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An efficient wear-leveling-aware multi-grained allocator for persistent memory file systems

Frontiers of Information technology & Electronic Engineering 2023年第5期24卷 688-702页

作者： Zhiwang YU Runyu ZHANG Chaoshu YANG Shun NIE Duo LIU State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550000China College of Computer Science Chongqing UniversityChongqing 404100China

Persistent memory(PM)file systems have been developed to achieve high performance by exploiting the advanced features of PMs,including nonvolatility,byte addressability,and dynamic random access memory(DRAM)like ***,these PMs suffer from limited write *** space management strategies of PM file systems can induce a severely unbalanced wear problem,which can damage the underlying PMs *** this paper,we propose a Wear-leveling-aware Multi-grained Allocator,called WMAlloc,to achieve the wear leveling of PMs while improving the performance of file *** adopts multiple min-heaps to manage the unused space of *** heap represents an allocation ***,WMAlloc allocates less-worn blocks from the corresponding min-heap for allocation ***,to avoid recursive split and inefficient heap locations in WMAlloc,we further propose a bitmap-based multi-heap tree(BMT)to enhance WMAlloc,namely,*** implement WMAlloc and WMAlloc-BMT in the Linux kernel based on NOVA,a typical PM file *** results show that,compared with the original NOVA and dynamic wear-aware range management(DWARM),which is the state-of-the-art wear-leveling-aware allocator of PM file systems,WMAlloc can,respectively,achieve 4.11×and 1.81×maximum write number reduction and 1.02×and 1.64×performance with four workloads on ***,WMAlloc-BMT outperforms WMAlloc with 1.08×performance and achieves 1.17×maximum write number reduction with four workloads on average.

关键词： File system Persistent memory Wear-leveling Multi-grained allocator

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data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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Bio-inspired visual neural network for stop-and-go wave perception in high-density crowd 4

Bio-inspired visual neural network for stop-and-go wave perc...

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4th International Conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Jiang, Wei Hu, Bin State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Department of Computer Science College of Computer Science & Technology Guizhou University Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In high-density crowd, a unique visual motion effect called stop-and-go wave occurs, which could evolve to trampling and compression incidents. However, few computational models have been reported for stop-and-go wave perception form the angle of computer vision. On the bases of the neural structures of the locusts’ vision systems and the frequency resonance properties of the interneuronal (INs) neurons in locust’s brains, this paper investigates a bio-inspired visual neural network (sgWPNN) for stop-and-go wave perception in high-density crowd scenes. The proposed sgWPNN firstly visually stabilizes the image frames;and then converts the spatial intensity visual information to the frequency domain for extracting crowd’s motion cues;finally, sgWPNN output membrane potential impulses to identify the occurring of stop-and-go wave in the field of view. Videos filmed high-density crowd activities in two scenarios demonstrate the effectiveness of sgWPNN in the perception against to the unique stop-and-go wave effect in moving crowd scenes. This work discusses the biological inspired processing of dynamic visual information in crowd activity perception, which can provide some new ideas for crowd activity detection and behavior analysis. © 2025 SPIE.

关键词： Frequency domain analysis

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A DL Intrusion Detection Method for Low-Rate Denial of Service Attacks

A DL Intrusion Detection Method for Low-Rate Denial of Servi...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Wu, Yuhong Lv, Yunshan Hu, Xiangdong School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Key Laboratory of Public Big Data Security Technology Chongqing Chongqing China School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665457033

Denial of service (DoS) attacks is among the most harmful network threats. Low-rate DoS (LDoS) attacks feature good concealment and are likely to evade detection by conventional signal analysis-based methods. A LDoS attack detection method that combines the convolutional neural network and the gated recurrent unit is proposed in the present work. To evaluate the proposed method, we collect real user traffic from a university's website, label the traffic with LDoS attacks as normal traffic. In order to get the real attack traffic, this paper has collected some new LDoS attack programs, such as slowloris, slowhttptest, pwnlloris, torhammer and httplog, which can be obtained from public channels, and constructed a LAN environment that conformed to the working scenario of the program. By running the attack program, the attack traffic could be obtained. In a large sample scenario, the average detection rate of our proposed method can reach 98.68%. This article focuses on the research: the average detection rate of the detection method proposed in this article in the case that the security agency can only intercept a small number of attack samples. That is, in the small sample scenario, the detection of the collected traffic shows that the average detection rate of the proposed detection method can reach 89.54% in a small sample scenario. Its effective detection rate is obviously superior to the existing similar detection methods. © 2023 IEEE.

关键词： Denial-of-service attack

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Evaluation of the quality of grassroots statistics based on a combined empowerment cloud model 4

Evaluation of the quality of grassroots statistics based on ...

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4rth International Conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2024

作者： Wang, Kexin Huang, Xing Lu, Qinghua Mao, Jiayuan Key Laboratory of Public Big Data Security Technology Chongqing College of Mobile Communication Chongqing401420 China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420 China Chongqing College of Mobile Communication Chongqing401420 China College of Science Chongqing University of Technology Chongqing400054 China

ISBN: (纸本)9781510681637

High-quality grass-roots statistics are an important guarantee to objectively reflect the development of grass-roots social and economic operations and provide strong data support for government decision-making. A combined empowerment cloud model is constructed to evaluate the quality of grass-roots statistics. Firstly, the index system for evaluating the quality of grassroots statistics is proposed based on GB/T 36344-2018 national standard framework;secondly, to calculate the weights of subjective and objective indicators, AHP-CRITIC is used in combination with game theory to estimate combination weights;then, the cloud model is constructed to evaluate the quality of grassroots statistics;finally, the proposed method is proved to be reasonable and effective through example analysis, and the cloud The model takes into account the randomness and fuzziness of some indicators and can provide a scientific and reliable decision basis for the evaluation of the quality of grass-roots statistics. © 2024 SPIE.

关键词： Statistics

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A multi-target UAV fault diagnosis method based on feature discriminant dimension reduction under small samples 4

A multi-target UAV fault diagnosis method based on feature d...

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4th International Symposium on computer technology and Information science, ISCTIS 2024

作者： An, Xue Zhang, Yizong Li, Shaobo Xiong, Xuan Guizhou University School of Mechanical Engineering Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350354560

In recent years, deep learning has provided many efficient and accurate solutions to UAV fault diagnosis, but most of the current deep learning can only deal with a single task, whereas the UAV system is a complex coupled system, which needs to perform several different fault position diagnostics (FPD) and fault degree diagnostics (FDD) at the same time. Therefore, this paper proposed a multi-target UAV fault diagnosis method based on feature discriminant dimensionality reduction (MFD-FDDR) under small samples. Firstly, We connect specific features of different tasks with the same spatial definition through the channel dimension. Then, we perform feature discriminant dimensionality reduction (FDDR) for each task feature, and the output characteristics satisfy the input size of the following layer of LSTM. Finally, we validate the effectiveness of our proposed method with extensive experiments on real UAV flight data. © 2024 IEEE.

关键词： discriminant dimensionality reduction fault diagnosis multi-task learning small samples

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A mobile energy trading scheme based on Lightning Network

A mobile energy trading scheme based on Lightning Network

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作者： Tan, Chaoyue Chen, Yuling Ren, Xiaojun Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology GuiZhou University Guiyang China Block Chain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Shouguang China

Current blockchain-based energy trading models raise serious concerns regarding the high and capped transaction latency and expensive service charges. To solve these problems, In this article, we combine Lightning Network (LN) and smart contract to present a mobile energy trading scheme based on LN. The focal point of the scheme lies in transfer of value occurs off-blockchain, which addresses the problem of transaction latency. Next, to solve the security of proposed scheme, we design a mechanism to delivery secret R. Participants exchange some parameters to calculate R instead of delivery it directly. The found security is guaranteed by committing funds into a multi-signature address in proposed scheme. Then, we conduct a comprehensive experiment to evaluate the proposed scheme. The simulation and analysis results show that the proposed scheme is efficient than traditional trading. In addition, we analyze our entire scheme and conclude that it defenses anti-tampering attacks and replay attack capacity effectively. © 2021 John Wiley & Sons, Ltd.

关键词： Blockchain

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Research on a technology of Mail Anti-virus Gateway for Industrial Internet

Research on a Technology of Mail Anti-virus Gateway for Indu...

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2023 IEEE International Conference on Image Processing and computer Applications, ICIPCA 2023

作者： Wu, Yuhong Lv, Yunshan Zhang, Ling School of Big Data & Computer Science Engineering Chongqing College of Mobile Communication Key Laboratory of Public Big Data Security Technology Chongqing China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350314670

With the rapid rise of the Industrial Internet, emails that convey a large amount of information require a strong network security environment. Aiming at the increasingly complex mail virus prevention and control work under the background of the industrial Internet, this paper designs and implements a mail anti-virus gateway system. First, design the overall process and structure of the mail virus processing system;then, design an extended mail receiving and sending protocol on the basis of the public framework, thereby ensuring the convenience of the system for use in different roles and environments. In the detection and filtering part of mail virus, an anti-virus engine containing multiple sub-function modules is designed to detect and filter viruses in mail text and attachments. Through the function test of 1000 kinds of virus mails, it is found that the mail anti-virus gateway system designed in this article can detect and filter most types of mail viruses. Compared with the virus detection on the client and server, the anti-virus gateway system has better performance And efficiency. © 2023 IEEE.

关键词： Network security

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Future Healthcare Recommender Systems: Applications, Open Issues, and Challenges

Future Healthcare Recommender Systems: Applications, Open Is...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Ju, Hongzheng Jin, Kebing Tang, Jianhang Zhang, Yang Wang, Bo Xiong, Zehui State Key Laboratory of Public Big Data Guizhou University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (纸本)9798350350548

With the enhancement of health awareness and the development of artificial intelligent technology, healthcare recommender systems (HRS) play an increasingly important role in individual health management. Meanwhile, the widespread usage of large models has significantly improved the efficiency and accuracy in the analysis and utilization of medical data. In this paper, we comprehensively summarize the basic types of recommender systems as well as the new trends in utilizing large models. Then we introduce the recommendation applications in healthcare areas from six aspects, i.e., disease risk prediction, medication recommendation, medical resource recommendation, mental health support, health life management, and health education. At last, we explore some current issues and challenges within HRS, as well as the development of potential solutions and directions in the future. © 2024 IEEE.

关键词： Electronic health record

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Steel surface Defect Detection Algorithm Based on Improved YOLOv5s 6

Steel surface Defect Detection Algorithm Based on Improved Y...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Su, Yingying Deng, Yuanyuan Zhou, Nengyang Si, Hongyun Peng, Jie Chongqing University of Science and Technology College of Electrical Engineering Chongqing China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing China

ISBN: (纸本)9798350316520

To address challenges in steel surface defect detection, such as low accuracy and slow processing speed, an enhanced algorithm is proposed. The C3 module is replaced with GSConv (multi-channel shuffle convolution) to improve parameter efficiency and detection accuracy. The nearest neighbor up-sampling is substituted with CARAFE (a lightweight operator) to enhance the receptive field and utilize semantic information. GhostNetV2 replaces the CBS convolutional module for efficient feature extraction through stacking operations. Additionally, the Hard Swish activation function is incorporated to boost accuracy. Experimental results on the NEU-DET dataset demonstrate that the average accuracy of the improved YOLOv5s algorithm is 81.2%, a 1.7% increase compared to the original algorithm, the amount of computation is 17.2GFLOPs and the number of model parameters is 8.65M. The model effectively enhances detection accuracy. © 2024 IEEE.

关键词： Surface defects

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