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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Shuang Yin Peng Ke Ling Tao College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Wuhan University of Science and Technology Wuhan China

In cloud computing environment, task scheduling is one of the most critical issues to be solved. Efficient task scheduling mechanism not only meets users' requirements but also ensures cloud resources' high utilization, so as to improve the overall performance of the cloud computing environment. Aiming at this problem, a new scheduling algorithm based on double-fitness algorithm-load balancing and task completion cost genetic algorithm(LCGA) is proposed. The scheduling guarantees load balancing and makes task completion cost less. At the same time, this paper brings in not just variance to represent the load among computing workers but weights multi-fitness function. Through the simulation experiment, the proposed algorithm is being compared with the genetic algorithm based on load balancing (LGA) and the genetic algorithm based on task completion cost (CGA). It proves the effectiveness of the scheduling algorithm and the availability of the optimization method.

关键词： Task analysis Cloud computing Processor scheduling Genetic algorithms Load management Sociology Statistics

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Dense dilated network with probability regularized walk for vessel detection

arXiv

引用

arXiv 2019年

作者： Mou, Lei Chen, Li Cheng, Jun Gu, Zaiwang Zhao, Yitian Liu, Jiang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan430081 China Cixi Institute of Biomedical Engineering Chinese Academy of Sciences Zhejiang315201 China UBTech Research UBTech Robotics Corp Ltd Guangdong518055 China Department of Computer Science and Engineering Southern University of Science and Technology Guangdong518055 China Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Zhejiang315201 China

The detection of retinal vessel is of great importance in the diagnosis and treatment of many ocular diseases. Many methods have been proposed for vessel detection. However, most of the algorithms neglect the connectivity of the vessels, which plays an important role in the diagnosis. In this paper, we propose a novel method for retinal vessel detection. The proposed method includes a dense dilated network to get an initial detection of the vessels and a probability regularized walk algorithm to address the fracture issue in the initial detection. The dense dilated network integrates newly proposed dense dilated feature extraction blocks into an encoder-decoder structure to extract and accumulate features at different scales. A multi-scale Dice loss function is adopted to train the network. To improve the connectivity of the segmented vessels, we also introduce a probability regularized walk algorithm to connect the broken vessels. The proposed method has been applied on three public data sets: DRIVE, STARE and CHASE_DB1. The results show that the proposed method outperforms the state-of-the-art methods in accuracy, sensitivity, specificity and also area under receiver operating characteristic curve. Copyright © 2019, The Authors. All rights reserved.

关键词： Diagnosis

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Enabling the Disagreement among Crowds: A Collaborative Crowdsourcing Framework

Enabling the Disagreement among Crowds: A Collaborative Crow...

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International Conference on computer Supported Cooperative Work in Design

作者： Meihong Wang Yuling Sun Jing Yang Liang He Department of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China

Crowdsourcing is quite cheap and effective to get a data set labeled by multiple annotators in a short amount of time. Although there are many traditional methods focusing on quality control of crowdsourcing, little pays close attention to the intrinsic ambiguity in dataset, which is hard for workers to make the right decision. In this paper, we introduce DCRB, a collaborative crowdsourcing framework that can obtain high quality output even for ambiguous tasks. We enable users' disagreement and encourage them to provide in-depth explanations when the disagreement occurs. We use these explanations to analyze the ambiguous tasks and design collaborative crowdsourcing to improve the output. In addition, we also design “reward brave” incentive mechanism to encourage users' valuable explanations. The experimental results show that our method significantly improves the accuracy of crowdsourcing, especially for those ambiguous crowdsourcing tasks.

关键词： Crowdsourcing Task analysis Collaboration Labeling Dogs Guidelines Machine learning

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A Generation Method of Automation Test Cases Based on Android

A Generation Method of Automation Test Cases Based on Androi...

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2017 5th International Conference on Machinery,Materials and Computing technology(ICMMCT 2017)

作者： Ming Zhang Baolei Cheng Weizhong Zha Jiwen Yang School of Computer Science and Technology Soochow University Provincial Key Laboratory for Computer Information Processing Technology

Aiming at the problems that complex testing process, incomplete testing and poor reusability of test cases for Android automated testing methods, we propose a generation method of automation test cases based on control traversal under the guidance of standard path. The method firstly records a test script as a standard path by the tester, then automatically acquires the control in the interface and generates the control relationship graph. Finally, the test case generation method based on the depth-first search was used to traverse the control relationship graph to generate test cases. The test case was used to test the Android mobile App. The results show that the test case generated by this method improves the test coverage and script reusability, simplifies the test operation and proves the feasibility of the method.

关键词： Automated Testing Test Case Standard Path Control Traversal Depth-First Search

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Realization of remote sensing image segmentation based on K-means clustering

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IOP Conference Series: Materials Science and Engineering 2019年第7期490卷

作者： Yanhong Wang Denghui Li Yuying Wang Guangxi Key Lab of Wireless Wideband Communication & Signal Processing Guilin China Institute Of Information Technology of Guilin University of Electronic Technology Key Laboratory of Cognitive Radio and Information Processing Ministry of Education (Guilin University of Electronic Technology) GUET Guilin China School of Computer Science and Information Security of GUET

Segmentation of remote sensing image is the key technology of positioning system. Firstly, we transform the remote sensing image from RGB pace to Lab space. Then, three centres are iterated by using K- means algorithm. Finally, in order to eliminate the influence, the closed operation of mathematical morphology is used to correct the segmented image. The results show that it can segment the road from the remote sensing image in lab mode, by using K-means clustering algorithm. Moreover, Lab mode is more suitable for k-mean than other modes.

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Simulating evolutional symport/antiport by evolution-communication and vice versa in tissue p systems with parallel communication 18th

Simulating evolutional symport/antiport by evolution-communi...

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18th International Conference on Membrane Computing, CMC 1018

作者： Adorna, Henry Alhazov, Artiom Pan, Linqiang Song, Bosheng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan430074 China University of the Philippines Diliman Quezon City1101 Philippines Institute of Mathematics and Computer Science Academy of Science of Moldova Academiei 5 Chişinău2028 Moldova School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

We aim to compare functionality of symport/antiport with embedded rewriting to that of symport/antiport accompanied by rewriting, by two-way simulation, in case of tissue P systems with parallel communication. A simul... 详细信息

ISBN: (纸本)9783319733586

关键词： Tissue

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Efficient vulnerability detection based on an optimized rule-checking static analysis technique

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Frontiers of information technology & Electronic Engineering 2017年第3期18卷 332-345页

作者： Deng CHEN Yan-duo ZHANG Wei WEI Shi-xun WANG Ru-bing HUANG Xiao-lin LI Bin-bin QU Sheng JIANG Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of TechnologyWuhan 430205China Industrial Robot Engineering Center Wuhan Institute of TechnologyWuhan 430205China School of Computer and Information Engineering Henan Normal UniversityXinxiang 453007China School of Computer Science and Telecommunication Engineering Jiangsu UniversityZhenjiang 212013China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Static analysis is an efficient approach for software assurance. It is indicated that its most effective usage is to perform analysis in an interactive way through the software development process, which has a high performance requirement. This paper concentrates on rule-based static analysis tools and proposes an optimized rule-checking algorithm. Our technique improves the performance of static analysis tools by filtering vulnerability rules in terms of characteristic objects before checking source files. Since a source file always contains vulnerabilities of a small part of rules rather than all, our approach may achieve better performance. To investigate our technique's feasibility and effectiveness, we implemented it in an open source static analysis tool called PMD and used it to conduct experiments. Experimental results show that our approach can obtain an average performance promotion of 28.7% compared with the original PMD. While our approach is effective and precise in detecting vulnerabilities, there is no side effect.

关键词： Rule-based static analysis Software quality Software validation Performance improvement

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A Novel modeling approach for all-dielectric metasurfaces using deep neural networks

arXiv

引用

arXiv 2019年

作者： An, Sensong Fowler, Clayton Zheng, Bowen Shalaginov, Mikhail Y. Tang, Hong Li, Hang Zhou, Li Ding, Jun Agarwal, Anuradha Murthy Rivero-Baleine, Clara Richardson, Kathleen A. Gu, Tian Hu, Juejun Zhang, Hualiang Department of Electrical & Computer Engineering University of Massachusetts Lowell LowellMA United States Department of Materials Science & Engineering Massachusetts Institute of Technology CambridgeMA United States Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Lockheed Martin Corporation OrlandoFL United States CREOL University of Central Florida OrlandoFL United States

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) response, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep neural network approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to assemble metasurface-based devices. Our neural network approach overcomes three key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch, accurate EM-wave phase prediction, as well as adaptation to 3-D dielectric structures, and can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for meta-atoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Community discovery of complex network based on fuzzy density peak clustering algorithm

Community discovery of complex network based on fuzzy densit...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Ling Tao Wenjie Li Yu Jin Shuang Yin College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China

Based on the similarity measure of the complex network node and the improved density peak clustering algorithm with information entropy, a community discovery method is proposed in this paper. First of all, the distance between nodes is measured by the Jaccard similarity and the shortest path generation algorithm. Then, the core communities with the improved density peak algorithm are selected automatically. Thirdly, applying the fuzzy clustering mechanism to calculate the membership degree matrix of each point, thus completing the division of the remaining points. Finally, overlapping nodes are distinguished by setting a threshold of degree difference. Experiments are conducted on four real-life networks and the Purity and the Extended Modularity are employed to evaluate the proposed algorithm. The comparison of experiments with some classical algorithms on real networks are given to demonstrate the feasibility and effectiveness of the proposed method.

关键词： Clustering algorithms Complex networks Entropy information entropy Classification algorithms Clustering methods Dolphins

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Learning a discriminative prior for blind image deblurring

arXiv

引用

arXiv 2018年

作者： Li, Lerenhan Pan, Jinshan Lai, Wei-Sheng Gao, Changxin Sang, Nong Yang, Ming-Hsuan National Key Laboratory of Science and Technology on Multispectral Information Processing School of Automation Huazhong University of Science and Technology Electrical Engineering and Computer Science University of California Merced United States School of Computer Science and Engineering Nanjing University of Science and Technology

We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor clear images over blurred ones. In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN). The learned prior is able to distinguish whether an input image is clear or not. Embedded into the maximum a posterior (MAP) framework, it helps blind deblurring in various scenarios, including natural, face, text, and low-illumination images. However, it is difficult to optimize the deblurring method with the learned image prior as it involves a non-linear CNN. Therefore, we develop an efficient numerical approach based on the half-quadratic splitting method and gradient decent algorithm to solve the proposed model. Furthermore, the proposed model can be easily extended to non-uniform deblurring. Both qualitative and quantitative experimental results show that our method performs favorably against state-of-the-art algorithms as well as domainspecific image deblurring approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Image enhancement

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