IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第3页

IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information IEEE计算智能新兴主题汇刊

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-26 DOI: 10.1109/TETCI.2025.3548330

引用次数: 0

IEEE Computational Intelligence Society Information IEEE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 : 2025-03-26 DOI: 10.1109/TETCI.2025.3548332

引用次数: 0

Learning Error Refinement in Stochastic Gradient Descent-Based Latent Factor Analysis via Diversified PID Controllers 基于多元PID控制器的随机梯度下降潜因子分析的学习误差细化

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-24 DOI: 10.1109/TETCI.2025.3547854

Jinli Li;Ye Yuan;Xin Luo

{"title":"Learning Error Refinement in Stochastic Gradient Descent-Based Latent Factor Analysis via Diversified PID Controllers","authors":"Jinli Li;Ye Yuan;Xin Luo","doi":"10.1109/TETCI.2025.3547854","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3547854","url":null,"abstract":"In Big Data-based applications, high-dimensional and incomplete (HDI) data are frequently used to represent the complicated interactions among numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model can process such data efficiently. Unfortunately, a standard SGD algorithm trains a single latent factor relying on the stochastic gradient related to the current learning error only, leading to a slow convergence rate. To break through this bottleneck, this study establishes an SGD-based LFA model as the backbone, and proposes six proportional-integral-derivative (PID)-incorporated LFA models with diversified PID-controllers with the following two-fold ideas: a) refining the instant learning error in stochastic gradient by the principle of six PID-variants, i.e., a standard PID, an integral separated PID, a gearshift integral PID, a dead zone PID, an anti-windup PID, and an incomplete differential PID, to assimilate historical update information into the learning scheme in an efficient way; b) making the hyper-parameters adaptation by utilizing the mechanism of particle swarm optimization for acquiring high practicality. In addition, considering the diversified PID-variants, an effective ensemble is implemented for the six PID-incorporated LFA models. Experimental results on industrial HDI datasets illustrate that in comparison with state-of-the-art models, the proposed models obtain superior computational efficiency while maintaining competitive accuracy in predicting missing data within an HDI matrix. Moreover, their ensemble further improves performance in terms of prediction accuracy.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 5","pages":"3582-3597"},"PeriodicalIF":5.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128456","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

Leveraging Pixel Difference Feature for Deepfake Detection 利用像素差分特征进行深度伪造检测

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-21 DOI: 10.1109/TETCI.2025.3548803

Maoyu Mao;Chungang Yan;Junli Wang;Jun Yang

{"title":"Leveraging Pixel Difference Feature for Deepfake Detection","authors":"Maoyu Mao;Chungang Yan;Junli Wang;Jun Yang","doi":"10.1109/TETCI.2025.3548803","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3548803","url":null,"abstract":"The rise of Deepfake technology poses a formidable threat to the credibility of both judicial evidence and intellectual property safeguards. Current methods lack the ability to integrate the texture information of facial features into CNNs, despite the fact that fake contents are subtle and pixel-level. Due to the fixed grid kernel structure, CNNs are limited in their ability to describe detailed fine-grained information, making it challenging to achieve accurate image detection through pixel-level fine-grained features. To mitigate this problem, we propose a Pixel Difference Convolution (PDC) to capture local intrinsic detailed patterns via aggregating both intensity and gradient information. To avoid the redundant feature computations generated by PDC and explicitly enhance the representational power of a standard convolutional kernel, we separate PDC into vertical/horizontal and diagonal parts. Furthermore, we propose an Ensemble Dilated Convolution (EDC) to explore long-range contextual dependencies and further boost performance. We introduce a novel network, Pixel Difference Convolutional Network (PDCNet), which is built with PDC and EDC to expose Deepfake by capturing faint traces of tampering hidden in portrait images. By leveraging PDC and EDC in the information propagation process, PDCNet seamlessly incorporates both local and global pixel differences. Comprehensive experiments are performed on three databases, FF++, Celeb-DF, and DFDC to confirm that our PDCNet outperforms existing approaches. Our approach achieves accuracies of 0.9634, 0.9614, and 0.8819 in FF++, Celeb-DF, and DFDC, respectively.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3178-3188"},"PeriodicalIF":5.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687698","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

Efficient Weight Pruning for Optical Neural Networks: When Pruned Weights are Non-Zeros 光神经网络的有效权值剪枝：当剪枝权值非零时

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-19 DOI: 10.1109/TETCI.2025.3547856

Shuo Zhao;Kun Wu;Xin Li;Ying-Chi Chen

{"title":"Efficient Weight Pruning for Optical Neural Networks: When Pruned Weights are Non-Zeros","authors":"Shuo Zhao;Kun Wu;Xin Li;Ying-Chi Chen","doi":"10.1109/TETCI.2025.3547856","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3547856","url":null,"abstract":"Optical neural networks (ONNs) have emerged as a promising solution for energy-efficient deep learning. However, their resource-intensive manufacturing process necessitates efficient methods to streamline ONN architectures without sacrificing their performances. Weight pruning presents a potential remedy. Unlike the conventional neural networks, the pruned weights in ONNs are not necessarily zero in general, thereby making most traditional pruning methods inefficient. In this paper, we propose a novel two-stage pruning method tailored for ONNs. In the first stage, a first-order Taylor expansion of the loss function is applied to effectively identify and prune unimportant weights. To determine the shared value for the pruned weights, a novel optimization method is developed. In the second stage, fine-tuning is further applied to adjust the unpruned weights alongside the shared value of pruned weights. Experimental results on multiple public datasets demonstrate the efficacy of our proposed approach. It achieves superior model compression with minimum loss in accuracy over other conventional pruning techniques.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 5","pages":"3572-3581"},"PeriodicalIF":5.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128522","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

Latent Object Embedding for Self-Supervised Monocular Depth Estimation 基于自监督单目深度估计的潜在目标嵌入

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-18 DOI: 10.1109/TETCI.2025.3547851

Shuai Wang;Ting Yu;Shan Pan;Wei Chen;Zehua Wang;Victor C. M. Leung;Zijian Tian

{"title":"Latent Object Embedding for Self-Supervised Monocular Depth Estimation","authors":"Shuai Wang;Ting Yu;Shan Pan;Wei Chen;Zehua Wang;Victor C. M. Leung;Zijian Tian","doi":"10.1109/TETCI.2025.3547851","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3547851","url":null,"abstract":"Extracting 3D information from 2D images is highly significant, and self-supervised monocular depth estimation has demonstrated great potential in this field. However, existing methods primarily focus on estimating depth from immediate visual features, leading to severe foreground-background adhesion, which poses challenges for achieving precise depth estimation. In this paper, we propose a depth estimation method called LOEDepth, which can implicitly distinguish foreground objects from the background. In LOEDepth, a latent object embedding module is introduced, which leverages a set of learnable queries to generate latent object proposals from both immediate visual features extracted by the encoder and sparse object features derived through multi-scale deformable attention. These latent object proposals are utilized to perform soft classification on the decoded features to distinguish foreground objects from the background. Additionally, as depth boundaries do not always align with semantic boundaries, we propose a novel deep decoder to provide decoding features with rich spatial location retrieval and semantic information. Finally, two mask strategies are utilized to conceal pixels violating the scene's static assumption, so as to mitigate disruptions caused by abnormal pixels during self-supervised training. Experimental results on the KITTI and Make3D datasets demonstrate significant performance improvements and robust fine-grained scene depth estimation capabilities of the proposed method.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 5","pages":"3548-3559"},"PeriodicalIF":5.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128552","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

TUCA-HER: An Improved HER for Robot Manipulation Skill Learning via Trajectory Utility and Conservative Advantage 基于轨迹效用和保守优势的机器人操作技能学习改进HER

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-18 DOI: 10.1109/TETCI.2025.3548787

Peiliang Wu;Zhaoqi Wang;Yao Li;Wenbai Chen;Guowei Gao

{"title":"TUCA-HER: An Improved HER for Robot Manipulation Skill Learning via Trajectory Utility and Conservative Advantage","authors":"Peiliang Wu;Zhaoqi Wang;Yao Li;Wenbai Chen;Guowei Gao","doi":"10.1109/TETCI.2025.3548787","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3548787","url":null,"abstract":"In the realm of multi-goal reinforcement learning for robot manipulation, effectively addressing sparse rewards has been a key challenge. The hindsight experience replay (HER) mechanism has provided notable advancements in this domain, yet its efficiency and adaptability still require further improvement. This paper introduces TUCA-HER for robot manipulation skill learning via Trajectory Utility and Conservative Advantage. We start by computing trajectory utility for experience samples collected in the early stages of training, which allows for dynamic relabeling and significantly enhances sample efficiency. Furthermore, we integrate conservative advantage learning into the actor-critic framework, reshaping rewards to construct TUCA-HER. Finally, we apply TUCA-HER to robot manipulation skill learning tasks, providing details on algorithmic implementation and complexity analysis. Evaluations conducted on OpenAI Fetch and Hand environments demonstrate TUCA-HER's superior performance in sample efficiency and task success rate compared to other algorithms. Notably, in the FetchPickAndPlace task, TUCA-HER showcases a remarkable 46% improvement over the Double experience replay buffer Adaptive Soft Hindsight Experience Replay (DAS-HER). Furthermore, Sim-to-Real experiments are conducted to validate the effectiveness of TUCA-HER in real-world environments.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 5","pages":"3560-3571"},"PeriodicalIF":5.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128539","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

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification 一种微扰一致性驱动的医学图像分割与分类主动学习方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-18 DOI: 10.1109/TETCI.2025.3547635

Tao Wang;Xinlin Zhang;Yuanbo Zhou;Yuanbin Chen;Longxuan Zhao;Tao Tan;Tong Tong

{"title":"PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification","authors":"Tao Wang;Xinlin Zhang;Yuanbo Zhou;Yuanbin Chen;Longxuan Zhao;Tao Tan;Tong Tong","doi":"10.1109/TETCI.2025.3547635","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3547635","url":null,"abstract":"In recent years, supervised learning using convolutional neural networks (CNN) has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Moreover, effective utilization of annotation resources might not always be feasible during the annotation process. To optimize the utilization of annotation resources, a proposed active learning framework is introduced that is applicable to both 2D and 3D segmentation and classification tasks. This framework aims to reduce annotation costs by selecting more valuable samples for annotation from the pool of unlabeled data. Based on the perturbation consistency, we apply different perturbations to the input data and propose a perturbation consistency evaluation module to evaluate the consistency among predictions when applying different perturbations to the data. Subsequently, we rank the consistency of each data and select samples with lower consistency as high-value candidates. These selected samples are prioritized for annotation. We extensively validated our proposed framework on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our proposed framework can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The proposed framework enables more efficient utilization of annotation resources by annotating more representative samples, thus enhancing the model's robustness with fewer annotation costs.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 4","pages":"3162-3177"},"PeriodicalIF":5.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687735","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

PurifyFL: Non-Interactive Privacy-Preserving Federated Learning Against Poisoning Attacks Based on Single Server PurifyFL：针对单服务器中毒攻击的非交互式隐私保护联邦学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-17 DOI: 10.1109/TETCI.2025.3540420

Yanli Ren;Zhe Yang;Guorui Feng;Xinpeng Zhang

引用次数: 0

A Learning-Based Two-Stage Multi-Thread Iterated Greedy Algorithm for Co-Scheduling of Distributed Factories and Automated Guided Vehicles With Sequence-Dependent Setup Times 一种基于学习的两阶段多线程迭代贪心算法用于序列依赖的分布式工厂和自动导引车协同调度

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-14 DOI: 10.1109/TETCI.2025.3540405

Zijiang Liu;Hongyan Sang;Biao Zhang;Leilei Meng;Tao Meng

{"title":"A Learning-Based Two-Stage Multi-Thread Iterated Greedy Algorithm for Co-Scheduling of Distributed Factories and Automated Guided Vehicles With Sequence-Dependent Setup Times","authors":"Zijiang Liu;Hongyan Sang;Biao Zhang;Leilei Meng;Tao Meng","doi":"10.1109/TETCI.2025.3540405","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3540405","url":null,"abstract":"Automated guided vehicles are widely utilized in the real production environment for tasks such as job transfer and inter-factory collaboration, yet they remain relatively underexplored in academic research. This study addresses the distributed permutation flow shop co-scheduling problem with sequence-dependent setup times (DPFCSP-SDST). We propose a novel solution that leverages an optimization algorithm, specifically a learning-based two-stage multi-thread iterated greedy algorithm (LTMIG). First, a problem-specific initialization method is designed to generate the initialization solution in two stages. Second, a Q-learning-based operator adaptation strategy is adopted to guide the evolutionary direction of factory assignment to reduce the makespan. Then, the proposed destructive-construction strategy builds an archive set to share historical knowledge with different stages of search, ensuring exploration capability. Local search effectively combines the parallel computing power of multi-threading with the inherent exploitation capability of LTMIG, and fully utilizes the information of elite solutions. Extensive experimental results demonstrate that LTMIG is significantly better than the comparison algorithms mentioned in the paper, and it turns out that LTMIG is the most suitable algorithm for solving DPFCSP-SDST.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2208-2218"},"PeriodicalIF":5.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148133","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