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Health monitoring using wearable artificial intelligence (AI) sensors with sensing and cognitive capabilities has garnered significant attention. The development of self‐contained AI sensors that can operate with low power consumption, akin to the human brain, is necessary. Physical reservoir computing (PRC), which mimics the human brain using physical phenomena, offers a low‐power consumption architecture. Nevertheless, creating a flexible and easily disposable sensors using PRC capable of processing optical signals with sub‐second response times suitable for biological signals presents a challenge. In this study, a disposable and flexible paper‐based optoelectronic synaptic devices are designed, which are composed of nanocellulose and ZnO nanoparticles, for PRC. This device exhibits synaptic photocurrent in response to optical input. To assess its performance, a classification and time‐series forecasting tasks are conducted. The memory capacity of short‐term memory task, indicating the device's ability to store past information, is 1.8. The device can recognize handwritten digits with an accuracy of 88%. These results highlight the potential of the device for PRC. In addition, subjecting the device to 1000 rounds of bending do not affect its accuracy. Furthermore, the device burn in a few seconds, much like regular office paper, demonstrating its disposability. [ABSTRACT FROM AUTHOR]

The Journal of Management Information Systems has published a special section on cognitive reapportionment in relation to artificial intelligence (AI) and advances in computing. The section explores the allocation of tasks between humans and machines as AI becomes more capable of cognitive tasks. The limitations of current AI systems are discussed, as well as the potential for collaboration between humans and AI. The journal also includes papers on topics such as knowledge-aware models, crowdsourcing, social media effects, and the impact of government contracting on high-tech firms. [Extracted from the article]

Generative artificial intelligence (GAI) is a rapidly growing field with a wide range of applications. In this paper, a thorough examination of the research landscape in GAI is presented, encompassing a comprehensive overview of the prevailing themes and topics within the field. The study analyzes a corpus of 1319 records from Scopus spanning from 1985 to 2023 and comprises journal articles, books, book chapters, conference papers, and selected working papers. The analysis revealed seven distinct clusters of topics in GAI research: image processing and content analysis, content generation, emerging use cases, engineering, cognitive inference and planning, data privacy and security, and Generative Pre-Trained Transformer (GPT) academic applications. The paper discusses the findings of the analysis and identifies some of the key challenges and opportunities in GAI research. The paper concludes by calling for further research in GAI, particularly in the areas of explainability, robustness, cross-modal and multi-modal generation, and interactive co-creation. The paper also highlights the importance of addressing the challenges of data privacy and security in GAI and responsible use of GAI. [ABSTRACT FROM AUTHOR]

Contribution: This article features a systematic literature review with the objective of presenting a study that reflects the current scenario of research on the cognitive load theory (CLT) in the domain of teaching and learning computer programming. Background: Computer programming is a highly cognitive skill, requiring mastering multiple competencies, and recognized as being difficult to learn, for this reason, the cognitive load (CL) in the learner’s working memory emerged as an influential concept, making CLT one of the most common theories in computing education research. Research Questions: What are the approaches that relate CLT to teaching and learning computer programming? What CLT-related concepts are covered? What evidence is reported with respect to this context? Methodology: Following a formal protocol, a survey was performed for papers linking CLT to teaching and learning programming published between 2010 and 2020. The selection of papers was based on a set of criteria established to drive the selection process, including alignment with the research questions and paper quality assessment. Findings: The approaches applied in the papers are based on measuring the CL; through instructional design based on the development or use of resources or tools, a range of different pedagogy strategies and the CLT concepts. With respect to the concepts, the subjective measurement technique and worked example effect are the most commonly deployed. As far as the evidence reported, the better part is related to the worked example effect and measuring CLs. [ABSTRACT FROM AUTHOR]

Kazuo Ishiguro's novel Klara and the Sun (2021) philosophizes on how in the current technologically saturated culture, the gradual evolution of the empathetic humanoids has, on one hand, problematized our normative notions of cognitive and affective categories, and on the other, has triggered an order of emotional uncanniness due to our reliance on hyperreal real objects for receiving solace and companionship. The novel may be conceived to be a commentary on the emerging discourse in the domain of cognitive and emotional computing that aspires to transform the inner life and social relationships of the human community. The novelty of the paper lies in its ability to showcase how Kazuo Ishiguro's Klara and the Sun (2021) creates a rupture in the existing research and literary narrative by critiquing the theoretical underpinnings of emotional computing that optimistically foresees a future where simulated empathetic minds will be able to decode the complexities of the human emotions. It discusses how literature turns into an apt tool to reflect on the limitations of the programmed machines to decode the elusiveness of the human mind that defies the one-to-one correlation between words, multiple connotations, and their underlying emotions. Through the lenses of the fictional narrative, the paper foregrounds how the concept of the social robot designed to offer empathy, care, and companionship turns into a failed project. The paper draws on critical perspectives from disposability theory, posthuman affect, and immaterial bodies to foreground the noncodified feature of affective experientialities that emerge as a result of the interface between humans and nonanimate beings. [ABSTRACT FROM AUTHOR]

This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system. [ABSTRACT FROM AUTHOR]

With the ongoing integration of information technology and industrialization, strategic emerging industries are becoming an increasingly important force in guiding future economic and social development. As one of the strategic emerging industries' development priorities and a replacement for scarce labor resources, industrial robots will be widely used in labor-intensive industries. It is a contentious topic how to evaluate and improve robot knowledge education. There are modular features introduced in this paper, which builds a modular network for the evaluation of robot knowledge education quality and enhances the cognitive computing ability of artificial neural networks and their ability to process complex information. A modular neural network-based model of feature combination robot knowledge education quality evaluation is developed based on the three aspects of module division method, subnet structure selection, and feature combination output. The following are some of the paper's most important contributions: K-OD algorithm of density clustering optimized by K-means is proposed. Because of its high level of modularized partition simulating, this method has an excellent clustering effect, and it identifies the core points, boundary points, as well as outliers. Using K-OD algorithm, the calculation of density radius and threshold is optimized by using density clustering, which reduces the overall computational complexity. Find out how SOM neural networks learn from competition. SOM network's competition layer neuron weights are prone to falling into local optimal solutions, so an SASOM neural network with weight adjustment simulated by an annealing algorithm is proposed to address this issue. It is more accurate in terms of prediction and error, and it is better at identifying sample attribute features. This work builds a modular neural network for evaluating robot knowledge education quality using K-OD clustering algorithm and SASOM neural network, which introduces the simulated annealing mechanism. [ABSTRACT FROM AUTHOR]

In the era of artificial intelligence, cognitive computing, based on cognitive science; and supported by machine learning and big data, brings personalization into every corner of our social life. Recommendation systems are essential applications of cognitive computing in educational scenarios. They help learners personalize their learning better by computing student and exercise characteristics using data generated from relevant learning progress. The paper introduces a Learning and Forgetting Convolutional Knowledge Tracking Exercise Recommendation model (LFCKT-ER). First, the model computes students’ ability to understand each knowledge concept, and the learning progress of each knowledge concept, and the model consider their forgetting behavior during learning progress. Then, students’ learning stage preferences are combined with filtering the exercises that meet their learning progress and preferences. Then students’ ability is used to evaluate whether their expectations of the difficulty of the exercises are reasonable. Then, the model filters the exercises that best match students’ expectations again by students’ expectations. Finally, we use a simulated annealing optimization algorithm to assemble a set of exercises with the highest diversity. From the experimental results, the LFCKT-ER model can better meet students’ personalized learning needs and is more accurate than other exercise recommendation systems under various metrics on real online education public datasets. [ABSTRACT FROM AUTHOR]

Technology-aided learning is one of the important aspects of cognitive computing where education is provided through the means of digital media. This paper presents a comprehensive review of trends and the development of technology-based adaptive language learning systems. It also strives to highlight challenges and opportunities in the field of Technology-Assisted Language Learning (TALL). Articles from various electronic databases and reputed journals in the related field have been reviewed from 2011to 2021 to get an insight into state-of-the-art adaptive systems for TALL. To analyze results, the authors have proposed three dimensions viz. spatial and temporal aspects, system and targeted learners’ characteristics, and adaptation provided. The findings of the review indicate that research in this field has been gaining popularity since 2015, especially in the Asian region. English is the most frequently investigated language in TALL systems which have been employed majorly for university students. The study also analyzes various methods and sources of adaptation provided in such systems. Challenges, opportunities, limitations, and implications of research in this direction have also been discussed. The study also proposes new ways and ideas that can be taken up and implemented by language educators and local governments to make the research in the field of TALL commercially feasible and penetrable to various levels of society. [ABSTRACT FROM AUTHOR]

This paper analyses extant literature on how Industry 4.0 impacts Social Capital in Buyer-Supplier Relationships. We conduct a systematic literature review and identify 36 academic articles that are analysed in the research process. The study uncovers strategic changes Industry 4.0 implies for Social Capital in Buyer-Supplier Relationships. These include transformations in cognitive, structural and relational capital in terms of a shared vision, social interaction and trust. Therein, Social Capital in Buyer-Supplier Relationships is needed and further invested in aspects like common decision-making, information sharing and cross-company integration in Industry 4.0 contexts. We propose that Industry 4.0 implementation does require and foster Social Capital in Buyer-Supplier Relationships and that two diametrically opposed elementary forms of Buyer-Supplier Relationships co-exist in an Industry 4.0 context. The systematic literature review is the first to analyse the extant body of literature on Buyer-Supplier Relationships in Industry 4.0 to synthesise detailed transformations against the backdrop of Social Capital. It provides a comprehensive overview of the current state of research and develops several suggestions for future research and managerial practice, for example, concerning the role of humans in strategic tasks in Industry 4.0. [ABSTRACT FROM AUTHOR]

It is a matter of debate whether the discipline independence in discipline formation narrows its interdisciplinarity. It is also less well understood how disruptive works emerge in investigative practice rather than a theory‐driven approach. Aggregation‐induced emission (AIE) is an atypical photophysical phenomenon, in which the whole (aggregate) is brighter than the sum of its parts (single molecule). Through measuring and computing the cognitive extent and evolution of research on AIE, including topics, epistemic‐social collaborative networks, interdisciplinarity, emergent concepts, core concept networks and knowledge flow, this study shows that a cross‐research scales concept and its practice can establish new bridges in the sciences and promote disruptive work. Focusing on mesoscale entities, scientists from many different branches of science are involved in theoretical research on mechanisms, as well as developing different AIE systems for applications. The data analysis in this study provides details showing how non‐reductionist concepts based on new scientific discoveries cross traditional disciplinary boundaries and aggregate interdisciplinary research. The emergence and evolution of the AIE field implies that scientists may be motivated to embrace nonreductionist ideas at different research scales, leading to a more permeable field boundary. [ABSTRACT FROM AUTHOR]

Research background: On the basis of an analysis of the current situation and expectations in the field of implementation of the elements of the Industry 4.0 concept, the purpose of this paper is to identify the effects on the labor market in large manufacturing enterprises in the Slovak Republic. Purpose of the article: The presented work has a theoretical-empirical nature and consists of a theoretical section and a practical section, which includes statistical indicator analysis and quantitative research. In the theoretical section, the paper discusses the issue of Industry 4.0 in general, with a focus on its impact on the labor market, thus laying the groundwork for future research on the subject. Methods: The output of this work is an analysis of selected indicators of the manufacturing industry sector in the Slovak Republic, based on the most recent employment data analysis in the first stage and quantitative research survey in the second stage, with the respondents being manufacturing industry companies operating in the Slovak Republic, and whose primary objective is to determine the current status of the implementation of the elements and technologies of Industry 4.0 in production companies in the Slovak Republic, as well as the factors influencing this situation, such as digital twin simulation modeling, artificial intelligence-based Internet of Manufacturing Things systems, and virtual machine and cognitive computing algorithms. Findings & value added: The research findings indicate that the degree of digitization adopted by businesses in the Slovak Republic is comparatively less robust and more sluggish to adapt. This is primarily attributable to the underdeveloped educational system, population reluctance, self-actualization, and inadequate state support. Recommendations for the Slovak market aim to increase the digital proficiency of businesses and of the general populace through various means, such as reforming legislation, enhancing state support for entrepreneurs, and modifying the education system, constituting the added value of the work. [ABSTRACT FROM AUTHOR]

Background: Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults. Objective: To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model's rating consistency. Methods: The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE. The validity of the AAM-MMSE was assessed in term of the consistency between the AAM-MMSE rating and physician rating. Results: A total of 427 participants were recruited for this study. The average age of these participants was 60.6 years old (ranging from 19 to 104 years old). According to the intraclass correlation coefficient (ICC), the interrater reliability between physicians and the AAM-MMSE for the full MMSE scale AAM-MMSE was high [ICC (2,1)=0.952; with its 95% CI of (0.883,0.974)]. According to the weighted kappa coefficients results the interrater agreement level for audio-related items showed high, but for items "Reading and obey", "Three-stage command", and "Writing complete sentence" were slight to fair. The AAM-MMSE rating accuracy was 87%. A Bland-Altman plot showed that the bias between the two total scores was 1.48 points with the upper and lower limits of agreement equal to 6.23 points and −3.26 points. Conclusions: Our work offers a promising fully automated MMSE assessment system for cognitive screening with pretty good accuracy. [ABSTRACT FROM AUTHOR]

This article presents a simple, cognitive theory of science and learning. The first section of the paper develops the theory's two main propositions: (i) A wide range of scientific activities rely heavily on one type of reasoning, hypothetical thinking, and (ii) This type of reasoning is also useful to students for learning science content. The second section of the paper presents a taxonomy of multiple‐choice questions that use hypothetical thinking and the third section of the paper tests the theory using data from a college biology course. As expected by the theory, student responses to 24 scientific reasoning questions were consistent with a one‐dimensional psychometric construct. Student responses to the scientific reasoning questions explained 36% of the variance in exam grades. Several directions for additional research are identified, including studying the psychometric structure of scientific thinking in more detail, performing randomized, controlled experiments to demonstrate a causal relationship between scientific thinking and learning, and identifying the relative contribution of other factors to success in college. [ABSTRACT FROM AUTHOR]

Artificial intelligence and machine learning are becoming centrally relevant to a variety of sciences in supporting the construction of complex models from data. Overview of the contributions This issue of Cognitive artificial intelligence consists of 11 substantive papers, drawn roughly equally from the artificial intelligence and cognitive science communities, but each drawing on and having relevance to both. There is an increasing excitement concerning the potential of artificial intelligence to both transform human society and to understand cognition in humans and other animals. Research in the new area of cognitive artificial intelligence will aim to advance fundamental understanding for the key artificial intelligence technologies being developed. [Extracted from the article]

In this paper, a definition outlining the scope and goals of the field of Cognitive Aspects of Virtual Reality (cVR) is provided. Leading up to and alongside the definition, the paper includes a discussion on the background behind cVR – with a special focus on new human-AI capablities driven by cognitive, psychological, social and technological factors. Finally, the paper provides an outline of related research fields that can act as synergies in relation to cVR while at the same time formulates questions and hypotheses that may drive future research in c VR. [ABSTRACT FROM AUTHOR]

Cognitive information systems create a new class of intelligent systems focused on semantic data analysis tasks. Such systems are based on cognitive resonance processes, which use a knowledge-based perception model, to analyze and semantically classify visual data. Such systems can therefore be used for image analysis and classification, including semantic analysis of medical images, aimed at supporting diagnostic processes and determining the severity of lesions visualized by diagnostic imaging methods. This paper will describe various types of cognitive information systems designed for lesion recognition in selected abdominal and coronary structures, as well as skeletal parts of the human body, made visible by the application of various modalities in medical diagnostic imaging procedures. In this paper, a new generation of cognitive systems will also be described, and when compared to existing systems, will have the ability to perform extended cognitive resonance processes. Inference based on extended resonance inference allows the system to acquire additional knowledge, as well as expand the knowledge base used for semantic analysis. This paper will also propose the implementation of new efficient formal grammars, which increase the efficiency of lesion recognition in selected medical images to over 90%. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper was to investigate the relationships among the intellectual ability of artificial intelligence (AI), cognitive emotional processes and the positive and negative reactions of human members. The authors also examined the moderating role of AI status in teams. Design/methodology/approach: The authors designed an experiment and recruited 120 subjects who were randomly distributed into one of three groups classified by the upper, middle and lower organization levels of AI in the team. The findings in this study were derived from subjects' self-reports and their performance in the experiment. Findings: Regardless of the position held by AI, human members believed that its intelligence level is positively correlated with dependence behavior. However, when the AI and human members are at the same level, the higher the intelligence of AI, the more likely it is that its direct interaction with team members will lead to conflicts. Research limitations/implications: This paper only focuses on human–AI harmony in transactional work in hybrid teams in enterprises. As AI applications permeate, it should be considered whether the findings can be extended to a broader range of AI usage scenarios. Practical implications: These results are helpful for understanding how to improve team performance in light of the fact that team members have introduced AI into their enterprises in large quantities. Originality/value: This study contributes to the literature on how the intelligence level of AI affects the positive and negative behaviors of human members in hybrid teams. The study also innovatively introduces "status" into hybrid organizations. [ABSTRACT FROM AUTHOR]

Cognitive Computing breaks the boundary between two separate fields, neuroscience and computer science. The fifth paper [[5]] proposes a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. The fourth paper [[4]] proposes an improved intelligent clustering algorithm and applies it to the complex water system environment. [Extracted from the article]

The role of knowledge graph encompasses the representation, organization, retrieval, reasoning, and application of knowledge, providing a rich and robust cognitive foundation for artificial intelligence systems and applications. When we learn new things, find out that some old information was wrong, see changes and progress happening, and adopt new technology standards, we need to update knowledge graphs. However, in some environments, the initial knowledge cannot be known. For example, we cannot have access to the full code of a software, even if we purchased it. In such circumstances, is there a way to update a knowledge graph without prior knowledge? In this paper, We are investigating whether there is a method for this situation within the framework of Dalal revision operators. We first proved that finding the optimal solution in this environment is a strongly NP-complete problem. For this purpose, we proposed two algorithms: Flaccid_search and Tight_search, which have different conditions, and we have proved that both algorithms can find the desired results. [ABSTRACT FROM AUTHOR]

Improving and enhancing the effectiveness of software vulnerability detection methods is urgently needed today. In this study, we propose a new source code vulnerability detection method based on intelligent and advanced computational algorithms. It's a combination of four main processing techniques including (i) Source Embedding, (ii) Feature Learning, (iii) Resampling Data, and (iv) Classification. The Source Embedding method will perform the task of analyzing and standardizing the source code based on the Joern tool and the data mining algorithm. The Feature Learning model has the function of aggregating and extracting source code attribute based on node using machine learning and deep learning methods. The Resampling Data technique will perform equalization of the experimental dataset. Finally, the Classification model has the function of detecting source code vulnerabilities. The novelty and uniqueness of the new intelligent cognitive computing method is the combination and synchronous use of many different data extracting techniques to compute, represent, and extract the properties of the source code. With this new calculation method, many significant unusual properties and features of the vulnerability have been synthesized and extracted. To prove the superiority of the proposed method, we experiment to detect source code vulnerabilities based on the Verum dataset, details of this part are presented in the experimental section. The experimental results show that the method proposed in the paper has brought good results on all measures. These results have shown to be the best research results for the source code vulnerability detection task using the Verum dataset according to our survey to date. With such results, the proposal in this study is not only meaningful in terms of science but also in practical terms when the method of using intelligent cognitive computing techniques to analyze and evaluate source code has helped to improve the efficiency of the source code analysis and vulnerability detection process. [ABSTRACT FROM AUTHOR]

In recent years, the increasing environmental problems, especially the issue of global warming, have motivated demand for a cleaner, more sustainable, and economically viable energy source. In this context, wind energy plays a significant role due to the small negative impact it has on the environment, which makes it among the most widespread potential sustainable renewable fuel nowadays. However, wind turbine control systems are important factors in determining the efficiency and cost-effectiveness of a wind turbine (WT) system for wind applications. As wind turbines become more flexible and larger, it is difficult to develop a control algorithm that guarantees both efficiency and reliability as these are conflicting objectives. This paper reviews various control strategies for the three main control systems of WT, which are pitch, torque, and yaw control, in different operational regions considering multi-objective control techniques. The different control algorithms are generally categorized as classical, modern (soft computing) and artificial intelligence (AI) for each WT control system. Modern and soft computing techniques have been showing remarkable improvement in system performance with minimal cost and faster response. For pitch and yaw systems, soft computing control algorithms like fuzzy logic control (FLC), sliding mode control (SMC), and maximum power point tracking (MPPT) showed superior performance and enhanced the WT power performance by up to 5% for small-scale WTs and up to 2% for multi-megawatt WTs. For torque control systems, direct torque control (DTC) and MPPT AI-based techniques were suitable for reducing generator torque fluctuations and estimating the torque coefficient for different wind speed regions. Classical control techniques such as PI/PID resulted in poor dynamic response for large-scale WTs. However, to improve classical control techniques, AI algorithms could be used to tune the controller's parameters to enhance its response, as a WT is a highly non-linear system. A graphical abstract is presented at the end of the paper showing the pros/cons of each control system category regarding each WT control system. [ABSTRACT FROM AUTHOR]

The article discusses the growth and prospects of artificial intelligence (AI) technology, particularly following the launch of ChatGPT in 2023. The global AI market is expected to reach over USD 2.5 trillion by 2032, and ChatGPT has already gained 200 million users within a year. The article emphasizes the need for research papers on deep structure AI and interdisciplinary applications of large-scale language models (LLMs). It also highlights the importance of hardware-based deep learning AI and the transparency and interpretability of deep neural networks. The article concludes by mentioning promising areas in the AI section, such as electronic interfaces, self-driving cars, emotional systems, and healthcare systems. [Extracted from the article]

The global economy is growing faster and faster. Behavioral finance is a transformation of financial theory. Over the past decade, this shift has had strong repercussions in academia, challenging the dominance of traditional finance and forming its own theoretical system. With the development of the stock market, traditional financial theories and behavioral financial theories continue to converge, and traditional financial theories based on investor rationality and efficient market assumptions are subject to unprecedented conjectures. Financial markets are affected by subjective factors such as people's behaviors and emotions. Investors always make decisions based on bounded rationality, cognitive deficits, and, ultimately, rationality. In order to avoid the complex and unpredictable risks of financial markets and understand their changing laws, the analysis of the characteristics of financial instability is conducive to understanding the nature and internal principles of financial markets. Analysis of the volatility characteristics of financial markets must give priority to the analysis of financial chronological order. Financial time series are characterized by differences in financial markets, which are indeterminate orders, and the analysis of their fluctuations becomes crucial for stimulating the microstructure of financial behavior markets. Therefore, in order to give full play to the role of edge computing and promote the controllability of behavioral financial market volatility, this paper used the calculation task load model algorithm, time slot length optimization algorithm, asymmetric thick-tail random fluctuation, and volatility analysis application algorithm to study the subject of how to learn to reduce financial market volatility, summarizing and discussing the experiment. The research results showed that the behavioral financial market volatility mechanism based on edge computing constructed in this paper improved the predictability of financial market volatility by 15%. [ABSTRACT FROM AUTHOR]

Detecting and warning Advanced Persistent Threat (APT) malware in Endpoint is essential because the current trend of APT attacker groups is to find ways to spread malware to users and then escalate privileges in the system. In this study, to improve the ability to detect APT malware on Endpoint machines, we propose a novel intelligent cognitive calculation method based on a model combining graph embeddings and Attention using processes generated by executable files. The proposed intelligent cognitive computation method performs 3 main tasks: i) extracting behaviors of processes; ii) aggregating the malware behaviors based on the processes; iii) detecting APT malware based on behavior analysis. To carry out the task (i), we propose to use several data mining techniques: extracting processes from Event IDs in the operating system kernel; extracting abnormal behaviors of processes. For task (ii), a graph embedding (GE) model based on the Graph Convolutional Networks (GCN) network is proposed to be used. For task (iii), based on the results of task (ii), the paper proposes to use a combination of the Convolutional Neural Network (CNN) network and Attention network (called CNN-Attention). The novelty and originality of this study is an intelligent cognitive computation method based on the use, combination, and synchronization of many different data mining techniques to compute, extract, and represent relationships and correlations among APT malware behaviors from processes. Based on this new intelligent cognitive computation method, many meaningful anomalous features and behaviors of APT malware have been synthesized and extracted. The proposals related to data mining methods to extract malware's features and the list of malware's behaviors provided in this paper are new information that has not been published in previous studies. In the experimental section, to demonstrate the effectiveness of the proposed method in detecting APT malware, the study has compared and evaluated it with other approaches. Experimental results in the paper have shown the outstanding efficiency of the proposed method when ensuring all metrics from 96.6% or more (that are 2% to 6% higher than other approaches). Experimental results in the paper have proven that our proposed method not only has scientifically significant but also has practical meaning because the method has helped to improve the efficiency of analyzing and detecting APT malware on Endpoint devices. In addition, this research result also has opened up a new approach for the task of detecting other anomalies on the Endpoint such as malware, unauthorized intrusion, insider, etc. [ABSTRACT FROM AUTHOR]

Nowadays, cognitive computing has become the popular solution to many problems arising in the energy industry, such as the creation of renewable technologies, energy saving, and searching for new sources. Last decade, a substantial number of scientific papers aiming to support these tasks were published. On the other hand, some years ago, the "cognitive enterprise" (CE) concept was introduced by the IBM company, which assumes, among others, the cognitive technologies used to increase enterprise intelligence. On the road to obtaining the status of a "cognitive enterprise", it should overcome many challenges. Thus, the aim of the paper was to analyze the current state of research on the application of cognitive computing in the energy industry and to define the trends, challenges, milestones, and perspectives in scientific work's development. The aim has been achieved using the bibliometric approach. The preliminary analysis was made by Web of Science data sources; 4182 records were retrieved. The results comprise the research field, geographic distribution of research, time analysis, and affiliation analysis. Additionally, descriptive statistics, as well as simple forecasting, were provided to present the research results. As a result of the research, the publication history road was created as well as the milestone framework on the path toward "cognitive enterprise". The findings of this research can contribute to literature and practice by applying them to the process of cognitive enterprise models' development as well as by adapting the education programs and training courses for enterprises and universities to market requirements. [ABSTRACT FROM AUTHOR]

Manufacturers perpetually adapt their systems to meet unforeseen events, new objectives, competition, and improved understanding of processes. In that human-directed work, models mediate an enduring relationship between production resources and engineers. Accommodating new understanding in the models controlling production can lead to more effective manufacturing. That work has previously been the province of quality programs such as Six Sigma, but is now fertile ground to study human-computer interaction about that enduring relationship mediated by models. Can AI augment human capability in the arcane work of formulating and refining models? This question is relevant to complex system engineering generally, not just manufacturing. In answering this question, this paper adapts Klein's flexecution for use in adaptable manufacturing systems. Theory flexecution, the methodical refinement of models, points to human-computer interactions that emphasize the roles of models, explanation, and machine agents that recognize the engineer's goals. This perspective article illustrates these ideas with an example of formulating models for production scheduling. [ABSTRACT FROM AUTHOR]

With the rapid advancement of Artificial Intelligence-driven object recognition, the development of cognitive tunable imaging sensors has become a critically important field. In this paper, we demonstrate an infrared (IR) sensor with spectral tunability controlled by the applied bias between the long-wave and mid-wave IR spectral regions. The sensor is a Quantum Well Infrared Photodetector (QWIP) containing asymmetrically doped double QWs where the external electric field alters the electron population in the wells and hence spectral responsivity. The design rules are obtained by calculating the electronic transition energies for symmetric and antisymmetric double-QW states using a Schrödinger–Poisson solver. The sensor is grown and characterized aiming detection in mid-wave (~5 µm) to long-wave IR (~8 µm) spectral ranges. The structure is grown using molecular beam epitaxy (MBE) and contains 25 periods of coupled double GaAs QWs and Al0.38Ga0.62As barriers. One of the QWs in the pair is modulation-doped to provide asymmetry in potential. The QWIPs are tested with blackbody radiation and FTIR down to 77 K. As a result, the ratio of the responsivities of the two bands at about 5.5 and 8 µm is controlled over an order of magnitude demonstrating tunability between MWIR and LWIR spectral regions. Separate experiments using parameterized image transformations of wideband LWIR imagery are performed to lay the framework for utilizing tunable QWIP sensors in object recognition applications. [ABSTRACT FROM AUTHOR]

Introduction: Dementia is one of the major global health issues among the aging population, characterized clinically by a progressive decline in higher cognitive functions. This paper aims to apply various artificial intelligence (AI) approaches to detect patients with mild cognitive impairment (MCI) or dementia accurately. Methods: Quantitative research was conducted to address the objective of this study using randomly selected 343 Saudi patients. The Chi-square test was conducted to determine the association of the patient's cognitive function with various features, including demographical and medical history. Two widely used AI algorithms, logistic regression and support vector machine (SVM), were used for detecting cognitive decline. This study also assessed patients' cognitive function based on gender and developed the predicting models for males and females separately. Results: Fifty four percent of patients have normal cognitive function, 34% have MCI, and 12% have dementia. The prediction accuracies for all the developed models are greater than 71%, indicating good prediction capability. However, the developed SVM models performed the best, with an accuracy of 93.3% for all patients, 94.4% for males only, and 95.5% for females only. The top 10 significant predictors based on the developed SVM model are education, bedtime, taking pills for chronic pain, diabetes, stroke, gender, chronic pains, coronary artery diseases, and wake-up time. Conclusion: The results of this study emphasize the higher accuracy and reliability of the proposed methods in cognitive decline prediction that health practitioners can use for the early detection of dementia. This research can also stipulate substantial direction and supportive intuitions for scholars to enhance their understanding of crucial research, emerging trends, and new developments in future cognitive decline studies. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems. [ABSTRACT FROM AUTHOR]

This paper analyzes and investigates the quality assessment of spoken English pronunciation using a cognitive heuristic computing approach and designs a corresponding spoken pronunciation quality assessment system for practical training. Using the general Goodness of Pronunciation assessment algorithm as a benchmark, the shortcomings of the traditional Goodness of Pronunciation method are explored through statistical experiments, and the validity of the overall posterior probability output from the speech model for pronunciation quality assessment is verified. For the analysis of rhythm, there is no common algorithm framework, but in this paper, the F0 similarity algorithm based on dynamic time regularization and the stop similarity algorithm based on forced alignment is proposed for the two main factors of rhythm, intonation, and pause, respectively. After framing, the Hamming window processing is used to make the signal smoother, reduce the side lobe size after fast Fourier transform processing, and solve the problem of spectrum leakage. Compared with the ordinary rectangular window function, the Hamming window can obtain a higher quality spectrum. And combined with CTC for speech recognition modeling, the recognition rates are comparable in the case of using BLSTM and bidirectional threshold cyclic unit BGRU as the hidden layer unit, respectively, and the training time is 23% less than BLSTM using BGRU; in addition, the BGRU-CTC model is improved by using a 2-BGRU-CTC model with 256 hidden layer nodes, so that the error rate of phoneme recognition is reduced to 33%. The effectiveness of the algorithm framework is also verified through experiments, which further proves the effectiveness of our proposed phoneme segment feature and rhyme similarity algorithm. [ABSTRACT FROM AUTHOR]

This paper examines the prevalence of bias in artificial intelligence text-to-image models utilized in the architecture and design disciplines. The rapid pace of advancements in machine learning technologies, particularly in text-to-image generators, has significantly increased over the past year, making these tools more accessible to the design community. Accordingly, this paper aims to critically document and analyze the collective, computational, and cognitive biases that designers may encounter when working with these tools at this time. The paper delves into three hierarchical levels of operation and investigates the possible biases present at each level. Starting with the training data for large language models (LLM), the paper explores how these models may create biases privileging English-language users and perspectives. The paper subsequently investigates the digital materiality of models and how their weights generate specific aesthetic results. Finally, the report concludes by examining user biases through their prompt and image selections and the potential for platforms to perpetuate these biases through the application of user data during training. Graphical Abstract [ABSTRACT FROM AUTHOR]

The advent of 2D graphical user interfaces in the 1980s shifted user interactions from line-based terminals to icon- based interfaces. As smartphones emerged in the 2010s, portable 2D graphical interfaces became a reality, liberating users from being confined to a single location when accessing digital services. These transformations have profoundly altered our understanding of digital information systems, with impacts that cannot be easily quantified. Current advancements in virtual and augmented reality (VR/AR), the Internet of Things (IoT), and artificial intelligence (AI) are on the verge of ushering in the next significant leap in cognitive expansion, introducing portable and highly contextual spatial interfaces, also sometimes referred to as Digital Realities (DRS). As a result, users now anticipate the ability to engage with an increasing array and variety of digital content in ways that are more contextualized and tailored to their needs, taking into account factors such as time, location, personalized goals and user-specific histories. In this paper, we aim to give an overview of cognitive aspects relevant to content integration and management specifically in DR environments, and to propose solutions and/or best practices to address them. Our discussion is centered around a paradigm called the Doing- When-Seeing (DWS) paradigm, which we propose for the design of Digital Reality interfaces. We demonstrate the applicability of this paradigm to the design of interfaces for creating content, organizing content, and semantically representing and retrieving content within 3D Digital Reality environments. [ABSTRACT FROM AUTHOR]

Purpose: In the construction sector, the knowledge-based process outgrows its emphasis on technological aspects. Yet, there is a lack of applied studies showing how a procurement system (PS) could be selected in the digital age. In particular, there is a radical need to establish an innovative process to visualise novel PS decision. Therefore, this paper aims to present a knowledge visualised framework for aiding construction PS decision-making. Design/methodology/approach: This paper describes the construction of process innovation. The framework (process) is supported by four influential decision supporting methods (mean utility values, analytic hierarchy process, fuzzy set theory and Delphi method) and computer programming (Matlab). Findings: There are four stages of this framework: (1) uniform rating for decision alternatives; (2) group decision for determining the decision attribute; (3) determining the final choice; (4) reporting the cognitive computing process. Supported by individual and groups decision dynamics, this framework emphasises how the dashboard aided innovative approach enables the induction of understanding, cognitive computing for decision-making and how the information would precisely be represented, which are vital requirements of modern construction. Originality/value: The contribution of this paper presents two leverage points that support the modern PS decision. Firstly, this paper provides a holistic view of the decision supporting methods on the basis of how a suitable PS would be systematically sought. Based on the existing studies, this paper upgrades into a visualised knowledge decision supporting process. It helps the participants understand and improve their cognitive learning. Secondly, this framework allows the participants to have a view of the individual and group decisions. It sheds light on the development of the collaborative decision-making process. [ABSTRACT FROM AUTHOR]

The prevalence of dementia is currently increasing worldwide. This syndrome produces a deterioration in cognitive function that cannot be reverted. However, an early diagnosis can be crucial for slowing its progress. The Clock Drawing Test (CDT) is a widely used paper-and-pencil test for cognitive assessment in which an individual has to manually draw a clock on a paper. There are a lot of scoring systems for this test and most of them depend on the subjective assessment of the expert. This study proposes a computer-aided diagnosis (CAD) system based on artificial intelligence (AI) methods to analyze the CDT and obtain an automatic diagnosis of cognitive impairment (CI). This system employs a preprocessing pipeline in which the clock is detected, centered and binarized to decrease the computational burden. Then, the resulting image is fed into a Convolutional Neural Network (CNN) to identify the informative patterns within the CDT drawings that are relevant for the assessment of the patient's cognitive status. Performance is evaluated in a real context where patients with CI and controls have been classified by clinical experts in a balanced sample size of 3 2 8 2 drawings. The proposed method provides an accuracy of 7 5. 6 5 % in the binary case-control classification task, with an AUC of 0. 8 3. These results are indeed relevant considering the use of the classic version of the CDT. The large size of the sample suggests that the method proposed has a high reliability to be used in clinical contexts and demonstrates the suitability of CAD systems in the CDT assessment process. Explainable artificial intelligence (XAI) methods are applied to identify the most relevant regions during classification. Finding these patterns is extremely helpful to understand the brain damage caused by CI. A validation method using resubstitution with upper bound correction in a machine learning approach is also discussed. [ABSTRACT FROM AUTHOR]

Systems thinking is armored with a range of methodologies to aid practitioners work in complex situations. However, systems methodologies are often associated with a niche research group in operations research, management science and systems engineering (OR/MS/SE) thereby making their popularity and acceptance in general management and engineering challenging. In such a situation, methodological flexibility can offer greater liberty to a practitioner to use systems methodologies in a more flexible and creative manner without having to be bound by the rigor of the methodology itself. This paper presents a discussion on methodological flexibility in systems thinking highlighting two consultancy case studies. An orientation to the development of systems thinking in OR/MS/SE is provided leading to the presentation of Holistic Flexibility, a recently developed conceptual lens in systems thinking that calls for a more egalitarian and democratic stance for the discipline. The case-studies presented are analyzed in light of Holistic Flexibility to articulate the benefits and practitioner limitations of methodological flexibility. Recommendations to address the limitations are provided. This paper has two main contributions: First, it presents the proposition that methodological flexibility can also mean that systems methodologies can influence the design and deployment of interventions in management consultancy, without directly deploying such methodologies. Second, the practitioner experience, drawing from the journey of the projects presented in the case-studies, will substantiate recent arguments that call for systems thinking to be a cognitive discipline without having to be methodologically bounded. [ABSTRACT FROM AUTHOR]

"Improvising Packet Delivery and Reducing Delay Ratio in Mobile Ad-hoc Network Using Neighbor Coverage Based Topology Control Algorithm" - Authored by P, Punitha; J, Shanthini; S, Karthik: This research work [ 16 ] is focused on delay and neighbor connectivity-based constrain in MANET architectures. 2019; e4263. https://doi.org/10.1002/dac.4263 16 Rajakumari K, Punitha P, Lakshmana Kumar R, Suresh C. Improvising packet delivery and reducing delay ratio in mobile ad hoc network using neighbor coverage-based topology control algorithm. The papers in this issue introduce new challenges, solutions, and networking algorithms in cognitive communication systems, advancing capsule network research towards deeper communication pattern understanding and the ability to communicate about that understanding. We are delighted to present this special issue of IJCS on CapsNet-based computing in cognitive communications. [Extracted from the article]