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Mitigating Adversarial Attack through Randomization Techniques and Image Smoothing 认领 引用
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作者 Hyeong-Gyeong Kim Sang-Min Choi +1 位作者 Hyeon Seo Suwon Lee 《Computers, Materials & Continua》 SCIE EI 2025年第9期4381-4397,共17页
Adversarial attacks pose a significant threat to artificial intelligence systems by exposing them to vulnerabilities in deep learning models.Existing defense mechanisms often suffer drawbacks,such as the need for mode... Adversarial attacks pose a significant threat to artificial intelligence systems by exposing them to vulnerabilities in deep learning models.Existing defense mechanisms often suffer drawbacks,such as the need for model retraining,significant inference time overhead,and limited effectiveness against specific attack types.Achieving perfect defense against adversarial attacks remains elusive,emphasizing the importance of mitigation strategies.In this study,we propose a defense mechanism that applies random cropping and Gaussian filtering to input images to mitigate the impact of adversarial attacks.First,the image was randomly cropped to vary its dimensions and then placed at the center of a fixed 299299 space,with the remaining areas filled with zero padding.Subsequently,Gaussian×filtering with a 77 kernel and a standard deviation of two was applied using a convolution operation.Finally,the×smoothed image was fed into the classification model.The proposed defense method consistently appeared in the upperright region across all attack scenarios,demonstrating its ability to preserve classification performance on clean images while significantly mitigating adversarial attacks.This visualization confirms that the proposed method is effective and reliable for defending against adversarial perturbations.Moreover,the proposed method incurs minimal computational overhead,making it suitable for real-time applications.Furthermore,owing to its model-agnostic nature,the proposed method can be easily incorporated into various neural network architectures,serving as a fundamental module for adversarial defense strategies. 展开更多
关键词 Adversarial attacks deep learning artificial intelligence systems random cropping Gaussian filtering image smoothing
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Efficient Backbone Network Construction in Wireless Artificial Intelligent Computing Systems 认领 引用
2
作者 Ming Sun Xinyu Wu +2 位作者 Yi Zhou Jin-Kao Hao Zhang-Hua Fu 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2025年第5期2300-2319,共20页
In wireless artificial intelligent computing systems,the construction of backbone network,which determines the optimum network for a set of given terminal nodes like users,switches,and concentrators,can be naturally f... In wireless artificial intelligent computing systems,the construction of backbone network,which determines the optimum network for a set of given terminal nodes like users,switches,and concentrators,can be naturally formed as the Steiner tree problem.The Steiner tree problem asks for a minimum edge-weighted tree spanning a given set of terminal vertices from a given graph.As a well-known graph problem,many algorithms have been developed for solving this computationally challenging problem in the past decades.However,existing algorithms typically encounter difficulties for solving large instances,i.e.,graphs with a high number of vertices and terminals.In this paper,we present a novel partition-and-merge algorithm for effectively handle large-scale graphs.The algorithm breaks the input network into small subgraphs and then merges the subgraphs in a bottom-up manner.In the merging procedure,partial Steiner trees in the subgraphs are also created and optimized by an efficient local optimization.When the merging procedure ends,the algorithm terminates and reports the final solution for the input graph.We evaluated the algorithm on a wide range of benchmark instances,showing that the algorithm outperforms the best-known algorithms on large instances and competes favorably with them on small or middle-sized instances. 展开更多
关键词 Wireless Artificial Intelligent Computing Systems(WAICS) backbone network Steiner Tree Problem(STP) partition-and-merge
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Revolutionizing Treatment:AI-Driven Noninvasive Approaches for ODD and ADHD 认领 引用 被引量：2
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作者 Shiva Dalili Bahman Zohuri 《Management Studies》 2023年第4期215-220,共6页
Oppositional Defiant Disorder(ODD)and Attention Deficit/Hyperactivity Disorder(ADHD)are mental health conditions that have traditionally been managed through behavioral therapies and medication.However,the integration... Oppositional Defiant Disorder(ODD)and Attention Deficit/Hyperactivity Disorder(ADHD)are mental health conditions that have traditionally been managed through behavioral therapies and medication.However,the integration of Artificial Intelligence(AI)has brought about a revolutionary shift in treatment approaches.This article explores the role of AI-driven noninvasive treatments for ODD and ADHD.AI offers personalized treatment plans,predictive analytics,virtual therapeutic platforms,and continuous monitoring,enhancing the effectiveness and accessibility of interventions.Ethical considerations and the need for a balanced approach are discussed.As technology evolves,collaborative efforts between mental health professionals and technologists will shape the future of mental health care for individuals with ODD and ADHD. 展开更多
关键词 ODD ADHD Artificial Intelligence noninvasive treatment personalized treatment predictive analytics virtual therapeutic platforms continuous monitoring mental health care technology ethical considerations Artificial Intelligence Systems
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Artificial visual-tactile perception array for enhanced memory and neuromorphic computations 认领 引用 被引量：19
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作者 Jiaqi He Ruilai Wei +6 位作者 Shuaipeng Ge Wenqiang Wu Jianchao Guo Juan Tao Ru Wang Chunfeng Wang Caofeng Pan 《InfoMat》 SCIE CSCD 2024年第3期81-94,共14页
The emulation of human multisensory functions to construct artificial perception systems is an intriguing challenge for developing humanoid robotics and cross-modal human–machine interfaces.Inspired by human multisen... The emulation of human multisensory functions to construct artificial perception systems is an intriguing challenge for developing humanoid robotics and cross-modal human–machine interfaces.Inspired by human multisensory signal generation and neuroplasticity-based signal processing,here,an artificial perceptual neuro array with visual-tactile sensing,processing,learning,and memory is demonstrated.The neuromorphic bimodal perception array compactly combines an artificial photoelectric synapse network and an integrated mechanoluminescent layer,endowing individual and synergistic plastic modulation of optical and mechanical information,including short-term memory,long-term memory,paired pulse facilitation,and“learning-experience”behavior.Sequential or superimposed visual and tactile stimuli inputs can efficiently simulate the associative learning process of“Pavlov's dog”.The fusion of visual and tactile modulation enables enhanced memory of the stimulation image during the learning process.A machine-learning algorithm is coupled with an artificial neural network for pattern recognition,achieving a recognition accuracy of 70%for bimodal training,which is higher than that obtained by unimodal training.In addition,the artificial perceptual neuron has a low energy consumption of~20 pJ.With its mechanical compliance and simple architecture,the neuromorphic bimodal perception array has promising applications in largescale cross-modal interactions and high-throughput intelligent perceptions. 展开更多
关键词 artificial intelligent systems mechanoluminescence neuromorphic computing optoelectronic synapse visual-tactile perception
Natural Language Processing and Understanding:Enabling Machines to Comprehend Human Language 认领 引用
5
《China Book International》 2025年第4期32-35,共4页
This book introduces the basic algorithms and mathematica^models of artificial intelligence systems.It applies simple and easy-to-understand language,showing the^underlying logic and the basic laws of artificial intel... This book introduces the basic algorithms and mathematica^models of artificial intelligence systems.It applies simple and easy-to-understand language,showing the^underlying logic and the basic laws of artificial intelligence. 展开更多
关键词 algorithms underlying logic natural language processing mathematical models artificial intelligence artificial intelligence systems
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Revisiting the ODE Method for Recursive Algorithms:Fast Convergence Using Quasi Stochastic Approximation 认领 引用
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作者 CHEN Shuhang DEVRAJ Adithya +1 位作者 BERSTEIN Andrey MEYN Sean 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2021年第5期1681-1702,共22页
Several decades ago,Profs.Sean Meyn and Lei Guo were postdoctoral fellows at ANU,where they shared interest in recursive algorithms.It seems fitting to celebrate Lei Guo’s 60 th birthday with a review of the ODE Meth... Several decades ago,Profs.Sean Meyn and Lei Guo were postdoctoral fellows at ANU,where they shared interest in recursive algorithms.It seems fitting to celebrate Lei Guo’s 60 th birthday with a review of the ODE Method and its recent evolution,with focus on the following themes:The method has been regarded as a technique for algorithm analysis.It is argued that this viewpoint is backwards:The original stochastic approximation method was surely motivated by an ODE,and tools for analysis came much later(based on establishing robustness of Euler approximations).The paper presents a brief survey of recent research in machine learning that shows the power of algorithm design in continuous time,following by careful approximation to obtain a practical recursive algorithm.While these methods are usually presented in a stochastic setting,this is not a prerequisite.In fact,recent theory shows that rates of convergence can be dramatically accelerated by applying techniques inspired by quasi Monte-Carlo.Subject to conditions,the optimal rate of convergence can be obtained by applying the averaging technique of Polyak and Ruppert.The conditions are not universal,but theory suggests alternatives to achieve acceleration.The theory is illustrated with applications to gradient-free optimization,and policy gradient algorithms for reinforcement learning. 展开更多
关键词 Learning and adaptive systems in artificial intelligence reinforcement learning stochastic approximation
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