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arXiv 2020年

作者： Xia, Zhihua Gu, Qi Zhou, Wenhao Xiong, Lizhi Weng, Jian Xiong, Naixue N. College of Cyber Security Jinan University Guangzhou510632 China Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing210044 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China The College of Intelligence and Computing Tianjin University Tianjin300350 China

The rapid development of cloud computing probably benefits many of us while the privacy risks brought by semi-honest cloud servers have aroused the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks to servers while ensuring the security of private data. The tasks to be outsourced are countless;however, the computations involved are similar. In this paper, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) on arbitrary n ≥ 2 servers. All protocols only require constant rounds of interactions and achieve low computation complexity. Moreover, the proposed n-party protocols ensure the security of private data even though n − 1 servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols. Copyright © 2020, The Authors. All rights reserved.

关键词： Additives

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Android Malicious Code Detection Based on Secondary Pruning Optimization

Android Malicious Code Detection Based on Secondary Pruning ...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics

作者： Qing Yu Kuolang Ma Zuohua Wang Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China Tianjin Times Shengshi Technology Co. Ltd. No. 216 Hongqi Road Nankai District Tianjin China

In this paper, we propose a detection algorithm for Android malicious code based on integrated multi-feature. By decompiling and processing APK files, the multi-class behavior features of Android application are extracted, and the classification is achieved through an integrated learning framework based on quadratic pruning optimization. The prototype system automatically detects the malicious code of the Android platform and analyzes the validity of the algorithm through experimental verification.

关键词： Classification algorithms Malware Feature extraction Optimization Predictive models security

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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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Code Authority Control Method Based on File security Level and ACL in WebIDE

Code Authority Control Method Based on File Security Level a...

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IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Junhuai Li Wenchao Ji Jubo Tian Huaijun Wang Kan Wang School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

The following topics are dealt with: mobile robots; learning (artificial intelligence); control system synthesis; permanent magnet motors; motion control; optimisation; power engineering computing; machine control; sy... 详细信息

ISBN: (纸本)9781538674178

关键词： Programming Authorization Performance evaluation Privacy Task analysis

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Exploiting Gabor Feature Extraction Method for Chinese Character Writing Quality Evaluation

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhixiao Wang Wenyao Yan Mingtao Guo Jiulong Zhang School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048 China Shaanxi Key Laboratory Xi'an of Network Computing and Security Technology China School of Data Science and Computer Xi'an innovation college of Yan'an University Xi'an 710100 China

The automatic evaluation of Chinese character writing quality has a wide application prospect. Most of the existing evaluation methods of Chinese character writing quality are based on radical segmentation and feature judgment, which require the high accuracy of Chinese character segmentation. However, there are many problems in the real handwriting, such as continuous writing, uneven strength of writing, personalized writing style and so on, which lead to the difficulty of segmentation in the ordinary handwriting. To solve the above problems, we propose an effective method based on image texture where the uniformity of writing lines and writing style is taken as an effective criterion. In our method, Gabor transform is used to extract the image features of writing samples, and finally the statistical learning method of support vector machine is used to effectively evaluate the writing quality. Experiments on multiple real datasets including CHAED show that our method is effective and accurate. The advantage of this method is that it does not need to segment fonts, and the cost of global feature extraction is small.

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

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

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 *** 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 *** 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 *** results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.

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An attribute-based encryption scheme with LSSS key-policy from lattices

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and computing 2018年 613卷 217-230页

作者： Liu, Lihua Wang, Shangping Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an710054 China School of Mathematics and Computer Science Shaanxi University of Technology HanzhongShaanxi723001 China

ISBN: (纸本)9783319607436

A new attribute-based encryption scheme (ABE) from lattices with Linear Secret Sharing Scheme (LSSS) key-policy is presented. In the new scheme, the key of an attribute under an access policy represented by LSSS is generated by the use of the sample-pre algorithm on lattices. Every attribute has its own authority, and the private key of each attribute under an access policy is produced by its own authority alone. The new scheme has the advantage of fine-grained access control, and also could support multi-authority attribute management, thus it facilitates the practical management of attribute encryption system. The security of the new scheme is proved in the selective-attribute attack model under learning with errors (LWE) assumption, and the security parameter selecting is discussed in detail. © 2018, Springer International Publishing AG.

关键词： Access control

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A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

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7th International Conference on Computational Data and Social networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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