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IAENG International Journal of computer science 2024年第10期51卷 1546-1559页

作者： Wang, Zhibiao Tao, Ye Liu, Xu Yu, Jiayi Cui, Wenhua School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Current automatic segment extraction techniques for identifying target characters in videos have several limitations, including low accuracy, slow processing speeds, and poor adaptability to diverse scenes. This paper introduces an optimized algorithm to address these issues that enhance the RetinaFace and FaceNet models. We selected RetinaFace for face detection, employing MobileNetV1-0.25 as the backbone network and simplifying its Feature Pyramid Network (FPN) structure to boost detection speeds. Analysis of 460 images with a 720P resolution demonstrated an average speed improvement of 20.6%. For face recognition, we utilized FaceNet with MobileNetV3 as the backbone, augmenting its feature extraction capability by integrating four Receptive Field Block (RFB) structures and replacing the Squeeze-and-Excitation (SE) module with the Convolutional Block Attention Module (CBAM). Experimental results indicate that our enhancements elevate the maximum accuracy to 97%, outperforming the original model. Additionally, we integrated these refined algorithms and conducted disintegration experiments on segment extraction in 10 videos, evaluating various metrics. The findings show improvements in both precision and recall. We also compared our algorithm against the Dlib model;our system achieved an overall interception accuracy of 79.94%, surpassing Dlib’s 75.55%. This confirms the enhanced performance and feasibility of our proposed algorithm. © (2024), (International Association of Engineers). All rights reserved.

关键词： Face recognition

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Sawtooth-enhanced bend sensor for gesture recognition

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science China(Technological sciences) 2024年第6期67卷 1727-1736页

作者： BAI YanRu ZHANG ZiHang WANG HaoYu GUO Rui LI XiSheng School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China School of Advanced Engineering University of Science and Technology BeijingBeijing100083China School of Precision Instrument and Opto-Electronics Engineering Tianjin UniversityTianjin300072China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

Gesture recognition has diverse application prospects in the field of human-computer ***,gesture recognition devices based on strain sensors have achieved remarkable results,among which liquid metal materials have considerable advantages due to their high tensile strength and *** improve the detection sensitivity of liquid metal strain sensors,a sawtooth-enhanced bending sensor is proposed in this *** with the results from previous studies,the bending sensor shows enhanced resistance *** addition,combined with machine learning algorithms,a gesture recognition glove based on the sawtooth-enhanced bending sensor is also fabricated in this study,and various gestures are accurately *** the fields of human-computer interaction,wearable sensing,and medical health,the sawtooth-enhanced bending sensor shows great potential and can have wide application prospects.

关键词： liquid metal bending sensor gesture recognition machine learning

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Performance analysis of UAV-based mixed underwater PLC-RF systems

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Digital Communications and Networks 2024年第5期10卷 1332-1340页

作者： Shenghai Chen Liang Yang Xianfu Lei College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua UniversityHuaihua418000China School of Information Science and Technology Southwest Jiaotong UniversityChengdu610031China

In this study,we analyzed the performance of an Unmanned Aerial Vehicle(UAV)-based mixed Underwater Power Line Communication-Radio Frequency(UPLC-RF)*** this network,a buoy located at the sea is used as a relay to transmit signals from the underwater signal source to the UAV through the PLC *** assume that the UPLC channel obeys a log-normal distribution and that the RF link follows the Rician *** this model,we obtained the closed-form expressions for the Outage Probability(OP),Average Bit-error-rate(ABER),and Average Channel Capacity(ACC).In addition,the asymptotic analysis of the OP and ABER was performed,and an upper bound for the average capacity was ***,the analytical results were verified by Monte Carlo simulation thereby demonstrating the effect of impulse noise and the altitude of the UAV on network performance.

关键词： ABER Average bit-error-rate ACC Average channel capacity OP Outage probability UAV Unmanned aerial vehicle UPLC Underwater power line communication

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Temperature field prediction of steel-concrete composite decks using TVFEMD-stacking ensemble algorithm

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Journal of Zhejiang university-science A(Applied Physics & engineering) 2024年第9期25卷 732-748页

作者： Benkun TAN Da WANG Jialin SHI Lianqi ZHANG School of Civil Engineering Changsha University of Science&TechnologyChangsha 410114China School of Civil and Architecture Engineering Hunan University of Arts and ScienceChangde 415000China School of Civil Engineering Central South University of Forestry and TechnologyChangsha 410004China

This research aims to develop an advanced deep learning-based ensemble algorithm,utilizing environmental temperature and solar radiation as feature factors,to conduct hourly temperature field predictions for steel-concrete composite decks(SCCDs).The proposed model comprises feature parameter lag selection,two non-stationary time series decomposition methods(empirical mode decomposition(EMD)and time-varying filtering-based empirical mode decomposition(TVFEMD)),and a stacking ensemble prediction *** validate the proposed model,five machine learning(ML)models(random forest(RF),support vector regression(SVR),multilayer perceptron(MLP),gradient boosting regression(GBR),and extreme gradient boosting(XGBoost))were tested as base learners and evaluations were conducted within independent,mixed,and ensemble ***,predictions are made based on engineering *** results indicate that consideration of lag variables and modal decomposition can significantly improve the prediction performance of learners,and the stacking framework,which combines multiple learners,achieves superior prediction *** proposed method demonstrates a high degree of predictive robustness and can be applied to statistical analysis of the temperature field in *** time lag features helps account for the delayed heat dissipation phenomenon in concrete,while decomposition techniques assist in feature extraction.

关键词： Steel-concrete composite deck(SCCD) Temperature field Time-varying filtering-based empirical mode decomposition(TVFEMD) Feature selection Machine learning(ML)

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Convolutional Neural Network Image Classification Based on Different Color Spaces

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Tsinghua science and technology 2025年第1期30卷 402-417页

作者： Zixiang Xian Rubing Huang Dave Towey Chuan Yue School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The existing literature regarding the influence of color space use on the performance of CNNs is *** paper explores the impact of different color spaces on image classification using *** compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets,each converted to nine color *** find that color space selection can significantly affect classification accuracy,and that some classes are more sensitive to color space changes than *** color spaces may have different expression abilities for different image features,such as brightness,saturation,hue,*** leverage the complementary information from different color spaces,we propose a pseudo-Siamese network that fuses two color spaces without modifying the network *** experiments show that our proposed model can outperform the single-color-space models on most *** also find that our method is simple,flexible,and compatible with any CNN and image dataset.

关键词： color space Convolutional Neural Network(CNN) image classification pseudo-Siamese network

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Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem

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Tsinghua science and technology 2024年第5期29卷 1283-1299页

作者： Wei Li Xiangfang Yan Ying Huang School of Information Engineering Jiangxi University of Science and TechnologyGanzhou 341000China School of Mathematics and Computer Science Gannan Normal UniversityGanzhou 341000China

With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing *** Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research ***,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this *** algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for *** problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing ***,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its *** KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in *** effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.

关键词： Ant Colony Optimization(ACO) Job shop Scheduling Problem(JSP) knowledge learning cooperative guidance

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Finite-Time Sliding Mode Control of Vehicle Formation with Specified Performance

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engineering Letters 2025年第3期33卷 620-629页

作者： Guo, Dongyang Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

The current urban intelligent transportation is in a rapid development stage, and coherence control of vehicle formations has important implications in urban intelligent transportation research. This article focuses on the problem of urban intelligent transportation planning, and proposes a new control method for vehicle formation regulation tracking performance control with bounded disturbances and model uncertainty, combining the multiple advantages of sliding mode control and finite time control and utilizing the universal approximation of neural network. This method can ensure that the vehicle formation can achieve the specified tracking performance and be stable in finite time instead of asymptotically stable. First, the third-order dynamics model and spacing strategy of the vehicle are given. The tracking performance of the vehicle formation is specified. Then, the control objective of the system is transformed by using the error transformation to obtain a new system. An improved sliding mode surface is designed for the transformed system. Furthermore, the universal approximation property of the neural network is utilised to overcome the parameter uncertainty in the system. The reconstruction error of neural network is handled by a robust term. In addition, the jittery vibration phenomenon of sliding mode control has been overcome. Finally, the finite-time stability of the system is analysed by constructing the Lyapunov function, simulations were performed to validate and compare with conventional control methods, simulation results show that the novel control method proposed in this paper is significantly faster than the conventional control method, validated the effectiveness of the method proposed in this paper. © 2025, International Association of Engineers. All rights reserved.

关键词： Lyapunov functions

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How graph convolutions amplify popularity bias for recommendation?

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Frontiers of computer science 2024年第5期18卷 121-132页

作者： Jiajia CHEN Jiancan WU Jiawei CHEN Xin XIN Yong LI Xiangnan HE School of Information Science and Technology University of Science and Technology of ChinaHefei 230026China School of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computer Science and Technology Shandong UniversityQingdao 250100China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail items are less likely to be *** effect prevents the GCN-based RS from making precise and fair recommendations,decreasing the effectiveness of recommender systems in the long *** this paper,we investigate how graph convolutions amplify the popularity bias in *** theoretical analyses,we identify two fundamental factors:(1)with graph convolution(i.e.,neighborhood aggregation),popular items exert larger influence than tail items on neighbor users,making the users move towards popular items in the representation space;(2)after multiple times of graph convolution,popular items would affect more high-order neighbors and become more *** two points make popular items get closer to almost users and thus being recommended more *** rectify this,we propose to estimate the amplified effect of popular nodes on each node's representation,and intervene the effect after each graph ***,we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node,then remove the effect from the node embeddings at each graph convolution *** method is simple and generic-it can be used in the inference stage to correct existing models rather than training a new model from scratch,and can be applied to various GCN *** demonstrate our method on two representative GCN backbones LightGCN and UltraGCN,verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular *** are open-sourced^(1)).

关键词： recommendation graph convolution networks popularity bias

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A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

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Tsinghua science and technology 2024年第2期29卷 617-633页

作者： Weiqi Zhang Zhenzhen Xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer Science Georgia State UniversityAtlantaGA 30303USA College of Information Science and Technology Jinan UniversityGuangzhou 510632China

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

关键词： local-differential privacy stay points detection trajectory data areas of interest temporal-spatial clustering

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