IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第6页

IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information 电气和电子工程师学会《计算智能新课题论文集》出版信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-02 DOI: 10.1109/TETCI.2024.3465291

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Guest Editorial Special Issue on Resource Sustainable Computational and Artificial Intelligence 资源可持续计算与人工智能特刊客座编辑

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-02 DOI: 10.1109/TETCI.2024.3463048

Joey Tianyi Zhou;Ivor W. Tsang;Yew Soon Ong

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IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors 电气和电子工程师学会《计算智能新课题论文集》（IEEE Transactions on Emerging Topics in Computational Intelligence）给作者的信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-02 DOI: 10.1109/TETCI.2024.3465295

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IEEE Computational Intelligence Society Information 电气和电子工程师学会计算智能学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-02 DOI: 10.1109/TETCI.2024.3465293

引用次数: 0

Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node Implantation

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-01 DOI: 10.1109/TETCI.2024.3462817

Fan Li;Hang Zhou;Huafeng Li;Yafei Zhang;Zhengtao Yu

{"title":"Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node Implantation","authors":"Fan Li;Hang Zhou;Huafeng Li;Yafei Zhang;Zhengtao Yu","doi":"10.1109/TETCI.2024.3462817","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3462817","url":null,"abstract":"Person text-image matching, also known as text-based person search, aims to retrieve images of specific pedestrians using text descriptions. Although person text-image matching has made great research progress, existing methods still face two challenges. First, the lack of interpretability of text features makes it challenging to effectively align them with their corresponding image features. Second, the same pedestrian image often corresponds to multiple different text descriptions, and a single text description can correspond to multiple different images of the same identity. The diversity of text descriptions and images makes it difficult for a network to extract robust features that match the two modalities. To address these problems, we propose a person text-image matching method by embedding text-feature interpretability and an external attack node. Specifically, we improve the interpretability of text features by providing them with consistent semantic information with image features to achieve the alignment of text and describe image region features. To address the challenges posed by the diversity of text and the corresponding person images, we treat the variation caused by diversity to features as caused by perturbation information and propose a novel adversarial attack and defense method to solve it. In the model design, graph convolution is used as the basic framework for feature representation and the adversarial attacks caused by text and image diversity on feature extraction is simulated by implanting an additional attack node in the graph convolution layer to improve the robustness of the model against text and image diversity. Extensive experiments demonstrate the effectiveness and superiority of text-pedestrian image matching over existing methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1202-1215"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tensorlized Multi-Kernel Clustering via Consensus Tensor Decomposition

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-19 DOI: 10.1109/TETCI.2024.3425329

Fei Qi;Junyu Li;Yue Zhang;Weitian Huang;Bin Hu;Hongmin Cai

{"title":"Tensorlized Multi-Kernel Clustering via Consensus Tensor Decomposition","authors":"Fei Qi;Junyu Li;Yue Zhang;Weitian Huang;Bin Hu;Hongmin Cai","doi":"10.1109/TETCI.2024.3425329","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3425329","url":null,"abstract":"Multi-kernel clustering aims to learn a fused kernel from a set of base kernels. However, conventional multi-kernel clustering methods typically suffer from inherent limitations in exploiting the interrelations and complementarity between the kernels. The noises and redundant information from original base kernels also lead to contamination of the fused kernel. To address these issues, this paper presents a Tensorlized Multi-Kernel Clustering (TensorMKC) method. The proposed TensorMKC stacks kernel matrices into a kernel tensor along the kernel space. To attain consensus extraction while mitigating the impact of noise, we incorporate the tensor low-rank constraint into the process of learning base kernels. Subsequently, a tensor-based weighted fusion strategy is employed to integrate the refined base kernels, yielding an optimized fused kernel for clustering. The process of kernel learning is formulated as a joint minimization problem to seek the promising fusion solution. Through extensive comparative experiments with fifteen popular methods on ten benchmark datasets from various fields, the results demonstrate that TensorMKC exhibits superior performance.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"406-418"},"PeriodicalIF":5.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Neuromorphic Auditory Perception by Neural Spiketrum

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-18 DOI: 10.1109/TETCI.2024.3419711

Huajin Tang;Pengjie Gu;Jayawan Wijekoon;MHD Anas Alsakkal;Ziming Wang;Jiangrong Shen;Rui Yan;Gang Pan

{"title":"Neuromorphic Auditory Perception by Neural Spiketrum","authors":"Huajin Tang;Pengjie Gu;Jayawan Wijekoon;MHD Anas Alsakkal;Ziming Wang;Jiangrong Shen;Rui Yan;Gang Pan","doi":"10.1109/TETCI.2024.3419711","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3419711","url":null,"abstract":"Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems. To realize the promised brain-like intelligence, it needs to solve the challenges of the neuromorphic hardware architecture design of biological neural substrate and the hardware amicable algorithms with spike-based encoding and learning. Here we introduce a neural spike coding model termed spiketrum, to characterize and transform the time-varying analog signals, typically auditory signals, into computationally efficient spatiotemporal spike patterns. It minimizes the information loss occurring at the analog-to-spike transformation and possesses informational robustness to neural fluctuations and spike losses. The model provides a sparse and efficient coding scheme with precisely controllable spike rate that facilitates training of spiking neural networks in various auditory perception tasks. We further investigate the algorithm-hardware co-designs through a neuromorphic cochlear prototype which demonstrates that our approach can provide a systematic solution for spike-based artificial intelligence by fully exploiting its advantages with spike-based computation.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"292-303"},"PeriodicalIF":5.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedLaw: Value-Aware Federated Learning With Individual Fairness and Coalition Stability

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-18 DOI: 10.1109/TETCI.2024.3446458

Jianfeng Lu;Hangjian Zhang;Pan Zhou;Xiong Wang;Chen Wang;Dapeng Oliver Wu

{"title":"FedLaw: Value-Aware Federated Learning With Individual Fairness and Coalition Stability","authors":"Jianfeng Lu;Hangjian Zhang;Pan Zhou;Xiong Wang;Chen Wang;Dapeng Oliver Wu","doi":"10.1109/TETCI.2024.3446458","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3446458","url":null,"abstract":"A long-standing problem remains with the heterogeneous clients in Federated Learning (FL), who often have diverse gains and requirements for the trained model, while their contributions are hard to evaluate due to the privacy-preserving training. Existing works mainly rely on single-dimension metric to calculate clients' contributions as aggregation weights, which however may damage the social fairness, thus discouraging the cooperation willingness of worse-off clients and causing the revenue instability. To tackle this issue, we propose a novel incentive mechanism named <italic>FedLaw to effectively evaluate clients' contributions and further assign aggregation weights. Specifically, we reuse the local model updates and model the contribution evaluation process as a convex coalition game among multiple players with a non-empty core. By deriving a closed-form expression of the Shapley value, we solve the game core in quadratic time. Moreover, we theoretically prove that <italic>FedLaw guarantees <italic>individual fairness, <italic>coalition stability, <italic>computational efficiency, <italic>collective rationality, <italic>redundancy, <italic>symmetry, <italic>additivity, <italic>strict desirability, and <italic>individual monotonicity, and also show that <italic>FedLaw can achieve a constant convergence bound. Extensive experiments on four real-world datasets validate the superiority of <italic>FedLaw in terms of model aggregation, fairness, and time overhead compared to the state-of-the-art five baselines. Experimental results show that <italic>FedLaw is able to reduce the computation time of contribution evaluation by about 12 times and improve the global model performance by about 2% while ensuring fairness.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"1049-1062"},"PeriodicalIF":5.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Utilizing a DenseSwin Transformer Model for the Classification of Maize Plant Pathology in Early and Late Growth Stages: A Case Study of Its Utilization Among Zambian Farmers

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-17 DOI: 10.1109/TETCI.2024.3444603

Chiuzu Chilumbu;Qi-Xian Huang;Hung-Min Sun

{"title":"Utilizing a DenseSwin Transformer Model for the Classification of Maize Plant Pathology in Early and Late Growth Stages: A Case Study of Its Utilization Among Zambian Farmers","authors":"Chiuzu Chilumbu;Qi-Xian Huang;Hung-Min Sun","doi":"10.1109/TETCI.2024.3444603","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3444603","url":null,"abstract":"Maize, which is the primarycrop in many sub-Saharan countries, including Zambia, is susceptible to a wide range of diseases that have a significant impact on food production. To tackle this challenge and improve disease detection efficiency, deep learning methods have been employed to accurately classify and identify plant diseases. In recent times, manual inspection of maize fields for disease detection has been the standard practice in many parts of Zambia. However, this approach is not only time-consuming but also impractical for large-scale agricultural operations. Hence, the development of precise and automated classification models has become crucial in modern agriculture. In this study, we propose a novel deep-learning model called DenseSwin, specifically designed for maize disease classification in both the early visible stage and late indisputable stage of the disease. DenseSwin combines the strengths of densely connected convolution blocks with a shifted windows-based multi-head self-attention mechanism. This unique fusion of techniques enables the model to effectively capture intricate patterns and features in maize plant images, thereby enhancing disease classification performance. Through extensive experimentation and evaluation, DenseSwin achieves an impressive accuracy of 97.18%. These results highlight the model's remarkable ability to accurately detect and classify maize diseases, offering promising potential for real-world applications in agricultural settings, particularly in Zambia.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1860-1872"},"PeriodicalIF":5.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Ship Dynamic Weapon-Target Assignment via Cooperative Distributional Reinforcement Learning With Dynamic Reward

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-17 DOI: 10.1109/TETCI.2024.3451338

Zhe Peng;Zhifeng Lu;Xiao Mao;Feng Ye;Kuihua Huang;Guohua Wu;Ling Wang

{"title":"Multi-Ship Dynamic Weapon-Target Assignment via Cooperative Distributional Reinforcement Learning With Dynamic Reward","authors":"Zhe Peng;Zhifeng Lu;Xiao Mao;Feng Ye;Kuihua Huang;Guohua Wu;Ling Wang","doi":"10.1109/TETCI.2024.3451338","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3451338","url":null,"abstract":"In fleet air defense, the efficient coordination of multiple ships to complete weapon-target assignment has always been a critical challenge, primarily due to the varying combat capabilities and duties associated with each ship. Consequently, the traditional “weapon-target” assignment mode has turned into a “ship-weapon-target” assignment mode in the multi-ship dynamic weapon-target assignment (MS-DWTA) problem we proposed, with a larger solution space. In this problem, different ships possess distinct attributes, such as defense duties, weapon types, and loaded missile quantities. To solve this problem, we proposed an Attention enhanced multi-agent Distributional reinforcement learning method with Dynamic Reward (ADDR). Different from standard reinforcement learning method, ADDR learns to estimate the distribution, as opposed to only the expectation of future return, enabling better adaptation to air defense scenarios with significant randomness. The multi-head attention network integrates both the ship situation and the target situation to appropriately adjust the output of each agent, which explicitly considers the agent-level impact of ships to the whole fleet. Moreover, due to the missile fight time, ships may not immediately receive rewards after executing actions. To address this delayed phenomenon, we designed a dynamic reward mechanism to accurately adjust the delayed rewards. Through extensive simulation experiments, ADDR has demonstrated superior performance over multiple evaluation metrics.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1843-1859"},"PeriodicalIF":5.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0