检索结果-内蒙古大学图书馆

How to Protect Ourselves From Overlapping Community detection in Social Networks

IEEE TRANSACTIONS ON BIG DATA 2022年第4期8卷 894-904页

作者： Liu, Dong Yang, Guoliang Wang, Yanwei Jin, Hu Chen, Enhong Henan Normal Univ Coll Comp & Informat Engn Key Lab Artificial Intelligence & Personalized Le Big Data Engn Lab Teaching Resources & Amp Assess Xinxiang 453007 Henan Peoples R China Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Hanyang Univ Div Elect Engn Ansan 5588 South Korea Henan Normal Univ Xinxiang 453007 Henan Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Anhui Prov Key Lab Big Data Anal & Applicat Hefei 230000 Anhui Peoples R China

In recent years, overlapping community detection algorithms have been paid more and more attention, which not only reveal the real social relations, but also expose the possible communication channels between communities. Those individuals (or people) in the overlapping area are very important to the communities that can promote communication between two or more communities. On the other hand, from the privacy perspective, some people may not want to be found out in the overlapping areas. With this in mind, we raise a question "Can individuals modify their relationships to avoid the community discovery algorithms locating them into overlapping areas?" If this problem could be solved, these people may not need to worry about being disturbed. In particular, we first give three heuristic hiding strategies, i.e., Random Hiding(RH), Based Degree Hiding(DH) and Betweenness Hiding(BH), as comparison, utilizing the randomly the node, information of node degree and node betweenness centrality, respectively. And then, we propose a novel hiding algorithm by exploiting the importance degree of nodes in communities based on which the corresponding social connections are added or deleted called name BIH. Through extensive experiments, we show the effectiveness of the proposed algorithm in moving out a target node from overlapped areas.

关键词： detection algorithms Social networking (online) Privacy Image edge detection Complex networks Big Data Optimization Overlapping community detection community deception hiding privacy protection

来源：评论

学校读者我要写书评

暂无评论

A Novel Defect detection Algorithm for Flexible Integrated Circuit Package Substrates

引用

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第2期69卷 2117-2126页

作者： Zhong, Zhiyan Ma, Zhichao Guangdong Polytech Normal Univ Sch Automat Guangzhou 510665 Peoples R China Jilin Univ Minist Educ Key Lab CNC Equipment Reliabil Sch Mech & Aerosp Engn Changchun 130025 Peoples R China Bionics Jilin Univ Weihai Inst Weihai 264207 Peoples R China

Efficient fabrication and high reliability of flexible integrated circuit package substrates (FICS) urgently need effective defect detection methods. Based on the defect categories of FICS images, including yellow circuit backgrounds, film backgrounds with speckles, and black backgrounds, a novel rapid detection algorithm of impurity defects in FICS images is proposed. Three successive procedures are indispensable to realize the rapid detection with high accuracy, including image contrast enhancement based on image gray value analysis, obtaining the standard templates to weaken the texture interferences, and the probability calculation of the background feature of each pixel in the edge region between background and defect. Then, the accurate identification of film/black backgrounds and impurity defects in high-density FICS images is achieved. Furthermore, the comparisons of time-accuracy tradeoff obtained by using existing methods (Lk-means, Ostu, GMEM, SPCNN, MBGCT, FTC) and the proposed algorithm are carried out to experimentally verify the feasibility. The defect detection algorithm will facilitate a solution for the high similarity between the inherent texture features of FICS and external impurity defects at micrometer scale and substantially improve the detection accuracy and efficiency of high-density FICS, which will meet the real-time industrial requirement for the rapid fabrication of high-density FICS.

关键词： Standards Substrates Impurities Mathematical model Fitting Probability detection algorithms Automatic optical inspection (AOI) image processing integrated circuit packaging

来源：评论

学校读者我要写书评

暂无评论

Asymptotic Performance Analysis of Distributed Non-Bayesian Quickest Change detection With Energy Harvesting Sensors

引用

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第4期58卷 3697-3707页

作者： Biswas, Sinchan Dey, Subhrakanti Lund Univ Dept Elect & Informat Technol S-22100 Lund Sweden Uppsala Univ Div Signals & Syst S-75105 Uppsala Sweden NUI Maynooth Hamilton Inst Maynooth W23 F2H6 Kildare Ireland

This article focuses on the distributed non-Bayesian quickest change detection based on the cumulative sum (CUSUM) algorithm in an energy harvesting wireless sensor network, where the distributions before and after the change point are assumed to be known. Each sensor is powered by randomly available harvested energy from the surroundings. It samples the observation signal and computes the log-likelihood ratios (LLRs) of the aforementioned two distributions if enough energy is available in its battery for sensing and processing the sample (E-s). Otherwise, the sensor decides to abstain from the sensing process during that time slot and waits until it accumulates enough energy to perform the sensing and processing of a sample. This LLR is used for performing the CUSUM test to arrive at local decisions about the change point, which are then combined at the fusion center (FC) by a predecided fusion rule to arrive at a global decision. In this article, we derive the asymptotic expressions (as the average time to a false alarm goes to infinity) for the expected detection delay and the expected time to a false alarm at the FC for three common fusion rules, namely, OR, AND, and r out of N majority rule, respectively, by considering the scenario, where the average harvested energy at each sensor is greater than the energy required for sensing and processing a sample E-s. To this end, we use the theory of order statistics and the asymptotic distribution of the first passage times of the local decisions. Numerical results are also provided to support the theoretical claims.

关键词： Sensors Wireless sensor networks Energy harvesting Delays Sensor fusion detection algorithms Change detection algorithms

来源：评论

学校读者我要写书评

暂无评论

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第3期71卷 1429-1442页

作者： Chen, Yangyang Feng, Huiyu Song, Suwen Wang, Zhongfeng Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Sun Yat Sen Univ Sch Integrated Circuits Shenzhen 518107 Peoples R China

The expectation propagation (EP) algorithm is near-optimal in massive multiple-input multiple-output (MIMO) systems but suffers from high computation complexity. Most of the previous works exploit the channel hardening property and introduce iterative matrix inversion algorithms to simplify the EP algorithm, but the performance degrades dramatically in non-ideal channels. In this paper, we propose a more universal EP-based detector, which can perform well in both ideal and non-ideal channels. Firstly, two general methods are proposed to effectively improve the detection performance and convergence speed of iterative matrix inversion algorithms under correlated channels. The proposed diagonal preprocessing (DP) method can improve the detection performance by more than 2-dB compared to not using this method;the novel eigenvalue parameter estimation method guarantees the convergence of all the frames. These two methods are applied to the second-order Richardson iteration (SORI) algorithm to derive the DP-SORI algorithm, which converges more than twice as fast as the state-of-the-art design. Secondly, for another important part of EP-based algorithms, namely the calculation of expectation and variance, complicated operations such as exponentiations, divisions, and inversions are all removed by algorithmic optimization. Moreover, based on the proposed approximate EP with DP-SORI (EPA-DP-SORI) algorithm, an efficient hardware design is developed, combining multiple optimization methods such as efficient matrix multiplication architecture design and low-complexity LDL decomposition. In addition to better detection performance compared with the state-of-the-art design, the presented EPA-DP-SORI detector can also deliver 1.27xand 1.57 x higher area and energy efficiency.

关键词： Detectors Approximation algorithms Massive MIMO detection algorithms Interference Matrix decomposition Iterative algorithms MIMO detection expectation propagation (EP) correlated channels diagonal preprocessing (DP) high throughput hardware implementation

来源：评论

学校读者我要写书评

暂无评论

Redefining Malware Sandboxing: Enhancing Analysis Through Sysmon and ELK Integration

引用

IEEE ACCESS 2024年 12卷 68624-68636页

作者： Mahmoud, Rasmi-Vlad Anagnostopoulos, Marios Pastrana, Sergio Pedersen, Jens Myrup Aalborg Univ Dept Elect Syst CMI Sect Cyber Secur Grp DK-2450 Copenhagen Denmark Univ Carlos III Madrid Escuela Politecn Super Dept Comp Sci Comp Secur COSEC Lab Leganes 28911 Spain

In cybersecurity, adversaries employ a myriad of tactics to evade detection and breach defenses. Malware remains a formidable weapon in their arsenal. To counter this threat, researchers unceasingly pursue dynamic analysis, which aims to comprehend and thwart established malware strains. This paper introduces an innovative methodology for dynamic malware analysis while critically evaluating prevailing technologies and their limitations. The proposed approach hinges on harnessing the capabilities of an open-source Security Information and Event Management (SIEM) toolset, namely the Elastic-Stack. This toolset is utilized to capture, structure, and analyze the behavioral patterns of malware without relying on any pre-existing sandbox framework. This augmentation facilitates a profound understanding of the activities exhibited by malware samples. With the help of the proposed ecosystem, we compile the AAU_MalData dataset that encompasses distinctly the benign and malicious behavior. Specifically, we analyzed the behavior of the 2,800 malware within a realistic network topology and systematically collected Windows event logs, which serve as a comprehensive record of the malware's actions. These event logs are precious as they are organized in a timestamped format, providing a chronological list of system activities, such as event descriptions, process and file details, and registry modifications. These are pivotal in comprehending the malware's functionality and repercussions on the compromised system. Furthermore, by incorporating the MITRE ATT&CK framework, we leveraged the event logs to correlate the malware's mode of operation, delve into its Command and Control operations, and investigate its persistence mechanisms, enabling a structured and practical approach to malware analysis. The AAU_MalData dataset, organized in a JSON format data structure, is offered to the research community first as a proof of concept to demonstrate the toolset's feasibility for dynamic m

关键词： Malware Monitoring Virtualization Virtual machining Virtual machine monitors Vectors Computer security detection algorithms Performance evaluation Dynamic malware analysis sandbox Elasticsearch MITRE ATT&CK Sysmon

来源：评论

学校读者我要写书评

暂无评论

A Novel Subpixel Circle detection Method Based on the Blurred Edge Model

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1-11页

作者： Liu, Weihua Yang, Xianqiang Sun, Hao Yang, Xuebo Yu, Xinghu Gao, Huijun Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150001 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Ningbo Inst Intelligent Equipment Technol Co Ltd Ningbo 315200 Peoples R China Peng Cheng Lab Shenzhen 518038 Peoples R China

Circle detection is a critical issue in computer vision and image processing. Whether in natural images or industrial images, the accuracy of circle detection has a significant impact on advanced vision applications. Conventional methods, such as circle Hough transform, random circle detection, and EDCircles, only reach pixel-level edge accuracy. This article proposes a subpixel circle detection method based on subpixel edges with accuracy of one-tenth of one pixel. All candidate circles are first detected by EDCircles. The circle scoring formula based on polarity, radius, and contour is then proposed to sort the detected circles, and the circle with the highest score is selected as the target. The 2-D subpixel calculation problem is transformed into the 1-D fitting problem, and the subpixel edge region is selected according to the gradient direction of the circular edge. To reduce the error of the step model and the real edge, the blurred edge model is proposed to fit the region. Subsequently, the parameters of the edge model are transformed into subpixel coordinates. To solve the problem that the traditional L2-loss function is not robust to outliers, the Huber loss function is finally applied to the circle fitting, and the gradient descent method is adopted to calculate the circle parameters. Experiments on natural and industrial images show that the proposed method has good performance on robustness and accuracy.

关键词： Image edge detection Image segmentation Transforms Least mean squares methods detection algorithms Feature extraction Computational modeling Blurred edge circle detection circle Hough transform (CHT) EDCircles subpixel

来源：评论

学校读者我要写书评

暂无评论

Fault Information Processing of Power Dispatching Communication System Combining MMSE Algorithm and LMS

引用

IEEE ACCESS 2024年 12卷 184529-184540页

作者： Pan, Lianrong Yang, Xiao Fu, Yuan Hu, Yuyang Yuan, Shangbin Guangxi Power Grid Co Ltd Elect Power Dispatching & Control Ctr Nanning 530000 Peoples R China

As the scale of power dispatching communication system expands day by day, it becomes more and more difficult for operators to deal with fault information. Therefore, it is imperative to develop an excellent fault information processing method for power dispatching communication system. Aiming at the defects of the existing power dispatching communication system, such as difficult to locate the fault information, low efficiency and poor accuracy, the research first introduces the minimum mean square error detection algorithm, and improves this algorithm. Then, the study utilizes the programmable logic controller communication system to complete the processing and analysis of the noise signal information. Second, the study also selects orthogonal frequency division multiplexing to improve the least mean square filtering algorithm in order to improve the accuracy of prediction. Finally, the study establishes a novel fault information processing model for the power dispatching communication system by combining the least mean square error detection algorithm and the least mean square filtering algorithm. The outcomes indicated that the relative errors of this model in predicting electrical and mechanical faults and environmental faults were only 0.41% and 0.21%, respectively. In addition, compared with the rest of the commonly used models, the fault localization accuracy of this model reached 99.08%. It not only effectively improved the fault localization accuracy by more than 13.77%, but also reduced the average number of iterations by more than 47.23. It can be concluded that the fault information processing model by combining the two improved algorithms can effectively improve the accuracy of fault localization and provide an efficient and feasible solution for the fault information processing of power dispatching communication system.

关键词： Noise Communication systems detection algorithms Interference Accuracy Mathematical models Information processing Power grids Mean square error methods Detectors Minimum mean square error least mean square power dispatching communication faults programmable logic controller

来源：评论

学校读者我要写书评

暂无评论

CGC-NET: Aircraft detection in Remote Sensing Images Based on Lightweight Convolutional Neural Network

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 2805-2815页

作者： Wang, Ting Zeng, Xiaodong Cao, Changqing Li, Wei Feng, Zhejun Wu, Jin Yan, Xu Wu, Zengyan Xidian Univ Xian 710071 Peoples R China Shenzhen Aerosp New Power Technol Co Ltd Shenzhen 518057 Peoples R China

In the past few years, aircraft detection in remote sensing (RS) images has been an important research hotspot, and it is very crucial in plenty of military applications. Based on the high computational cost of the model and numerous parameters, deep convolution neural networks-based algorithms have excellent performance in the aircraft detection task. However, it is still difficult to detect aircraft due to the complex background of RS images, various types of aircraft, and so on. In addition, it is difficult and costly to make labels for satellite-based optical RS images. Consequently, we propose an end-to-end lightweight aircraft detection framework called CGC-NET (a network based on circle grayscale characteristics), which can accurately detect aircraft with a few training samples. There are only a small number of trainable parameters in CGC-NET, which greatly reduces the need for large datasets. Extensive evaluations indicate the excellent performance of CGC-NET, in which the F-score can reach 91.06% and the model size is only 0.88 M. Therefore, CGC-NET can be used to accurately detect aircraft targets simply and effectively.

关键词： Aircraft Feature extraction Object detection detection algorithms Military aircraft Classification algorithms Support vector machines Circle frequency filter (CFF) deep learning framework few-shot learning lightweight convolutional neural network (CNN) small samples

来源：评论

学校读者我要写书评

暂无评论

Research on Dynamic Community detection Method Based on an Improved Pity Beetle Algorithm

引用

IEEE ACCESS 2022年 10卷 43914-43933页

作者： Wang, Yan-Jiao Song, Jia-Xu Sun, Peng Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China China United Telecommun Co Ltd Jilin Branch Changchun 130000 Peoples R China

In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clustering are promising for dynamic community detection, there is still room for further improvement in the snapshot quality and the temporal cost. In this study, a dynamic community detection algorithm based on optional pathway guide pity beetle algorithm (DYN-OPGPBA), which is a novel dynamic community detection method based on the framework of evolutionary clustering, is proposed. We propose an improved PBA for community detection of the network at the first time step, including a discrete search strategy based on adjacent nodes, a closeness-based community modification strategy and a crowded community split strategy. Compared with many representative static community detection methods, the proposed method has some superior detection accuracy. A neighbour vector competition-based individual update strategy and an external population size restriction mechanism are also proposed for community detection at subsequent time steps. Results show that DYN-OPGPBA has a better balance between snapshot quality and temporal cost than two representative dynamic community detection methods.

关键词： Heuristic algorithms Clustering algorithms Optimization Image edge detection detection algorithms Costs Statistics Community detection dynamic network evolutionary clustering Pity Beetle algorithm

来源：评论

学校读者我要写书评

暂无评论

A Multilayer General Type-2 Fuzzy Community detection Model in Large-Scale Social Networks

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第10期30卷 4494-4503页

作者： Mansoureh, Naderipour Hossein, Fazel Zarandi Mohammad Susan, Bastani Amirkabir Univ Technol Polytech Tehran Dept Ind Engn Tehran 158754413 Iran Alzahra Univ Dept Sociol Tehran *** Iran

In this article, a novel general type-2 fuzzy community detection model is presented based on different interactions in multilayer graphs to detect communities in a graph with an interaction between nodes, called a multiplex graph considering both structural and attribute similarities. The focus of this model is mostly on the uncertainty related to the interactions between nodes. This model regards a set of layers as the variety of relations among a set of nodes. Based on multilayers, the membership functions of the communities are delineated as "general type-2 fuzzy sets" comprising "primary and secondary variables." The former indicate the degree of belonging to communities in the multiplex graph based on different interactions in multilayers, and the latter indicate the degree of belonging of the multilayers to the multiplex graph. In the suggested community detection algorithm, no type-reduction or defuzzification exists to update the community prototypes. Moreover, an index, in accordance with the suggested model, is defined to validate the suggested model and its results;and several experimental results on different network sizes including large-scale social networks are given to determine the efficiency of the suggested model compared to the other models.

关键词： Multiplexing Fuzzy sets Uncertainty Indexes Social networking (online) Clustering algorithms detection algorithms General type-2 fuzzy community detection interactions uncertainty multilayer graphs multiplex graph validity index

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：