Pub Date : 2024-04-25DOI: 10.1109/tcds.2024.3393635
Stefano Silvoni, Simon Desch, Florian Beier, Robin Bekrater-Bodmann, Annette Löffler, Dieter Kleinböhl, Stefano Tamascelli, Herta Flor
{"title":"Adaptive Framework for Long term Sensory Home Training: a Feasibility Study","authors":"Stefano Silvoni, Simon Desch, Florian Beier, Robin Bekrater-Bodmann, Annette Löffler, Dieter Kleinböhl, Stefano Tamascelli, Herta Flor","doi":"10.1109/tcds.2024.3393635","DOIUrl":"https://doi.org/10.1109/tcds.2024.3393635","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"46 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140797954","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}
Pub Date : 2024-04-23DOI: 10.1109/tcds.2024.3392745
Jing Jin, Xinjie He, Brendan Z Allison, Ke Qin, Xingyu Wang, Andrzej Cichocki
{"title":"Leveraging Spatio Temporal Estimation for Online Adaptive Steady State Visual Evoked Potential Recognition","authors":"Jing Jin, Xinjie He, Brendan Z Allison, Ke Qin, Xingyu Wang, Andrzej Cichocki","doi":"10.1109/tcds.2024.3392745","DOIUrl":"https://doi.org/10.1109/tcds.2024.3392745","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"83 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140797952","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}
{"title":"Minimizing EEG Human Interference: A Study of an Adaptive EEG Spatial Feature Extraction with Deep Convolutional Neural Networks","authors":"Haojin Deng, Shiqi Wang, Yimin Yang, W.G.Will Zhao, Hui Zhang, Ruizhong Wei, Q.M.Jonathan Wu, Bao-Liang Lu","doi":"10.1109/tcds.2024.3391131","DOIUrl":"https://doi.org/10.1109/tcds.2024.3391131","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"201 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627221","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}
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.
{"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}
Pub Date : 2024-04-17DOI: 10.1109/TCDS.2024.3390597
Timur Ibrayev;Amitangshu Mukherjee;Sai Aparna Aketi;Kaushik Roy
Deep neural network (DNN) based machine perception frameworks process the entire input in a one-shot manner to provide answers to both “ what object is being observed” and “ where it is located.” In contrast, the “two-stream hypothesis” 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 what and the where questions. In this work, we propose a machine learning framework inspired by the “two-stream hypothesis” and explore the potential benefits that it offers. Specifically, the proposed framework models the following mechanisms: 1) ventral ( what ) stream focusing on the input regions perceived by the fovea part of an eye (foveation); 2) dorsal ( where ) 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 and 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.
{"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</i>\u0000 object is being observed” and “\u0000<italic>where</i>\u0000 it is located.” In contrast, the \u0000<italic>“two-stream hypothesis”</i>\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</i>\u0000 and the \u0000<italic>where</i>\u0000 questions. In this work, we propose a machine learning framework inspired by the \u0000<italic>“two-stream hypothesis”</i>\u0000 and explore the potential benefits that it offers. Specifically, the proposed framework models the following mechanisms: 1) ventral (\u0000<italic>what</i>\u0000) stream focusing on the input regions perceived by the fovea part of an eye (foveation); 2) dorsal (\u0000<italic>where</i>\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</i>\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}
Pub Date : 2024-04-16DOI: 10.1109/TCDS.2024.3390005
Sayantani Ghosh;Amit Konar;Atulya K. Nagar
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.
{"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：