检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Qian, Ya-Guan Ma, Dan-Feng Wang, Bin Pan, Jun Wang, Jia-Min Chen, Jian-Hai Zhou, Wu-Jie Lei, Jing-Sheng School of Big-Data Science Zhejiang University of Science and Technology Hangzhou310023 China Network and Information Security Laboratory of Hangzhou Hikvision Digital Technology Co. Ltd. Hang Zhou310052 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

Recent studies have shown convolutional neural networks (CNNs) for image recognition are vulnerable to evasion attacks with carefully manipulated adversarial examples. Previous work primarily focused on how to generate adversarial examples closed to source images, by introducing pixel-level perturbations into the whole or specific part of images. In this paper, we propose an evasion attack on CNN classifiers in the context of License Plate Recognition (LPR), which adds predetermined perturbations to specific regions of license plate images, simulating some sort of naturally formed spots (such as sludge, etc.). Therefore, the problem is modeled as an optimization process searching for optimal perturbation positions, which is different from previous work that consider pixel values as decision variables. Notice that this is a complex nonlinear optimization problem, and we use a genetic-algorithm based approach to obtain optimal perturbation positions. In experiments, we use the proposed algorithm to generate various adversarial examples in the form of rectangle, circle, ellipse and spots cluster. Experimental results show that these adversarial examples are almost ignored by human eyes, but can fool HyperLPR with high attack success rate over 93%. Therefore, we believe that this kind of spot evasion attacks would pose a great threat to current LPR systems, and needs to be investigated further by the security community. Copyright © 2019, The Authors. All rights reserved.

关键词： Genetic algorithms

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A comprehensive analysis of video service quality on IQIYI from large-scale data sets 13th

A comprehensive analysis of video service quality on IQIYI f...

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13th International Conference on Heterogeneous networking for Quality, Reliability, Security and Robustness, QShine 2017

作者： Guo, Yao Lv, Qiujian Liu, Fang Yang, Jie Gao, Zhe Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Center for Data Science BUPT Beijing100876 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Technology Research Institute Aisino Corporation Beijing100086 China

ISBN: (纸本)9783319780771

With the proliferation of online video, measuring the quality of the video service has become a vital aspect for improving user’s experience. Recent work shows that measurable quality metrics such as buffering, bitrate, and video resolutions impact user’s experience, but none of them reveal the real relationships between these metrics and user’s actual experience. This paper attempts to solve the problem above. We use IQIYI as the sample, and our large-scale dataset consists of 7 days real Internet traffic in a northern city of China. We quantify user’s experience at per-video level (or view). Using Apache Spark, we extract some video events and calculate several quality metrics. In order to investigate the relationship between the metrics and user’s experience, we use the FP-Growth algorithm to mining the implicit association rules and get some interesting results. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2018.

关键词： Large dataset

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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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DisCANTree: A Distributed Algorithm for Incremental Frequent Itemset Mining based on MapReduce

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

作者： Wen Xiao Juan Hu Key Laboratory of Unmanned Aerial Vehicle Development & Data Application of Anhui Higher Education Institutes Wanjiang University of Technology Maanshan 243000 China Ma'anshan Engineering Technology Research Center for Wireless Sensor Network and IntelliSense Wanjiang University of Technology Maanshan 243000 China

Frequent itemset mining is one of the most important data mining tasks. Classical frequent itemset mining algorithms need to store data in a centralized way and run in a batch way, which cannot meet the requirements of fast updating big data mining. In this paper, we propose a distributed incremental frequent itemset mining algorithm, DisCANTree, which uses CANTree to store the conditional database, achieves the load balance between nodes by grouping all items, updates the new transaction to the existing CANTree to avoid the load of tree reconstruction, and uses the efficient FPGrowth algorithm to mine CANTree to generate frequent itemsets. The popular distributed programming model MapReduce and its open source system Hadoop are used to implement the DisCANTree algorithm. The experimental results show that the DisCANTree algorithm has more advantages than the most popular PFP algorithm in performance as well as the number of transferred records between nodes, and especially suits for the fast updating sparse big data.

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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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Deep well construction of big data platform based on multi-source heterogeneous data fusion

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International Journal of Internet Manufacturing and Services 2019年第4期6卷 371-388页

作者： Zhang, Yu Wang, Yange Ding, Hongwei Li, Yongzhen Bai, Yanping School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture No. 1 Zhanlanguan Road Xicheng District Beijing100044 China China University of Mining and Technology No. 16 Qinghua Road Haidian District Beijing100083 China School of Software Beijing University of Technology Beijing100124 China Network Information Center Beijing University of Civil Engineering and Architecture Beijing100044 China College of Management Capital Normal University Beijing100048 China

At present, energy saving and emission reduction had become a problem of great concern for mankind. At the same time, there were some problems in the mining industry, such as waste of resources, low efficiency and easy occurrence of industrial accidents. Therefore, this paper had designed a deep well construction big data platform. The high precision and bear great pressure sensors were added to the system to solve the difficult problem of collecting information in deep wells by ordinary sensors. The multi-source heterogeneous data fusion algorithm was added to the system to solve the problem that the format of the data acquisition was different. In conclusion, the completion of the platform could achieve data monitoring in the process of mines. It not only helps to enhance the safety of mine construction, but also provides data analytical tools for further theoretical research of mine construction. © 2019 Inderscience Enterprises Ltd.

关键词： Big data

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Recovery and repair schemes for shift-XOR regenerating codes

arXiv

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arXiv 2019年

作者： Fu, Ximing Yang, Shenghao Xiao, Zhiqing School of Science and Engineering Chinese University of Hong Kong Shenzhen China University of Science and Technology of China China School of Science and Engineering Chinese University of Hong Kong Shenzhen Guangdong518172 China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Chinese University of Hong Kong Shenzhen Guangdong518172 China Shenzhen Research Institute of Big Data Shenzhen Guangdong518172 China Tsinghua University

Recovery and repair schemes are proposed for shift-exclusive-or (shift-XOR) product-matrix (PM) regenerating codes, which outperform those of the existing PM codes in terms of both communication and computation costs. In particular, for the minimum bandwidth regenerating (MBR) codes, our recovery and repair schemes have the optimal transmission bandwidth, zero decoding auxiliary data space and lower time complexity;for the minimum storage regenerating (MSR) codes, our recovery and repair schemes have smaller transmission bandwidth, smaller decoding auxiliary data space and lower time complexity. Moreover, our schemes involve only XOR operations in recovery and repair and are practical for system implementation. Technically, a procedure is first proposed for solving a system of shift-XOR equations, which plays a similar fundamental role as Gaussian elimination for solving systems of linear equations, and is of independent interest. The recovery and repair of shift-XOR MBR/MSR codes are then decomposed into a sequence of systems of shift-XOR equations, and hence can be solved by a sequence of calls to the procedure for solving a system of shift-XOR equations. As the decomposition of the recovery and repair depends only on the PM construction, but not the specific shift and XOR operations, our recovery and repair schemes can be extended to other MBR/MSR codes using the PM construction. Copyright © 2019, The Authors. All rights reserved.

关键词： Matrix algebra

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Revisiting Cloud Migration: Strategies and Methods

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

作者： Zhixiao Wang Wenyao Yan Wendong Wang 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 Yan'an University Xi'an 716000 China

Cloud computing attracts users with its advantage of unlimited resource supply where resources can be elastically expanded on demand and balanced-load at the same time. This means that the application in the cloud environment should run in the way of elastic expansion cluster. At present, most of the elastic expansion and load balancing technologies provided by IaaS level are oriented to virtual machines (VM), with little consideration of application level, which does not fundamentally meet the needs of users. Based on this, starting from the motivation of cloud migration, we first analyse the cloud migration technology from three aspects: migration object, migration means and migration objectives, then explore the cloud migration criteria and four cloud migration strategies at the application level in the cloud environment, and point out that cloud migration should be optimized and maintained in terms of elastic expansion, load balancing, security, etc.

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Dense small cell networks: From noise-limited to dense interference-limited

Dense small cell networks: From noise-limited to dense inter...

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作者： Yang, Bin Mao, Guoqiang Ding, Ming Ge, Xiaohu Tao, Xiaofeng School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China School of Computing and Communication University of Technology Sydney UltimoNSW2007 Australia Data61 CSIRO CanberraACT2601 Australia National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing100876 China

Considering both non-line-of-sight (NLoS) and line-of-sight (LoS) transmissions, the transitional behaviors from noise-limited regime to dense interference-limited regime have been investigated for the fifth generation (5G) small cell networks (SCNs). Besides, we identify four performance regimes based on base station (BS) density, i.e., 1) the noise-limited regime, 2) the signal-dominated regime, 3) the interference-dominated regime, and 4) the interference-limited regime. To characterize the performance regime, we propose a unified framework analyzing the future 5G wireless networks over generalized shadowing/fading channels, in which the user association schemes based on the strongest instantaneous received power and the strongest average received power can be studied, while NLoS/LoS transmissions and multislop path loss model are considered. Simulation results indicate that different factors, i.e., noise, desired signal, and interference, successively and separately dominate the network performance with the increase of BS density. Hence, our results shed new light on the design and management of SCNs in urban and rural areas with different BS deployment densities. © 1967-2012 IEEE.

关键词： Wireless networks

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A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural network

arXiv

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arXiv 2020年

作者： Hua, Xuan Han, Jungang Zhao, Chen Tang, Haipeng He, Zhuo Tang, Jinshan Chen, Qing-Hui Tang, Shaojie Zhou, Weihua College of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing in Shaanxi Xi’an710121 China College of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China College of Computing Michigan Technological University HoughtonMI49931 United States School of Computing University of Southern Mississippi Long Beach39560 United States Deprtment of Kinesiology and Integrative Physiology Michigan Technological University HoughtonMI49931 United States School of Automation Xi’an University of Posts and Telecommunications Xi’an710121 China

This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural networks (CNN) to aid the classification of ECG signals. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) for segmenting the original ECG data into segments in order to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments. The performance of the classification models for 5-class and 6-class are verified with ECG signals from 48 records of the MIT-BIH arrhythmia database. As the heartbeat types are divided into 5 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, and paced beat) in the MIT-BIH, the best classification accuracy, the area under the curve (AUC), the sensitivity and the F1-score reach 99.24%, 0.9994, 0.99 and 0.99, respectively. As the heartbeat types are divided into 6 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, paced beat and other beats) in the MIT-BIH, the beat classification accuracy, the AUC, the sensitivity, and the F1-score reach 97.02%, 0.9966, 0.97, and 0.97, respectively. Meanwhile, according to the recommended practice from the Association for Advancement of Medical Instrumentation (AAMI), the heartbeat types are divided into 5 classes (i.e., normal beat, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unclassifiable beats), the beat classification accuracy, the sensitivity, and the F1-score reach 97.45%, 0.97, and 0.97, respectively. The experimental results show that the proposed method achieves better performance than the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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