IEEE Transactions on Cognitive and Developmental Systems最新文献_第6页

Small Object Detection Based on Microscale Perception and Enhancement-Location Feature Pyramid 基于微尺度感知和增强的小物体检测--位置特征金字塔

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-05-07 DOI: 10.1109/tcds.2024.3397684

Guang Han, Chenwei Guo, Ziyang Li, Haitao Zhao

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LITE-SNN: Leveraging Inherent Dynamics to Train Energy-Efficient Spiking Neural Networks for Sequential Learning LITE-SNN：利用固有动态性训练高能效尖峰神经网络以进行序列学习

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-05-03 DOI: 10.1109/tcds.2024.3396431

Nitin Rathi, Kaushik Roy

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Machine Unlearning for Seizure Prediction 用于癫痫发作预测的机器学习

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-05-01 DOI: 10.1109/tcds.2024.3395663

Chenghao Shao, Chang Li, Rencheng Song, Xiang Liu, Ruobing Qian, Xun Chen

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HR-SNN: An End-to-End Spiking Neural Network for Four-class Classification Motor Imagery Brain-Computer Interface HR-SNN：用于四级分类运动图像的端到端尖峰神经网络脑机接口

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-30 DOI: 10.1109/tcds.2024.3395443

Yulin Li, Liangwei Fan, Hui Shen, Dewen Hu

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Adaptive Framework for Long term Sensory Home Training: a Feasibility Study 长期感官家庭训练的适应性框架：可行性研究

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-25 DOI: 10.1109/tcds.2024.3393635

Stefano Silvoni, Simon Desch, Florian Beier, Robin Bekrater-Bodmann, Annette Löffler, Dieter Kleinböhl, Stefano Tamascelli, Herta Flor

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Leveraging Spatio Temporal Estimation for Online Adaptive Steady State Visual Evoked Potential Recognition 利用时空估计进行在线自适应稳态视觉诱发电位识别

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-23 DOI: 10.1109/tcds.2024.3392745

Jing Jin, Xinjie He, Brendan Z Allison, Ke Qin, Xingyu Wang, Andrzej Cichocki

引用次数: 0

Minimizing EEG Human Interference: A Study of an Adaptive EEG Spatial Feature Extraction with Deep Convolutional Neural Networks 最小化脑电图人为干扰：利用深度卷积神经网络进行自适应脑电图空间特征提取的研究

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-18 DOI: 10.1109/tcds.2024.3391131

Haojin Deng, Shiqi Wang, Yimin Yang, W.G.Will Zhao, Hui Zhang, Ruizhong Wei, Q.M.Jonathan Wu, Bao-Liang Lu

引用次数: 0

MAVIDSQL: A Model-Agnostic Visualization for Interpretation and Diagnosis of Text-to-SQL Tasks MAVIDSQL：用于解释和诊断文本到 SQL 任务的模型诊断可视化工具

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-18 DOI: 10.1109/TCDS.2024.3391278

Jingwei Tang;Guodao Sun;Jiahui Chen;Gefei Zhang;Baofeng Chang;Haixia Wang;Ronghua Liang

{"title":"MAVIDSQL: A Model-Agnostic Visualization for Interpretation and Diagnosis of Text-to-SQL Tasks","authors":"Jingwei Tang;Guodao Sun;Jiahui Chen;Gefei Zhang;Baofeng Chang;Haixia Wang;Ronghua Liang","doi":"10.1109/TCDS.2024.3391278","DOIUrl":"10.1109/TCDS.2024.3391278","url":null,"abstract":"Significant advancements in semantic parsing for text-to-SQL (T2S) tasks have been achieved through the employment of neural network models, such as LSTM, BERT, and T5. The exceptional performance of large language models, such as ChatGPT, has been demonstrated in recent research, even in zero-shot scenarios. However, the inherent transparency of T2S models presents them as black boxes, concealing their inner workings from both developers and users, which complicates the diagnosis of potential error patterns. Despite the fact that numerous visual analysis studies have been conducted in natural language processing communities, scant attention has been paid to addressing the challenges of semantic parsing, specifically in T2S tasks. This limitation hinders the development of effective tools for model optimization and evaluation. This article presents an interactive visual analysis tool, MAVIDSQL, to assist model developers and users in understanding and diagnosing T2S tasks. The system comprises three modules: the model manager, the feature extractor, and the visualization interface, which adopt a model-agnostic approach to diagnose potential errors and infer model decisions by analyzing input–output data, facilitating interactive visual analysis to identify error patterns and assess model performance. Two case studies and interviews with domain experts demonstrate the effectiveness of MAVIDSQL in facilitating the understanding of T2S tasks and identifying potential errors.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"1887-1903"},"PeriodicalIF":5.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627741","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

Toward Two-Stream Foveation-Based Active Vision Learning 实现基于视觉的双流主动视觉学习

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-17 DOI: 10.1109/TCDS.2024.3390597

Timur Ibrayev;Amitangshu Mukherjee;Sai Aparna Aketi;Kaushik Roy

{"title":"Toward Two-Stream Foveation-Based Active Vision Learning","authors":"Timur Ibrayev;Amitangshu Mukherjee;Sai Aparna Aketi;Kaushik Roy","doi":"10.1109/TCDS.2024.3390597","DOIUrl":"10.1109/TCDS.2024.3390597","url":null,"abstract":"Deep neural network (DNN) based machine perception frameworks process the entire input in a one-shot manner to provide answers to both “\u0000<italic>what\u0000 object is being observed” and “\u0000<italic>where\u0000 it is located.” In contrast, the \u0000<italic>“two-stream hypothesis”\u0000 from neuroscience explains the neural processing in the human visual cortex as an active vision system that utilizes two separate regions of the brain to answer the \u0000<italic>what\u0000 and the \u0000<italic>where\u0000 questions. In this work, we propose a machine learning framework inspired by the \u0000<italic>“two-stream hypothesis”\u0000 and explore the potential benefits that it offers. Specifically, the proposed framework models the following mechanisms: 1) ventral (\u0000<italic>what\u0000) stream focusing on the input regions perceived by the fovea part of an eye (foveation); 2) dorsal (\u0000<italic>where\u0000) stream providing visual guidance; and 3) iterative processing of the two streams to calibrate visual focus and process the sequence of focused image patches. The training of the proposed framework is accomplished by label-based DNN training for the ventral stream model and reinforcement learning (RL) for the dorsal stream model. We show that the two-stream foveation-based learning is applicable to the challenging task of weakly-supervised object localization (WSOL), where the training data is limited to the object class or its attributes. The framework is capable of both predicting the properties of an object \u0000<italic>and\u0000 successfully localizing it by predicting its bounding box. We also show that, due to the independent nature of the two streams, the dorsal model can be applied on its own to unseen images to localize objects from different datasets.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"1843-1860"},"PeriodicalIF":5.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140615331","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

Cognitive Assessment of Scientific Creative Skill by Brain-Connectivity Analysis Using Graph Convolutional Interval Type-2 Fuzzy Network 利用图卷积-间隔-2 型模糊网络的脑连接性分析对科学创新技能进行认知评估

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-04-16 DOI: 10.1109/TCDS.2024.3390005

Sayantani Ghosh;Amit Konar;Atulya K. Nagar

{"title":"Cognitive Assessment of Scientific Creative Skill by Brain-Connectivity Analysis Using Graph Convolutional Interval Type-2 Fuzzy Network","authors":"Sayantani Ghosh;Amit Konar;Atulya K. Nagar","doi":"10.1109/TCDS.2024.3390005","DOIUrl":"10.1109/TCDS.2024.3390005","url":null,"abstract":"Scientific creativity refers to natural/automated genesis of innovations in science, propelling scientific, technological, industrial, and/or societal progress. Mental paper folding (MPF) requires spatial reasoning, which is an important attribute to determine creative potential of people. The article proposes a novel approach to determine creative potential of people from their brain-connectivity network (BCN) during their participation in MPF tasks using functional near-infrared spectroscopy (fNIRS). The work involves three phases. The first phase includes construction of BCN using Pearson's correlation method. The centrality features of the nodes in the network are assessed in the second phase and transferred to a proposed graph convolutional-interval type-2 fuzzy network (GC-IT2FN) in the third phase to classify the creative potential of individuals in four grades. The novelty of the work includes: 1) a novel self-attention mechanism in the network to guide graph convolution layers to focus on the most relevant nodes; 2) selection of a new activation function, Logish, after graph convolution to enhance classifier accuracy; and 3) utilizing the promising region in the footprint of uncertainty (FOU) of the used fuzzy sets of IT2FN-based classifier to reduce the effect of uncertainty in brain data on classifier performance. Experiments conducted demonstrate the efficacy of the proposed framework in contrast to traditional approaches.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"1872-1886"},"PeriodicalIF":5.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140615380","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