检索结果-内蒙古大学图书馆

6th International Conference on Image and Signal Processing and their Applications, ISPA 2019

作者： Zemmal, Nawel Azizi, Nabiha Ziani, Amel Benzebouchi, Nacer Eddine Aldwairi, Monther Badji Mokhtar University Labged Laboratory Annaba23000 Algeria Department of Mathematics and Computer Science Mohamed Cherif Messaadia University Souk-Ahras41000 Algeria Lri Laboratory: Information Research Laboratory Computer Science Department Annaba23000 Algeria Jordan University of Science and Technology Department of Network Engineering and Security Irbid22110 Jordan

ISBN: (纸本)9781728131566

Breast cancer is the most feared disease in the female population. Early detection plays an important role to improve prognosis. Mammography is the best examination for the detection of breast cancer. However, in some cases, reading mammograms is difficult for radiologists. For this reason, several researches have been conducted to develop computer Aided Diagnosis tools (CAD) for this disease which aims to interpret mammography images. This paper investigates a new CAD system based on Transductive scheme and Mutual Information for breast abnormalities diagnosis. In the proposed method, a feature vector contains a combination of two features extraction method: Grey Level Co-occurrence Matrix and local Binary Pattern. In the next step, a novel scheme combining Mutual Information and Correlation-based feature selection was applied for selecting the most relevant features. Finally, the classification was achieved using a Transductive Support Vector Machine classifier. The effectiveness of the proposed CAD is examined on the DDSM dataset using classification accuracy, recall and precision. Experimental results demonstrate that the proposed CAD system is clinically significant and can be used to classify the abnormalities of the breast. © 2019 IEEE.

关键词： computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Velocity-driven particle swarm optimization 8

Velocity-driven particle swarm optimization

引用

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

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. © 2019 Association for Computing Machinery.

关键词： Velocity

来源：评论

学校读者我要写书评

暂无评论

反向连边在大型分层网络中的影响（英文）

引用

engineering 2024年第5期 240-249页

作者：曹浩森胡斌斌莫小雨陈都鑫高建喜袁烨陈关荣 Tamás Vicsek 管晓宏张海涛 MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Mechanical and Aerospace Engineering Nanyang Technological University Jiangsu Key Laboratory of Networked Collective Intelligence School of Mathematics Southeast University Department of Computer Science Rensselaer Polytechnic Institute Department of Electronic Engineering City University of Hong Kong Department of Biological Physics E?tv?s University MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi'an Jiaotong University

Hierarchical networks are frequently encountered in animal groups, gene networks, and artificial engineering systems such as multiple robots, unmanned vehicle systems, smart grids, wind farm networks,and so forth. The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes, such as lagging birds' howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group. This study reveals that, for most large-scale real hierarchical networks, the majority of the reverse edges do not affect the synchronization process of the entire network; the synchronization process is influenced only by a small part of these reverse edges along specific paths. More surprisingly, a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%. The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork. The overwhelming majority of active reverse edges turn out to have some kind of ‘‘bunchingo effect on the information flows of hierarchical networks, which slows down synchronization processes. This finding refines the current understanding of the role of reverse edges in many natural, social, and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks. Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Investigating Bi-level optimization for learning and vision from a unified perspective: A survey and beyond

arXiv

引用

arXiv 2021年

作者： Liu, Risheng Gao, Jiaxin Zhang, Jin Meng, Deyu Lin, Zhouchen DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian116024 China Department of Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an Shaanxi China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems, such as hyper-parameter optimization, multi-task and meta learning, neural architecture search, adversarial learning and deep reinforcement learning, actually all contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of BLO. Then we construct a best-response-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies, covering aspects ranging from fundamental automatic differentiation schemes to various accelerations, simplifications, extensions and their convergence and complexity properties. Last but not least, we discuss the potentials of our unified BLO framework for designing new algorithms and point out some promising directions for future research. © 2021, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A personalized privacy protection data uploading scheme for mobile crowdsensing 2

A personalized privacy protection data uploading scheme for ...

引用

2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Ma, Rong Chen, Lei Xiong, Jinbo Tian, Youliang Li, Qi Liu, Ximeng Fujian Provincial Key Laboratory of Network Security and Cryptology College of Mathematics and Informatics Fujian Normal University Fuzhou350117 China College of Engineering and Computing Georgia Southern University GA30458 United States Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Fujian Provincial Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China

ISBN: (纸本)9781728129778

Mobile crowdsensing is a typical application of the Industrial Internet of hings and brings a promising opportunity to construct powerful industrial systems and services. The existing privacy protection schemes usually adopt a unified privacy protection policy for all sensing data due to lack of effective privacy metrics, thereby resulting in excessive or insufficient protection of privacy information and low accuracy of sensing data. To tackle these issues, in this paper, we propose a personalized privacy protection data uploading (PPDU) scheme based on game theory and sensing user's historical spatio-temporal data. In PPDU, a personalized privacy measurement algorithm is designed to obtain the personalized privacy degree of user sensing data. Based on the privacy degree, a user uploading behavior game is designed by employing an incomplete information game, and a game-based spatio-temporal sensing data uploading method is proposed for user privacy protection. Simulation experiments with real trajectory data show that the PPDU scheme makes a reasonable balance between retaining high quality of crowdsensing service and protecting user's privacy. © 2019 IEEE.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

An improved LBG algorithm for user clustering in ultra-dense network 5th

An improved LBG algorithm for user clustering in ultra-dense...

引用

5th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2018

作者： Liang, Yanxia Liu, Yao Sun, Changyin Liu, Xin Jiang, Jing Gao, Yan Xie, Yongbin Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’an710121 China Department of Computer Science and Engineering University of South Florida TampaFL33647 United States School of Information Engineering Xi’an Eurasia University Xi’an710065 China

ISBN: (纸本)9783030037659

A novel LBG-based user clustering algorithm is proposed to reduce interference efficiently in Ultra-Dense network (UDN). There are two stages, weight design and user clustering. Because a user could interfere and be interfered by other users at the same time, a balanced cooperative transmission strategy is utilized in weight design. The improved LBG algorithm is used for user clustering, which overcomes the shortcoming of local optimum of conventional LBG. Moreover, this algorithm is superior to conventional LBG in computational complexity. Simulation results show that the sum rate of cell-edge users increases a lot compared to the reference algorithm, and the average system throughput gets higher obviously. © Springer Nature Switzerland AG 2019.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Semantically Secure and Verifiable Multi-keyword Search in Cloud Computing 1

引用

2nd International Conference on Machine Learning for Cyber security, ML4CS 2019

作者： Zhang, Lili Wang, Wenjie Zhang, Yuqing National Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China Henan International Joint Laboratory of Cyberspace Security Applications Information Engineering College Henan University of Science and Technology Luoyang471023 China National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

ISBN: (数字)9783030306199

ISBN: (纸本)9783030306182

In cloud computing model, the data are usually encrypted before outsourced to the cloud server, which protects the data privacy, but also leaves keyword searches over ciphertext data a challenging problem. A keyword search scheme over encrypted data should achieve both index privacy and query privacy;moreover, verification of search results is desirable because the incorrectf results can be returned owing to system defects or the cloud server’s motivation to save computation recourses. Many multi-keyword search schemes have been proposed;however, few of these schemes are verifiable and adaptively index-hiding and adaptively query-hiding. In this paper, a semantically secure multi-keyword search scheme is constructed, which is adaptively index-hiding and adaptively query-hiding, also supports the correctness verification of search results. We provide a detailed performance comparison and give a thorough security proof by a sequence of games. The combined results demonstrate that our scheme is secure and practical. © 2019, Springer Nature Switzerland AG.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

衰亡链揭示互利共生生态系统中安全与崩溃的中间阶段（英文）

引用

engineering 2024年第12期 89-98页

作者：王光炜刘雪明肖颖袁烨潘林强管晓宏高建喜张海涛 The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and Technology Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi'an Jiaotong University Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Department of Computer Science Rensselaer Polytechnic Institute

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities, such as overfishing and deforestation, and the effects of such damage on ecological stability are uncertain. Despite recent advances in experimental and theoretical studies on regime shifts and tipping points, theoretical tools for understanding the extinction chain, which is the sequence of species extinctions resulting from overexploitation, are still lacking, especially for large-scale nonlinear networked systems. In this study, we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and densities. We discovered five phases during the exploitation process: safe,partial extinction, bistable, tristable, and collapse, which enabled the optimal design of restoration strategies for degraded or collapsed systems. We validated our approach using a 20-year dataset from an eelgrass restoration project. Counterintuitively, we also found a specific region in the diagram spanning exploitation rates and competition intensities, where exploiting more species helps increase *** computational tool provides insights into harvesting, fishing, exploitation, or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient Malware Originated Traffic Classification by Using Generative Adversarial networks

Efficient Malware Originated Traffic Classification by Using...

引用

IEEE Symposium on computers and Communications (ISCC)

作者： Zhicheng Liu Shuhao Li Yongzheng Zhang Xiaochun Yun Zhenyu Cheng Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

With the booming of malware-based cyber-security incidents and the sophistication of attacks, previous detections based on malware sample analysis appear powerless due to time-consuming and labor-intensive analysis process. The existing detection methods based on traffic analysis rely heavily on the available traffic patterns, which hinder detecting the zero-day attacks caused by malware variants. In this paper, we propose an approach based on deep learning referred to as TrafficGAN, which analyzes (HTTP) traffic sessions to distinguish between malware-related and normal traffic. We first try to explore traffic patterns of malware variants by adding noise and category condition to the Generative Adversarial networks (GAN), thus generating various similar but slightly different traffic. And then, we use discriminative model to seek the deviation between abnormal traffic and normal traffic by extracting the essential difference. Notablely, we increase the diversity of data by generating samples adversarially, which enhances the robustness of the system to detect zero-day attacks and highlights the lack of sensitive data in the security community. We conduct extensive experiments on the public dataset and our data collected for specific targets. The results demonstrate that our method achieves superior performance to other methods and protects specific targets from the susceptibility of malware.

关键词：

来源：评论

学校读者我要写书评

暂无评论

In-situ data scheduling optimization based on rainbow DQN for IIoT

引用

Future Generation computer Systems 2026年 174卷

作者： Peiying Zhang Siyi Li Lizhuang Tan Neeraj Kumar Jian Wang Kai Liu Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Department of Computer Science University of Economics and Human Sciences Warszawa 01-043 Poland Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia College of Science China University of Petroleum (East China) Qingdao 266580 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Space Network and Communications Tsinghua University Beijing 100084 China

In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：