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Fuzzing:Progress,Challenges,and Perspectives

computers, Materials & Continua 2024年第1期78卷 1-29页

作者： Zhenhua Yu Zhengqi Liu Xuya Cong Xiaobo Li Li Yin Institute of Systems Security and Control College of Computer Science and TechnologyXi'an University of Science and TechnologyXi'an710054China School of Mathematics and Information Science Baoji University of Arts and SciencesBaoji721013China Institute of Systems Engineering Macao University of Science and TechnologyTaipaMacaoChina

As one of the most effective techniques for finding software vulnerabilities,fuzzing has become a hot topic in software *** feeds potentially syntactically or semantically malformed test data to a target program to mine vulnerabilities and crash the *** recent years,considerable efforts have been dedicated by researchers and practitioners towards improving fuzzing,so there aremore and more methods and forms,whichmake it difficult to have a comprehensive understanding of the *** paper conducts a thorough survey of fuzzing,focusing on its general process,classification,common application scenarios,and some state-of-the-art techniques that have been introduced to improve its ***,this paper puts forward key research challenges and proposes possible future research directions that may provide new insights for researchers.

关键词： Fuzzing vulnerability software testing software security

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Prediction of BDS-3 satellite clock bias based on EWT-LSTM ensemble model

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GPS Solutions 2025年第3期29卷 1-15页

作者： Lv, Kaihui Cai, Chenglin Cai, Yihao Guan, Wenhui Li, Zexian Cheng, Lingfeng Fang, Yun School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China School of Automation and Electronic Information Xiangtan University Hunan Xiangtan411105 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physical Science and Technology ShanghaiTech University Shanghai201210 China School of Materials Science and Engineering Xiangtan University Hunan Xiangtan411105 China

The accuracy of Satellite Clock Bias (SCB) is a crucial factor affecting the performance of precise point positioning. However, the post-processed precise clock error products provided by the International GNSS Service (IGS) often fail to meet real-time requirements, and the broadcast ephemeris transmitted by satellites does not achieve the necessary precision. To address this issue, this paper proposes an ensemble model that combines Empirical Wavelet Transform (EWT) and Long Short-Term Memory (LSTM) neural networks—referred to as the EWT-LSTM ensemble model. This integration enables the model to effectively capture key features of SCB sequences, ultimately improving prediction accuracy and stability. We compared the EWT-LSTM ensemble model with the Backpropagation Neural Network model (BP), the LSTM model, and the combined model of the Improved Sparrow Search Algorithm and Gated Recurrent Unit Neural Network (ITSSA-GRU). The analysis included all available BeiDou-3 satellites, considering various orbital and atomic clock types. The experimental results show that the EWT-LSTM ensemble model significantly improves SCB prediction accuracy. For 12-h prediction tasks, the accuracy increased by 76.67%, 69.52%, and 61.51% compared to the BP, LSTM, and ITSSA-GRU models, respectively. For 24-h prediction tasks, the accuracy increased by 93.88%, 81.45%, and 74.25%, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Long short-term memory

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OAAFormer:Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transformer

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Journal of computer science & technology 2024年第4期39卷 755-770页

作者： Jun-Jie Gao Qiu-Jie Dong Rui-An Wang Shuang-Min Chen Shi-Qing Xin Chang-He Tu Wenping Wang School of Computer Science and Technology Shandong UniversityQingdao 266237China School of Information and Technology Qingdao University of Science and TechnologyQingdao 266061China College of Engineering Texas A&M UniversityTexasTX 77843U.S.A.

In the domain of point cloud registration,the coarse-to-fine feature matching paradigm has received significant attention due to its impressive *** paradigm involves a two-step process:first,the extraction of multilevel features,and subsequently,the propagation of correspondences from coarse to fine ***,this approach faces two notable ***,the use of the Dual Softmax operation may promote one-to-one correspondences between superpoints,inadvertently excluding valuable ***,it is crucial to closely examine the overlapping areas between point clouds,as only correspondences within these regions decisively determine the actual *** these issues,we propose OAAFormer to enhance correspondence *** the one hand,we introduce a soft matching mechanism to facilitate the propagation of potentially valuable correspondences from coarse to fine *** the other hand,we integrate an overlapping region detection module to minimize mismatches to the greatest extent ***,we introduce a region-wise attention module with linear complexity during the fine-level matching phase,designed to enhance the discriminative capabilities of the extracted *** on the challenging 3DLoMatch benchmark demonstrate that our approach leads to a substantial increase of about 7%in the inlier ratio,as well as an enhancement of 2%-4%in registration ***,to accelerate the prediction process,we replace the Conventional Random Sample Consensus(RANSAC)algorithm with the selection of a limited yet representative set of high-confidence correspondences,resulting in a 100 times speedup while still maintaining comparable registration performance.

关键词： point cloud registration coarse-to-fine overlapping region feature matching Transformer

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Translation and rotation of particles in different flow pattern areas of a silo

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Particuology 2023年第2期73卷 68-77页

作者： Ran Li Quan Chen Hongze Li Wenzheng Xiu Vladimir Zivkovic Hui Yang School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai200093China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China School of Engineering Newcastle UniversityNE17RUUnited Kingdom

The present paper reports the results obtained for translational and rotational velocity profiles of spherical particles for the mixed flow in a conical *** discrete element method(DEM)based on Hertz-Mindlin(no slip)with RVD rolling friction contact model is used for *** correlations are found between translational and rotational velocities in different flow areas of the *** particular,the abrasion caused by rotation is dominant in the funnel flow *** addition,increase of the mass flow rate of silo can effectively reduce the abrasion induced by *** highlights that understanding of dynamic characteristics of particles is helpful for optimization of silos and reduction of granular material abrasion.

关键词： Translational velocity Rotational velocity Rolling contribution rate Mass flow index

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Review on operation control of cold thermal energy storage in cooling systems

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Energy and Built Environment 2025年第3期6卷 509-523页

作者： Wang, Huan Xie, Baoshan Li, Chuanchang Key Laboratory of Renewable Energy Electric-Technology of Hunan Province School of Energy and Power Engineering Changsha University of Science and Technology Changsha410114 China

Cold storage technology is useful to alleviate the mismatch between the cold energy demand and supply. The integration of cold energy storage in cooling system is an effective approach to improve the system reliability and performance. This review provides an overview and recent advances of the cold thermal energy storage (CTES) in refrigeration cooling systems and discusses the operation control for system optimization. Firstly, the composition and principles of cooling systems coupled with CTES are presented. Special attention was paid to cold storage medium of phase change material (PCM) with high energy density and stable phase change temperature. Then, based on the classification of driven energy, the different applications of passive or active cooling systems with CTES are classified, including building cooling, cold chain logistics, and other refrigeration systems. Most importantly, the operation control which is necessary to performance optimization is presented, including operational control strategies, cold load predictions, and economic evaluation methods. Three types of operational control strategies are summarised using water storage and cooling system as an example. Two types of cold load predictions, parametric regression and artificial neural network method, are introduced. Three aspects of economic costs are summarized in terms of initial equipment investment cost, operational cost, and life-cycle cost are summarized. Finally, an outlook on the development of cooling systems using CTES is given. © 2024

关键词： Cooling systems

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Video Action Recognition Method Based on Personalized Federated Learning and Spatiotemporal Features

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computers, Materials & Continua 2025年第6期83卷 4961-4978页

作者： Rongsen Wu Jie Xu Yuhang Zhang Changming Zhao Yiweng Xie Zelei Wu Yunji Li Jinhong Guo Shiyang Tang School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer Science Chengdu University of Information TechnologyChengdu610225China Shanghai Key Lab of Intelligent Information Processing School of CSFudan UniversityShanghai200433China School of Sensing Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China School of Mechanical and Manufacturing Engineering University of New South WalesSydney2052Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

With the rapid development of artificial intelligence and Internet of Things technologies,video action recognition technology is widely applied in various scenarios,such as personal life and industrial ***,while enjoying the convenience brought by this technology,it is crucial to effectively protect the privacy of users’video ***,this paper proposes a video action recognition method based on personalized federated learning and spatiotemporal *** the framework of federated learning,a video action recognition method leveraging spatiotemporal features is *** the local spatiotemporal features of the video,a new differential information extraction scheme is proposed to extract differential features with a single RGB frame as the center,and a spatialtemporal module based on local information is designed to improve the effectiveness of local feature extraction;for the global temporal features,a method of extracting action rhythm features using differential technology is proposed,and a timemodule based on global information is *** translational strides are used in the module to obtain bidirectional differential features under different action ***,to address user data privacy issues,the method divides model parameters into local private parameters and public parameters based on the structure of the video action recognition *** approach enhancesmodel training performance and ensures the security of video *** experimental results show that under personalized federated learning conditions,an average accuracy of 97.792%was achieved on the UCF-101 dataset,which is non-independent and identically distributed(non-IID).This research provides technical support for privacy protection in video action recognition.

关键词： Video action recognition personalized federated learning spatiotemporal features data privacy

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On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1755-1773页

作者： Jinyu Chen Long Shi Qisheng Huang Taotao Wang Daojing He School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing210094China School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen518055China College of Electronics and Information Engineering Shenzhen UniversityShenzhen518060China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault ***,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal *** a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain *** address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional *** paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided ***,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart ***,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing *** particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation ***,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s ***,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

关键词： Blockchain reputation management PoA throughput security decentralization

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials & Continua 2025年第6期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoNSW 2007Australia School of Data Science and Engineering Indian Institute of Science Education and ResearchBhopal462066India Research Institute of Marine Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and TechnologySRM Institute of Science and TechnologyDelhi NCR CampusGhaziabad201204India Battery System Modelling Department CLARIOS VARTA Hannover GmbHHanover30419Germany Science and Innovation Group Bureau of MeteorologyBrisbane4000Australia

Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric *** study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 *** indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s *** computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction *** GRU achieves the best overall performance for SOC estimation,attaining an R^(2) of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational *** contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery *** of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep *** findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained *** work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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An adaptive compensation control method for the transmission error of the mechanical system based on the characteristic model

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International Journal of Manufacturing technology and Management 2023年第3-4期37卷 241-256页

作者： Ren, Wanwan Wu, You Du, Ronghua School of Automotive and Mechanical Engineering Changsha University of Science and Technology Hunan Changsha410004 China

Applying the characteristic model-based golden section adaptive (GSA) control theory, an adaptive error compensation control method for the transmission error of the mechanical system is proposed in this paper. A mechanical transmission system with a compensation mechanism was established, taking the ball screw as the main transmission component. The dynamics characteristic of the mechanical transmission system is analysed. The GSA control theory based on the characteristic model is introduced. For the established characteristic model of the transmission system, the projection gradient method is used to identify its characteristic parameters online. The stability analysis of the closed-loop system consisting of characteristic model and GSA controller is carried out. The experimental results show that the characteristic model-based GSA control has a good effect on the transmission error compensation, as well as simple on-site debugging. The effectiveness and superiority of the method are verified. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Error compensation

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Robust Alzheimer’s Patient Detection and Tracking for Room Entry Monitoring Using YOLOv8 and Cross Product Analysis

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computers, Materials & Continua 2025年第6期83卷 4215-4238页

作者： Praveen Kumar Sekharamantry Farid Melgani Roberto Delfiore Stefano Lusardi Department of Information Engineering and Computer Science University of TrentoTrento38123Italy Department of Computer Science and Engineering GITAM School of TechnologyGITAM(Deemed to be University)Visakhapatnam530045India TeiaCare S.r.l Milan20127Italy

Recent advances in computer vision and artificial intelligence(AI)have made real-time people counting systems extremely reliable,with experts in crowd control,occupancy supervision,and *** improve the accuracy of people counting at entry and exit points,the current study proposes a deep learning model that combines You Only Look Once(YOLOv8)for object detection,ByteTrack formulti-object tracking,and a unique method for vector-based movement *** system determines if a person has entered or exited by analyzing their movement concerning a predetermined boundary *** different logical strategies are used to record entry and exit *** leveraging object tracking,cross-product analysis,and current frame state updates,the system effectively tracks human flow in and out of a roomand maintains an accurate count of the *** present approach is supervised on Alzheimer’s patients or residents in the hospital or nursing home environment where the highest level of monitoring is essential.A comparison of the two strategy frameworks reveals that robust tracking combined with deep learning detection yields 97.2%and 98.5%accuracy in both controlled and dynamic settings,*** model’s effectiveness and applicability for real-time occupancy and human management tasks are demonstrated by performance measures in terms of accuracy,computing time,and robustness in various *** integrated technique has a wide range of applications in public safety systems and smart buildings,and it shows considerable gains in counting reliability.

关键词： computer vision YOLOv8 ByteTrack cross-product analysis frame-based counting

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