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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 cloud data centers, mobile applications and human activities. Managing energy efficiency and power consumption is one of the important issues in green IoT-enabled technologies. This paper presents an overview on the energy management solutions in the IoT based on Systematic Literature Review (SLR). The main goal of this SLR-based overview is to recognize significant research trends in the field of energy management and power consumption techniques which need additional consideration to highlight more efficient and effective methods in IoT. Also, a taxonomy is proposed to categorize the existing research studies on energy management solutions. A statistical and technical analysis of reviewed existing papers are provided, and evaluation factors and attributes are discussed. We observed that variety of published research papers in smart home have highest percentage to evaluate energy management in the IoT. Also, deep learning and clustering methods are must popular techniques that were applied to evaluate the energy management in IoT case studies. Finally, new challenges and forthcoming issues of the energy management and efficient power consumption methods are presented. [ABSTRACT FROM AUTHOR]

Previous reports show that a substantial proportion of (near) medical errors in the operating theatre is attributable to ineffective communication between healthcare professionals. Speaking up about observed medical errors is a safety behaviour which promotes effective communication between health care professionals, consequently optimising patient care by reducing medical error risk. Speaking up by healthcare professionals (e.g., nurses, residents) remains difficult to execute in practice despite increasing awareness of its importance. Therefore, this paper discourses a computational model concerning the mechanisms known from psychological, observational, and medical literature which underlie the speaking up behaviour of a health care professional. It also addresses how a doctor may respond to the communicated message. Through several scenarios we illustrate what pattern of factors causes a healthcare professional to speak up when witnessing a (near) medical error. We moreover demonstrate how introducing an observant agent can facilitate effective communication and help to ensure patient safety through speaking up when a nurse can not. In conclusion, the current paper introduces an adaptive computational model which predicts speaking up behaviour from the perspective of the speaker and receiver, with the addition of a virtual coach to further optimise patient safety when a patient could be in harm's way. [ABSTRACT FROM AUTHOR]

Despite lacking a generally accepted definition, Artificial General Intelligence (AGI) is commonly understood to refer to artificial agents possessing the capacity to build up a context-independent understanding of itself and the world and to generalize this knowledge across a multitude of contexts. In human agents, this capacity is, to a large degree, facilitated by processes of self-directed learning , during which agents voluntarily control the conditions under which episodes of learning and problem solving occur. Since self-directed learning depends on the degree of knowledge the agent has about various aspects of themselves (their bodily skills, their learning goal, etc.), an AGI implementation of this type of learning must build on a theory of how this self-knowledge is actualized and modified during the learning process. In this paper, we employ the pattern theory of self in order to characterize different aspects of an agent's self that are relevant for self-directed learning. Such aspects include agent-internal cognitive states such as thoughts, emotions, and intentions, but also relational states such as action possibilities in the environment. Combinations of these aspects form a characteristic pattern, which is unique to each individual agent, with no one aspect being necessary or sufficient for the individuation of that agent's self. Here, we focus on the interdependence of narrative and embodied aspects of the self-pattern, since they involve particularly salient challenges consisting in conceptualizing the interaction between propositional and motor representations. In our paper, we model the reciprocal interaction of these aspects of the self-pattern within an individual cognitive agent. We do so by extending an approach by Ryan, Agrawal, & Franklin (2020), who laid the groundwork for the implementation of the pattern theory of self in the LIDA (Learning Intelligent Decision Agent) model. We describe how embodied and narrative aspects of an agent's self-pattern are realized by patterns of interaction between different LIDA modules over time, and how interactions at multiple temporal scales allow the agent's self-pattern to be both dynamically variable and relatively stable. Finally, we investigate the implications this view has for the creation of artificial agents that can benefit from self-directed learning, both in the context of deliberate planning and adaptive motor execution. [ABSTRACT FROM AUTHOR]

This paper describes an extension of a safety culture within hospital organizations providing more transparency and acknowledgement of all actors, and in particular the parents. It contributes a model architecture to support a hospital to develop such an extended safety culture. It is illustrated for prevention of postpartum depression. Postpartum depression is a commonly known consequence of childbirth for both mothers and fathers. In this research, we computationally analyze the risk factors and lack of support received by fathers. Therefore, we use shared mental models to model the effects of poor and additional communication by healthcare practitioners to mitigate the development of postpartum depression in both the mother and the father. Both individual mental models and shared mental models are considered in the design of the computational model. The paper illustrates the benefits of simple support in terms of communication during childbirth, which has lasting effects, even outside the hospital. For the impact of additional communication, a Virtual Safety Coach is designed that intervenes when necessary to provide support, i.e., when a health care practitioner doesn't. Moreover, organizational learning is also modelled to improve the mental models of both the Safety Coach and the Health Care Practitioner. [ABSTRACT FROM AUTHOR]

The need for engaging treatment approaches in mental health care has led to the developing of new applications based on serious game approaches. These approaches have facilitated the development of promising tools for dealing with attention deficit hyperactivity disorder. This paper presents a novel system called CogniDron-EEG. This system is based on a brain–computer interface for flying indoor a drone for cognitive training purposes. We conducted a controlled trial with ten healthy children aged 7–14 to test the functional suitability and usability of the CogniDron-EEG system. Also, this study allowed us to evaluate the preference between our system and another system based on video games. Therefore, participant subjects used our CogniDron-EEG system and a system called Nexus to identify the users' preferences concerning these two systems. The findings suggest that participants were satisfied with the CogniDron-EEG and provide the basis for further development and research on the CogniDron-EEG system. Therefore, the proposed system in this paper opens a new branch of research on drones to study their advantages and disadvantages of using them for cognitive training purposes. Additionally, implications for developing human–robot interaction and serious games in the mental health context are discussed. [ABSTRACT FROM AUTHOR]

Affective computing is a rising interdisciplinary field of research spanning the areas from artificial intelligence, natural language processing to cognitive and social sciences. Potential applications comprise of man–machine interaction, healthcare, entertainment, teaching, marketing and many more. Despite the increasing number of papers published in the domains of affective computing, emotion recognition, and human–computer interaction (HCI), there are still gaps in the comprehensive literature review that covers all relevant studies in a single study, which this review attempts to address. As a result, this study provides a systematic literature review (SLR) on existing modalities (unimodals) for emotion recognition, emotion models, and trends in relevant studies by selecting articles published from January 2010 to June 2021. To ensure the retrieval of all relevant studies, a review protocol is used that includes both automatic and manual searches. Based on the research questions, the final 129 papers are reviewed and relevant information is extracted. This SLR provides future research directions to assist novice researchers and practitioners in more efficiently utilizing affective computing techniques. [ABSTRACT FROM AUTHOR]

Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. With the introduction of deep networks, the process of activity detection is clustered into two groups known as hand-crafted feature based approach and automatic feature extraction approach. This paper focuses on various approaches used in recent literature based on traditional and automatic approach. Moreover, hierarchy for different approaches under them such as space based, motion based, genetic based, fuzzy based, dictionary based are discussed. With introduction of Convolutional Neural Networks and Recurrent Neural Networks, automatic learning capability from input modality makes them first choice to be implemented for activity recognition. In this paper various approaches have been analyzed according to methodology, accuracy, classifier and datasets. [ABSTRACT FROM AUTHOR]

The paper presents repeatable effects of synchronizing visual and auditory alarms with heartbeats on the availability of cognitive resources. A perception-driven situation awareness model is proposed and studied by implementing two distinct experimental protocols with different groups of participants. Results of a first study with a single-screen configuration are repeated by those of a second one on a multiple-screen context. Both experimental protocols rely on manipulating a between-subjects factor to compare two conditions - one with alarms activated synchronously with heart rate and one with alarms non-synchronized with heart rate - and a within-subjects factor to compare the impact of workload by increasing the level of task difficulty. Results about mono-screen and multi-screen configurations are homogenous. The synchronous condition makes people produce significantly more errors and fewer visual scans of the alarm display area. This degradation of perceptual abilities is non-conscious and is correlated with workload. Main people are not aware about their actual performance and this is confirmed by the evolution of subjective performance and frustration regarding task difficulty, display configuration and alarm activation condition. Such discrepancies between what it is looked at with what it is actually perceived and between actual and perceived indicators like performance are perceptual dissonances that are relevant for perception-driven situation awareness. The application of synchronizing dynamic events with heartbeats will be studied for different individual and collective work contexts in order to extend the proposed perception-driven situation awareness model based on perceptual dissonance management and on human capability parameters. [ABSTRACT FROM AUTHOR]

The paper considers the task-driven approach to artificial intelligence. It is shown that, on the one hand, it generalizes such approaches as the agent-based approach and general artificial intelligence, and, on the other hand, accurately reflects the cognitive processes and purposeful behavior described in the physiological Theory of functional brain systems. [ABSTRACT FROM AUTHOR]

This paper presents a framework based on natural language processing and first-order logic aiming at instantiating cognitive chatbots. The proposed framework leverages two types of knowledge bases interacting with each other in a meta-reasoning process. The first one is devoted to the reactive interactions within the environment, while the second one to conceptual reasoning. The latter exploits a combination of axioms represented with rich semantics and abduction as pre-stage of deduction, dealing also with some of the state-of-the-art issues in the natural language ontology domain. As a case study, a Telegram chatbot system has been implemented, supported by a module which automatically transforms polar and wh-questions into one or more likely assertions, so as to infer Boolean values or snippets with variable length as factoid answer. The conceptual knowledge base is organized in two layers, representing both long- and short-term memory. The knowledge transition between the two layers is achieved by leveraging both a greedy algorithm and the engine's features of a NoSQL database, with promising timing performance if compared with the adoption of a single layer. Furthermore, the implemented chatbot only requires the knowledge base in natural language sentences, avoiding any script updates or code refactoring when new knowledge has to income. The framework has been also evaluated as cognitive system by taking into account the state-of-the art criteria: the results show that AD-Caspar is an interesting starting point for the design of psychologically inspired cognitive systems, endowed of functional features and integrating different types of perception. [ABSTRACT FROM AUTHOR]

• Big headed ants (Pheidole megacephala) are able to forage in a Y-maze for food. • Previously, investigators modeled P. megacephala with a two-pheromone model. • We account for empirical results of P. megacephala foraging with a one-pheromone model. • Our one-pheromone model recapitulates key patterns and is biologically plausible. • This work demonstrates principles of sensory-cognitive modeling and ant foraging. In a 2009 paper, Dussutour et al. proposed that big headed ants (Pheidole megacephala) employ two attractant pheromones during foraging: one for exploration and another during food gathering. This claim was consistent with, and argued to be supported by, laboratory studies of ant exploration and food-gathering in a Y-maze apparatus. The authors measured foraging activity and colony foraging choice in terms of the number of ants choosing different branches over time, where experimental conditions modified the history of food availability at the end of each branch. They built a two-pheromone mathematical model to account for observed rates and proportions of ants traversing the left versus right branch. Here we show that the main reported experimental observations can be explained by a one-pheromone model. Our findings show that it is plausible, but unnecessary, to hypothesize that these ants employ two distinct pheromones in order to account for the two principal results of the Dussutour et al. study, and therefore, the study falls short of dispositive evidence for a two-pheromone model. More broadly, we highlight that patterns of animal behavior can be ambiguous with respect to sensory and cognitive mechanisms, hopefully motivating future modeling efforts that perform formal comparison across models with different structure. [ABSTRACT FROM AUTHOR]

This paper introduces a novel controlled adaptive mental causal network model addressing how dreams overnight can influence creativity in waking life. The network model depicts in a causal, dynamic, and generic manner which adaptive mental processes underlie the connection between dreams and creativity and is shown to be validated with the existing cognitive neuroscience literature. [ABSTRACT FROM AUTHOR]

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 can be considered informational criteria for activation of a neuron. This then is viewed as a form of emergent information formation. Activation of neurons is determined by such information via a process termed criterial causation. In the current paper, the relationship of criterial causation with the principle of temporal factorisation for the dynamics of the world in general is explored. Temporal factorisation describes how the world represents information about its past in its present state, which then in turn determines the world's future. In the paper, it is shown how these processes are analysed in more detail and modeled by (adaptive) network models. [ABSTRACT FROM AUTHOR]

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 stakeholders to rapidly locate victims and acquire their relevant information, which in turn can boost up the efficiency and effectiveness of Search and Rescue (SAR) missions. In order to capture state-of-the-art development and technological challenges, this paper presents an extensive review on the flood SAR systems, highlighting some of the key emerging IoT technologies that prove or are potentially useful in improving the SAR operation by the rescuers. Furthermore, a comprehensive study on different existing communication technologies for SAR is provided, covering the system architecture, communication network compositions and applications. Based on the critical analysis of existing works, this paper puts forward a proposal on an IoT-aided integrated flood management framework to support SAR in the flood-catchment areas, leveraging upon three-domain (ground, water and air) collaborative wireless networks. [ABSTRACT FROM AUTHOR]

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 with the aim of ontologically reducing computing to cognition. We defend, however, that cognition and computation are in action, thus cognition is just a form of computing and that cognition is the explanatory basis for computation. We state that: 1. Representational theories of computing recurring to intentional content run into metaphysical problems. 2. Functional non-representational theories do not incur this metaphysical problem when describing computing in terms of the abstract machine. 3. Functional theories are consistent with enactive in describing computing machines not in a strictly functional way, but especially in terms of their organization. 4. Enactive cognition is consistent with the computationalism in describing Turing machines as functionally and organizationally closed systems. 5. The cognitive explanatory basis for computing improves the computational theory of cognition. When developed in the human linguistic domain, computer science is seen as a product of human socionatural normative practices, however, cognition is just an explanatory, not ontological, basis for computing. The paper concludes by supporting that computation is in action, that cognition is just one form of computing in the world and the explanatory basis for computation. [ABSTRACT FROM AUTHOR]

The terms "extended cognition" and the "extended mind" identify two strands of philosophical argument that are commonly subsumed under the general heading of active externalism. The present paper describes an integrated approach to understanding extended cognition and the extended mind—one that papers over the differences between these two, ostensibly distinct, forms of cognitive extension. As an added bonus, the paper describes how active externalism might be applied to the realm of non-cognitive phenomena, thereby yielding an expansion in the theoretical and empirical scope of the active externalist enterprise. Both these points of progress stem from what is called the dispositional hypothesis. According to the dispositional hypothesis, extended cognition occurs when the mechanisms responsible for the manifestation of dispositional properties include components that lie beyond the borders of the thing to which the dispositional properties are ascribed. • A clearer explication of active externalist theses is required. • According to the dispositional hypothesis, cognitive extension occurs when the mechanisms responsible for the manifestation of dispositional properties extend beyond the borders of the entity to which dispositional properties are ascribed. • A consideration of dispositional properties provides the basis for a unified approach to understanding extended cognition and the extended mind. • Active externalism can be applied to phenomena that do not fall within the empirical orbit of cognitive science. [ABSTRACT FROM AUTHOR]

This paper present a multi-adaptive network model integrating multiple adaptation mechanisms, specifically focusing on five types of such adaptation mechanisms. Two of them address first-order adaptation by learning of responding on others and first-order adaptation by bonding with others based on homophily. Three other adaptation mechanisms addressed are second-order adaptation of the speed of both Hebbian learning and bonding by homophily, and second-order adaptation of the homophily tipping point. The paper provides a comprehensive explanation of these concepts and their role in controlled adaptation within the diverse contextual scenarios of the paper. [ABSTRACT FROM AUTHOR]

Discourse parsing for multiparty dialogue aims to detect the discourse structure and relations in a dialogue to obtain a discourse dependency graph. Existing models for this task have proven the effect of speaker information. However, no model explicitly learns representations of the speaker for parsing the discourse structure of dialogues. In this paper, to further exploit the effect of speaker information, we propose a novel model HG-MDP , which uses a heterogeneous graph neural network to encode dialogue graphs and we use an iterative update for the aggregation of speaker nodes and utterance nodes. Finally, we adopt updated utterance nodes to predict discourse dependency links and relations using the biaffine module. To validate our HG-MDP model, we perform experiments on the two existing benchmarks STAC and Molweni corpus. The results prove the effectiveness of the speaker modeling module on two datasets and we achieve the state-of-the-art on the Molweni dataset. [ABSTRACT FROM AUTHOR]

Transformative Organizational Change becomes more and more significant both practically and academically, especially in the context of organizational culture and learning. However computational modeling and a formalization of organizational change and learning processes are still largely unexplored. This paper aims to provide an adaptive network model of transformative organizational change and translate a selection of organizational learning and change processes into computationally modelled processes. Additionally, it sets out to connect the dynamic systems view of organizations to self-modelling network models. The creation of the model and the implemented mechanisms of organizational processes are based on extrapolations of an extensive literature study and grounded in related work in this field, and then applied to a specified hospital-related case scenario in the context of safety culture. The model was evaluated by running several simulations and variations thereof. The results of these were investigated by qualitative analysis and comparison to expected emergent behaviour based on related available academic literature. The simulations performed confirmed the occurrence of an organizational transformational change towards a constant learning culture by offering repeated and effective learning and changes to organizational processes. Observations about various interplays and effects of the mechanism have been made, and they exposed that acceptance of mistakes as a part of learning culture facilitates transformational change and may foster sustainable change in the long run. Further, the model confirmed that the self-modelling network model approach applies to a dynamic systems view of organizations and a systems perspective of organizational change. The created model offers the basis for the further creation of self-modelling network models within the field of transformative organizational change and the translated mechanisms of this model can further be extracted and reused in a forthcoming academic exploration of this field. [ABSTRACT FROM AUTHOR]

Van Duijn and colleagues propose that minimal cognition can be understood in terms of sensorimotor coordination – that is the coordination between sensory apparatus of an organism and the motor processes that move it around the environment (2006). Despite there being much to recommend this account it still faces some challenges. For example, it does not accord with the intuition that there are cognitive processes that have little to do with sensorimotor coupling, it relies on a strong distinction between metabolic and cognitive processes, and perhaps counterintuitively it denies cognition to plants but grants it to the bacteria on their roots that perform various functions for them. Moreover, it is difficult to see how to account for cognition over the transition from single to multicellular organisms. This paper proposes taking a more radical view of cognition to be what happens in swarms – the coordination of multiple processes through the traces of their actions in the environment. Key to this approach is the observation that the structure of the environment plays an active coordinative role and that this structure results in part from the actions of the system that it helps coordinate. Systems develop sensitivity to the appropriate trace variables in the environment. Viewed as collections of processes bacteria, biofilms, plants, animals can be viewed as cognitive systems in this framework and it has the potential to be applied to the social world. [ABSTRACT FROM AUTHOR]

In a socialized multi-agent system, the preferences of individuals will be inevitably influenced by others. This paper introduces an extended empathy structure to characterize the coupling process of preferences under specific relations and make it cover scenarios including human society, human–machine system, and even abiotic engineering applications. In this model, empathy is abstracted as a stochastic experience process in the form of Markov chain, and the coupled empathy utility is defined as the expectation of obtaining preferences under the corresponding probability distribution. The self-other separation is the core concept with which our structure can exhibit social attributes, including attraction of implicit states, inhibition of excessive empathy, attention of empathetic targets, and anisotropy of the utility distribution. Compared with the previous empirical models, our model has a better performance on the data set and can provide a new perspective for designing and analyzing the cognitive layer of the human–machine network, as well as the information fusion and semi-supervised clustering methods in engineering. [ABSTRACT FROM AUTHOR]

Job burnout has been on the rise in the past decade, especially amongst the younger working generation. While work environmental aspects play an important role in predicting burnout, variations in personality traits are integral for understanding the syndrome's risk factors, processes, and outcomes. This paper studies the complex interaction of personality factors on the one hand and work environment aspects on the other through the relatively novel adaptive causal network modelling paradigm. Due to the adaptive nature of the model, it can investigate the effects of changes in particular job demands and resources on the symptoms of burnout and their dependence on different personality traits. The model can also demonstrate how an individual's personality traits, environmental perception, and burnout symptoms can adaptively be altered by individual therapy, in this case, mindfulness-based cognitive therapy. Using the dedicated software environment in MATLAB to simulate the designed adaptive causal network model, two main scenarios were explored, focusing on the neuroticism personality trait. The results demonstrate that neuroticism increases due to interpersonal conflict, indicating that neuroticism can be treated as an adaptive trait. Furthermore, when mindfulness-based cognitive therapy was introduced into the simulation, the likelihood of developing burnout decreased because the perception of the work environment was positively changed due to the therapy. This model contributes to the field of burnout modelling by representing personality traits as adaptive factors that can be changed through individual interventions. More detailed research is needed to understand how organisational-level interventions can also impact burnout development through changes in environmental perception and personality. [ABSTRACT FROM AUTHOR]

The deployment of autonomous driving technology is hindered by "corner cases": unusual nuanced conditions that the self-driving software cannot understand and act fully. We argue that some corner cases originate from a "narrow AI" approach, which lacks the general knowledge that humans exploit when dealing with these cases. We propose an alternative that can be seen as a step toward features of Artificial General Intelligence. We exploit the biological principle of affordance competition in layered control architectures to create an artificial agent that realizes emergent, adaptive, and logical behaviors without programming case-specific rules or algorithms. We give six different examples of simple and complex emergent behaviors. For the case study of merge scenarios, we contrast the approach of this paper with an algorithmic solution of the literature. The ideas presented here (if not the whole agent's sensorimotor organization) could be used to improve the robustness and flexibility of self-driving technology. [ABSTRACT FROM AUTHOR]

We store and retrieve words from our brain during a communicative intention. These words form what linguistics and psycholinguistics refer to as the mental lexicon —a cognitive construct—. In this paper, we study the effectiveness of such a cognitive-based model for selecting the relevant lexicon that an automatic classifier can leverage for learning to distinguish personality traits from short communication intentions. Our proposed approach can automatically detect lexical units that are more suitable to train a machine learning algorithm to identify a subject's personality trait. We evaluated our method in two Mexican Spanish datasets, labeled according to the Big Five personality model. Experimental results indicate some personality traits are more transparent than others, e.g., Extroversion and Openness with 71% and 72% F-scores, respectively. An analysis of the information our proposed method selects identifies relevant psycholinguistic cues that complement psychologists' a prior knowledge. Overall, contrary to deep NNs based models, our proposed approach represents a less expensive and more interpretable technique, the desired combination for systems that aim to support the decisions made by a specialist. [ABSTRACT FROM AUTHOR]

The paper puts forward an intelligent approach that deals with the computation of an optimal path with collision avoidance for the stick-carrying twin moving from a pre-assumed start position to a predefined goal position. It has been solved through the efficient implementation of modified cuckoo search, sine cosine algorithm, and particle swarm optimization to design a hybrid algorithm aimed at using the communal advantages of the search and position update ability of these algorithms. The benefits are realized by integrating the egg-laying behavior of the cuckoo species to achieve an efficient global search strategy with modified parameters, local search strategy of particle swarm optimization, and greedy approach of sine cosine algorithm. The proposed algorithm is validated using 10 standard benchmark functions, computer simulation using C language, and real robot platform using Epuck robot to illustrate minimal time, shortest distance, collision avoidance, path smoothness, synchronized action, and reduced energy usage in terms of the path traveled, execution time, the number of steps, and the number of turns in the static as well as the dynamic environment. [ABSTRACT FROM AUTHOR]

Joint decision-making can be seen as the synchronization of actions and emotions, usually via nonverbal interaction between people while they show empathy. The aim of the current paper was (1) to develop an adaptive computational model for the type of synchrony that can occur in joint decision-making for two persons modeled as agents, and (2) to visualize the two persons by avatars as virtual agents during their decision-making. How to model joint decision-making computationally while taking into account adaptivity is rarely addressed, although such models based on psychological literature have a lot of future applications like online coaching and therapeutics. We used an adaptive network-oriented modelling approach to build an adaptive joint decision-making model in an agent-based manner and simulated multiple scenarios of such joint decision-making processes using a dedicated software environment that was implemented in MATLAB. Programming in the Unity 3D engine was done to virtualize this process as nonverbal interaction between virtual agents, their internal and external states, and the scenario. Although our adaptive joint decision model has general application areas, we have selected a therapeutic session as example scenario to visualize and interpret the example simulations. [ABSTRACT FROM AUTHOR]

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 the framework of theories without types, and they, in turn, are considered as special counterparts of typed theories. Similar mixing was used in model studies for lambda calculus. To a contrast with them, in this paper, information processes correspond to parameterized metadata objects, which are variable domain constructs. Transformations of variable domains correspond to the spread of the process. Directional transformation provides the generation of metadata targets in the form of parameterized concepts. This simulates the development of the process, which corresponds to the spread of cognitive interference and allows the interpretation of a hidden time factor. The emerging model is purely process based and provides such a conceptual framework. The possibility of coding this framework with a system of interdependent lambda terms is reflected. [ABSTRACT FROM AUTHOR]

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 requires consciousness. Despite these successes, many conceptual challenges remain, and bridging the divide between LIDA's conceptual model and its implementations has been challenging. The contributions of this paper are threefold: We present a detailed survey of learning in LIDA, during which we clarify, elaborate on, and synthesize together ideas from numerous papers, using updated terminology that reflects the continuing evolution of LIDA. We explore foundational issues in learning, such as, "What must be innate or built-in?" versus "What can be learned?", the nature of LIDA's representations, and the relationship between the LIDA conceptual model and its computational realizations. Finally, we provide a roadmap for future work. We believe that this paper will direct and catalyze our research endeavors, and provide a thorough introduction to the conceptual foundations of LIDA's learning mechanisms that will be useful to anyone that would like a deeper understanding of LIDA or for those that plan to implement LIDA-based agents. [ABSTRACT FROM AUTHOR]

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 cope with stress, using mathematical modelling. We present practical implementations in the form of client–server software, incorporating the computational model which describes therapy effects for overcoming stress based on quantitative neuropsychological research. The underlying network model simulates the elicitation of an extremely stressful emotion due to a strong stress-inducing event as an external stimulus, followed by a therapy practice simulation leading to a reduction of the stress level. Each simulation is based on user input and preferences, integrating a parameter tuning process; it fits a simulation for a particular user. The client–server architecture software which has been designed and developed completely fulfills this objective. It includes server part with embedded MATLAB interaction and API for client communication. [ABSTRACT FROM AUTHOR]

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 learn decision making through abstraction of experiences. However, most of the control algorithms used today in current autonomous systems such as driverless vehicle prototypes or mobile robots are controlled through supervised learning methods or manually designed rule-based policies. Additionally, many emerging autonomous systems such as driverless cars, are set in a multi-agent environment, often with partial observability. Learning decision making policies in multi-agent environments is a challenging problem, because the environment is not stationary from the perspective of a learning agent, and hence the Markov properties assumed in single agent RL does not hold. This paper focuses on learning decision-making policies in multi-agent environments, both in cooperative settings with full observability and dynamic environments with partial observability. We present experiments in simple, yet effective, new multi-agent environments to simulate policy learning in scenarios that could be encountered by an autonomous navigating agent such as a CAV. The results illustrate how agents learn to cooperate in order to achieve their objectives successfully. Also, it was shown that in a partially observable setting, an agent was capable of learning to roam around its environment without colliding in the presence of obstacles and other moving agents. Finally, the paper discusses how data-driven multi-agent policy learning can be extended to real-world environments by augmenting the intelligence of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Homo-sapiens suffer from psychogenic pain due to current day lifestyle. According to psychologists, s tress 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 proposes the development of mechanized and efficient Speech Emotion Recognition (SER) for stress level analysis. The paper investigates the performance of perceptual based speech features like Revised Perceptual Linear Prediction Coefficients, Bark Frequency Cepstral Coefficients, Perceptual Linear Predictive Cepstrum, Gammatone Frequency Cepstral coefficient, Mel Frequency Cepstral Coefficient, Gammatone Wavelet Cepstral Coefficient and Inverted Mel Frequency Cepstral Coefficients on SER. The novelty of this work involves application of a SemiEager (SemiE) learning algorithm for evaluating auditory cues. SemiE offers advantages over eager and lazy based learning by reducing the computational cost. Stress level recognition being the main objective, the Speech Under Simulated and Actual Stress (SUSAS) benchmark database is used for performance analysis. A comparative analysis is presented to demonstrate the improvement in the SED performance. An overall accuracy of 90.66% recognition of stress related emotions is achieved. [ABSTRACT FROM AUTHOR]

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 forecast error of TCTs data set is the distance between real position and forecast position. Compared with the stepwise regression method widely used in TCTs, 8.26 km accuracy improvement is obtained by our model based on the dataset with 70/1680 testing/training records. By contrast, the improvement using this model is 16.49 km based on a smaller dataset with 30/720 testing/training records. Results show that the regularized ELM bagging has a general better generalization capacity on TCTs data set. [ABSTRACT FROM AUTHOR]

Computational models of emotion (CMEs) are software systems designed to emulate the diverse aspects of the human emotion process. This type of model is commonly incorporated into cognitive agent architectures to provide mechanisms underlying affective behavior. The construction of CMEs involve theories that explain human emotion as well as computational artifacts related to the design and implementation of software systems. Although most CMEs reported in the literature provide details on their theoretical foundations, it is uncommon to find details about the computational practices and artifacts utilized during their design and implementation phases. This paper presents and discusses some challenges associated with the computational nature of this type of model: (i) Software quality attributes in CMEs, (ii) Interoperability between CMEs and cognitive components, (iii) Formal procedures for the design of CMEs, and (iv) Reference schemes to validate CMEs. Software engineering is used as a reference to propose and discuss these challenges. In addition, a reference architecture designed by following software engineering practices and artifacts is proposed to discuss the implications of addressing these challenges. The present research is at the intersection of human emotion modeling and software engineering to contribute to the software development process of affective systems. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive network model simulating the role of the gut microbiome for triggering biological mechanisms that alter the circadian cycle, mood and insomnia via the microbiome-gut-brain axis. Simulation graphs provide insight into how these immune and endocrine pathways interact with each other when the levels of the probiotics, Lactobacillus and Bifidobacteria, and pathogenic bacteria were altered. Varying these factors in simulations produced different outcomes for insomnia and sleep deprivation. [ABSTRACT FROM AUTHOR]

Intelligent Virtual Agents (IVAs) can provide users with a friendly experience and have a wide range of applications in the era of artificial intelligence. However, most of existing IVAs are designed for personal computers. Design and user studies of IVAs on smartphones are uncommon. Therefore, developing IVAs for smartphones is an interesting topic. Considering Augmented Reality (AR) technology can provide more potential application value for IVAs, we mainly investigate users' experiences of AR IVAs on smartphones in this paper. To make an IVA more suitable for a smartphone, a lightweight IVA's cognitive architecture is proposed. To find out the factors that affect users' interaction experiences, the effects of humanoid embodiment and emotional expressions of IVAs on users' perceptions and experiences are explored. A museum is used as a specific task scenario to measure users' experiences. Three forms of AR agents are evaluated in this scenario: a voice assistant without an entity, a humanoid IVA without emotional expressions, and a humanoid IVA with emotional expressions. The results show that compared with the voice assistant, a humanoid embodiment can significantly improve the user's experience, and compared with humanoid IVA without emotional expressions, a humanoid IVA with emotional expressions is more welcome. Moreover, we use the cloud model to describe the uncertainty of IVAs' actions (blinking and body orientation). The results show that the uncertainty of actions can increase the believability of IVAs. [ABSTRACT FROM AUTHOR]