Irrigation practices can be advanced by the aid of cognitive computing models. Repeated droughts, population expansion and the impact of global warming collectively impose rigorous restrictions over irrigation practices. Reference evapotranspiration (ET0) is a vital factor to predict the crop water requirements based on climate data. There are many techniques available for the prediction of ET0. An

Previous research has demonstrated that Distributional Semantic Models (DSMs) are capable of reconstructing maps from news corpora (Louwerse & Zwaan, 2009) and novels (Louwerse & Benesh, 2012). The capacity for reproducing maps is surprising since DSMs notoriously lack perceptual grounding. In this paper we investigate the statistical sources required in language to infer maps, and the resulting constraints

In this paper, it is addressed by mathematical analysis how network-oriented modeling relates to the dynamical systems perspective on mental processes. It has been mathematically proven that any dynamical system can be modeled as a temporal-causal network model and that any adaptive dynamical system (of any order) can be modeled by a self-modeling network (of the same order).

Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations

The main promise of voice assistants is their ability to correctly interpret and learn from user input as well as the ability to utilize this knowledge to achieve specific goals and tasks. These systems need predetermined activation actions to start a conversation. Unfortunately, the typically used solution, wake-words, force an unnatural interaction. Furthermore, this method can also confuse when

Social media addiction has spread rapidly among young people in recent years. Individuals with social media addiction are more likely to avoid and suppress negative emotions instead of reappraising these emotions, which can cause psychological and even physical harm. This study presents a second-order adaptive mental network model to simulate the process of emotion regulation in social media addicts

Recently, deep reinforcement learning (DRL) has attracted considerable attention. The well-known deep Q-network (DQN) architecture successfully combines deep learning and Q-learning which is a representative reinforcement learning (RL) method. In general, RL and DRL require many trial-and-error searches. To overcome this limitation, alternative approaches called exploitation-oriented learning (XoL)

Resource allocation and task scheduling is a complex task in fog computing environment because of the inherent heterogeneity among the fog devices. The proposed work attempts to solve the problem by using the popular multi criteria decision making methods such as AHP and TOPSIS. The goal of this paper is to propose a model for performance oriented task - resource mapping in a fog computing environment

In recent years the neural networks have received considerable success on classification tasks. The randomly initialized network is generally expected to adjust the weights for reducing the loss, which is also indicated as the total distance from the samples to the corresponding vertices. However, the output space of the conventional neural networks suffers from the fixed relation between the labels

Wireless Sensor Networks is one of the most significant area of research where large number of sensor nodes that are distributed in a geographical area operate on limited battery power. As these networks, depending on the application, are sometimes deployed in in-hostile environment, which makes energy conservation one of the major challenge faced in WSN. To reduce energy consumption, clustering is

In this paper, an overview of the wide variety of occurrences of mental models in the literature is discussed. They are classified according to two dimensions obtaining four categories of them: static-dynamic and world-mental, where static refers to mental models for static world states or for static mental states and dynamic refers to mental models for world processes or for mental processes. In addition

In this paper, I deploy Gallagher et al.’s theory of Direct Social Perception (DSP) to help explain how we perceive others’ subjective time. This process of second-person temporal perception plays an important role in interpersonal interaction, yet is often glossed over in discussions of intersubjectivity. Using A.D. Craig (2009) ‘awareness’ model of subjective time to unify converging evidence that

In this paper, the concept of context-dependent realisation of mental models is introduced and discussed. Literature from neuroscience is discussed showing that different types of mental models can use different types of brain areas. Moreover, it is discussed that the same occurs for the formation and adaptation of mental models and the control of these processes. This makes that it is hard to claim

In this paper, joint decision making processes are studied and the role of cognitive metaphors as mental models in them. A second-order self-modeling network model is introduced based on mechanisms known from cognitive and social neuroscience and cognitive metaphor and mental model literature. The cognitive metaphors were modeled as specific forms of mental models providing a form of modulation within

Goal To apply signal processing and machine learning skills and knowledge in processing the EEG and MEG signal and further localize and evaluate the source of the finger stimulation. Methods Cognitive control is usually applied in information processing and behavioral response. In the preprocessing, baseline correction is implemented to analyze the pre-stimuli, combining ERP to mark the event related

In this work, we attempted to find out the relationship between different gait patterns and their corresponding cognitive states by using different statistical and machine learning approaches. This paper strongly focusses on the simulations followed by implementation of the proposed cognitive states i.e. (i) EmotionOriented State (EOS) (ii) Thinking Oriented State (TOS) (iii) Memory Oriented State(MOS)

Artificial intelligence has made great strides since the deep learning revolution, but AI systems still struggle to extrapolate outside of their training data and adapt 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 predict the existence of phenomena which have never

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

Abstract 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

Abstract 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

Abstract 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

When it comes to cognitive architecture and human information processing, chunks are one of the best known and most recognized constructs. Nevertheless, the nature of chunks is still very elusive, especially when it comes to chunks in procedural knowledge. This study deals with basic features of procedural information processing and examines the manifestation of chunks in procedural knowledge. The

Intrusion Detection Systems (IDSs) is a system that monitors network traffic for suspicious activity and issues alert when such activity is revealed. Moreover, the existing IDSs-based methods are based on outdated attacks that unable to identify modern attacks or malicious trends. For this reason, in this study we developed a new multi-swarm adaptive grasshopper optimization algorithm to utilize adaptation

Intelligent information systems that contain emergent elements often encounter trust problems because results do not get sufficiently explained and the procedure itself can not be fully retraced. This is caused by a control flow depending either on stochastic elements or on the structure and relevance of the input data. Trust in such algorithms can be established by letting users interact with the

The actions of intelligent agents, such as chatbots, recommender systems, and virtual assistants are typically not fully transparent to the user. Consequently, users take the risk that such agents act in ways opposed to the users’ preferences or goals. It is often argued that people use trust as a cognitive shortcut to reduce the complexity of such interactions. Here we formalise this by using the

The miniaturization of electronic devices has facilitated increasing interest in new applications of wireless communication. In this paper, a new miniature microstrip fed boomerang-shaped patch antenna has been proposed and designed for a wireless sensor network (WSN) application. In this design, the proposed antenna possesses a wide bandwidth by loading of a boomerang-shaped patch; with a 50 Ω microstrip

This article presents a cognitive model of distraction and mind-wandering that combines and formalizes several existing theories. It assumes that task-related goals and opportunities for distraction are continuously in competition for mental resources. If the task-related goal does not need a particular resource at a particular moment, the likelihood that it is captured by a distraction is high. We

The persistence of information communicated between humans is difficult to measure as it is affected by many features. This paper presents an approach to computationally model the cognitive processes of information sharing to describe persistence or extinction of communication in Twitter over time. The adaptive mental network model explains, for example, how an individual can experience information

Elman recurrent network is a representative model with feedback mechanism. Although gradient descent method has been widely used to train Elman network, it frequently leads to slow convergence. According to optimization theory, conjugate gradient method is an alternative strategy in searching the descent direction during training. In this paper, an efficient conjugate gradient method has been presented

Propagated activation of neurons through their network is an important process in the brain. Another crucial part of neural processing concerns adaptation over time of characteristics of this network such as connection strengths or excitability thresholds. This adaptation can be slow, as in learning from a multiple experiences, or it can be fast, as in memory formation. These adaptive network characteristics

The deep belief network model, which is widely used in deep learning, consists of a multi-layer constrained Boltzmann machine and a back-propagation network. The authors have conducted parameter sensitivity experiments on the number of iterations, the number of hidden layers and the number of hidden layer nodes in the DBN network for remote sensing image classification, and obtained a set of optimal

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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 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. Sensory input vectors are processed by robust association circuitry and then propagated to a navigational temporary map. Instinctive and learned objects and procedures are applied to the same temporary map

Abstract 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

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

Abstract 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.

Abstract 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

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

The retinal blood vessel segmentation is required for continuously monitoring the blood vessel in most of the retinal disease diagnosis. Deep learning approaches are accepted as the promising techniques for biomedical image segmentation. In this paper, Encoder enhanced Atrous architecture is proposed for retinal blood vessel segmentation. The encoder section is enhanced by improving the depth concatenation

The climate and weather dynamics in the past few years has driven a massive increase in the number and intensity of flood disasters, which severely claim casualties in human, goods and properties. Aimed to reduce these casualties, emerging software-defined internet protocol-based communication technologies in the form of Internet of Things (IoT) have attracted strong interests from disaster mitigation

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

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

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

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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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

Abstract 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