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2020 Index IEEE Transactions on Cognitive and Developmental Systems Vol. 12 IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2021-01-11
Presents the 2020 subject/author index for this publication.
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Table of contents IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-09-08
Presents the table of contents for this issue of the publication.
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IEEE Transactions on Cognitive and Developmental Systems publication information IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-09-08
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Special Issue on Artificial Intelligence and Edge Computing for Trustworthy Robots and Autonomous Systems IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-09-08
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Special Issue on Toward Autonomous Evolution, (Re)production, and Learning in Robotic Ecosystems IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-09-08
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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TechRxiv IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-09-08
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Introducing IEEE Collabratec IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-09-08
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Member Get-A-Member (MGM) Program IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-09-08
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IEEE Computational Intelligence Society Information IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-09-08
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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IEEE Transactions on Cognitive and Developmental Systems information for authors IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-09-08
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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Understanding Decisions in Collective Risk Social Dilemma Games Using Reinforcement Learning IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-07-13 Medha Kumar; Varun Dutt
Prior research has used reinforcement-learning models to investigate human decisions in choice games. However, research has not investigated how reinforcement-learning models expectancy valence learning (EVL) and prospect valence learning (PVL) would explain human decisions in applied judgment games where people face a collective risk social dilemma (CRSD) against societal problems such as climate
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Table of contents IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-06-10
Presents the table of contents for this issue of the publication.
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IEEE Transactions on Cognitive and Developmental Systems publication information IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-06-10
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Guest Editorial Special Issue on Multidisciplinary Perspectives on Mechanisms of Language Learning IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-06-10 Malte Schilling; Katharina J. Rohlfing; Paul Vogt; Chen Yu; Michael Spranger
Humans excel at learning from other humans [item 1) in the Appendix). Language facilitates such learning and plays a crucial role. On the one hand, it coordinates our interactions and cooperative behavior [item 2) in the Appendix). On the other hand, language and communication allow to directly incorporate novel knowledge gathered from social interaction or from reading [item 3) in the Appendix). It
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Special Issue on Artificial Intelligence and Edge Computing for Trustworthy Robots and Autonomous Systems IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-06-10
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Special Issue on Intrinsically Motivated Open-Ended Learning IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-06-10
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Special Issue on Artificial Intelligence and Edge Computing for Trustworthy Robots and Autonomous Systems IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-06-10
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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Introducing IEEE Collabratec IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-06-10
Advertisement.
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IEEE Computational Intelligence Society Information IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-06-10
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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IEEE Transactions on Cognitive and Developmental Systems information for authors IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-06-10
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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Models of Cross-Situational and Crossmodal Word Learning in Task-Oriented Scenarios IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-05-15 Brigitte Krenn; Sepideh Sadeghi; Friedrich Neubarth; Stephanie Gross; Martin Trapp; Matthias Scheutz
We present two related but different cross-situational and crossmodal models of incremental word learning. Model 1 is a Bayesian approach for co-learning object-word mappings and referential intention which allows for incremental learning from only a few situations where the display of referents to the learning system is systematically varied. We demonstrate the robustness of the model with respect
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A Method for Napping Time Recommendation Using Electrical Brain Activity IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-04-29 Sheng-Fu Liang; Yu-Hsuan Shih; Yu-Han Hu; Chih-En Kuo
Napping in the workplace has become popular. Knowing how to nap for brain benefits is important. We designed a nap experiment to investigate how napping after different sleep stages impacts procedural memory and sleepiness. In total, 45 nonhabitual nappers were randomly assigned to the Wake group (no napping), N2 group (napping and being woken after enough N2 sleep), and slow-wave sleep (SWS) group
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Intraindividual Completion Time Modulates the Prediction Error Negativity in a Virtual 3-D Object Selection Task IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-04-29 Avinash Kumar Singh; Hsiang-Ting Chen; Klaus Gramann; Chin-Teng Lin
A prediction error negativity (PEN) can be observed in the human electroencephalogram when there is a mismatch between the predicted and the perceived changes in the environment. Our previous study using a virtual object selection task demonstrated an impact of the level of avatar realism on the PEN, reflecting a mismatch between visual and proprioceptive feedback about the object selection. To investigate
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A Concealed Information Test System Based on Functional Brain Connectivity and Signal Entropy of Audio–Visual ERP IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-04-29 Wenwen Chang; Hong Wang; Zhiguo Lu; Chong Liu
Deception is a human behavior and its cognitive process and mechanism involve complex neuronal activities of the brain. In this article, we develop a simple and feasible concealed information test (CIT) method which is based on the audio–visual event-related potentials (ERPs) and its spatial and temporal features. The main purpose of this article is to extend a pattern recognition method with functional
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Table of contents IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-03-11
Presents the table of contents for this issue of this publication.
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IEEE Transactions on Cognitive and Developmental Systems publication information IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-03-11
Provides a listing of current staff, committee members and society officers.
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A Furcated Visual Collision Avoidance System for an Autonomous Microrobot IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-07-23 Hamid Isakhani; Nabil Aouf; Odysseas Kechagias-Stamatis; James F. Whidborne
This paper proposes a secondary reactive collision avoidance system for microclass of robots based on a novel approach known as the furcated luminance-difference processing (FLDP) inspired by the lobula giant movement detector, a wide-field visual neuron located in the lobula layer of a locust nervous system. This paper addresses some of the major collision avoidance challenges: obstacle proximity
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Can the Evidence for Explanatory Reasoning Be Explained Away? IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-07-31 Igor Douven
Recent evidence appears to show a close connection between explanation and belief revision, specifically, the revision of graded beliefs. Insofar as this is also evidence of violations of Bayesian norms of reasoning, the question arises whether we are facing a new bias here, on a par with previously discovered biases in probabilistic reasoning. We consider an apparently successful attempt by Costello
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Cooperative Manipulation for a Mobile Dual-Arm Robot Using Sequences of Dynamic Movement Primitives IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-09-06 Ting Zhao; Mingdi Deng; Zhijun Li; Yingbai Hu
In order to extend promising robot applications in human daily lives, robots need to perform dextrous manipulation tasks, particularly for a mobile dual-arm robot. This paper propose a novel control strategy, which consists of a first trial process and a learning phase, to enable a mobile dual-arm robot to complete a grasp-and-place task which can be decomposed into movement sequences, such as reaching
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Abnormal Event Detection From Videos Using a Two-Stream Recurrent Variational Autoencoder IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-11-26 Shiyang Yan; Jeremy S. Smith; Wenjin Lu; Bailing Zhang
With the massive deployment of distributed video surveillance systems, the automatic detection of abnormal events in video streams has become an urgent need. An abnormal event can be considered as a deviation from the regular scene; however, the distribution of normal and abnormal events is severely imbalanced, since the abnormal events do not frequently occur. To make use of a large number of video
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Robotic-Assisted Rehabilitation Trainer Improves Balance Function in Stroke Survivors IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-11-27 Jiancheng Ji; Tao Song; Shuai Guo; Fengfeng Xi; Hua Wu
Nerve injury after stroke leads to disorders of locomotion and a declining balance function, which increases the risk of falling. Restriction of pelvic motions can hinder successful rehabilitation, hence a robotic-assisted rehabilitation trainer (RART) is proposed to assist patients in controlling the pelvic motions via force field. The mechanical design, kinetic framework, and the intention-based
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DeepFeat: A Bottom-Up and Top-Down Saliency Model Based on Deep Features of Convolutional Neural Networks IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-01-23 Ali Mahdi; Jun Qin; Garth Crosby
A deep feature-based saliency model (DeepFeat) is developed to leverage understanding of the prediction of human fixations. Conventional saliency models often predict the human visual attention relying on few image cues. Although such models predict fixations on a variety of image complexities, their approaches are limited to the incorporated features. In this paper, we aim to utilize the deep features
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Selective Perception as a Mechanism to Adapt Agents to the Environment: An Evolutionary Approach IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-01-30 Mirza Ramicic; Andrea Bonarini
Rapid advancement of machine learning makes it possible to consider large amounts of data to learn from. Learning agents may get data ranging on real intervals directly from the environment they interact with, in a process usually time expensive. To improve learning and manage these data, approximated models and memory mechanisms are adopted. In most of the implementations of reinforcement learning
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Zero-Shot Classification Based on Multitask Mixed Attribute Relations and Attribute-Specific Features IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-02-28 Ping Gong; Xuesong Wang; Yuhu Cheng; Z. Jane Wang; Qiang Yu
Zero-shot classification is a hot topic in computer vision and pattern recognition. Most zero-shot classification methods are based on the intermediate level representation of attributes to achieve knowledge transfer from the training classes to the unseen test classes. Recently, multitask learning (MTL) has been shown as one of state-of-the-art approaches for attribute learning and zero-shot classification
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Semantic Relational Object Tracking IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-06-24 Andreas Persson; Pedro Zuidberg Dos Martires; Luc De Raedt; Amy Loutfi
This paper addresses the topic of semantic world modeling by conjoining probabilistic reasoning and object anchoring. The proposed approach uses a so-called bottom-up object anchoring method that relies on rich continuous attribute values measured from perceptual sensor data. A novel anchoring matching function learns to maintain object entities in space and time and is validated using a large set
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Memory Mechanisms for Discriminative Visual Tracking Algorithms With Deep Neural Networks IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-02-21 Lituan Wang; Lei Zhang; Jianyong Wang; Zhang Yi
Deep-neural-networks-based online visual tracking methods have achieved state-of-the-art results. One of the core components of these methods is the memory pool, in which a number of samples consisting of image patches and the corresponding labels are stored to update the online tracking network. Hence, the mechanism of updating the stored samples determines the performance of the tracking method.
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Usage-Based Learning in Human Interaction With an Adaptive Virtual Assistant IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-07-09 Clément Delgrange; Jean-Michel Dussoux; Peter Ford Dominey
Today users can interact with popular virtual assistants such as Siri to accomplish their tasks on a digital environment. In these systems, links between natural language requests and their concrete realizations are specified at the conception phase. A more adaptive approach would be to allow the user to provide natural language instructions or demonstrations when a task is unknown by the assistant
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A Brain-Inspired Visual Fear Responses Model for UAV Emergent Obstacle Dodging IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-09-04 Feifei Zhao; Qingqun Kong; Yi Zeng; Bo Xu
Dodging emergent dangers is an innate cognitive ability for animals, which helps them to survive in the natural environment. The retina-superior colliculus (SC)-pulvinar–amygdala–periaqueductal gray pathway is responsible for the visual fear responses, and it is able to quickly detect the looming obstacles for innate dodging. Inspired by the mechanism of the visual fear responses pathway, we propose
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IEEE Computational Intelligence Society Information IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-03-11
Provides a listing of current committee members and society officers.
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IEEE Transactions on Cognitive and Developmental Systems information for authors IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-03-11
Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
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Extended Interaction With a BCI Video Game Changes Resting-State Brain Activity IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-04-02 Avinash Kumar Singh; Yu-Kai Wang; Jung-Tai King; Chin-Teng Lin
Video games are a widespread leisure activity and essential for a substantial field of research. In several kinds of research, video games show positive effects on cognition. Video games’ ability to change the brain in a way that improves cognition is already evident in the research world. The underlying brain dynamics assessed by coherence (Coh) and partial-directed coherence (PDC) can shed light
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Table of contents IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-12-09
Presents the table of contents for this issue of this publication.
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IEEE Transactions on Cognitive and Developmental Systems publication information IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-12-09
Provides a listing of current staff, committee members and society officers.
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Evaluation of Internal Models in Autonomous Learning IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-08-22 Simón C. Smith; J. Michael Herrmann
Internal models (IMs) can represent relations between sensors and actuators in natural and artificial agents. In autonomous robots, the adaptation of IMs and the adaptation of the behavior are interdependent processes which have been studied under paradigms for self-organization of behavior such as homeokinesis. We compare the effect of various types of IMs on the generation of behavior in order to
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Anomalous Behaviors Detection in Moving Crowds Based on a Weighted Convolutional Autoencoder-Long Short-Term Memory Network IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-08-23 Biao Yang; Jinmeng Cao; Nan Wang; Xiaofeng Liu
We propose an anomaly detection approach by learning a generative model of moving pedestrians to guarantee public safety. To resolve the existing challenges of anomaly detection in complicated definitions, complex backgrounds, and local occurrence, a weighted convolutional autoencoder-long short-term memory network is proposed to reconstruct raw data and their corresponding optical flow and then perform
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A Workload Balanced Algorithm for Task Assignment and Path Planning of Inhomogeneous Autonomous Underwater Vehicle System IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-08-23 Mingzhi Chen; Daqi Zhu
Task assignment is an important research topic in multiple autonomous underwater vehicle (AUV) cooperative working system. However, many studies concentrate on minimizing total distance of AUVs serving targets at different locations, and mostly do not pay attention to workload balance among inhomogeneous AUVs. What is more, most of them do not think of the effect of ocean current while distributing
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Symbol Emergence in Cognitive Developmental Systems: A Survey IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-08-30 Tadahiro Taniguchi; Emre Ugur; Matej Hoffmann; Lorenzo Jamone; Takayuki Nagai; Benjamin Rosman; Toshihiko Matsuka; Naoto Iwahashi; Erhan Oztop; Justus Piater; Florentin Wörgötter
Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding
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Electroencephalogram Emotion Recognition Based on Empirical Mode Decomposition and Optimal Feature Selection IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-08-31 Zhen-Tao Liu; Qiao Xie; Min Wu; Wei-Hua Cao; Dan-Yun Li; Si-Han Li
Electroencephalogram (EEG) emotion recognition based on a hybrid feature extraction method in empirical mode decomposition domain combining with optimal feature selection based on sequence backward selection is proposed, which can reflect subtle information of multiscale components of unstable and nonlinear EEG signals and remove the reductant features to improve the performance of emotion recognition
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Brain Teleoperation Control of a Nonholonomic Mobile Robot Using Quadrupole Potential Function IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-09-24 Wang Yuan; Zhijun Li
This paper presents the development of a brain-machine interfacing teleoperation control framework of a mobile robot using quadrupole potential function (QPF). The online brain-computer interface is based on steady-state visually evoked potentials, which employs the multivariate synchronization index classification algorithm to decode the human electroencephalograph (EEG) signals. In this way, human
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Semi-Supervised Learning Based on GAN With Mean and Variance Feature Matching IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-10-11 Cong Hu; Xiao-Jun Wu; Josef Kittler
The improved generative adversarial network (improved GAN) is a successful method using a generative adversarial model to solve the problem of semi-supervised learning (SSL). The improved GAN learns a generator with the technique of mean feature matching which penalizes the discrepancy of the first-order moment of the latent features. To better describe common attributes of a distribution, this paper
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Optimal Control of Eye Movements During Visual Search IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-10-22 Alexander Vasilyev
In this paper, we study the problem of an optimal oculomotor control during the execution of visual search tasks. We introduce a computational model of human eye movements, which takes into account various constraints of the human visual and oculomotor systems. In the model, the choice of the subsequent fixation location is posed as a problem of a stochastic optimal control, which relies on reinforcement
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Automatic Pupillary Light Reflex Detection in Eyewear Computing IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-11-09 Hoe Kin Wong; Julien Epps; Siyuan Chen
There are many benefits to facilitating “always-on” pupillary light reflex (PLR)-aware pupil size measurement in eyewear, including improving the reliability of pupil-based cognitive and affective load monitoring and enabling PLR-based diagnosis of cognitive and eye-related diseases which have neurological symptoms manifested in the form of aberrant PLR responses. However, the detection of PLR responses
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Multitask Learning for Object Localization With Deep Reinforcement Learning IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2018-12-10 Yan Wang; Lei Zhang; Lituan Wang; Zizhou Wang
In object localization, methods based on a top-down search strategy that focus on learning a policy have been widely researched. The performance of these methods relies heavily on the policy in question. This paper proposes a deep Q-network (DQN) that employs a multitask learning method to localize class-specific objects. This DQN agent consists of two parts, an action executor part and a terminal
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Project R-CASTLE: Robotic-Cognitive Adaptive System for Teaching and Learning IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-09-12 Daniel Tozadore; Adam H. M. Pinto; João Valentini; Marcos Camargo; Rodrigo Zavarizz; Victor Rodrigues; Fernando Vedrameto; Roseli Romero
Robots are already present in people's lives as receptionists, caregivers, and tutors. In human-robot interaction, social behavior is not only expected but often associated with users' confidence. Although several studies have been researching in this direction, the robot adaptation and the existing gap between the system and nonprogramming designers still need more effort to achieve success. In this
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Call for Papers: Special Issue on Human Friendly Cognitive Robotics IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-12-09
Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.
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IEEE Computational Intelligence Society Information IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-12-09
Provides a listing of current committee members and society officers.
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IEEE Transactions on Cognitive and Developmental Systems information for authors IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2019-12-09
Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
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A Multitier Reinforcement Learning Model for a Cooperative Multiagent System IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-01-30 Haobin Shi; Liangjing Zhai; Haibo Wu; Maxwell Hwang; Kao-Shing Hwang; Hsuan-Pei Hsu
In multiagent cooperative systems with value-based reinforcement learning, agents learn how to complete the task by an optimal policy learned through value-policy improvement iterations. But how to design a policy that avoids cooperation dilemmas and comes to a common consensus between agents is an important issue. A method that improves the coordination ability of agents in cooperative systems by
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Perceptual Modeling of Tinnitus Pitch and Loudness IEEE Trans. Cogn. Dev. Syst. (IF 2.667) Pub Date : 2020-01-08 Richard Gault; Thomas Martin McGinnity; Sonya Coleman
Tinnitus is the phantom perception of sound, experienced by 10%–15% of the global population. Computational models have been used to investigate the mechanisms underlying the generation of tinnitus-related activity. However, existing computational models have rarely benchmarked the modeled perception of a phantom sound against recorded data relating to a person’s perception of tinnitus characteristics