filtering combination model

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Journal of Physics: Conference Series 2020年第1期1682卷

作者： Wei Wei Zhuo Wang Changlong Fu Robertas Damaševičius Rafal Scherer Marcin Wožniak School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048 Shaanxi Key Laboratory for Network Computing and Security Technology China.

Nowadays, data plays a unique role in various fields. Based on the background of the era of big data, this paper collects user evaluations of certain commodities and labels the evaluations into positive emotions and negative emotions. We propose an intelligent recommendation algorithm based on Naive Bayes algorithm, which determines the user's preference for a product according to the user's opinion on a product, then uses the collaborative filtering algorithm to recommend other similar products according to the similarity between the product and other products, so as to realize the recommendation of other similar products. This can not only help users choose more goods in the environment, but also maximize the value of goods. In this paper, bayesian classifier was used for prediction, and the accuracy reached about 97%.

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A Hole Repairing Method Based on Edge-Preserving Projection 12th

A Hole Repairing Method Based on Edge-Preserving Projection

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Wang, Yinghui Zhao, Yanni Wang, Ningna Ning, Xiaojuan Shi, Zhenghao Zhao, Minghua Lv, Ke Huang, Liangyi Institute of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an China Department of Computer Science Shannxi Vocational and Technical College Xi’an China Booking B.V. Herengracht 597 Amsterdam1017 CE Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington D.C. United States

ISBN: (纸本)9783030237110

A point cloud hole repairing method based on edge-preserving projection is proposed in order to maintain sharp features of holes. First, the hole boundary points are acquired by quadrants and angles, then connections among 3D hole boundary points are projected onto the two-dimensional plane using the edge-preserving projection. Secondly, the two-dimensional region obtained from projection is subjected to point collectivization for obtaining filling points, at the same time, the radial-basis interpolation mapping technique is used to construct the hole surface according to the 3D hole boundary point. Finally, the two-dimensional filling points are reflected onto the surface of the constructed hole to complete hole repairing. Experimental results show that our method can effectively repair the hole of the point cloud and restore the sharp features of the hole. © 2019, Springer Nature Switzerland AG.

关键词： Repair

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A Hole Repairing Method Based on Slicing 12th

A Hole Repairing Method Based on Slicing

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Zhao, Yanni Wang, Yinghui Wang, Ningna Ning, Xiaojuan Shi, Zhenghao Zhao, Minghua Lv, Ke Huang, Liangyi Institute of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an China Department of Computer Science Shannxi Vocational and Technical College Xi’an China Booking B.V. Herengracht 597 Amsterdam1017 CE Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington D.C. United States

ISBN: (纸本)9783030237110

The repairing of 3D point cloud holes has an important meaning to ensure the integrity of cloud data. We present a slice-based repairing method for 3D point cloud in this paper. Firstly, the model is horizontal sliced and each slice is projected on a two-dimensional plane, then the band-shaped points obtained during projection are clustered to select boundary points of the hole. Combined with optimal fitting points, the hole repairing point sets in the projection layer are re-sampled based on the cubic B-spline curve to fit boundary points. Finally, all hole repairing point sets in the projection layer are combined in 3D space to finish the entire hole recovery. The experimental results show that the proposed method can effectively repair complex holes for various point cloud models. © 2019, Springer Nature Switzerland AG.

关键词： Curve fitting

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A Slice-Guided Method of Indoor Scene Structure Retrieving 12th

A Slice-Guided Method of Indoor Scene Structure Retrieving

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Wang, Lijuan Wang, Yinghui Wang, Ningna Ning, Xiaojuan Lv, Ke Huang, Liangyi Xi’an University of Technology South Jinhua Road no. 5 Xi’an710032 China Xi’an Technology University Mid Xuefu Road no. 2 Xi’an710021 China Shaanxi Key Laboratory of Network Computing and Security Technology South Jinhua Road no. 5 Xi’an710032 China *** B.V. Vijzelstraat 66-80 Amsterdam1017HL Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington DC20052 United States

ISBN: (纸本)9783030237110

The structure information of indoor scene is necessary for a robot who works in a room. In order to achieve structure of an indoor scene, a slice-guided method of indoor scene structure retrieving is proposed in this paper. We present a slicing based approach that transforms three-dimensional (3D) segmentations into two-dimensional (2D) segmentation and segments different kinds of primitive shapes while keeping the global topology structure of the indoor scene. The global topology structure is represented by a graph. The graph is compared with the given indoor scene template. The matched objects and the topology relation between them are finally presented. Our experiment results show that the proposed method performs well on several typical indoor scenes, even if some data are missing. © 2019, Springer Nature Switzerland AG.

关键词： Topology

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Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs 3

Exploiting Meta-Path with Attention Mechanism for Fine-Grain...

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2019 3rd International Conference on Data Mining, Communications and Information technology, DMCIT 2019

作者： Wang, Zhixiao Yan, Wenyao Wang, Wendong Gao, Ang Yu, Lei Yang, Shaobo Nie, GaoYang School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China Telematics Group University of Goettingen Goettingen37077 Germany Shaanxi Key Laboratory of Network Computing and Security Xi'an710048 China School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an710048 China College of Xi'an Innovation Yan'an University Xi'an710100 China Yan'an University Yan'an716000 China National Satellite Meteorological Center China Meteorological Administration Beijing100081 China

Based on the huge volumes of user check-in data in LBSNs, users' intrinsic mobility patterns can be well explored, which is fundamental for predicting where a user will visit next given his/her historical check-in records. As there are various types of nodes and interactions in LBSNs, they can be treated as Heterogeneous Information network (HIN) where multiple semantic meta-paths can be extracted. Inspired by the recent success of meta-path context based embedding techniques in HIN, in this paper, we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location prediction. Experimental results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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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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