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

Event Causal Relation Extraction in Brain Connectomics: A Model Utilizing Weighted Joint Constrained Learning 脑连接组学中的事件因果关系提取：一个利用加权联合约束学习的模型

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-09 DOI: 10.1109/TETCI.2024.3462173

Lianfang Ma;Jianhui Chen;Jiajin Huang;Yiyu Yao;Ning Zhong

{"title":"Event Causal Relation Extraction in Brain Connectomics: A Model Utilizing Weighted Joint Constrained Learning","authors":"Lianfang Ma;Jianhui Chen;Jiajin Huang;Yiyu Yao;Ning Zhong","doi":"10.1109/TETCI.2024.3462173","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3462173","url":null,"abstract":"Brain science research has entered the era of connectomics, characterized by a significant increase in published articles investigating brain structure and functional connections. Automatically and accurately extracting scientific evidence from these articles has become an urgent concern. Unlike early brain mechanism studies at the functional area level, brain connectomics studies feature more intricate experimental designs and yield complex findings. Traditional neuroimaging text mining techniques, operating at the term level, are insufficient for effectively extracting scientific evidence from brain connectomics articles. This paper addresses a key challenge in event-level neuroimaging text mining, i.e., event causal relation extraction in brain connectomics. We introduce a novel model named Brain Connectomics Event Relation Miner (BCERM), leveraging weighted joint constrained learning. By integrating a bidirectional long short-term memory (BiLSTM) network with a multi-layer perceptron (MLP), we develop a lightweight model for jointly extracting multiple event causal relations from brain connectomics articles. Given the scarcity of annotated brain connectomics corpora, we propose a weighted joint constrained learning framework. This framework integrates double consistency constraints, encompassing common sense and domain constraints, and combines them with adaptive weight learning to enhance the model's few-shot learning capability. Experimental evaluations on a real brain connectomics article dataset demonstrate that our method achieves an F-score of 70%, outperforming state-of-the-art event relation extraction methods in the low-resource environment.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1885-1896"},"PeriodicalIF":5.3,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706664","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

Decentralized Triggering and Event-Based Integral Reinforcement Learning for Multiplayer Differential Game Systems 多人差分游戏系统的分散触发和基于事件的积分强化学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-07 DOI: 10.1109/TETCI.2024.3372389

Chaoxu Mu;Ke Wang;Song Zhu;Guangbin Cai

{"title":"Decentralized Triggering and Event-Based Integral Reinforcement Learning for Multiplayer Differential Game Systems","authors":"Chaoxu Mu;Ke Wang;Song Zhu;Guangbin Cai","doi":"10.1109/TETCI.2024.3372389","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372389","url":null,"abstract":"Multiplayer differential games are typically characterized by multiple control loops, where communication resources are periodically transmitted and control policies are updated in a time-triggered manner. In this paper, two different event-triggered mechanisms are proposed for a class of multiplayer nonzero-sum differential game systems. Specifically, by defining a global sampled state, a centralized triggering rule is devised to manage state sampling and control updating in a synchronized manner. By considering each player's preferences, the decentralized triggering rule is devised in which a local event generator produces the triggering sequence independently. On the other hand, with experience replay and integral reinforcement learning, an event-based adaptive learning scheme is developed, which is implemented by critic neural networks and only requires partial knowledge of system dynamics. The theoretical results indicate that both two triggering mechanisms can guarantee the asymptotic stability and weight convergence. Finally, simulation results on a three-player numerical system and a two-player supersonic transport system substantiate the effectiveness of two learning-based triggering mechanisms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"3727-3741"},"PeriodicalIF":5.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691785","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

Dual-Scale Attributed Graph Transformer for Extracting Spatial-Temporal Features With Applications in Quality Index Prediction 基于双尺度属性图转换器的时空特征提取及其在质量指标预测中的应用

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-10-04 DOI: 10.1109/TETCI.2024.3462486

Kesheng Zhang;Wen Yu;Tianyou Chai

{"title":"Dual-Scale Attributed Graph Transformer for Extracting Spatial-Temporal Features With Applications in Quality Index Prediction","authors":"Kesheng Zhang;Wen Yu;Tianyou Chai","doi":"10.1109/TETCI.2024.3462486","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3462486","url":null,"abstract":"This paper presents a novel deep learning architecture, the Dual-scale Attribute Graph Transformer (DAGT), for extracting spatial-temporal features from attributed graph data. DAGT addresses the challenge of inconsistent sampling periods in industrial data streams by utilizing two key modules: 1) Dual-Scale Spatial-temporal Graph Convolution Network (DSGCN): This module captures both spatial and temporal information within attributed graphs, enabling effective feature extraction for tasks like quality index prediction. 2) Spatial-temporal Graph Attention Block (SGAB): This module employs an attention mechanism to selectively focus on crucial areas of the graph sequence. By assigning higher weights to regions with significant spatial-temporal features, SGAB refines the feature representation. The contributions of DAGT lie in the construction of a dual-scale adjacency matrix for efficient temporal and spatial dimensionality reduction and the design of a graph pooling module via spatial clustering. These innovations enhance the model's ability to learn from attributed graph sequences. The proposed method for quality index prediction is validated using real-world industrial data of the mineral processing process and various comparative experiments.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1873-1884"},"PeriodicalIF":5.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706663","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

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

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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 基于文本特征可解释性嵌入和外部攻击节点植入的文本-图像匹配

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

Distillation-Based Domain Generalization for Cross-Dataset EEG-Based Emotion Recognition 基于提取的跨数据集eeg情感识别领域泛化

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-30 DOI: 10.1109/TETCI.2024.3449926

Wei Li;Siyi Wang;Shitong Shao;Kaizhu Huang

{"title":"Distillation-Based Domain Generalization for Cross-Dataset EEG-Based Emotion Recognition","authors":"Wei Li;Siyi Wang;Shitong Shao;Kaizhu Huang","doi":"10.1109/TETCI.2024.3449926","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3449926","url":null,"abstract":"Electroencephalogram (EEG)-based emotion recognition has gradually become a research hotspot with extensive real-world applications. Differences in EEG signals across subjects usually lead to the unsatisfactory performance in subject-independent emotion recognition. To handle this challenge, many researchers have paid attention to the development of transfer learning techniques, which yield promising results. Currently, most researchers focus on transfer learning between subjects within one single dataset. However, cross-dataset transfer learning presents a great challenge, because, in this case, the data collected from different environments and equipments have much more severe variations. Domain Generalization (DG) has great potential to handle the unseen data without involving them in training. Besides, Knowledge Distillation (KD), which can transfer the knowledge learned from the teacher to the student model, has shown promise in generalization for the student model. Inspired by DG and KD, we propose a novel and effective method, Distillation-Based Domain Generalization (DBDG), for cross-dataset EEG-based emotion recognition. Specifically, DBDG contains the modules of feature extraction, online distillation and self-distillation. The feature extraction module can learn the discriminative emotional features from EEG signals; the online distillation and self-distillation modules can enhance the method generalizability. Experimental results on three public benchmark datasets, SEED, SEED-IV and DEAP, have demonstrated the effectiveness of our method for cross-dataset EEG-based emotion recognition.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2474-2490"},"PeriodicalIF":5.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148172","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

DFEN: A Dual-Feature Extraction Network-Based Open-Set Domain Adaptation Method for Optical Remote Sensing Image Scene Classification 基于双特征提取网络的开集域自适应光学遥感图像场景分类方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-27 DOI: 10.1109/TETCI.2024.3462429

Zhunga Liu;Xinran Ji;Zuowei Zhang;Yimin Fu

{"title":"DFEN: A Dual-Feature Extraction Network-Based Open-Set Domain Adaptation Method for Optical Remote Sensing Image Scene Classification","authors":"Zhunga Liu;Xinran Ji;Zuowei Zhang;Yimin Fu","doi":"10.1109/TETCI.2024.3462429","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3462429","url":null,"abstract":"Open-set domain adaptation methods aim to correctly classify data when there is a disparity in distribution and class spaces between the test and training sets. However, existing methods mainly concentrate on natural images, and their performance is hindered in more complex remote sensing image scene classification tasks by various inherent factors, in particular, diverse imaging conditions, different resolutions, and geographical semantics. To address this issue, we propose a dual-feature extraction network-based open-set domain adaptation method (DFEN). Specifically, we design a dual-feature extraction network model, comprising a local feature extractor and a global feature extractor. The local feature extractor is primarily used to capture local distinctive features from images to enhance the discriminative ability of the model for similar categories and improve its resistance to interference in multi-object images. By contrast, the global feature extractor focuses on extracting semantic correlations that are independent of image style and target scale to improve the model's understanding and generalization ability for scene categories. Besides, to boost the classification accuracy for unknown categories in open environments, we introduce an adaptive unknown class weighting mechanism based on the similarity between samples and known classes. By comprehensively measuring across the entire classification space and individual categories, samples with high weights are considered as unknown class. Experimental results demonstrate that the proposed method achieves promising performance in various open and cross-domain scenarios.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2462-2473"},"PeriodicalIF":5.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148080","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