Cognitive Systems Research Articles

Cognitive Systems for the Energy Efficiency Industry

This review underscores the pivotal role of Cognitive Systems (CS) in enhancing energy efficiency within the industrial sector, exploring the application of sophisticated algorithms, data analytics, and machine learning techniques to the real-time optimization of energy consumption. This methodology has the potential to reduce operational expenses and further diminish environmental repercussions; however, it also leverages data-driven insights and predictive maintenance to foresee equipment malfunctions and modulate energy utilization accordingly. The viability of integrating renewable energy sources is emphasized, supporting a transition towards sustainability. Furthermore, this research includes a bibliometric literature analysis from the past decade on the deployment of CS and Artificial Intelligence in enhancing industrial energy efficiency.

Prediction of Degraded Infrastructure Conditions for Railway Operation.

In the railway sector, rolling stock and infrastructure must be maintained in perfect condition to ensure reliable and safe operation for passengers. Climate change is affecting the urban and regional infrastructure through sea level rise, water accumulations, river flooding, and other increased-frequency extreme natural situations (heavy rains or snows) which pose a challenge to maintenance. In this paper, the use of artificial intelligence based on predictive maintenance implementation is proposed for the early detection of degraded conditions of a bridge due to extreme climatic conditions. For this prediction, continuous monitoring is proposed, with the aim of establishing alarm thresholds to detect dangerous situations, so restrictions could be determined to mitigate the risk. However, one of the main challenges for railway infrastructure managers nowadays is the high cost of monitoring large infrastructures. In this work, a methodology for monitoring railway infrastructures to define the optimal number of transductors that are economically viable and the thresholds according to which infrastructure managers can make decisions concerning traffic safety is proposed. The methodology consists of three phases that use the application of machine learning (Random Forest) and artificial cognitive systems (LSTM recurrent neural networks).

Visual attention methods in deep learning: An in-depth survey

Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed attention to boost performance for many applications. Interestingly, the same attention design can suit processing different data modalities and can easily be incorporated into large networks. Furthermore, multiple complementary attention mechanisms can be incorporated into one network. Hence, attention techniques have become extremely attractive. However, the literature lacks a comprehensive survey on attention techniques to guide researchers in employing attention in their deep models. Note that, besides being demanding in terms of training data and computational resources, transformers only cover a single category in self-attention out of the many categories available. We fill this gap and provide an in-depth survey of 50 attention techniques, categorizing them by their most prominent features. We initiate our discussion by introducing the fundamental concepts behind the success of the attention mechanism. Next, we furnish some essentials such as the strengths and limitations of each attention category, describe their fundamental building blocks, basic formulations with primary usage, and applications specifically for computer vision. We also discuss the challenges and general open questions related to attention mechanisms. Finally, we recommend possible future research directions for deep attention. All the information about visual attention methods in deep learning is provided at https://github.com/saeed-anwar/VisualAttention

Cyber-Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era-A Review.

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber-physical optimization system.

Loss of mGlu5 receptors in somatostatin-expressing neurons alters negative emotional states.

Subtype 5 metabotropic glutamate receptors (mGlu5) are known to play an important role in regulating cognitive, social and valence systems. However, it remains largely unknown at which circuits and neuronal types mGlu5 act to influence these behavioral domains. Altered tissue- or cell-specific expression or function of mGlu5 has been proposed to contribute to the exacerbation of neuropsychiatric disorders. Here, we examined how these receptors regulate the activity of somatostatin-expressing (SST+) neurons, as well as their influence on behavior and brain rhythmic activity. Loss of mGlu5 in SST+ neurons elicited excitatory synaptic dysfunction in a region and sex-specific manner together with a range of emotional imbalances including diminished social novelty preference, reduced anxiety-like behavior and decreased freezing during retrieval of fear memories. In addition, the absence of mGlu5 in SST+ neurons during fear processing impaired theta frequency oscillatory activity in the medial prefrontal cortex and ventral hippocampus. These findings reveal a critical role of mGlu5 in controlling SST+ neurons excitability necessary for regulating negative emotional states.

AI and the transformation of industrial work: Hybrid intelligence vs double-black box effect

It is uncertain how the application of artificial intelligence (AI) technology transforms industrial work. We address this question from the perspective of cognitive systems, which, in this case, includes considerations of AI and process transparency, resilience, division of labor, and worker skills. We draw from a case study on glass tempering that includes a machine-vision-based quality control system and an advanced automation process control system. Based on task analysis and background literature, we develop the concept of hybrid intelligence that implies balanced AI transparency that supports upskilling and resilience. So-called fragmented intelligence, in turn, may result from the combination of the complexity of advanced automation along with the complexity of the process physics that places critical emphasis on expert knowledge. This combination can result in the so-called “double black box effect”, given that designing for understandability for the line workers might not be feasible: expert networks are needed for resilience.

Personality systems interactions theory: an integrative framework complementing the study of the motivational and volitional dynamics underlying adjustment to chronic pain.

In the endeavor to advance our understanding of interindividual differences in dealing with chronic pain, numerous motivational theories have been invoked in the past decade. As they focus on relevant, yet different aspects of the dynamic, multilevel processes involved in human voluntary action control, research findings seem fragmented and inconsistent. Here we present Personality Systems Interactions theory as an integrative meta-framework elucidating how different motivational and volitional processes work in concert under varying contextual conditions. PSI theory explains experience and behavior by the relative activation of four cognitive systems that take over different psychological functions during goal pursuit. In this way, it may complement existing content-related explanations of clinical phenomena by introducing a functional, third-person perspective on flexible goal management, pain acceptance and goal maintenance despite pain. In line with emerging evidence on the central role of emotion regulation in chronic pain, PSI theory delineates how the self-regulation of positive and negative affect impacts whether behavior is determined by rigid stimulus-response associations (i.e., habits) or by more abstract motives and values which afford more behavioral flexibility. Along with testable hypotheses, multimodal interventions expected to address intuitive emotion regulation as a central process mediating successful adaptation to chronic pain are discussed.

The Use of Chunking Technique Combined with the Writing Is Thinking Technique to Control Students' Cognitive Load When Learning About the Human Reproductive System

Cognitive load is related to working memory capacity, which affects the cognitive processing system. This study aimed to control the cognitive load of students by using chunking techniques combined with the writing is thinking technique when learning about the human reproductive system. The research was conducted at a senior high school in Tasikmalaya City during the 2020/2021 academic year. 56 research subjects were divided into two groups - the experimental group and the control group. The research method used was quasi-experimental with a post-test-only control group design. Measurement of students’ cognitive load included intrinsic cognitive load (ICL), extraneous cognitive load (ECL), and germane cognitive load (GCL), which was measured using an instrument in the form of a subjective rating scale questionnaire with a scale of 1 – 8. The results showed that learning about the human reproductive system using the chunking technique combined with the writing and thinking technique obtained the average value of a student’s ability to receive and process information was 38.4 (low ICL). The average value of mental effort was 32.8 (low ECL), and the average value of the cognitive load related to student learning outcomes was 73.7 (high GCL). Based on the results of statistical tests with the Mann Whitney two-mean difference test, there were significant differences in the ICL, ECL, and GCL components between the experimental group and the control group, so the use of chunking techniques combined with writing is a thinking technique that can control the cognitive load. The chunking technique combined with writing is a thinking technique that is able to optimize working memory capacity by managing pieces of information and helping students construct knowledge by rewriting the information obtained using their own language.
 Keywords: chunking techniques, writing is thinking technique, human reproductive system

Unmanned aerial vehicle-assisted wideband cognitive radio network based on DDQN-SAC

Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems.

In the mind of Narcissus: The mediating role of emotional regulation in the emergence of distorted cognitions.

Currently narcissism is considered one of the most widespread phenomenon. As a consequence, its different types (grandiose and vulnerable narcissism) have been investigated from several different perspectives. The present research attempts to explore the differences between the two types of narcissism and their links with different cognitive components that are connected to these personality traits. The primary aim of our study is to investigate the possible connections among maladaptive schemas (entitlement, vulnerability, emotional deprivation) and cognitive evaluation systems (self-esteem, systemizing-empathizing) and narcissism. We applied both correlation and path analyses to explore the hypothesized associations. The results show that early maladaptive schemas are strongly associated with narcissism and the empathizing system. The different subtypes of narcissism have different connections with self-esteem. Our results show that the two types of narcissism have different manifestations and connections with the early maladaptive schemas, Emphatizing Quotient, and self-esteem. Our empirical results serve as important and empirically supported inputs to counseling and clinical practice.

Understanding behavioral and cognitive dispositions as subsystem topologies within cognitive models: A proposal.

In our 2023 paper, entitled "Modeling interactions between the embodied and the narrative self: Dynamics of the self-pattern within LIDA," Kugele, Newen, Franklin, and I propose a functional description and implementation of a central element of Gallagher & Newen's pattern theory of self, which identifies an agent's self with a dynamic pattern of so-called cognitive aspects which govern their thought and behavior (Gallagher, 2013; Newen, 2018; Gallagher & Daly, 2018). The pattern theory explicitly rejects the traditional conceptualization of the self as a unitary entity with certain properties that resides within agents, with the idea of a pattern of aspects being central to its ability to account for the dynamic, yet relatively stable development of most natural agents' selves. Implementing the pattern theory within Learning Intelligent Distribution Agent revealed that, in order for a cognitive architecture to account for both the dynamic and stable nature of an agent's self-pattern, aspects of that pattern had to be realized by dispositions of the agent to either think or act in a certain way. In this commentary, I argue that this fundamental role of dispositions extends to cognitive processes in general and that cognitive systems should be understood in terms of the dynamical interactions of dispositions over time. In order to facilitate such an understanding, dispositions will have to be identified with topologies of cognitive (sub)systems. I provide an example of such a topology by reference to informational topologies in neuronal systems.

Guest Editorial Special Issue on Movement Sciences in Cognitive Systems

Guest Editorial Special Issue on Movement Sciences in Cognitive Systems

Passive Model-Predictive Impedance Control for Safe Physical Human–Robot Interaction

Various cognitive systems have been designed to model the position and stiffness profiles of human behavior and then to drive robots by mimicking the human’s behavior to accomplish physical human-robot interaction tasks through a properly designed impedance controller. However, some studies have shown that variable stiffness parameters of the impedance controller can cause the violation of the passivity constraint of the robot states, and make the robot’s stored energy exceed the external energy injected from the human user, thus leading to the unsafe human-robot interaction. To solve this problem, this paper proposes a novel passive model predictive impedance control method including two control loops. In the bottom-loop of the proposed controller, the robot is driven by a variable impedance controller to achieve the desired compliant interaction behavior. In the top-loop of the proposed controller, the model predictive control (MPC) is used to ensure that the robot states satisfy the passivity constraint by calculating a complementary torque to limit the stored energy of the robot. The passivity of the closed-loop robot system and the feasibility of MPC are guaranteed by theoretical analysis, ensuring the safety of the robotic movement in the human-robot interaction. The effectiveness of the proposed method is demonstrated by the simulation and experiment on the Franka Emika Panda robot.

Raising the Roof: Situating Verbs in Symbolic and Embodied Language Processing.

Recent investigations on how people derive meaning from language have focused on task-dependent shifts between two cognitive systems. The symbolic (amodal) system represents meaning as the statistical relationships between words. The embodied (modal) system represents meaning through neurocognitive simulation of perceptual or sensorimotor systems associated with a word's referent. A primary finding of literature in this field is that the embodied system is only dominant when a task necessitates it, but in certain paradigms, this has only been demonstrated using nouns and adjectives. The purpose of this paper is to study whether similar effects hold with verbs. Experiment 1 evaluated a novel task in which participants rated a selection of verbs on their implied vertical movement. Ratings correlated well with distributional semantic models, establishing convergent validity, though some variance was unexplained by language statistics alone. Experiment 2 replicated previous noun-based location-cue congruency experimental paradigms with verbs and showed that the ratings obtained in Experiment 1 predicted reaction times more strongly than language statistics. Experiment 3 modified the location-cue paradigm by adding movement to create an animated, temporally decoupled, movement-verb judgment task designed to examine the relative influence of symbolic and embodied processing for verbs. Results were generally consistent with linguistic shortcut hypotheses of symbolic-embodied integrated language processing; location-cue congruence elicited processing facilitation in some conditions, and perceptual information accounted for reaction times and accuracy better than language statistics alone. These studies demonstrate novel ways in which embodied and linguistic information can be examined while using verbs as stimuli.

Art therapy/occupational and play therapy: plastic expressiveness as a means of reducing loneliness, anxiety, sadness – research carried out during the period 2008-2022 with the theme: creation in / with elements from nature at the placement center

IntroductionOccupational therapy - which also includes art therapy - is an activity/test with a purpose, it involves coordination between the sensory, motor, cognitive, and psychosocial systems of the individual. “Sciences recognize the role of observation in research… All artists who practice art therapy are based on their own artistic activity and present a common recurring feature: they are always in line with “essential pragmatism”. (McNIFF, Shaun, Trust the process: an artist’s guide to letting go. Creative ability. Psychological aspects. Self-actualization (Psychology). Artist-Psychology, Shambhala Publication, Boston, 1998, p. 78)ObjectivesWe seek to find new development solutions through stimulation, creativity, catharsis, and socialization to be authentic, spontaneous, feel fulfilled, emotionally balanced, and transformed, with the aim of fulfilling one’s social role through contact with human and environmental factors. (Emilia Chirilă, ART THERA PY IN EMOTIONAL DISORDERS OF CHILDREN AND ADOLESCENTS, printed edition 2018) ISBN 978-973-0-27683-1)MethodsThrough the graphic gesture, the child expresses various issues related to his feelings, like the search for his identity, the generated anxieties, the family and professional environment, and the situations of neglect and abuse. (MALCHIODI, Cathy A, Handbook of Art Therapy, The Guilford Press, New York and London, 2003, p. 157).ResultsThe following reactions can be identified: aggression, frustration, dominance tendency, low self-esteem, fraternal rivalry, hopelessness, sadness, compensation mechanisms, self-defense, other significant psycho-traumatic aspects. The disappearance of frustrations and negative feelings due to the disinterest of parents who do not visit the beneficiaries was achieved by gaining authority over the environment and by improving pre-existing skills. emotional disorders of children and adolescents” - Journal of Neurology and Psychiatry of Children and Adolescents from Romania - 2012 - vol. 15 - no. 3- p 121-136 - ISSN (printed): 2068-8040)Image:Image 2:Image 3:ConclusionsThrough the creative process and symbolic communication, associated with narration and imitation, we realize the development of outstanding and hidden abilities, we develop new ways of communication, new ways of self-expression, and new ways of seeing things, to increase the ability to face existential problems.(2018 Emilia Chirilă - Art therapy in emotional disorders of children and adolescents: “Festina lente - Hurry slowly!” Harmonizing the rhythm with those around us ,print edition, ISBN 978-973-0-27683)Disclosure of InterestNone Declared

IEEE Transactions on Cognitive and Developmental Systems Information for Authors

IEEE Transactions on Cognitive and Developmental Systems Information for Authors

Dual routes of chunking social interaction: Insights from grouping two agent actions in working memory.

Humans have evolved the sophisticated ability to extract social relations embedded in interactive entities. One typical demonstration is a social chunking phenomenon wherein the cognitive system chunks individual actions into a unified episode basing on perceived interactive actions. However, the mechanisms underlying social chunking remain to be elucidated. Most studies have adopted static images and manipulated interactions through agents' facingness (face-to-face vs. back-to-back). Connecting agents via directed contingent actions is crucial in forming real-life social interaction. Hence, we employed dynamic actions as stimuli, separated physical- and communicative-contingency interactive actions, and predicted that domain-general physical regularities and domain-specific social relationships are crucial in social interactions, respectively. We tested this prediction by using an involuntary chunking effect in working memory, wherein two individual actions are involuntarily chunked when containing task-irrelevant interactive information. We found that involuntary chunking occurred for both types of upright interactive actions (Experiments 1, 3, 5, and 6). Inverting actions erased the chunking of communicative- but not physical-contingency actions (Experiments 2, 4, and 5). The facingness of dyads did not participate in chunking physical-contingency actions but was a prerequisite for chunking communicative-contingency actions (Experiments 3 and 6). These results reveal the dual routes of chunking interactive actions. Moreover, they suggest that the chunking mechanisms of dynamic social interaction are distinct from those of static images, highlighting the importance of using dynamic stimuli to explore the mechanisms of social interaction in emerging people-watching interdisciplinarity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The products and multi-disciplinarity of data-centric tasks: Influences on data searchers' behaviors and cognition

The product type and multi-disciplinarity level of tasks may be associated with the behaviors of data searchers and their utilization of cognitive System 1 and System 2. To test these associations, a user experiment was conducted. Findings suggest that due to differences in the uncertainty of data needs and the difficulty of data evaluation, when executing tasks with intellectual products, users devote more effort to data evaluation rather than query optimization and rely more on System 2. Moreover, due to differences in knowledge complexity and the availability of data resources, when performing multi-discipline tasks, users devote more effort to query optimization and data evaluation but gain lower search performance. When performing intellectual-multi-discipline tasks, data searchers use System 1 and System 2 in a complementary way to support systematic and efficient evaluation. A task classification has been developed, offering a framework to identify behavioral and cognitive patterns linked to specific tasks.

데이터로 본 판소리의 性 인지 체계: 신재효本을 중심으로

This study analyzes the sexual cognitive system in pansori works using Shin Jae-hyo’s pansori texts as the research subject. To that end, I first extracted excerpts of sexual expressions from Shin Jae-hyo’s pansori texts such as Chunhyang-ga(NamChang), Simcheong-ga, Bak-Taryeong, Tobyol-ga, Jeokbyeok-ga, and Byeongangsoe-ga. Then, I input them on a sentence-by-sentence basis with metadata including the speaker (narrator), target of description, related characters, works, and excerpts. As a preliminary step for the quantitative analysis, I performed morphological analysis and preprocessing on the data entered in the text column. This made the language of the pansori texts suitable for digital analysis tools with the input of new information or stop words into the user dictionary.
 Three analyses were attempted using the data extracted from Shin Jae-hyo’s pansori texts. First, basic statistical analysis was conducted to visualize which work had the most occurrences of sexual expressions and to examine the frequency of speaker-description target pairs when sexual expressions were uttered and their targets. Second, Latent Dirichlet Allocation (LDA) topic modeling was performed to extract topics from a large collection of documents. Third, I used co-occurrence word analysis to reveal the semantic network of words constituting sexual expressions.
 The findings revealed that the sexual cognitive system in pansori works is based on “deviation and return.” That is, while enjoying pansori, one can realize that forbidden desires are, indeed, common desires and can satisfy sexual fantasies. However, such desires should not persist beyond exceptional circumstances and should ultimately be concealed and transformed to abide byto societal norms. Listening to and reading pansori involves repeatedly deviating from and returning to sexuality as a theme that persists throughout the whole story.

AHP Based Consistency of Pairwise Comparison Matricesgeneralized Jpinion Assessment and Conclusion on the Example of Cognitive Radio System

AHP Based Consistency of Pairwise Comparison Matricesgeneralized Jpinion Assessment and Conclusion on the Example of Cognitive Radio System