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An Incentive Mechanism for Federated Learning:A Continuous Zero-Determinant Strategy Approach

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 88-102页

作者： Changbing Tang Baosen Yang Xiaodong Xie Guanrong Chen Mohammed A.A.Al-qaness Yang Liu IEEE the College of Physics and Electronic information engineering Zhejiang Normal UniversityUniversityJinhua 321004China the School of Mathematical sciences Zhejiang Normal UniversityJinhua 321004China the Department of Electrical engineering City University of Hong KongHong KongChina Key Laboratory of intelligent Education Technology and Application of Zhejiang Province Jinhua 321004China

As a representative emerging machine learning technique, federated learning(FL) has gained considerable popularity for its special feature of “making data available but not visible”. However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable *** shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant(CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to ***, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.

关键词： Federated learning(FL) game theory incentive mechanism machine learning zero-determinant strategy

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Underwater RGB-D imaging system with millimetric precision

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Science China(information sciences) 2024年第10期67卷 383-385页

作者： Yajun GAO Yang CONG Xu TANG Mingxue LI State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of sciences College of Automation Science and engineering South China University of Technology

In recent years, underwater fine three-dimensional reconstruction technology has emerged as a focal point in research, exhibiting extensive potential applications, particularly in underwater archaeology and resource e... 详细信息

关键词： SINGLE CAMERA

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Brain-inspired artificial intelligence research: A review

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Science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and information engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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Computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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On the Probability of Generating a Primitive Matrix

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Journal of Systems Science & Complexity 2024年第4期37卷 1755-1771页

作者： CHEN Jingwei FENG Yong LIU Yang WU Wenyuan Chongqing Key Laboratory of Automated Reasoning and Cognition Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Chongqing College University of Chinese Academy of SciencesChongqing 400714China Key Laboratory of Advanced Manufacturing Technology of Ministry of Education Guizhou UniversityyGuiyang 550025China information Science and engineering Chongqing Jiaotong UniversityChongqing 400074China

Given a k×n integer primitive matrix A(i.e.,a matrix can be extended to an n×n unimodular matrix over the integers)with the maximal absolute value of entries‖A‖bounded by an integer λ from above,the authors study the probability that the m×n matrix extended from A by appending other m-k row vectors of dimension n with entries chosen randomly and independently from the uniform distribution over{0,1,…,λ-1}is still *** authors present a complete and rigorous proof of a lower bound on the probability,which is at least a constant for fixed m in the range[k+1,n-4].As an application,the authors prove that there exists a fast Las Vegas algorithm that completes a k×n primitive matrix A to an n×n unimodular matrix within expected O(n^(ω)log‖A‖)bit operations,where O is big-O but without log factors,ω is the exponent on the arithmetic operations of matrix multiplication.

关键词： Integer matrix matrix completion probabilistic algorithm unimodular matrix

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Resilient sliding mode control for 2D CPSs under denial-of-service attacks

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Science China(information sciences) 2024年第6期67卷 262-278页

作者： Lingling LI Rongni YANG Zhiguang FENG Ligang WU College of intelligent Systems Science and engineering Harbin Engineering University School of Control Science and engineering Shandong University School of Astronautics Harbin Institute of Technology

This study addresses the resilient sliding mode control(SMC) problem for two-dimensional cyber-physical systems(2D CPSs) characterized by the Roesser model under denial-of-service attack(DoSA), which can interfere with signal transmission over the communication network. First, the DoS-A model is established by introducing constraints on the Do S frequency and duration. Then, based on active or silent attack situations, the considered system is described as a switched mode. Furthermore, together with Lyapunov theory, the average dwell time technique is employed to deduce sufficient criteria that assure the existence of the desired sliding mode controller. Finally, verification examples are provided to show the validity of the established SMC scheme.

关键词： two-dimensional systems cyber-physical systems denial-of-service attack resilient sliding mode control

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Sparse convolutional model with semantic expression for waste electrical appliances recognition

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Science China(Technological sciences) 2024年第9期67卷 2881-2893页

作者： HAN HongGui LIU YiMing LI FangYu DU YongPing Faculty of information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and intelligent System Beijing 100124China engineering Research Center of Digital Community Ministry of EducationBeijing 100124China Beijing Artificial Intelligence Institute and Beijing Laboratory for intelligent Environmental Protection Beijing 100124China

Deep neural networks play an important role in the recognition of waste electrical appliances. However, deep neural network components still lack reliability in decision-making features. To address this problem, a sparse convolutional model with semantic expression(SCMSE) is proposed. First, a low-rank sparse semantic expression component, combining the benefits of residual networks and sparse representation, is adapted to enhance sparse feature extraction and semantic expression. Second, a reliable network architecture is obtained by iterating the optimal sparse solution, enhancing semantic expression. Finally, the results of visualization experiments on the waste electrical appliances dataset demonstrate that the proposed SCMSE can obtain excellent semantic performance.

关键词： sparse convolutional model deep neural network semantic expression visualization computer vision

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Modified NiFe-MOF with defects induced by-NH_(2)and-SH for enhanced adsorption and photocatalytic reduction of CO_(2)

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Journal of Materials Science & Technology 2024年第3期170卷 167-176页

作者： Ruting Huang Yeyin Zhang Wenqiang Li Wanxia Zhang Yong Fang Wenrui Zhang Anqi Cui Yu Ying Xianyang Shi information Materials and intelligent Sensing Laboratory of Anhui Province Anhui Province Key Laboratory of Wetland Ecosystem Protection and RestorationSchool of Resource and Environmental EngineeringAnhui UniversityHefei230601China Key Laboratory of Nutrient Cycling Resources and Environment of Anhui Institute of Soil and FertilizerAnhui Academy of Agricultural SciencesHefei230001China

In this study,a Ni-Fe metal-organic framework modified by-NH_(2)and-SH was synthesized using a sim-ple hydrothermal process for enhancing photocatalytic CO_(2)*** expected,the N2S-NiFe(NH_(2)-BDC/H_(4)DSBDC,2:1)displayed excellent photocatalytic CO_(2)reduction activity and high CO selectivity(12,412.23μmol g^(−1)h^(−1))under visible light irradiation(λ≥420 nm),which is 5 times that of ***,the excellent photocatalytic performance of N2S-NiFe can benefit from the rich defect trap site caused by the introduction of amino and sulfhydryl groups,accelerating charge transfer and promoting space charge separation,as supported by the photo-electrochemical *** better understand the CO_(2)adsorption mechanism,density functional theory calculations were performed,which revealed that the Niov site has more negative adsorption energy compared with the Feov *** study provides a simple strategy to establish efficient photocatalysts for CO_(2)reduction through the modification of organic ligands.

关键词： NiFe-MOF CO_(2)reduction Defect structure Photocatalytic

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Quantifying trapping stability of optical tweezers with an external flow

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Photonics Research 2024年第8期12卷 1741-1749页

作者： FENG XU YARONG YU YANG LIU YAO CHANG WENXIANG JIAO LIN WANG HOPUI HO BEI WU FEI XU YANQING LU YUANJIE PANG GUANGHUI WANG College of engineering and Applied sciences Nanjing UniversityNanjing 210093China Key Laboratory of intelligent Optical Sensing and Integration of the Ministry of Education Nanjing UniversityNanjing 210093China School of Optical and Electronic information Wuhan National Laboratory of OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China Department of Biomedical engineering The Chinese University of Hong KongHong Kong 999077China

Optical tweezers(OTs)can immobilize and manipulate objects with sizes that span between nano-and micrometer *** manipulating ability of OTs is traditionally characterized by stability factor(S),which can only indicate an empirical“hit-or-miss”***,the current quantitative models for trapping stability rarely consider the influence of external *** this paper,a comprehensive analysis to quantify the optical trapping stability in a perturbed asymmetric potential well is presented from the perspective of statistics,especially for weak trapping *** analytical formulation takes experimentally measurable parameters including particle size,optical power,and spot width as inputs and precisely outputs a statistically relevant mean trapping ***,this formulation takes into account general and realistic cases including fluidic flow velocity and other *** verify the model,a back-focal-plane-interferometer-monitored trapping experiment in a flow is set up and the statistical characteristics of trapping time demonstrate good agreement with theoretical *** total,the model quantitatively reveals the effects of external disturbance on trapping time,which will find applications where optical trapping stability is challenged by external perturbations in weak trapping conditions.

关键词： process flow trapping

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and information Technology The Superior UniversityLahore54600Pakistan intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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