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Operator State Estimation to Enable Adaptive Assistance in Manned-Unmanned-Teaming Cogn. Syst. Res. (IF 1.902) Pub Date : 2021-01-18 Simon Schwerd; Axel Schulte
With the continued development of unmanned aerial vehicle (UAV) technologies, the UAV on-board automation is increasingly more capable of performing tasks formerly done by human operators. Thereby, the role of UAVs is changing from being mere tools to become members of integrated manned-unmanned systems. However, the high automation necessary to achieve this cooperation, introduces a new set of negative
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A proposal of bioinspired motor-system cognitive architecture focused on feed-forward-control movements Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-12-17 Carlos Johnnatan Sandoval; Felix Francisco Ramos
The objective of this article is to present a conceptual motor-system cognitive architecture inspired in the human nervous system, and a cognitive architecture focused on the voluntary movement controlled by feed-forward. The article first focuses on describing the brain cortex areas that make up the motor system, presenting the supplementary motor area (SMA), premotor cortex and primary cortex, the
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Energy management solutions in the Internet of Things applications: Technical analysis and new research directions Cogn. Syst. Res. (IF 1.902) Pub Date : 2021-01-03 Dayu Wang; Daojun Zhong; Alireza Souri
By advancement of Internet of Things (IoT) technology in smart life such as smart city, smart home, smart healthcare and smart transportation, interconnections between smart things are growing that complicate evaluation of efficiency factors on the intelligent systems. Energy consumption as one of the most challenging issues is increasing with the growing IoT devices and existing interconnections between
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Russian-language Neurosemantics: Clustering of Word Meaning and Sense from Oral Narratives Cogn. Syst. Res. (IF 1.902) Pub Date : 2021-01-11 Liudmila Zaidelman; Zakhar Nosovets; Artemiy Kotov; Vadim Ushakov; Vera Zabotkina; Boris M. Velichkovsky
This article is a part of a large-scale brain mapping project aimed at finding the relations among semantic categories in oral Russian-language texts and brain activity as measured using functional magnetic resonance imaging (fMRI). The goal of present study in particular is to examine the nature of lexical semantic relations and find an appropriate lexical space, homeomorphic to the activation patterns
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An adaptive network model covering metacognition to control adaptation for multiple mental models Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-12-04 Jan Treur
Learning processes can be described by adaptive mental (or neural) network models. If metacognition is used to regulate learning, the adaptation of the mental network becomes itself adaptive as well: second-order adaptation. In this paper, a second-order adaptive mental network model is introduced for metacognitive regulation of learning processes. The focus is on the role of multiple internal mental
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The Enactive Computational Basis of Cognition and the Explanatory Cognitive Basis for Computing Cogn. Syst. Res. (IF 1.902) Pub Date : 2021-01-09 Leonardo Lana de Carvalho; João Eduardo Kogler
The computational theory of cognition, or computationalism, holds that cognition is a form of computation. Two issues related to this view are comprised by the goal of this paper: A) Computing systems are traditionally seen as representational systems, but functional and enactive approaches support non-representational theories; B) Recently, a sociocultural theory against computationalism was proposed
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Conceptual processing system for a companion robot Cogn. Syst. Res. (IF 1.902) Pub Date : 2021-01-07 Artemiy Kotov; Liudmila Zaidelman; Anna Zinina; Nikita Arinkin; Alexander Filatov; Kirill Kivva
Companion robots should perceive speech, recognize objects in the real world, and further react with speech utterances and nonverbal communicative cues. Robots should also remember the interaction history and accumulate knowledge from external text sources: news, blogs, and e-mails. We have designed a conceptual representation system for a companion robot, able to support this list of interactive tasks
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Prototypes of the “natural” concepts discovery Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-12-16 Evgenii Vityaev; Bayar Pak
In works of Eleanor Rosch “natural” concepts was introduced that reflect a high correlated structure of features of objects of the external world. Prototypes of the “natural” concepts are clearest cases of objects that reflect this highly correlated structure. The same high correlated structure manifested in the “natural” phenotypical classification. To formalize this highly correlated structure, we
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Applying Deutsch’s concept of good explanations to artificial intelligence and neuroscience – An initial exploration Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-12-23 Daniel C. Elton
Artificial intelligence has made great strides since the deep learning revolution, but AI systems remain incapable of learning principles and rules which allow them to extrapolate outside of their training data to new situations. For inspiration we look to the domain of science, where scientists have been able to develop theories which show remarkable ability to extrapolate and sometimes even predict
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Risk assessment of knowledge fusion in an innovation ecosystem based on a GA-BP neural network Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-12-15 Lin Wang; Xinhua Bi
The risk assessment of knowledge fusion in innovation ecosystems is directly related to these ecosystems’ success or failure. A back-propagation (BP) neural network optimized by a genetic algorithm (GA) is thus proposed to evaluate the risk of knowledge fusion in innovation ecosystems. First, an index system is constructed for evaluating the risk of knowledge fusion in innovation ecosystems, and data
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Learning in LIDA Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-21 Sean Kugele; Stan Franklin
LIDA is a systems-level, biologically-inspired cognitive architecture. More than a decade of research on LIDA has seen much conceptual work on its learning mechanisms, and resulted in a set of conceptual commitments that constrain those mechanisms; perhaps the most essential of these constraints is the Conscious Learning Hypothesis from Global Workspace Theory, which asserts that all significant learning
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Multi-modal actuation with the activation bit vector machine Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-12-06 H.R. Schmidtke
Research towards a new approach to the abstract symbol grounding problem showed that through model counting there is a correspondence between logical/linguistic and coordinate representation in the visuospatial domain. The logical/verbal description of a spatial layout directly gives rise to a coordinate representation that can be drawn, with the drawing reflecting what is described. The main characteristic
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Decision-making bioinspired model for target definition and “satisfactor” selection for physiological needs Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-12-01 Raymundo Ramirez-Pedraza; Felix Ramos
Every person, from an early age, has to make decisions to resolve situations that arise in life. In general, different people make different decisions in the same situation, since decision-making takes into account different factors such as age, emotional state, experience, among others. We can make decisions about situations that we classify as: more important than others, routine, unexpected, or
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Bio-inspired cognitive model of motor learning by imitation Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-28 Zandor Machaen; Luis Martin; Jonathan-Hernando Rosales
Learning, and even more so by imitation, is an essential Cognitive Functions because it is carried out throughout life and allows us to adapt our behaviors from other beings through observation. In this work, we propose a model, and implementation of the cognitive function of imitation motor learning (IML), based on psychological and neuroscientific evidence. According to the evidence, learning by
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An expanded model for perceptual visual single object recognition system using expectation priming following neuroscientific evidence Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-19 Ivan Axel Dounce; Felix Ramos
Under numerous circumstances, humans recognize visual objects in their environment with remarkable response times and accuracy. Existing artificial visual object recognition systems have not yet surpassed human vision, especially in its universality of application. We argue that modeling the recognition process in an exclusive feedforward manner hinders those systems’ performance. To bridge that performance
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Complementary interactions between classical and top-down driven inhibitory mechanisms of attention Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-12-18 S.C. Low; V. Vouloutsi; P.F.M.J. Verschure
Selective attention informs decision-making by biasing perceptual processing towards task-relevant stimuli. In experimental and computational literature, this is most often implemented through top-down excitation of selected stimuli. However, physiological and anatomical evidence shows that in certain situations, top-down signals could instead be inhibitory. In this study, we investigated how such
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A Two-Part Evaluation Approach for Measuring the Usability and User Experience of an Augmented Reality-based Assistance System to Support the Temporal Coordination of Spatially Dispersed Teams Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-12-10 Lisa Thomaschewski; Benjamin Weyers; Annette Kluge
One key predictor of team performance and productivity is the teams’ ability to coordinate its subtasks as precisely as possible over time. Thereby, the quality of the temporal coordination in teams is highly dependent on several cognitive team skills, like for instance the ability to build a precise and stable mental model of the situation (shared situation awareness) and of the team task process
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Declarative working memory: A bio-inspired cognitive architecture proposal Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-19 Luis Martin; Karina Jaime; Félix Ramos; Francisco Robles
Memory is considered one of the most important functions since it allows us to code, store and retrieve knowledge. These qualities make it an indispensable function for a virtual creature. In general, memory can be classified based on the durability of the stored data in working memory and long-term memory. Working memory refers to the capacity to maintain temporarily a limited amount of information
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A bioinspired model of short-term satiety of hunger influenced by food properties in virtual creatures Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-19 Diana G. Gómez-Martínez; Marco Ramos; Juan Luis del Valle-Padilla; Jonathan-Hernando Rosales; Francisco Robles; Félix Ramos
The behavior of the human is continually changed as a consequence of various drives which human is predisposed also of your survival instinct. Among the basic drives of the human, there are the physiological needs and is precisely the hunger that motivates the food intake to get the energy that the body requires via food. The regulation of hunger allows to stop the food intake by means of the homeostatic
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Causal cognitive architecture 1: Integration of connectionist elements into a navigation-based framework Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-19 Howard Schneider
The brain-inspired Causal Cognitive Architecture 1 (CCA1) tightly integrates the sensory processing capabilities found in neural networks with many of the causal abilities found in human cognition. Causality emerges not from a central controlling stored program but directly from the architecture. Sensory input vectors are processed by robust association circuitry and then propagated to a navigational
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Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-18 Zalimhan Nagoev; Inna Pshenokova; Olga Nagoeva; Zaurbek Sundukov
The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making
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Does change in ethical education influence core moral values? Towards history- and culture-aware morality model with application in automatic moral reasoning Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-21 Jagna Nieuważny; Karol Nowakowski; Michal Ptaszyński; Fumito Masui; Rafal Rzepka; Kenji Araki
In this study, we focus on ethical education as a means to improve artificial companion’s conceptualization of moral decision-making process in human users. In particular, we focus on automatically determining whether changes in ethical education influenced core moral values in humans throughout the century. We analyze ethics as taught in Japan before WWII and today to verify how much the pre-WWII
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Cognitive Evaluation of Machine Learning Agents Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-29 Suvarna Kadam; Vinay Vaidya
Advances in applying statistical Machine Learning (ML) led to several claims of human-level or near-human performance in tasks such as image classification & speech recognition. Such claims are unscientific primarily for two reasons, (1) They incorrectly enforce the notion that task-specific performance can be treated as manifestation of General Intelligence and (2) They are not verifiable as currently
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A proposal for an auditory sensation cognitive architecture and its integration with the motor-system cognitive function Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-19 G. Palacios; C. Sandoval; F. Ramos
The auditory system is capable of producing a wide range of information through the acquisition and perception of the vibrations present in the environment, even when the receptor is not directly facing the stimulus’s source. Said information can be crucial for survival and useful for a variety of systems like the visual system and the motor system. Despite that, the quantity of studies involving the
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Brain-inspired distributed cognitive architecture Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-17 Leendert A. Remmelzwaal; Amit K. Mishra; George F.R. Ellis
In this paper we present a brain-inspired cognitive architecture that incorporates sensory processing, classification, contextual prediction, and emotional tagging. The cognitive architecture is implemented as three modular web-servers, meaning that it can be deployed centrally or across a network for servers. The experiments reveal two distinct operations of behaviour, namely high- and low-salience
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Modeling spread, interlace and interchange of information processes with variable domains Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-24 Viacheslav Wolfengagen; Larisa Ismailova; Sergey Kosikov; Denis Babushkin
In this paper a semantic metalanguage is developed and designed to study the occurrence, spread and safe interaction of semantic processes in information modeling systems, including cognitive interference. An approach to construe a semantic network is proposed and based on a computational model in which both nodes and arcs are information processes. Concepts are represented by intensional objects within
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A brain-inspired cognitive support model for stress reduction based on an adaptive network model Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-10 Andrei Andrianov; S. Sahand Mohammadi Ziabari; Charlotte Gerritsen
Stress is often seen as a negative factor which affects every individual’s life quality and decision making. To help avoid or deal with extreme emotions caused by an external stressor, a number of practices have been introduced. In the scope of this paper, we take three kinds of therapy into account: mindfulness, humor, and music therapy. This paper aims to see how various practices help people to
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Cogmic space for narrative-based world representation Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-13 Taisuke Akimoto
Representing a world or a physical/social environment in an agent’s cognitive system is essential for creating human-like artificial intelligence. This study takes a story-centered approach to this issue. In this context, a story refers to an internal representation involving a narrative structure, which is assumed to be a common form of organizing past, present, future, and fictional events and situations
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A Multi-Level Cognitive Architecture for Self-Referencing, Self-Awareness and Self-Interpretation Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-23 Jan Treur; Gerrit Glas
In this paper, a multilevel cognitive architecture is introduced that can be used to model mental processes in clients of psychotherapeutic sessions. The architecture does not only cover base level mental processes but also mental processes involving self-referencing, self-awareness and self-interpretation. To this end, the cognitive architecture was designed according to four levels, where (part of)
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Toward integrating cognitive components with computational models of emotion using software design patterns Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-03 Enrique Osuna; Luis-Felipe Rodríguez; J. Octavio Gutierrez-Garcia
Computational models of emotion (CMEs) are software systems designed to imitate particular aspects of human emotions. The main purpose of this type of computational model is to capture the complexity of the human emotion process in a software system that is incorporated into a cognitive agent architecture. However, creating a CME that closely imitates the actual functioning of emotions demands to address
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A uniform model of computational conceptual blending Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-04 Marco Schorlemmer; Enric Plaza
We present a mathematical model for the cognitive operation of conceptual blending that aims at being uniform across different representation formalisms, while capturing the relevant structure of this operation. The model takes its inspiration from amalgams as applied in case-based reasoning, but lifts them into category theory so as to follow Joseph Goguen’s intuition for a mathematically precise
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Multi-template supervised descent method for face alignment Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-10-27 Cheng Ding; Weidong Tian; Chao Geng; Xijing Zhu; Qinmu Peng; Zhongqiu Zhao
Supervised Descent Method (SDM) is a highly efficient and accurate approach for facial landmark locating and face alignment. In the training phase, it learns a sequence of descent directions to minimize the difference between the estimated shape and the ground truth in feature space. Then in the testing phase, it utilizes these descent directions to predict shape increment iteratively. However, when
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Earthquake Disaster Avoidance Learning System Using Deep Learning Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-13 Muhammad Sadiq Amin; Huynsik Ahn
The popularity of deep learning has influenced the field of surveillance and human safety. We adopt the advantages of deep learning techniques to recognize potentially harmful objects inside living rooms, offices, and dining rooms during earthquakes. In this study, we propose an educational system to teach earthquake risks using indoor object recognition based on deep learning algorithms. The system
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Speech stress recognition using semi-eager learning Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-10-29 Vaijanath V. Yerigeri; L.K. Ragha
Homo-sapiens suffer from psychogenic pain due to current day lifestyle. According to psychologists, stress is the most destructive form of psychalgia and it is a vicious companion for this species. Immoderate levels of stress may lead to the death of many individuals. Normally, the presence of stress gives rise to certain emotions which can be detected to predict stress levels of a person. This paper
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Multilevel metric rank match for person re-identification Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-10-22 Chao Wang; ZhengGao Pan; XueZhu Li
Metric learning is one of the important ways to improve the person re-identification (ReID) accurate, of which triplet loss is the most effect metric learning method. However, triplet loss only ranks the extracted feature at the end of the network, in this paper, we propose a multilevel metric rank match (MMRM) method, which ranks the extracted feature on multilevel of the network. At each rank level
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On Modeling the Creativity and the Concept of Chef-D’oeuvre Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-09 Olga Chernavskaya; Yaroslav Rozhylo
The problem of modeling the creativity is shown to be entirely connected with another mysterious challenge — the “Explanatory Gap” between the concepts of “Brain” and “Mind”. We use the Natural Constructive Cognitive Architecture, with its important feature being the combination of two subsystems, for generation of new information and for its conservation. “Brain” is considered as records of the raw
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An Adaptive Network Model for Pain and Pleasure through Spicy Food and its Desensitization Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-11-07 Mandy Choy; Suleika El Fassi; Jan Treur
This paper aims to map out the adaptive causal pathways of processes underlying capsaicin consumption and the desensitization process of the TRPV1 receptor as a feedback loop together with pain and pleasure perception. In order to map out these causal capsaicin pathways, adaptive causal network modeling was applied, which is a way of modeling biological, neural, mental and social processes from an
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Hierarchical Deep Q-Network from imperfect demonstrations in Minecraft Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-10-22 Alexey Skrynnik; Aleksey Staroverov; Ermek Aitygulov; Kirill Aksenov; Vasilii Davydov; Aleksandr I. Panov
We present Hierarchical Deep Q-Network (HDQfD) that won first place in the MineRL competition. The HDQfD works on imperfect demonstrations and utilizes the hierarchical structure of expert trajectories. We introduce the procedure of extracting an effective sequence of meta-actions and subgoals from the demonstration data. We present a structured task-dependent replay buffer and an adaptive prioritizing
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Cognitive control models of multiple access IoT networks using LoRa technology Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-10-13 Mohammed Saleh Ali Muthanna; Ping Wang; Min Wei; Abdelrahman Abuarqoub; Ahmad Alzu’bi; Hina Gull
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Regularized ELM bagging model for Tropical Cyclone Tracks prediction in South China Sea Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-10-01 Maocan Yang; Jun Zhang; Hong Lu; Jian Jin
This paper aims to improve the prediction accuracy of Tropical Cyclone Tracks (TCTs) over the South China Sea (SCS) with 24 h lead time. The model proposed in this paper is a regularized extreme learning machine (ELM) ensemble using bagging. The method which turns the original problem into quadratic programming (QP) problem is proposed in this paper to solve lasso and elastic net problem in ELM. The
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Learning data-driven decision-making policies in multi-agent environments for autonomous systems Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-09-28 Joosep Hook; Seif El-Sedky; Varuna De Silva; Ahmet Kondoz
Autonomous systems such as Connected Autonomous Vehicles (CAVs), assistive robots are set improve the way we live. Autonomous systems need to be equipped with capabilities to Reinforcement Learning (RL) is a type of machine learning where an agent learns by interacting with its environment through trial and error, which has gained significant interest from research community for its promise to efficiently
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Adaptive sampling for active learning with genetic programming Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-09-19 Sana Ben Hamida; Hmida Hmida; Amel Borgi; Marta Rukoz
Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some known issues such as the computational cost, the over-fitting problem and the imbalanced databases. The traditional dynamic sampling for GP gives to the algorithm a new sample periodically
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The model of a simple self-reproducing system Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-09-28 David B. Saakian; Vladimir G. Red'ko
The model of a simple self-reproducing system has been investigated. The current model has been developed in the framework of syser systems. The term syser is the abbreviation of the words “SYstem of SElf-Reproduction”. The syser model can be considered as a reasonable model of the prebiological macromolecular self-reproducing systems. The syser includes a polynucleotide matrix, a replication enzyme
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Using spotted hyena optimizer for training feedforward neural networks Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-09-16 Qifang Luo; Jie Li; Yongquan Zhou; Ling Liao
Spotted hyena optimizer (SHO) is a novel metaheuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for training feedforward neural network (FNN), which is regarded as a challenging task since it is easy to fall into local optima. Our objective is to apply metaheuristic optimization algorithm
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A fuzzy-based driver assistance system using human cognitive parameters and driving style information Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-09-09 Juan Pablo Vasconez, Michelle Viscaino, Leonardo Guevara, Fernando Auat Cheein
Reducing the number of traffic accidents due to human errors is an urgent need in several countries around the world. In this scenario, the use of human-robot interaction (HRI) strategies has recently shown to be a feasible solution to compensate human limitations while driving. In this work we propose a HRI system which uses the driver’s cognitive factors and driving style information to improve safety
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PSO-GA based hybrid with Adam Optimization for ANN training with application in Medical Diagnosis Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-09-08 Rajesh K. Yadav, Anubhav
This paper introduces a novel PSO-GA based hybrid training algorithm with Adam Optimization and contrasts performance with the generic Gradient Descent based Backpropagation algorithm with Adam Optimization for training Artificial Neural Networks. We aim to overcome the shortcomings of the traditional algorithm, such as slower convergence rate and frequent convergence to local minima, by employing
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The theory of learning styles applied to distance learning Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-09-06 Roberto D. Costa, Gustavo F. Souza, Ricardo A.M. Valentim, Thales B. Castro
Distance Education (DE) associated with the use of Virtual Learning Environments (VLE) as interaction tools between the student and the educator has become a large research niche spread around the world. Techniques to improve learning effectiveness in VLEs seek to find relation-ships connections between pedagogical advances and educational technological resources available in VLEs. In this context
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Toward ethical cognitive architectures for the development of artificial moral agents. Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-09-03 Salvador Cervantes,Sonia López,José-Antonio Cervantes
New technologies based on artificial agents promise to change the next generation of autonomous systems and therefore our interaction with them. Systems based on artificial agents such as self-driving cars and social robots are examples of this technology that is seeking to improve the quality of people’s life. Cognitive architectures aim to create some of the most challenging artificial agents commonly
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Segment routing based energy aware routing for software defined data center Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-09-02 B. Balakiruthiga, P. Deepalakshmi, Sachi Nandan Mohanty, Deepak Gupta, P. Pavan Kumar, K. Shankar
Despite the fact that most of the data centers are software-defined, the multifaceted network architecture and increase in network traffic make data centers to suffer from overhead. Multipath TCP supports multiple paths for a single routing session and ensures proper utilization of bandwidth over all available links. As rise in number of nodes in data center is frequent and drastic, scalability issue
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A Classification Framework using a Diverse Intensified Strawberry Optimized Neural Network (DISON) for Clinical Decision-making Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-09-01 S. Sreejith, H. Khanna Nehemiah, A. Kannan
A novel classification framework for clinical decision making that uses an Extremely Randomized Tree (ERT) based feature selection and a Diverse Intensified Strawberry Optimized Neural network (DISON) is proposed. DISON is a Feed Forward Artificial Neural Network where the optimization of weights and bias is done using a two phase training strategy. Two algorithms namely Strawberry Plant Optimization
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Evaluating agents’ trustworthiness within virtual societies in case of no direct experience Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-08-31 Alessandro Sapienza, Rino Falcone
A great deal of effort has been made to introduce trust models to assess trustworthiness within virtual societies. The great majority of them makes extensive use of direct experience as the main source of information, considering recommendation/reputation and inferential processes just later, as a secondary mechanism to refine trust assessment. In this kind of networks, unfortunately, direct experience
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A multi-modal approach to cognitive training and assistance in minimally invasive surgery Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-08-06 Tina Vajsbaher, Tim Ziemer, Holger Schultheis
Minimally-invasive surgery (MIS) offers many benefits to patients, but is considerably more difficult to learn and perform than is open surgery. One main reason for the observed difficulty is attributable to the visuo-spatial challenges that arise in MIS, taxing the surgeons’ cognitive skills. In this contribution, we present a new approach that combines training and assistance as well as the visual
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Planet Braitenberg: Experiments in virtual psychology Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-08-01 Paul R. Smart
Braitenberg vehicles are simple robotic platforms, equipped with rudimentary sensor and motor components. Such vehicles have typically featured as part of thought experiments that are intended to show how complex behaviours are apt to emerge from the interaction of inner control mechanisms with aspects of bodily structure and features of the wider (extra-agential) environment. The present paper describes
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Integrating a cognitive assistant within a critique-based recommender system Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-07-24 Marc Güell, Maria Salamó, David Contreras, Ludovico Boratto
Recommender systems are cognitive computing systems designed to support humans in their decision-making processes through convincing, timely product suggestions. In the field of recommender systems, critique-based recommenders have been widely applied as an effective approach for guiding users through a product space in pursuit of suitable products. To date, no critique-based approach has included
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Role of the secondary visual cortex in HMAX model for object recognition Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-07-24 Hiwa Sufikarimi, Karim Mohammadi
The models inspired by visual systems of life creatures (e.g., human, mammals, etc.) have been very successful in addressing object recognition tasks. For example, Hierarchical Model And X (HMAX) effectively recognizes different objects by modeling the V1, V4, and IT regions of the human visual system. Although HMAX is one of the superior models in the field of object recognition, its implementation
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Review for Cognitive Systems Research of the book The Brain and AI, by authors Karl Schlagenhauf and Fanji Gu Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-07-23 Hans Liljenström
The human brain is often considered the most complex system known. It has a fantastic capacity to learn and remember, to recognize patterns in space and time, solve problems of all kinds, innovate tools and machines, create beautiful art and science. Is it reasonable to believe that we, in a foreseeable future, will be able to understand all the wonders of our own brain, enough to be able to mimic
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Fuzzy rough sets: Survey and proposal of an enhanced knowledge representation model based on automatic noisy sample detection Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-07-10 Abdelkhalek Hadrani, Karim Guennoun, Rachid Saadane, Mohammed Wahbi
Fuzzy Rough Set (FRS) theory, which has been emerged thanks to unifying Rough Set and Fuzzy Set ones, is a powerful mathematical tool for handling and processing real data of imprecise, incomplete, inconsistent and uncertain nature. It has drawn attention of many researchers, scientists and industrials in various domains over the last three decades. However, different studies have showed that its classical
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Safe and optimal navigation for autonomous multi-rotor aerial vehicle in a dynamic known environment by a decomposition-coordination method Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-06-02 Imane Nizar, Youssef Illoussamen, Hala El Ouarrak, El Hossein Illoussamen, Manuel Grana (Graña), Mohammed Mestari
In this paper, we present a new solution for the Autonomous navigation problem, using a Decomposition-Coordination Method (DCM) 1. The main purpose of this work is to compute an optimal and safe path for the multi-rotor Unmanned Aerial Vehicle (UAV) in a dynamic environment, moving from an initial location to the desired state. We assume that the flight environment is totally known to a supervisory
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ELM-HTM guided bio-inspired unsupervised learning for anomalous trajectory classification Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-05-23 Arif Ahmed Sekh, Debi Prosad Dogra, Samarjit Kar, Partha Pratim Roy, Dilip K. Prasad
Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose
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Hierarchical growing grid networks for skeleton based action recognition Cogn. Syst. Res. (IF 1.902) Pub Date : 2020-05-23 Zahra Gharaee
In this paper, a novel cognitive architecture for action recognition is developed by applying layers of growing grid neural networks. Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowledge of the input space, which increases the processing
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