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Converting Artificial Neural Networks to Ultra-Low-Latency Spiking Neural Networks for Action Recognition IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-03-14 Hong You, Xian Zhong, Wenxuan Liu, Qi Wei, Wenxin Huang, Zhaofei Yu, Tiejun Huang
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EEG-based Auditory Attention Detection with Spiking Graph Convolutional Network IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-03-12 Siqi Cai, Ran Zhang, Malu Zhang, Jibin Wu, Haizhou Li
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Robust Perception-based Visual Simultaneous Localization and Tracking in Dynamic Environments IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-28 Song Peng, Teng Ran, Liang Yuan, Jianbo Zhang, Wendong Xiao
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Brain Connectivity Analysis for EEG-based Face Perception Task IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-27 Debashis Das Chakladar, Nikhil R Pal
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D-FaST: Cognitive Signal Decoding with Disentangled Frequency-Spatial-Temporal Attention IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-26 WeiGuo Chen, Changjian Wang, Kele Xu, Yuan Yuan, Yanru Bai, Dongsong Zhang
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DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-26 Zhiwen Xiao, Xin Xu, Huanlai Xing, Bowen Zhao, Xinhan Wang, Fuhong Song, Rong Qu, Li Feng
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Deep Reinforcement Learning with Multi-Critic TD3 for Decentralized Multi-Robot Path Planning IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-20 Heqing Yin, Chang Wang, Chao Yan, Xiaojia Xiang, Boliang Cai, Changyun Wei
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Agree to Disagree: Exploring Partial Semantic Consistency against Visual Deviation for Compositional Zero-Shot Learning IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-20 Xiangyu Li, Xu Yang, Xi Wang, Cheng Deng
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Compressed Video Anomaly Detection of Human Behavior Based on Abnormal Region Determination IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-20 Lijun He, Miao Zhang, Hao Liu, Liejun Wang, Fan Li
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Editorial IEEE Transactions on Cognitive and Developmental Systems IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-02 Huajin Tang
As we usher into the new year of 2024, in my capacity as the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems (TCDS), I am happy to extend to you a tapestry of New Year greetings, may this year be filled with prosperity, success, and groundbreaking achievements in our shared fields.
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Guest Editorial Special Issue on Cognitive Learning of Multiagent Systems IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-02 Yang Tang, Wei Lin, Chenguang Yang, Nicola Gatti, Gary G. Yen
The development and cognition of biological and intelligent individuals shed light on the development of cognitive, autonomous, and evolutionary robotics. Take the collective behavior of birds as an example, each individual effectively communicates information and learns from multiple neighbors, facilitating cooperative decision making among them. These interactions among individuals illuminate the
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IEEE Transactions on Cognitive and Developmental Systems Information for Authors IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-02
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IEEE Transactions on Cognitive and Developmental Systems Publication Information IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-02-02
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An Electroencephalography-Based Brain-Computer Interface for Emotion Regulation with Virtual Reality Neurofeedback IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-29 Kendi Li, Weichen Huang, Wei Gao, Zijing Guan, Qiyun Huang, Jin-Gang Yu, Zhu Liang Yu, Yuanqing Li
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Depression Detection Using an Automatic Sleep Staging Method with an Interpretable Channel-Temporal Attention Mechanism IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-26 Jiahui Pan, Jie Liu, Jianhao Zhang, Xueli Li, Dongming Quan, Yuanqing Li
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Husformer: A Multi-Modal Transformer for Multi-Modal Human State Recognition IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-23 Ruiqi Wang, Wonse Jo, Dezhong Zhao, Weizheng Wang, Arjun Gupte, Baijian Yang, Guohua Chen, Byung-Cheol Min
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PLOT: Human-like Push-grasping Synergy Learning in Clutter with One-shot Target Recognition IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-22 Xiaoge Cao, Tao Lu, Liming Zheng, Yinghao Cai, Shuo Wang
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Kernel Ridge Regression-based Randomized Network for Brain Age Classification and Estimation IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-18 Raveendra Pilli, Tripti Goel, R Murugan, M Tanveer, P. N. Suganthan
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The Effect of Expressive Robot Behavior on Users’ Mental Effort: A Pupillometry Study IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-15 Marieke van Otterdijk, Bruno Laeng, Diana Saplacan Lindblom, Jim Torresen
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TR-TransGAN: Temporal Recurrent Transformer Generative Adversarial Network for Longitudinal MRI Dataset Expansion IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-08 Chen-Chen Fan, Hongjun Yang, Liang Peng, Xiao-Hu Zhou, Sheng Chen, Zeng-Guang Hou
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MIMo: A Multi-Modal Infant Model for Studying Cognitive Development IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-05 Dominik Mattern, Pierre Schumacher, Francisco M. López, Marcel C. Raabe, Markus R. Ernst, Arthur Aubret, Jochen Triesch
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Reconfiguration of cognitive control networks during a long-duration flanker task IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-05 Jia Liu, Yongjie Zhu, Zheng Chang, Tiina Parviainen, Christian Antfolk, Timo Hämäläinen, Fengyu Cong
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Multiple Instance Learning for Cheating Detection and Localization in Online Examinations IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-05 Yemeng Liu, Jing Ren, Jianshuo Xu, Xiaomei Bai, Roopdeep Kaur, Feng Xia
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Dual-GCL: Dual-Graph Contrastive Learning for Unsupervised Person Re-Identification IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2024-01-03 Lin Zhang, Ran Song, Yifan Wang, Qian Zhang, Wei Zhang
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Guest Editorial Special Issue on Hybrid Brain–Computer Collaborative Intelligent System IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-12-14 Edmond Q. Wu, Pengwen Xiong, Aiguo Song, Peter X. Liu
Brain–machine fusion, also known as hybrid intelligence or brain–computer interface (BCI), is considered one of the most promising technologies of the 21st century. Its potential impact spans a wide range of disciplines, including cognitive science, information science, artificial intelligence, biology, neuroscience, and engineering. The research in this field aims to seamlessly integrate biological
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Guest Editorial Special Issue on Emerging Topics on Development and Learning IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-12-14 Dingsheng Luo, Angelo Cangelosi, Alessandra Sciutti, Weiwei Wan, Ana Tanevska
This special issue will encompass state-of-the-art research on emerging topics related to development and learning in natural and artificial systems. The primary focus of this special issue is to explore the facets of development and learning from a multidisciplinary perspective by convening researchers from the fields of computer science, robotics, psychology, and developmental studies. We invited
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Generalized Feature Learning for Detection of Novel Objects IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-10-25 Jierui Liu, Xilong Liu, Zhiqiang Cao, Junzhi Yu, Min Tan
Few-shot object detection (FSOD) aims at heuristically detecting novel objects with limited labeled data. Typical methods focus on the advanced classifications using the features extracted from common backbones. However, these features are usually base domain-biased, which trap the methods due to insufficient knowledge learned by common backbones. In this correspondence, a novel FSOD network is designed
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Human-Collaborative Artificial Intelligence Along With Social Values in Industry 5.0: A Survey of the State-of-the-Art IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-10-20 Mahdi Khosravy, Neeraj Gupta, Antoine Pasquali, Nilanjan Dey, Rubén González Crespo, Olaf Witkowski
The expected fifth industrial revolution or Industry 5.0 (I-5.0) is human-centered and concerns societal values, and sustainability. I-5.0 focuses on human and machine coworking by augmenting human-collaborative intelligent robots. The current developments in information communications and the increasing market need for high agility and innovative ways to tailor products urge the world for an I-5.0
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Sequential Learning Network With Residual Blocks: Incorporating Temporal Convolutional Information Into Recurrent Neural Networks IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-10-17 Dongjing Shan, Kun Yao, Xiongwei Zhang
Temporal convolutional networks (TCNs) have shown remarkable performance in sequence modeling and surpassed recurrent neural networks (RNNs) in a number of tasks. However, performing exceptionally on extremely long sequences remains an obstacle due to the restrained receptive field of temporal convolutions and a lack of forgetting mechanism. Although RNNs can carry state transmission down the full
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EMG-Based Cross-Subject Silent Speech Recognition Using Conditional Domain Adversarial Network IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-09-18 Yakun Zhang, Huihui Cai, Jinghan Wu, Liang Xie, Minpeng Xu, Dong Ming, Ye Yan, Erwei Yin
Machine learning techniques have achieved great success in electromyography (EMG) decoding, but EMG-based cross-subject silent speech recognition (SSR) received less attention because of its high individual variability. Therefore, this article explores the field of cross-subject SSR to improve the recognition performance of EMG data collected from new subjects. First, this article reports on applying
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An Overview of Brain Fingerprint Identification Based on Various Neuroimaging Technologies IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-09-12 Shihao Zhang, Wenting Yang, Haonan Mou, Zhaodi Pei, Fangyi Li, Xia Wu
As a novel category of biometric features, research on brain fingerprints has become a hot topic in neuroscience, not only for its reliable performance on individual identification but also for specifying the brain activity of different humans. Such unique biometric data are extracted using various neuroimaging technologies. Although the literature has reviewed the extraction and application of brain
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Guest Editorial Special Issue on Prediction and Perception in Humans and Robots IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-09-07 Alejandra Ciria, Guido Schillaci, Bruno Lara, Emily S. Cross, Angelo Cangelosi
This special issue addresses perceptual optimization processes related to attentional and predictive mechanisms. Optimal interaction with the environment requires that agents learn to anticipate and evaluate which sensory information is relevant for a task in a specific context so as to prioritize its processing. It has been suggested that during perception, the selection of sensory information depends
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Multiagent Multiobjective Decision Making and Game for Saving Public Resources IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-08-23 Xiwen Ma, Yibo Zhang, Wei Xie, Jingsong Yang, Weidong Zhang
Uncertain environments and inefficient decision analysis restrict the efficient utilization of depletable public resources by multiagents, especially for the scenario involved with multiobjective game dilemmas and weak scalability of decision making. To address the above conundrums, this article proposes a multilayer games framework that integrates cognition, decision making, and countermeasures (CDCs)
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Unsupervised Multimodal Word Discovery Based on Double Articulation Analysis With Co-Occurrence Cues IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-08-22 Akira Taniguchi, Hiroaki Murakami, Ryo Ozaki, Tadahiro Taniguchi
Human infants acquire their verbal lexicon with minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other sensory stimuli. This study proposes a novel fully unsupervised learning method for discovering speech units using phonological information as a distributional cue and object information as a co-occurrence cue. The proposed
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CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-07-28 Ali Ayub, Alan R. Wagner
For most real-world applications, robots need to adapt and learn continually with limited data in their environments. In this article, we consider the problem of few-shot incremental learning (FSIL), in which an AI agent is required to learn incrementally from a few data samples without forgetting the data it has previously learned. To solve this problem, we present a novel framework inspired by theories
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Learning Skills From Demonstrations: A Trend From Motion Primitives to Experience Abstraction IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-07-18 Mehrdad Tavassoli, Sunny Katyara, Maria Pozzi, Nikhil Deshpande, Darwin G. Caldwell, Domenico Prattichizzo
The uses of robots are changing from static environments in factories to encompass novel concepts such as human–robot collaboration in unstructured settings. Preprogramming all the functionalities for robots becomes impractical, and hence, robots need to learn how to react to new events autonomously, just like humans. However, humans, unlike machines, are naturally skilled in responding to unexpected
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pFedEff: An Efficient and Personalized Federated Cognitive Learning Framework in Multiagent Systems IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-06-26 Hongjian Shi, Jianqing Zhang, Shuming Fan, Ruhui Ma, Haibing Guan
With the increase in data volume and environment complexity, real-world problems require more advanced algorithms to acquire useful information for further analysis or decision making. Cognitive learning (CL) effectively handles incomplete information, and multiagent systems can provide enough data for analysis. Inspired by distributed machine learning, federated learning (FL) has become an efficient
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Guest Editorial Special Issue on Intrinsically Motivated Open-Ended Learning (IMOL) IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-06-08 Kathryn Kasmarik, Gianluca Baldassarre, Vieri Giuliano Santucci, Jochen Triesch
The concept of lifelong [1] , continual [2] , progressive [3] , or open-ended [4] , [5] , [6] learning by artificial agents or robots is of interest to researchers because it permits robots to adapt to multiple tasks over the course of their life and progressively accumulate knowledge [1] , [3] , [7] . This means that the agent or robot is less likely to become obsolete due to environmental changes
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Individual-Level fMRI Segmentation Based on Graphs IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-05-30 Kevin W. Tong, Xiao-Yan Zhao, Yong-Xia Li, Ping Li
Aiming at the high complexity of fMRI data and the great spatial dependence of existing methods, a whole-brain functional segmentation algorithm with low computational overhead and low spatial structure dependence is proposed for individual-level fMRI segmentation in 3-D space. First, the spatial information and functional connectivity of each voxel in fMRI are utilized for presegmentation to create
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REAL-X—Robot Open-Ended Autonomous Learning Architecture: Building Truly End-to-End Sensorimotor Autonomous Learning Systems IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-05-24 Emilio Cartoni, Davide Montella, Jochen Triesch, Gianluca Baldassarre
Open-ended learning is a core research field of developmental robotics and AI aiming to build learning machines and robots that can autonomously acquire knowledge and skills incrementally as infants. The first contribution of this work is to highlight the challenges posed by the previously proposed benchmark “REAL competition” fostering the development of truly open-ended learning robots. The benchmark
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Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-05-17 Xintong Yang, Ze Ji, Jing Wu, Yu-Kun Lai
As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the environment. Briefly, it captures the possibilities and effects of the actions of an agent applied to a specific object or, more generally, a part of the environment. This article provides a short review of the recent developments
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ElectrodeNet—A Deep-Learning-Based Sound Coding Strategy for Cochlear Implants IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-05-12 Enoch Hsin-Ho Huang, Rong Chao, Yu Tsao, Chao-Min Wu
ElectrodeNet, a deep-learning-based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks. The extended ElectrodeNet-CS strategy further incorporates the channel selection (CS). Network models of deep neural network (DNN), convolutional neural
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Parallel Self-Attention and Spatial-Attention Fusion for Human Pose Estimation and Running Movement Recognition IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-05-12 Qingtian Wu, Yu Zhang, Liming Zhang, Haoyong Yu
Human pose estimation (HPE) is a fundamental yet promising visual recognition problem. Existing popular methods (e.g., Hourglass and its variants) either attempt to directly add local features element-wisely, or (e.g., vision transformers) try to learn the global relationships among different human parts. However, it remains an open problem to effectively integrate the local–global representations
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Observer-Based Event-Triggered Iterative Learning Consensus for Locally Lipschitz Nonlinear MASs IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-05-10 Hongyi Li, Jinhao Luo, Hui Ma, Qi Zhou
This article aims to realize the robust tracking for nonidentical locally Lipschitz nonlinear multiagent systems (MASs) with unmeasurable states, for which an observer-based distributed event-triggered iterative learning control (ILC) framework is proposed. With this framework, distributed state observers provide the indispensable state information for agents to learn to complete the task. An initial
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SalDA: DeepConvNet Greets Attention for Visual Saliency Prediction IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-05-10 Yihan Tang, Pan Gao, Zhengwei Wang
Predicting salient regions in images requires the capture of contextual information in the scene. Conventional saliency models typically use the encoder–decoder architecture and multiscale feature fusion for modeling contextual features, which, however, possess huge computational cost and model parameters. In this article, we address the saliency prediction task by capturing long-range dependencies
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AdaDet: An Adaptive Object Detection System Based on Early-Exit Neural Networks IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-05-09 Le Yang, Ziwei Zheng, Jian Wang, Shiji Song, Gao Huang, Fan Li
Recent researchers have proposed adaptive inference methods with an early-exiting mechanism, which stops the inference procedure of input if the prediction is with high confidence, leading to a fine-grained resource allocation based on the complexity of inputs. However, the adaptive inference strategy can only be applied for image classification tasks, such a system for object detection is still under-investigated
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CSC-Net: Cross-Color Spatial Co-Occurrence Matrix Network for Detecting Synthesized Fake Images IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-05-09 Tong Qiao, Yuxing Chen, Xiaofei Zhou, Ran Shi, Hang Shao, Kunye Shen, Xiangyang Luo
Recently, the generative adversarial networks (GANs) generated images have been spread over the social networks, which brings the new challenge in the community of media forensics. Although some reliable forensic tools have advanced the study of detecting GAN generated images, while the detection accuracy cannot be guaranteed when facing the malicious post-processing attacks, especially in the practical
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Spatial–Temporal Feature Network for Speech-Based Depression Recognition IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-05-08 Zhuojin Han, Yuanyuan Shang, Zhuhong Shao, Jingyi Liu, Guodong Guo, Tie Liu, Hui Ding, Qiang Hu
Depression is a serious mental disorder that has received increased attention from society. Due to the advantage of easy acquisition of speech, researchers have tried to propose various automatic depression recognition algorithms based on speech. Feature selection and algorithm design are the main difficulties in speech-based depression recognition. In our work, we propose the spatial–temporal feature
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DisTop: Discovering a Topological Representation to Learn Diverse and Rewarding Skills IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-04-27 Arthur Aubret, Laetitia Matignon, Salima Hassas
An efficient way for a deep reinforcement learning (RL) agent to explore in sparse-rewards settings can be to learn a set of skills that achieves a uniform distribution of terminal states. We introduce DisTop, a new model that simultaneously learns diverse skills and focuses on improving rewarding skills. DisTop progressively builds a discrete topology of the environment using an unsupervised contrastive
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EEG-Based Emotion Recognition Using Trainable Adjacency Relation Driven Graph Convolutional Network IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-04-25 Wei Li, Mingming Wang, Junyi Zhu, Aiguo Song
In recent years, there has been a growing research interest in using deep learning to resolve the issue of electroencephalogram (EEG)-based emotion recognition. Current research emphasizes exploiting the useful information from each single EEG channel or each individual set of multichannel EEG, but overlooks the correlation information among different multichannel EEG sets. To explore such discriminative
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Prior Knowledge-Augmented Broad Reinforcement Learning Framework for Fault Diagnosis of Heterogeneous Multiagent Systems IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-04-13 Li Guo, Yiran Ren, Runze Li, Bin Jiang
A heterogeneous multiagent system (MAS) can easily experience unpredictable faults due to its complex structure and involvement of different individuals. However, existing approaches have several issues, including complicated network architecture, insufficient feature extraction, and poor generalization ability. This study proposes a novel framework called prior knowledge-augmented broad reinforcement
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Iterative Pseudo-Sparse Partial Least Square and Its Higher Order Variant: Application to Inference From High-Dimensional Biosignals IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-04-13 Aref Einizade, Sepideh Hajipour Sardouie
Partial least square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem at hand and select relevant features. Addressing these issues improves the generalizability of decoding the unseen data, with the severe challenge of high computational complexity. In order to avoid directly solving the L1 norm optimization problem
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Hand Movement Recognition and Salient Tremor Feature Extraction With Wearable Devices in Parkinson’s Patients IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-04-13 Fang Lin, Zhelong Wang, Hongyu Zhao, Sen Qiu, Ruichen Liu, Xin Shi, Cui Wang, Wenchao Yin
Tremor is one of the earliest signs of Parkinson’s disease (PD), which seriously disrupts patients’ daily lives. It is important to study upper limb tremors quantitatively to control PD progression. In this study, surface electromyography (sEMG) signals from wearable devices are used to recognize rest, posture, and kinetic tremor action from six upper limb clinical actions and to quantify features
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A Cognitive Robotics Implementation of Global Workspace Theory for Episodic Memory Interaction With Consciousness IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-04-11 Wenjie Huang, Antonio Chella, Angelo Cangelosi
Artificial general intelligence revived in recent years after people achieved significant advances in machine learning and deep learning. This leads to the thinking of how real intelligence could be created. Consciousness theories believe that general intelligence is essentially conscious, yet no universal definition is agreed upon. In this work, global workspace (GW) theory is implemented and integrated
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D2IFLN: Disentangled Domain-Invariant Feature Learning Networks for Domain Generalization IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-04-05 Zhengfa Liu, Guang CHen, Zhijun Li, Sanqing Qu, Alois Knoll, Changjun Jiang
Domain generalization (DG) aims to learn a model that generalizes well to an unseen test distribution. Mainstream methods follow the domain-invariant representational learning philosophy to achieve this goal. However, due to the lack of priori knowledge to determine which features are domain specific and task-independent, and which features are domain invariant and task relevant, existing methods typically
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Relationship Between Decision Making and Resting-State EEG in Adolescents With Different Emotional Stabilities IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-04-03 Yajing Si, Lin Jiang, Peiyang Li, Baodan Chen, Feng Wan, Jing Yu, Dezhong Yao, Fali Li, Peng Xu
Despite the varied decision responses are revealed between adolescents with emotional stability and instability (ES and EI), the possible association underlying decision making and resting-state activity remains unknown. The study explored the potential relationship between the resting-state electroencephalogram network and decision responses when adolescents with different emotional stabilities participated
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A Distributed Dynamic Framework to Allocate Collaborative Tasks Based on Capability Matching in Heterogeneous Multirobot Systems IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-04-03 Hoi-Yin Lee, Peng Zhou, Bin Zhang, Liuming Qiu, Bowen Fan, Anqing Duan, Jingtao Tang, Tin Lun Lam, David Navarro-Alarcon
Collaboration among a group of robots with heterogeneous capabilities is an important research problem that enables to combine different robot functionalities, and thus, conducts complex tasks that may be difficult to achieve by a single robot with limited resources. In this article, we propose a new distributed task allocation framework based on the capability matching of heterogeneous robots. The
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Enhancing Overt and Covert Attention Using a Real-Time Neurofeedback Game With Consumer-Grade EEG IEEE Trans. Cogn. Dev. Syst. (IF 5.0) Pub Date : 2023-03-22 T. A. Suhail, A. P. Vinod
Neurofeedback training is emerging as a promising tool for cognitive enhancement in healthy as well as cognitive-deficit patients. In this article, we propose the design of a real-time neurofeedback game using a consumer-grade wireless electroencephalography (EEG) system and examine its efficacy in enhancing overt and covert attention abilities for healthy individuals. The game uses a simulated car