IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第9页

A Novel Dual-Stage Evolutionary Algorithm for Finding Robust Solutions 寻找稳健解决方案的新型双阶段进化算法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-14 DOI: 10.1109/TETCI.2024.3369710

Wei Du;Wenxuan Fang;Chen Liang;Yang Tang;Yaochu Jin

{"title":"A Novel Dual-Stage Evolutionary Algorithm for Finding Robust Solutions","authors":"Wei Du;Wenxuan Fang;Chen Liang;Yang Tang;Yaochu Jin","doi":"10.1109/TETCI.2024.3369710","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369710","url":null,"abstract":"In robust optimization problems, the magnitude of perturbations is relatively small. Consequently, solutions within certain regions are less likely to represent the robust optima when perturbations are introduced. Hence, a more efficient search process would benefit from increased opportunities to explore promising regions where global optima or good local optima are situated. In this paper, we introduce a novel robust evolutionary algorithm named the dual-stage robust evolutionary algorithm (DREA) aimed at discovering robust solutions. DREA operates in two stages: the peak-detection stage and the robust solution-searching stage. The primary objective of the peak-detection stage is to identify peaks in the fitness landscape of the original optimization problem. Conversely, the robust solution-searching stage focuses on swiftly identifying the robust optimal solution using information obtained from the peaks discovered in the initial stage. These two stages collectively enable the proposed DREA to efficiently obtain the robust optimal solution for the optimization problem. This approach achieves a balance between solution optimality and robustness by separating the search processes for optimal and robust optimal solutions. Experimental results demonstrate that DREA significantly outperforms five state-of-the-art algorithms across 18 test problems characterized by diverse complexities. Moreover, when evaluated on higher-dimensional robust optimization problems (100-\u0000<inline-formula><tex-math>$D$</tex-math></inline-formula>\u0000 and 200-\u0000<inline-formula><tex-math>$D$</tex-math></inline-formula>\u0000), DREA also demonstrates superior performance compared to all five counterpart algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3589-3602"},"PeriodicalIF":5.3,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376994","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

Dynamic Secure Multi Broad Network for Privacy Preserving of Streaming Data 保护流数据隐私的动态安全多宽网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-14 DOI: 10.1109/TETCI.2024.3370005

Xiao-Kai Cao;Man-Sheng Chen;Chang-Dong Wang;Jian-Huang Lai;Qiong Huang;C. L. Philip Chen

{"title":"Dynamic Secure Multi Broad Network for Privacy Preserving of Streaming Data","authors":"Xiao-Kai Cao;Man-Sheng Chen;Chang-Dong Wang;Jian-Huang Lai;Qiong Huang;C. L. Philip Chen","doi":"10.1109/TETCI.2024.3370005","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3370005","url":null,"abstract":"Distributed computing as a widely concerned research direction needs to use the data training model of users, making the security of users' private data become a challenging problem to be solved. At present, federated learning is the mainstream research method to solve this problem. However, federated learning is not good at distributed training on streaming data. In real scenarios, the client's data is usually continuously updated streaming data. In this paper, we propose Dynamic Secure Multi Broad Network (DSMBN), which is a novel privacy computing framework completely different from federated learning. In DSMBN, we design three interactive communication protocols to handle streaming data in different scenarios. The function of the protocol is to use random mapping to encrypt data during the interaction. The protocol ensures that the client's original data does not leave the local server when generating mapped features. The central server uses the resulting mapped features (essentially encrypted data) instead of the original data to train machine learning models. In theoretical analysis, we analyze the first protocol's security, communication costs, and computational complexity. In the experiment, we design seven experimental scenarios, including quantity balance, Non-IID data distribution and streaming data, and compare them with several mainstream privacy protection machine learning methods. The experimental results show that compared with centralized training without privacy protection, DSMBN can achieve the same test accuracy under the premise of protecting private data security. Compared with mainstream federated learning methods, DSMBN can achieve higher accuracy in the Non-IID scenarios and save computing time and communication resources.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"3152-3165"},"PeriodicalIF":5.3,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964789","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

A Novel Random Forest Variant Based on Intervention Correlation Ratio 基于干预相关比的新型随机森林变体

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-14 DOI: 10.1109/TETCI.2024.3369320

Tao Zhang;Tao Li;Zaifa Xue;Xin Lu;Le Gao

引用次数: 0

A Global Self-Attention Memristive Neural Network for Image Restoration 用于图像修复的全局自注意力记忆神经网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-14 DOI: 10.1109/TETCI.2024.3369447

Wenhao Zhang;He Xiao;Dirui Xie;Yue Zhou;Shukai Duan;Xiaofang Hu

{"title":"A Global Self-Attention Memristive Neural Network for Image Restoration","authors":"Wenhao Zhang;He Xiao;Dirui Xie;Yue Zhou;Shukai Duan;Xiaofang Hu","doi":"10.1109/TETCI.2024.3369447","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369447","url":null,"abstract":"Recently, using the idea of non-local operations, various non-local networks and the Vision Transformer have been proposed to model the long-range pixel dependencies, addressing the limitation of Convolutional neural networks(CNNs). However, most of these models cannot adaptively process images with different resolutions, and their large number of parameters and computational complexity make them unfavorable for edge devices. In this paper, we propose an efficient Global Self-Attention Memristive Neural Network (GSA-MNN) for image restoration and present a memristive circuits implementation scheme for GSA-MNN. GSA-MNN can both extract global and local information from images, which can be flexibly applied to different resolution images. Specifically, the Global Spatial Attention Module (GSAM) and the Global Channel Attention Module (GCAM) are designed to complete the modeling and inference of global relations. The GSAM is used to model global spatial relations between the pixels of the feature maps, while the GCAM explores global relations across the channels. Moreover, a multi-scale local information extraction module is proposed to deal with image regions with complex textures. Furthermore, we provide a modular designed circuit implementation scheme for these three modules and the entire GSA-MNN. Benefiting from the programmability of the memristor crossbars, three kinds of image restoration tasks: image deraining, low-light image enhancement, and image dehazing are realized on the same circuit framework by adjusting the configuration parameters. Experimental comparisons with over 20 state-of-the-art methods on 10 public datasets show that our proposed GSA-MNN has superiority.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2613-2624"},"PeriodicalIF":5.3,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096167","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

Self-Supervised Multi-Granularity Graph Attention Network for Vision-Based Driver Fatigue Detection 基于视觉的驾驶员疲劳检测的自监督多粒度图注意网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-13 DOI: 10.1109/TETCI.2024.3369937

Yiming Huang;Chunsheng Liu;Faliang Chang;Yansha Lu

{"title":"Self-Supervised Multi-Granularity Graph Attention Network for Vision-Based Driver Fatigue Detection","authors":"Yiming Huang;Chunsheng Liu;Faliang Chang;Yansha Lu","doi":"10.1109/TETCI.2024.3369937","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369937","url":null,"abstract":"Driver fatigue is one of the main causes of traffic accidents. Current vision-based methods for detecting driver fatigue lack robustness in the presence of interfering images, and exhibit insufficient ability to focus on frames containing crucial information. To address these issues, we propose a \u0000<italic>Self-supervised Multi-granularity Graph Attention Network</i>\u0000 (SMGA-Net) for driver fatigue detection. The network mainly contains the following contributions: Firstly, with the multi-task self-supervised learning strategy, a novel method called \u0000<italic>Image Restoration based Self-supervised Learning</i>\u0000 (IRS-Learning) is proposed to enhance the network's robustness when processing interfering images. Secondly, with the graph attention mechanism, a \u0000<italic>Multi-head Graph Attention</i>\u0000 (MG-Attention) module is designed to concentrate on frames that contain crucial information by assigning importance weights to each frame. In addition, a \u0000<italic>Cross Attention Feature Fusion</i>\u0000 (CAF-Fusion) method is proposed to adaptively merge the multi-granularity features and emphasize effective information contained therein. Experiments performed on the National TsingHua University Drowsy Driver Detection (NTHU-DDD) dataset show that the proposed SMGA-Net based driver fatigue detection method outperforms the state-of-art methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"3067-3080"},"PeriodicalIF":5.3,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964887","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 Processing of Spiking Neural Networks via Task Specialization 通过任务专业化高效处理尖峰神经网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-13 DOI: 10.1109/TETCI.2024.3370028

Muath Abu Lebdeh;Kasim Sinan Yildirim;Davide Brunelli

引用次数: 0

WLR-Net: An Improved YOLO-V7 With Edge Constraints and Attention Mechanism for Water Leakage Recognition in the Tunnel WLR-Net：带边缘约束和注意力机制的改进型 YOLO-V7 隧道漏水识别系统

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-13 DOI: 10.1109/TETCI.2024.3369999

Junxin Chen;Xu Xu;Gwanggil Jeon;David Camacho;Ben-Guo He

引用次数: 0

Selective Transfer Based Evolutionary Multitasking Optimization for Change Detection 基于选择性转移的变化检测进化多任务优化

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-12 DOI: 10.1109/TETCI.2024.3360331

Hao Li;Tianshi Luo;Liwen Liu;Maoguo Gong;Wenyuan Qiao;Fei Xie;A. K. Qin

{"title":"Selective Transfer Based Evolutionary Multitasking Optimization for Change Detection","authors":"Hao Li;Tianshi Luo;Liwen Liu;Maoguo Gong;Wenyuan Qiao;Fei Xie;A. K. Qin","doi":"10.1109/TETCI.2024.3360331","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3360331","url":null,"abstract":"Change detection in multitemporal remote sensing images aims to generate a difference image (DI) and then analyze it to identify the unchanged/changed areas. The current change detection techniques always investigate a single change detection task of two images from the image series one by one and may ignore the relevant information across the different tasks. Furthermore, theoretical results have demonstrated that the distribution of DI can be interpreted by a Rayleigh-Rice mixture model (RRMM). The parameters of RRMM are usually estimated by the expectation-maximization (EM) algorithm, which is easy to be trapped into local minima. In order to address these issues, a selective transfer based evolutionary multitasking change detection method is proposed to deal with multiple change detection tasks concurrently. For each change detection task, the log-likelihood function and centroid distance function are considered as two objectives to be optimized simultaneously. In the proposed method, a RRMM parameter estimation driven initialization method with random partition of the data is designed by maximum likelihood estimates of the parameters. Then the next population is generated by the intra-task and inter-task genetic transfer operators. A selective knowledge transfer based local search strategy is proposed to further improve the population by applying EM algorithm. In this strategy, the samples in the unchanged class of multiple tasks are utilized to estimate the parameters to acquire knowledge transferred from the other task. Experiments on three real remote sensing data sets demonstrate that the selective transfer based evolutionary multitasking change detection method is able to accelerate the convergence and achieve superior performance in terms of accuracy.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2197-2212"},"PeriodicalIF":5.3,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096297","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

Node Clustering on Attributed Graph Using Anchor Sampling Strategy and Debiasing Strategy 使用锚点采样策略和去重策略对归属图进行节点聚类

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-12 DOI: 10.1109/TETCI.2024.3369849

Qian Tang;Yiji Zhao;Hao Wu;Lei Zhang

{"title":"Node Clustering on Attributed Graph Using Anchor Sampling Strategy and Debiasing Strategy","authors":"Qian Tang;Yiji Zhao;Hao Wu;Lei Zhang","doi":"10.1109/TETCI.2024.3369849","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369849","url":null,"abstract":"Contrastive representation learning has been widely employed in attributed graph clustering and has demonstrated significant success. However, these methods have two problems: 1)According to an assumption that clusters are formed around a minority of central anchor nodes, the contrastive relationships between these anchors are not explored in previous works. 2)They fail to deal with biased sample pairs, which may degrade the representation quality and cause poor clustering performance. To solve the problems, we propose a framework termed GE-S-D for both node representation learning and clustering, which consists of an anchor sampling strategy, a low-pass graph encoder, and a debiasing strategy. Specifically, to reveal the contrastive relationships between anchors, we design a sampling strategy to select a small number of anchors and then construct a training set of positive and negative sample pairs for contrastive learning. Then, we introduce a low-pass graph encoder to propagate contrastive messages to all nodes and learn cluster-friendly node representations. Furthermore, to alleviate the interference of biased sample pairs, we design a debiasing strategy using K-Means on the node representations to obtain the clustering information and remove the false positive and false negative sample pairs in the training set for improving contrastive learning. The clustering performance is verified on five benchmark datasets, and our method is superior to many state-of-the-art methods according to quantitive and qualitative analysis.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"3017-3028"},"PeriodicalIF":5.3,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964860","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

Mutual Retinex: Combining Transformer and CNN for Image Enhancement Mutual Retinex：结合变换器和 CNN 增强图像效果

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-03-12 DOI: 10.1109/TETCI.2024.3369321

Kui Jiang;Qiong Wang;Zhaoyi An;Zheng Wang;Cong Zhang;Chia-Wen Lin

{"title":"Mutual Retinex: Combining Transformer and CNN for Image Enhancement","authors":"Kui Jiang;Qiong Wang;Zhaoyi An;Zheng Wang;Cong Zhang;Chia-Wen Lin","doi":"10.1109/TETCI.2024.3369321","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3369321","url":null,"abstract":"Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"2240-2252"},"PeriodicalIF":5.3,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096206","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