Journal articles on the topic '170201 Computer Perception, Memory and Attention'

The purpose of the study is to determine the visual and auditory reaction time, perception, attention and memory levels of PE teacher candidates and computer teacher candidates.For this purpose, after giving necessary information to the teacher candidates of the Department of Physical Education and Sports and the candidates of Computer Teaching Department of Kocaeli University, 88 voluntary students, were applied Benton Perception, Attention-Memory test and Newtest 1000 which determined their visual and auditory reaction time. Data was analyzed by using SPSS 21.00 software. The Man Whitney U test was used for the comparison of the paired groups since the data in the normality analysis did not follow a normal distribution. As a result, it was found that computer teacher candidates had higher levels of attention-perception and memory scores than those of PE teacher candidates. PE teacher candidates were found to be faster to perceive auditory stimulus and give reactions.

Researchers have introduced side information such as social networks or knowledge graphs to alleviate the problems of data sparsity and cold starts in recommendation systems. However, most of the methods ignore the exploration of feature differentiation aspects in the knowledge propagation process. To solve the above problem, we propose a new attention recommendation method based on an enhanced knowledge propagation perception. Specifically, to capture user preferences in a fine-grained manner in a knowledge graph, an asymmetric semantic attention mechanism is adopted. It identifies the influence of propagation neighbors on user preferences through a more precise representation of the preference semantics for head and tail entities. Furthermore, in consideration of the memory and generalization of different propagation depth features and adaptively adjusting the propagation weights, a new propagation feature exploration framework is designed. The performance of the proposed model is validated by two real-world datasets. The baseline model averagely increases by 9.65% and 9.15% for the Area Under Curve (AUC) and Accuracy (ACC) indicators, which proves the effectiveness of the model.

(1) Background: An abacus is an instrument used to perform different arithmetic operations. The objective was to analyze the benefits of mathematical calculations made with an abacus to improve the concentration, attention, memory, perceptive attitudes, and creativity cognitive abilities of primary school students. (2) Methods: A total of 65 children, aged 7–11 years (8.49 ± 1.65) participated in this randomized controlled clinical trial. The children were randomly distributed into a control group (n = 34) and experimental group (n = 31). The questionnaires used were the D2 test to measure attention and concentration, the Difference Perception Test (FACE-R) test for the perception of differences, the test of immediate auditory memory (AIM), and the test to evaluate creative intelligence (CREA). (3) Results: No significant differences were found between both groups before the intervention. Significant improvements were observed in the cognitive parameters of concentration, memory, perceptive attitudes, and creativity after the intervention, using the abacus, with respect to the control group. (4) Conclusions: It is demonstrated that a calculation program based on the use of the abacus for 8 weeks has beneficial effects on the cognitive capacities of concentration, immediate auditory memory, perceptive attitudes, and creativity. In addition, the benefits of using the abacus to improve cognitive attitudes are reported.

Designing efficient deep learning models for 3D point cloud perception is becoming a major research direction. Point-voxel convolution (PVConv) Liu et al. (2019) is a pioneering research work in this topic. However, since with quite a few layers of simple 3D convolutions and linear point-voxel feature fusion operations, it still has considerable room for improvement in performance. In this paper, we propose a novel pyramid point-voxel convolution (PyraPVConv) block with two key structural modifications to address the above issues. First, PyraPVConv uses a voxel pyramid module to fully extract voxel features in the manner of feature pyramid, such that sufficient voxel features can be obtained efficiently. Second, a sharable attention module is utilized to capture compatible features between multi-scale voxels in pyramid and point cloud for aggregation, as well as to reduce the complexity via structure sharing. Extensive results on three point cloud perception tasks, i.e., indoor scene segmentation, object part segmentation and 3D object detection, validate that the networks constructed by stacking PyraPVConv blocks are efficient in terms of both GPU memory consumption and computational complexity, and are superior to the state-of-the-art methods.

Eyes as the unique organ possess intensively direct connections to the brain and dynamically perceptual accessibility to the mind. This paper analyzes the cognitive mechanisms of eyes not only as the sensory of vision, but also the browser of internal memory in thinking and perception. The browse function of eyes is created by abstract conditioning of the eye's tracking pathway for accessing internal memories, which enables eye movements to function as the driver of the perceptive thinking engine of the brain. The dual mechanisms of the eyes as both the external sensor of the brain and the internal browser of the mind are explained based on evidences and cognitive experiences in cognitive informatics, neuropsychology, cognitive science, and brain science. The finding on the experiment's internal browsing mechanism of eyes reveals a crucial role of eyes interacting with the brain for accessing internal memory and the cognitive knowledge base in thinking, perception, attention, consciousness, learning, memorization, and inference.

Abstract Reliable duration perception is an integral aspect of daily life that impacts everyday perception, motor coordination, and subjective passage of time. The Scalar Expectancy Theory (SET) is a common model that explains how an internal pacemaker, gated by an external stimulus-driven switch, accumulates pulses during sensory events and compares these accumulated pulses to a reference memory duration for subsequent duration estimation. Second-order mechanisms, such as multisensory integration (MSI) and attention, can influence this model and affect duration perception. For instance, diverting attention away from temporal features could delay the switch closure or temporarily open the accumulator, altering pulse accumulation and distorting duration perception. In crossmodal duration perception, auditory signals of unequal duration can induce perceptual compression and expansion of durations of visual stimuli, presumably via auditory influence on the visual clock. The current project aimed to investigate the role of temporal (stimulus alignment) and nontemporal (stimulus complexity) features on crossmodal, specifically auditory over visual, duration perception. While temporal alignment revealed a larger impact on the strength of crossmodal duration percepts compared to stimulus complexity, both features showcase auditory dominance in processing visual duration.

The importance of emotional expression as part of human communication has been understood since Aristotle, and the subject has been explored scientifically since Charles Darwin and others in the nineteenth century. Advances in computer technology now allow machines to recognize and express emotions, paving the way for improved human–computer and human–human communications. Recent advances in psychology have greatly improved our understanding of the role of affect in communication, perception, decision-making, attention and memory. At the same time, advances in technology mean that it is becoming possible for machines to sense, analyse and express emotions. We can now consider how these advances relate to each other and how they can be brought together to influence future research in perception, attention, learning, memory, communication, decision-making and other applications. The computation of emotions includes both recognition and synthesis, using channels such as facial expressions, non-verbal aspects of speech, posture, gestures, physiology, brain imaging and general behaviour. The combination of new results in psychology with new techniques of computation is leading to new technologies with applications in commerce, education, entertainment, security, therapy and everyday life. However, there are important issues of privacy and personal expression that must also be considered.

Tridimensional representations stimulate cognitive processes that are the core and foundation of human-computer interaction (HCI). Those cognitive processes take place while a user navigates and explores a virtual environment (VE) and are mainly related to spatial memory storage, attention, and perception. VEs have many distinctive features (e.g., involvement, immersion, and presence) that can significantly improve HCI in highly demanding and interactive systems such as brain-computer interfaces (BCI). BCI is as a nonmuscular communication channel that attempts to reestablish the interaction between an individual and his/her environment. Although BCI research started in the sixties, this technology is not efficient or reliable yet for everyone at any time. Over the past few years, researchers have argued that main BCI flaws could be associated with HCI issues. The evidence presented thus far shows that VEs can (1) set out working environmental conditions, (2) maximize the efficiency of BCI control panels, (3) implement navigation systems based not only on user intentions but also on user emotions, and (4) regulate user mental state to increase the differentiation between control and noncontrol modalities.

Human relations are driven by social events—people interact, exchange information, share knowledge and emotions, and gather news from mass media. These events leave traces in human memory, the strength of which depends on cognitive factors such as emotions or attention span. Each trace continuously weakens over time unless another related event activity strengthens it. Here, we introduce a novel cognition-driven social network (CogSNet) model that accounts for cognitive aspects of social perception. The model explicitly represents each social interaction as a trace in human memory with its corresponding dynamics. The strength of the trace is the only measure of the influence that the interactions had on a person. For validation, we apply our model to NetSense data on social interactions among university students. The results show that CogSNet significantly improves the quality of modeling of human interactions in social networks.

Presence refers to the emotional state of users where their motivation for thinking and acting arises based on the perception of the entities in a virtual world. The immersion level of users can vary when they interact with different media content, which may result in different levels of presence especially in a virtual reality (VR) environment. This study investigates how user characteristics, such as gender, immersion level, and emotional valence on VR, are related to the three elements of presence effects (attention, enjoyment, and memory). A VR story was created and used as an immersive stimulus in an experiment, which was presented through a head-mounted display (HMD) equipped with an eye tracker that collected the participants’ eye gaze data during the experiment. A total of 53 university students (26 females, 27 males), with an age range from 20 to 29 years old (mean 23.8), participated in the experiment. A set of pre- and post-questionnaires were used as a subjective measure to support the evidence of relationships among the presence effects and user characteristics. The results showed that user characteristics, such as gender, immersion level, and emotional valence, affected their level of presence, however, there is no evidence that attention is associated with enjoyment or memory.

The article deals with the formation of cognitive interest in the subject of informatics, as well as the skills of independent work on the acquisition of new knowledge, the development of thinking, memory, attention. A modern Informatics teacher is called upon not only to educate and educate schoolchildren, to develop students' interest in the subject, which has a great effect in the formation of knowledge and contributes to the activation of the process of knowledge perception and mental activity, but must also be able to organize extracurricular activities. The relevance of this topic meets the new requirements of education in the field of Informatics. The article describes the experience of carrying out extracurricular work in the study of teaching methods of Informatics. Therefore, targeted methodological training of future teachers of informatics for extracurricular work on the subject significantly increases their overall methodological training.

The article deals with the formation of cognitive interest in the subject of informatics, as well as the skills of independent work on the acquisition of new knowledge, the development of thinking, memory, attention. A modern Informatics teacher is called upon not only to educate and educate schoolchildren, to develop students' interest in the subject, which has a great effect in the formation of knowledge and contributes to the activation of the process of knowledge perception and mental activity, but must also be able to organize extracurricular activities. The relevance of this topic meets the new requirements of education in the field of Informatics. The article describes the experience of carrying out extracurricular work in the study of teaching methods of Informatics. Therefore, targeted methodological training of future teachers of informatics for extracurricular work on the subject significantly increases their overall methodological training.

<p class="IATED-Affiliation">Objectivized phenomenon in science education and their elements, revealed for students in the homogenized visual form, stimulate development of the Subject consciousness within the context of learning. Visualization is visual artefact represented in visual way aiming to code or decode information. By stressing this, the use of visualization during lessons of Biology, Physics, Geography and Mathematics becomes very important – it helps for perception, attention, memory and imagination that are needed during learning processes. This article encloses empirical data about effect of computer based visualization on students’ cognitive processes. There were asked 2708 ninth and tenth form classes students in Lithuania. The results show that visualization is useful homogenously both for males and females, but females statistically significantly are sure about the positive effect of visual artifacts in the classroom.</p>

Higher education has delved into a new stage of rapid development focusing on quality improvement, while encountering new challenges and obstacles. In this research work, an artificial intelligence algorithm for education improvement is proposed. Firstly, deep feature abstraction in temporal and special dimensions is performed using Long Short-Term Memory (LSTM) artificial neural network and convolutional networks. Consequently, multiscale attention fusion techniques are used to improve the articulateness of the characteristics and come up with better recommendations with the assistance of multilayer perceptron. Moreover, the proposed model helps in improving the cognitive capability of students and enhances their overall quality of perception. Moreover, it has been proven that the performance of the proposed model provides better recommendation outcomes and better robustness compared to existing models through conducting extensive experiments based on real data.

The lack of spatial orientation often serves as a warning light for a diffused cerebral impairment, such as the one at the origin of a confusional state or of a mental disorder. Topographical orientation disorders may be the expression of memory or attention deficits, unilateral spatial negligence, or elementary visuoperceptive disorders. In a minority of cases, spatial disorientation presents as an isolated disorder and is the expression of a focal brain lesion. The paper describes the clinical rationale and the technical characteristics of the Virtual Environment for Topographical Orientation (VETO). VETO is used both as a complementary tool for the assessment of topographical orientation disorders and to increase the possibilities of management of these patients from a rehabilitative point of view. VETO is based on the recent theoretical model of wayfinding in virtual environments proposed by Chen and Stanney (1999). This model suggests that wayfinders generally start from the direct perception of the environment or from the recall of a cognitive map. In terms of direct perception of the environment, landmark knowledge is acquired by directly viewing indirect representation such as photographs. In terms of cognitive mapping, procedure/route knowledge is acquired through direct experience or through simulated experience and stored in memory. Our hypothesis is that the study of spatial orientation through specific VETO tasks, both in normal samples and in subjects affected by topographical disorientation, can bring greater comprehension and validation of the cognitive models of spatial orientation present in literature.

Training models to predict click and order targets at the same time. For better user satisfaction and business effectiveness, multitask learning is one of the most important methods in e-commerce. Some existing researches model user representation based on historical behaviour sequence to capture user interests. It is often the case that user interests may change from their past routines. However, multi-perspective attention has broad horizon, which covers different characteristics of human reasoning, emotions, perception, attention, and memory. In this paper, we attempt to introduce the multi-perspective attention and sequence behaviour into multitask learning. Our proposed method offers better understanding of user interest and decision. To achieve more flexible parameter sharing and maintaining the special feature advantage of each task, we improve the attention mechanism at the view of expert interactive. To the best of our knowledge, we firstly propose the implicit interaction mode, the explicit hard interaction mode, the explicit soft interaction mode, and the data fusion mode in multitask learning. We do experiments on public data and lab medical data. The results show that our model consistently achieves remarkable improvements to the state-of-the-art method.

A multimodal approach to prevent the cognitive impairment in patients with cardiovascular diseases combines the control of major cardiovascular risk factors, cognitive recovery, and physical training. Here we discuss current advances in computer-aided (also called computer-assisted) cognitive recovery to prevent the cognitive impairment in patients with cardiovascular diseases, as this approach has a number of advantages in comparison with the conventional tools. We describe a cognitive training software to stimulate perception, attention, short-term memory, executive functions, speech, and thinking. Baseline neurological examination and neuropsychological testing are mandatory before starting a cognitive recovery. A particular attention is paid to the high prevalence of cognitive impairment in cardiac surgery patients. Around half of them have pre-operative cognitive impairments, and almost half of the patients suffer from a postoperative cognitive dysfunction. Among the brain regions, prefrontal and parietal cortex and hippocampus are the most sensitive to circulatory disorders as they are supplied by the terminal branches of the cerebral arteries. Therefore, cognitive rehabilitation of cardiac surgery patients should include computer-aided cognitive training tasks activating these brain regions.

Although the study of visual perception has made more progress in the past 40 years than any other area of cognitive science, there remain major disagreements as to how closely vision is tied to cognition. This target article sets out some of the arguments for both sides (arguments from computer vision, neuroscience, psychophysics, perceptual learning, and other areas of vision science) and defends the position that an important part of visual perception, corresponding to what some people have called early vision, is prohibited from accessing relevant expectations, knowledge, and utilities in determining the function it computes – in other words, it is cognitively impenetrable. That part of vision is complex and involves top-down interactions that are internal to the early vision system. Its function is to provide a structured representation of the 3-D surfaces of objects sufficient to serve as an index into memory, with somewhat different outputs being made available to other systems such as those dealing with motor control. The paper also addresses certain conceptual and methodological issues raised by this claim, such as whether signal detection theory and event-related potentials can be used to assess cognitive penetration of vision.A distinction is made among several stages in visual processing, including, in addition to the inflexible early-vision stage, a pre-perceptual attention-allocation stage and a post-perceptual evaluation, selection, and inference stage, which accesses long-term memory. These two stages provide the primary ways in which cognition can affect the outcome of visual perception. The paper discusses arguments from computer vision and psychology showing that vision is “intelligent” and involves elements of “problem solving.” The cases of apparently intelligent interpretation sometimes cited in support of this claim do not show cognitive penetration; rather, they show that certain natural constraints on interpretation, concerned primarily with optical and geometrical properties of the world, have been compiled into the visual system. The paper also examines a number of examples where instructions and “hints” are alleged to affect what is seen. In each case it is concluded that the evidence is more readily assimilated to the view that when cognitive effects are found, they have a locus outside early vision, in such processes as the allocation of focal attention and the identification of the stimulus.

Introduction. The period from 5 to 7 years attracts special attention from scientists, as it is a sensitive and critical stage of individual development of the child's body.Most domestic and foreign scholars emphasize the need of selecting the content, forms and methods of teaching in accordance with age and gender. However, the peculiarity of the study is that during training should also take into account the psycho-physiological and neurodynamic characteristics of the development of participants in the educational process and adjust teaching methods based on them for more effective perception of certain information by children.The formation of visual and auditory perception for children 5-7 years, as an indicator of readiness, is one of the main cognitive functions, which is formed due to interanalytic complexes of nerve connections and maturity of the cerebral cortex.Therefore, significant attention should be paid to the invariant and variable component of the "New Ukrainian School" of the Standard curriculum of general secondary education for grades 1-2 and preschools, developed under the leadership of O. Savchenko [1], for improving cognitive development and prevention of maladaptation changes in the body during training.Purpose. The aim is to study the features of neurodynamic and mental functions of children of preschool and primary school age with different levels of visual and auditory perception.20 Серія «Біологічні науки», 202 2Methods. The observation was carried out on children aged 5-7 of two educational institutions "Secondary schools of I degrees - preschool educational institutions" No 41 and No42 of Sumy (n = 246).The method of assessment of auditory memory "Learning 10 words" (A.R. Luria, 1973) for informative study of memory was chosen. Determination of the volume of visual random memory was performed according to the method of Shipitsyna LM.M.V. Makarenko’s method on the computer complex “Diagnost-1” was used to determine individual-typological features of higher nervous activity of children.Results. The highest rate of auditory memory was recorded for children aged 6 and 7 years, in contrast to children aged 5 years, in whom the development of cognitive processes was carried outthrough visual perception. Children 5 years of age had longer latent periods obtained during sensorimotor response of varying complexity and lower results of SNP, FRNP, PGM during mental stress in contrast to children 6 and 7 years.A similar trend was also observed among boys aged 5-7 compared to girls in this age group.According to the results of neurodynamic studies, it was found that the best values of SNP, FRNP, PGM and shorter latent periods of SVMR, RC1-2, RC1-3, RC2-3 were obtained for individualswith high levels of visual and auditory perception.Originality. The idea of neurodynamic and psychophysiological functions of children 5-7 years with different levels of visual and auditory perception is expanded.Conclusions. The importance of psychophysiological and neurodynamic functions of children 5-7 years with different levels of visual and auditory perception to prevent maladaptation in the educational process was substantiated.Key words: children; cognitive development; functional mobility of nervous processes; strength of nervous processes; sensorimotor systems.

Under the background of quality education, music learning is also changing, from the original shallow learning to deep learning gradually. In-depth learning is a new teaching concept, which pays full attention to students’ perception and exploration of music so that students can fully experience the charm of music. It can not only help students master more music knowledge and improve their music skills but also cultivate students’ music literacy and enhance their music ability (Świechowski, 2015). Therefore, in junior high school music teaching, teachers should actively apply the deep learning model and then improve the teaching level and comprehensively cultivate students’ music literacy (Whitenack and Swanson, 2003). In this paper, two convolution-based deep learning models, Breath1d and Breath2d, were designed and constructed, and a multilayer perceptron (MLP) was used as a benchmark method for performance evaluation, and a long short-term memory (LSTM) network is applied for the classification task. This paper discusses the value and application strategies of deep learning in junior high school music teaching and hopes to provide some reference for all educational colleagues (Zhang and Nauman 2020).

Background: Cognitive impairment as a consequence of stroke is a major cause affecting the patient’s functional independence, activity participation, daily living skills, and occupation. Almost 75% of post-stroke patients are diagnosed with significant cognitive impairment, which includes problems with attention, orientation, memory, language, and perception. Along with effective pharmaceutical cures, cognitive intervention as a part of rehabilitation approaches that may prevent, delay, or treat cognitive impairment is becoming increasingly important. Many studies have reported improvement in cognitive functions of post-stroke patients after using computer-based cognitive intervention (CBCI). CBCI can be an effective add-on to available rehabilitation programs. Objective: This article provides reviews related to relevant literature and, represents a structure to specify the efficacy of CBCI for the rehabilitation of post-stroke patients for future research. Methods: We searched many search engines namely MEDLINE, Web of Science, clinical key and The Cochrane Library, for studies investigating the effect of cognitive intervention based on a computer program for post-stroke patients. The results of selected studies were summarized. Total 19 publications from January 2007 to January 2019 are included in this review. The search terms entered were a combination of these search areas that defined (1) the population as adults who had suffered a stroke and cognitive dysfunction, (2) intervention search term included cognitive abilities, cognitive training, and computer-based training; computer-based cognitive intervention for rehabilitation. Results: The results after computer-based training showed improvement in various cognitive functions such as; memory, attention and executive functions of post-stroke patients. However, a significant difference between the study groups has not been observed in all the studies. Most studies analyzed in this research project indicated that such interventions might contribute to the improvement of cognitive function, especially attention concentration and memory. Of the 19 kinds of research that discussed CBCI outcomes 18 found significant improvements for one or more cognitive functions. When the effect size for CBCI was reported, effects were large in comparison to other traditional cognitive interventions of post-stroke patients. Conclusion: Studies related to cognitive functions strongly support CBCI except few have reported a significant difference. The review of all the studies suggests that CBCI may help to change the functional aspect of post-stroke patients by improving their cognitive functions. In this field, it is a challenge to conduct well designed and sufficiently powered studies due to low budgets availability, the limited number of available patients, heterogeneity of the population, and ethical considerations. Future studies should examine all the challenges, limitations, and valuable insights into the study and emphasize the need for a carefully designed computer-based cognitive intervention program for the future. Future studies should target to compare CBCI with active and passive control conditions and include a larger sample size.

Background: Tests of auditory perception, such as those used in the assessment of central auditory processing disorders ([C]APDs), represent a domain in audiological assessment where measurement of this theoretical construct is often confounded by nonauditory abilities due to methodological shortcomings. These confounds include the effects of cognitive variables such as memory and attention and suboptimal testing paradigms, including the use of verbal reproduction as a form of response selection. We argue that these factors need to be controlled more carefully and/or modified so that their impact on tests of auditory and visual perception is only minimal. Purpose: To advocate for a stronger theoretical framework than currently exists and to suggest better methodological strategies to improve assessment of auditory processing disorders (APDs). Emphasis is placed on adaptive forced-choice psychophysical methods and the use of matched tasks in multiple sensory modalities to achieve these goals. Together, this approach has potential to improve the construct validity of the diagnosis, enhance and develop theory, and evolve into a preferred method of testing. Research Design: Examination of methods commonly used in studies of APDs. Where possible, currently used methodology is compared to contemporary psychophysical methods that emphasize computer-controlled forced-choice paradigms. Results: In many cases, the procedures used in studies of APD introduce confounding factors that could be minimized if computer-controlled forced-choice psychophysical methods were utilized. Conclusions: Ambiguities of interpretation, indeterminate diagnoses, and unwanted confounds can be avoided by minimizing memory and attentional demands on the input end and precluding the use of response-selection strategies that use complex motor processes on the output end. Advocated are the use of computer-controlled forced-choice psychophysical paradigms in combination with matched tasks in multiple sensory modalities to enhance the prospect of obtaining a valid diagnosis.

Background: Acute consumption of cannabis or its primary psychoactive ingredient ∆9-tetrahydrocannabinol has been shown to impair memory, reaction time, time perception, and attention. However, it is difficult to measure these impairments in a brief test that can be used in a non-laboratory setting. Aims: We aim to develop and validate a prototype for a mobile phone application to measure ∆9-tetrahydrocannabinol-induced cognitive impairment. Methods: We conducted two double-blind, within-subjects studies examining impairments after oral doses of ∆9-tetrahydrocannabinol (0, 7.5, 15 mg) using both standardized computer-based tasks and our novel phone-based tasks. The tasks measured cognitive speed, reaction time, fine motor ability, and working memory and, in the second study, time perception. Study 1 ( n=24) provided initial data, and Study 2 ( n=24) was designed to refine the measures. In both studies, healthy non-daily cannabis users participated in three four-hour experimental sessions in which they received capsules containing ∆9-tetrahydrocannabinol (7.5, 15 mg) or placebo. Subjective and cardiovascular measures were obtained at regular intervals, and at the time of peak drug effect subjects completed both standardized, computer-based and brief, phone-based tasks. Results: ∆9-Tetrahydrocannabinol-induced impairment was detected on most of the computer tasks, but was not evident on most of the phone tasks. Conclusions: The phone tasks were brief, to facilitate use in a non-laboratory setting, but it is likely that this made them less sensitive to the impairing effects of ∆9-tetrahydrocannabinol. These findings confirm that ∆9-tetrahydrocannabinol impairs performance on several tasks at two recreationally relevant doses, but raises question about the feasibility of designing a phone application as a sensitive field sobriety test for cannabis.

Abstract Objectives The impact of glucocorticoids on neurocognitive performance in patients with SLE is not fully addressed. We aimed to study the effect of daily and cumulative glucocorticoid dose on neurocognitive performance in SLE patients. Methods Consecutive SLE patients and gender- and age-matched healthy controls (HCs) underwent the computer-based Automated Neuropsychological Assessment Matric (ANAM), which evaluates eight neurocognitive domains including learning, recall, visual perception, mental rotation, short-term memory, attention, sustained attention and working memory. The total and individual-domain throughput scores (TPSs) and the presence of cognitive dysfunction (total TPS &lt;1.5 s.d. below the mean TPS of HCs) were compared between SLE patients and HCs. Within the SLE group, univariate and independent associations between prednisolone dose (daily and cumulative) and individual-domain TPS were studied by univariate and multivariable linear regression, respectively. Results A total of 96 SLE patients and 96 HCs were studied. SLE patients scored significantly worse across all the neurocognitive domains and had a significantly lower mean total TPS (P &lt; 0.001) and a higher prevalence of cognitive dysfunction compared with HCs (25.0 vs 7.3%, P = 0.001). In SLE patients, daily prednisolone dose was significantly and negatively correlated with mathematical-processing TPS, which probes working memory (P = 0.018). No significant correlation between cumulative prednisolone dose and any of the individual-domain TPSs was found. In multivariable regression, higher daily prednisolone dose and doses &gt;9 mg daily remained independently associated with lower mathematical-processing TPSs (P = 0.031). Conclusion Daily prednisolone dose ≥9 mg, but not cumulative glucocorticoid dose, had an independent negative impact on mathematical processing in SLE patients.

Humans can infer concepts from image pairs and apply those in the physical world in a completely different setting, enabling tasks like IKEA assembly from diagrams. If robots could represent and infer high-level concepts, then it would notably improve their ability to understand our intent and to transfer tasks between different environments. To that end, we introduce a computational framework that replicates aspects of human concept learning. Concepts are represented as programs on a computer architecture consisting of a visual perception system, working memory, and action controller. The instruction set of this cognitive computer has commands for parsing a visual scene, directing gaze and attention, imagining new objects, manipulating the contents of a visual working memory, and controlling arm movement. Inferring a concept corresponds to inducing a program that can transform the input to the output. Some concepts require the use of imagination and recursion. Previously learned concepts simplify the learning of subsequent, more elaborate concepts and create a hierarchy of abstractions. We demonstrate how a robot can use these abstractions to interpret novel concepts presented to it as schematic images and then apply those concepts in very different situations. By bringing cognitive science ideas on mental imagery, perceptual symbols, embodied cognition, and deictic mechanisms into the realm of machine learning, our work brings us closer to the goal of building robots that have interpretable representations and common sense.

To effectively evaluate the risk situation between intelligent vehicles and surrounding traffic participants in complex scenes, a complex traffic environment perception technology based on dual multiline light detection and ranging (LiDAR) is proposed in this work. The vehicle motion state is predicted by fusing the multiview characteristics of point cloud timing and multitarget interaction information, and the risk assessment model is constructed via artificial potential field theory. The real-time point cloud information is used to obtain the time-sequence bird’s-eye view and range image. The improved VGG19 network model is used to extract the time-sequence high-level abstract combined features in the multiview scene. The constructed time-sequence feature vector is used as the input data of the attention mechanism, and the attention-bidirectional long short-term memory (Attention-BiLSTM) model is used for training to form the desired input-output mapping relationship. The motion state of the target vehicle can therefore be updated, and the static and dynamic risk fields of traffic participants surrounding the vehicle can be established based on artificial potential field theory, thereby allowing for the evaluation of the operational risk of the intelligent vehicle. The results of experiments demonstrate that the prediction effect of the target vehicle state parameters via the use of the proposed model is better than that of other compared models, and the prediction effect of the risk field of intelligent vehicle operation based on the multiview point cloud features and vehicle interaction information is good.

<p class="0abstract">Metacognition is one of the foremost cardinal factors of achievement in the 21st century. Despite extensive research, there is still the need to build a unique model based on multidisciplinary research illuminating questions as regards the real nature of metacognition and the methods to develop metacognitive abilities. The current study presents a new layered model of metacognition based on well-established theories derived from cognitive science, psychology, physical and computer sciences, environmental and other sciences, even from philosophy. We describe in detail the cognitive and metacognitive processes involved in each layer, while particular emphasis is placed on the relation between the control processes as well as the special role of attention. According to our model, each layer of metacognition describes a higher-order control system which operates under the rule of a series of attention processes at an ever more refined, abstract, united and holistic level. The same applies to the cognitive processes and abilities such as attention, memory, perception, pattern recognition. At each higher level, they display more advanced attributes and functions responding to the necessity of creating more abstract mental representations and upper class motivations, thoughts and emotions. In addition, we recommend a number of strategies that support the metacognitive development at each level of the hierarchy. The multi-layered model of metacognition targets at enriching our understanding of how metacognition evolves and it has the potential to guide the development of more effective strategies in educational system.<strong></strong></p>

Introduction: Interactive electronic games allow to access virtual environments and interact using a computer or TV screen. Anyone who has played a video game, or seen others playing, is aware of the importance of reaction speed and eye-hand coordination skills. Objective: To determine the differences in psychomotor performance between professional gamers and amateurs. Material and Methods: A total of 62 gamers took part in the study, including 31 people - professional video game users who had participated in e-tournaments in the last month (age: M = 20.6, SD = 6.3) and 31 people who did not play video games or played very rarely (they declared that they did not participate in e-sports tournaments), who constituted the control group (age: M = 17.9, SD = 5.4). Integrated computer SDP-System with an executive module for stimulus generation and reception was used to assess psychomotor performance. Results: Professional gamers have higher psychomotor skills than amateurs. They reveal better results for fast thinking, motor reactions, perception, attention, and working memory. Conclusion: Playing video games has a positive impact on players' psychomotor performance and can promote improvements in elementary cognitive functions. Key words: video-games; e-sport; psychomotor performance; cognitive functions

Aim. To estimate psychophysiological changes during workspace virtualization.Materials and Methods. We evaluated the psychophysiological profile of 10 healthy right-handed males aged 25 to 45 years before, during and after the working in a virtual reality (VR) headset. All participants had higher education, normal or corrected to normal vision, and were experienced computer users. Psychometric testing included a neurological examination, assessment of functional and feedback-related brain activity (reaction time, errors, and missed signals) and attention span, quantification of processed symbols in the 1st and 4th minutes of Bourdon test, analysis of short-term memory (10 words, 10 numbers and 10 meaningless syllables memorization) and spatial perception, and multi-channel electroencephalography recording in rest.Results. Deterioration of psychometric indicators after a cognitive load in a VR headset was documented only in the most difficult tasks: the number of errors increased by 93% in the brain performance test and by 65% in the attention distribution test. The analysis of electroencephalography data showed that the delta rhythm and theta1 rhythm activity decreased by 28 and 13%, respectively, after working in a VR headset as compared to baseline values, while alpha1 rhythm activity increased by 96%. Probably, the observed electroencephalography changes corresponded to the patterns of brain activation associated with cognitive load and the resulting fatigue.Conclusions. We developed a suitable approach for the psychometric testing before and after working in VR headset, which demonstrated general tolerance and acceptable subjective difficulties to VR load.

Background Serious games are now widely used in many contexts, including psychological research and clinical use. One area of growing interest is that of cognitive assessment, which seeks to measure different cognitive functions such as memory, attention, and perception. Measuring these functions at both the population and individual levels can inform research and indicate health issues. Attention is an important function to assess, as an accurate measure of attention can help diagnose many common disorders, such as attention-deficit/hyperactivity disorder and dementia. However, using games to assess attention poses unique problems, as games inherently manipulate attention through elements such as sound effects, graphics, and rewards, and research on adding game elements to assessments (ie, gamification) has shown mixed results. The process for developing cognitive tasks is robust, with high psychometric standards that must be met before these tasks are used for assessment. Although games offer more diverse approaches for assessment, there is no standard for how they should be developed or evaluated. Objective To better understand the field and provide guidance to interdisciplinary researchers, we aim to answer the question: How are digital games used for the cognitive assessment of attention made and measured? Methods We searched several databases for papers that described a digital game used to assess attention that could be deployed remotely without specialized hardware. We used Rayyan, a systematic review software, to screen the records before conducting a systematic review. Results The initial database search returned 49,365 papers. Our screening process resulted in a total of 74 papers that used a digital game to measure cognitive functions related to attention. Across the studies in our review, we found three approaches to making assessment games: gamifying cognitive tasks, creating custom games based on theories of cognition, and exploring potential assessment properties of commercial games. With regard to measuring the assessment properties of these games (eg, how accurately they assess attention), we found three approaches: comparison to a traditional cognitive task, comparison to a clinical diagnosis, and comparison to knowledge of cognition; however, most studies in our review did not evaluate the game’s properties (eg, if participants enjoyed the game). Conclusions Our review provides an overview of how games used for the assessment of attention are developed and evaluated. We further identified three barriers to advancing the field: reliance on assumptions, lack of evaluation, and lack of integration and standardization. We then recommend the best practices to address these barriers. Our review can act as a resource to help guide the field toward more standardized approaches and rigorous evaluation required for the widespread adoption of assessment games.

Attention is a basic and main mental task and can play an important role in the functioning of other brain abilities such as intelligence, memory, learning, and perception, and its deficit occurs in 80% of patients with traumatic brain injury. The use of game-based tools for rehabilitation is rapidly expanding. Cognitive rehabilitation via video games is an emerging hot topic in cognitive science. Serious games serve a specific purpose in addition to entertainment. They can be more engaging than exercises since they replace reward and motivation systems with real-world motivations as a complement for rehabilitation activities. This study was aimed at identifying and categorizing serious computer games used for attention rehabilitation and evaluating their effects. Six electronic databases (Scopus, PubMed, ISI, Embase, IEEE, and Cochrane) were searched in August 2021. The search strategy consisted of three main concepts of “serious game”, “cognitive deficits”, and “cognitive rehabilitation”. The inclusion criteria were (1) journal articles, (2) English language, (3) being published in the last 10 years, (4) human participants, and (5) game-based intervention. In the 30 included studies, 22 unique games were utilized for attention rehabilitation. Lumosity (20%), Brain Age (Dr. Kawashima’s Brain Training) (10%), and MoHRS (6.66%) were the most common games among the studies. There were (57%) casual, (23%) action, (10%) simulation, and (10%) multiple genres. Of the 47 tools used in the studies, 5 utilized cross-modal oddball attention tasks, 4 utilized game performance, 3 utilized the paced auditory serial additional test (PASAT), and the rest employed other tools. A total of 73 outcome measures were related to attention, 42 measures did not have significant results, 30 were significantly improved, 1 was significantly deteriorated, and 4 articles did not have any specific measures for attention evaluation. Thus, the results revealed the positive effect of serious games on attention. However, issues such as absence of scientific teams, the variety of the disorders that cause defects, the variety of criteria, differences in measurements, lack of long-term follow-up, insufficient RCT studies, and small sample sizes should be considered when designing, developing, and using game-based systems to prevent bias.

The presentation of common acute diseases in older age is often referred to as “atypical”. Frequently, the symptoms are neither single nor tissue related. In most cases, the onset of symptoms and diseases is the expression of a diminished reserve with a failure of the body system and imbalance of brain function. Delirium is one of the main devastating and prevalent atypical symptoms and could be considered as a geriatric syndrome. It encompasses an array of neuropsychiatric symptoms and represents a disarrangement of the cerebral function in response to one or more stressors. The most recent definition, reported in the DSM-V, depicts delirium as a clear disturbance in attention and awareness. The deficit is to be developed in a relatively short time period (usually hours or days). The attention disorder must be associated with another cognitive impairment in memory, orientation, language, visual-spatial or perception abilities. For the treatment, it is imperative to remove the potential causes of delirium before prescribing drugs. Even a non-pharmacological approach to reducing the precipitating causes should be identified and planned. When we are forced to approach the pharmacological treatment of hyperactive delirium in older persons, we should select highly cost-effective drugs. High attention should be devoted to the correct balance between improvement of psychiatric symptoms and occurrence of side effects. Clinicians should be guided in the correct choice of drugs following cluster symptoms presentation, excluding drugs that could potentially produce complications rather than advantages. In this brief point-of-view, we propose a novel pharmacological flow-chart of treatment in relation to the basic clusters of diseases of an older patient acutely admitted to the hospital and, in particular, we emphasize “What We Should Not Do!”, with the intention of avoiding possible side effects of drugs used.

The purpose of this study was to provide a statistical review of trends in dance medicine and science research over the last 38 years with regard to how much is being published, where it is being published, and what subject matter is attracting the most attention. All data were compiled through computer tabulations of entries in the authors’ Dance Medicine & Science Bibliography, 3rd Edition. When viewed in three-year intervals, the number of publications was seen to increase dramatically between 1977 and 1988, and decrease steadily at each interval through 2003. Over the time span in question, 141 authored books, 36 edited books, and 321 chapters have appeared, along with 405 articles in periodicals (led by Medical Problems of Performing Artists and the Journal of Dance Medicine & Science, with 151 and 136 articles, respectively). Eleven research subjects can be identified which have produced at least 100 references; the 3 most prolific subjects are Psychology/Personality/Perception/Memory/Stress, psychological (308); Technique/Teaching/Training (284); and Stress fracture/Overuse injury (241). Publications over the last 38 years in dance medicine and science portray it as a vigorous and diverse field, although there is potential cause for concern in the (at this time unexplained) decrease in number of publications since 1989.

Children demonstrate increasing early engagement with mobile media facilitated by its portability and interactivity. Parents are known to employ a range of mediation strategies for mobile media use but continue to have limited awareness about the impact of mobile media on their child’s executive functioning. Mobile media use has previously been shown to be negatively correlated with the executive functioning development of a child; however, little is known of how parents approach their child’s mobile media use. This study employed a survey design ( N = 281 ) to examine how parents access information related to mobile media and document their perspectives about the impact of mobile media on their child’s behavior and executive functioning. Correlational analyses and cooccurrence graphs showed that parents implement several mediation strategies but rarely access guidelines on mobile media use. A confirmatory factor analysis examined the model fit for four latent constructs of the Behavior Rating Inventory of Executive Function (BRIEF®), which included the Inhibit, Emotional Control, Initiate, and Working Memory scales. Structural equation modelling substantiated the association between parental perception of negative impacts of mobile media related to their child’s behavior, academics, and/or attention and a lower observed executive functioning. Overall, these findings suggest that parents recognize the negative impacts of mobile media on their child’s behavior, and this is associated with how they see the development of their child’s executive functioning. The results emphasize the importance of educating parents as to the role of mobile media in shaping their child’s behavior and associated executive functions.

PurposeThe aim of this article is to present the application value of a diagnostic toolkit for assessment of cognitive functions based on a proprietary Multidimensional Model of Cognitive Functioning which combines executive functions with the field of perception, language, and communication. ThesesThe developed tools assess the level of executive functions (attention control, working memory, cognitive flexibility, and reasoning), the speed of perceptual processes, as well as linguistic functions in terms of speech- and writing-based communication. ConclusionsAll tasks have been prepared in a computer-based version. The innovativeness of the proposed battery of tests stems from the fact that the adopted model creates a coherent theoretical framework for the entire spectrum of functions describing the effectiveness of cognitive processing in people of different ages and varying educational needs. In the context of the tools available in psychological and pedagogical counselling centres, this technically advanced tool will be the first test available on the Polish market covering such a broad spectrum of cognitive functions. The tool has been designed to enable the diagnosis of diverse groups of people with special educational needs – from gifted children, through people with intellectual disabilities, visual and hearing impairments, neurodevelopmental disorders (e.g. autism), and language disorders and dyslexia, to migrant children. This broad scope of application was made possible by implementing the WCAG 2.1 and universal design principles.

As the central processing unit of the human body, the human brain is in charge of several activities, including cognition, perception, emotion, attention, action, and memory. Emotions have a significant impact on human well-being in their life. Methodologies for accessing emotions of human could be essential for good user-machine interactions. Comprehending BCI (Brain-Computer Interface) strategies for identifying emotions can also help people connect with the world more naturally. Many approaches for identifying human emotions have been developed using signals of EEG for classifying happy, neutral, sad, and angry emotions, discovered to be effective. The emotions are elicited by various methods, including displaying participants visuals of happy and sad facial expressions, listening to emotionally linked music, visuals, and, sometimes, both of these. In this research, a multi-model fusion approach for emotion classification utilizing BCI and EEG data with various classifiers was proposed. The 10-20 electrode setup was used to gather the EEG data. The emotions were classified using the sentimental analysis technique based on user ratings. Simultaneously, Natural Language Processing (NLP) is implemented for increasing accuracy. This analysis classified the assessment parameters as happy, neutral, sad, and angry emotions. Based on these emotions, the proposed model’s performance was assessed in terms of accuracy and overall accuracy. The proposed model has a 93.33 percent overall accuracy and increased performance in all emotions identified.

The article represents the results of neurocognitive development study in progress. Clinical methods of investigation are supplemented with examination of children with Russian computer psychophysiological complex “Psichomat”, which allows to examine a number of cognitive functions — perception, attention, memory, visual-motor coordination, processes of analysis and synthesis — in mathematical function expression: milliseconds and percentage of mistakes. A child’s intrauterine and postnatal development is determined by various factors. Cognitive processes, which are ascending accordingly to a child’s age, play significant role in social development. The standard charts of children psychoneurological development, worked out by the authors, must become key factors both for pediatricians of different subspecialities and for parents and psychologists, in order not to miss the beginning of some pathology condition — from development delay to retardation. During the analysis of cognitive status of a child, speech development, behavior and emotions should be assessed. The deficiency of cognitive functions causes difficulties in education and contact with a child, which in some cases leads to formation of syndromal pathological conditions and disease entities (from mild development delays to different stages of oligophrenia). It is necessary to educate pediatricians of various subspecialities and pediatric neurologists the methods of children examination in order to determine the “cognitive profile” according to the age, health condition, presence of somatic and/or psychoneurological diseases and influence of the drug therapy both on a child’s organism and cognitive processes.

The subject of this research is the impact of information environment upon people&rsquo;s psyche. Currently, the volume of information accessible to human vastly exceeds the ability to digest it. The emerging information overload leads to nervous exhaustion, worsening of memory, lapse of attention concentration, and stress. Thus, there are created favorable conditions for negative influences of the policy of &ldquo;post-truth&rdquo; upon personality, mentality, perception of the world, and system of relationship. Using the instruments of rationality, the policy of &ldquo;post-truth&rdquo; turns them against rational thinking of a person. Affecting emotions and referring to personal beliefs, &ldquo;post-truth&rdquo; enhances stigmatization not even denying the facts. The research methodology combines the methods of fundamental psychological mechanisms and patterns of the origin and functioning of human psyche; methods general psychological research; methods of studying mental processes, mentality and personality; methods of cognitive psychology; technologies of examination of cognitive processes and communication in computer networks. The main conclusion consists in the statements that in the conditions of information overload and time deficit for analysis, a person makes hasty decisions based on his previous experience; while &ldquo;post-truth&rdquo; can be countered only through the development of the ability of critical thinking, giving assessment, learning to analyze and form own opinion regarding events ongoing in the world.

In humans, age-associated degrading changes, widely observed in molecular and cellular processes underly the time-dependent decline in spatial navigation, time perception, cognitive and psychological abilities, and memory. Cross-talk of biological, cognitive, and psychological clocks provides an integrative contribution to healthy and advanced aging. At the molecular level, genome, proteome, and lipidome instability are widely recognized as the primary causal factors in aging. We narrow attention to the roles of protein aging linked to prevalent amino acids chirality, enzymatic and spontaneous (non-enzymatic) post-translational modifications (PTMs SP), and non-equilibrium phase transitions. The homochirality of protein synthesis, resulting in the steady-state non-equilibrium condition of protein structure, makes them prone to multiple types of enzymatic and spontaneous PTMs, including racemization and isomerization. Spontaneous racemization leads to the loss of the balanced prevalent chirality. Advanced biological aging related to irreversible PTMs SP has been associated with the nontrivial interplay between somatic (molecular aging) and mental (psychological aging) health conditions. Through stress response systems (SRS), the environmental and psychological stressors contribute to the age-associated “collapse” of protein homochirality. The role of prevalent protein chirality and entropy of protein folding in biological aging is mainly overlooked. In a more generalized context, the time-dependent shift from enzymatic to the non-enzymatic transformation of biochirality might represent an important and yet underappreciated hallmark of aging. We provide the experimental arguments in support of the racemization theory of aging.

Fatigue caused by exercise or mentally demanding tasks can lead to an alteration in the cognitive functioning of athletes. Therefore, it is important to investigate whether and to what extent fatigue influences athletes cognitive performance in sports with high cognitive demands. This scoping review aims to map research articles dealing with the effects of acute fatigue on players cognitive performance in team sports. The main inclusion criterium was that studies had to examine the impact of any form of acute fatigue on (i) cognitive functions only, (ii) reactive agility, (iii) sport-specific skills with reactive components included. In total, 12 articles met our inclusion criteria. Results indicated that prolonged exercise causes a decline in the decision-making, attention, and perception abilities of players. Accuracy of sport-specific tasks with cognitive components included rather deteriorated after both exercise and mental fatigue inducement. However, alteration of players cognitive performance depends on the intensity and duration of fatigue-inducing tasks. Mental fatigue and consequent decision-making deterioration can be triggered by at least 30 min of a Stroop color-word task as well as smartphone application exposure. Analysis of the studies revealed a lack of research investigating the acute effect of fatigue on reactive agility, along with cognitive functions such as memory or learning. Due to possible acute negative effects of fatiguing exercise or mentally demanding tasks on human cognition, future research should consider the examination of different types and intensities of exercise on players’ cognitive performance.

The reports of committees investigating the causes of rail accidents indicate the significance of the so-called human factor (e.g. UTK, 2019). In addition to issues related to perso nality functioning, the organization and culture of work, this also includes variables related to the cognitive functioning of train drivers themselves. These are significant factors that determine the occurrence of accidents that result from ignoring or not spotting railway signals and signs. The purpose of this study was to verify the relationship between cognitive functioning and safety determinants, which was measured using an eye-tracking technique. The application of this technique was intended to check its usefulness in the field of railway traffic safety and to achieve an approximate simulation environment of the real working conditions of a train driver. In the present study, the Vienna Test System was used as a me - thod for the determination of cognitive functioning. Based on the variables described in the subject literature, the following tests were applied: Determination Test, Visual Memory Test, Visual Pursuit Test, Reaction Time, Cognitrone, Signal Detection and Vigilance. In addition to computer cognitive tests, an eye-tracking test method was designed based on a film recorded in real train-driving situation. Measures describing areas of interest (AOI) that are crucial for safety were analysed. Due to the pilot nature of the research, only 10 passenger train drivers participated in the study. The results of the study showed a link between the cognitive functioning of the train drivers and visual security determinants. Significant correlations were found with stress tolerance resulting from cognitive overload, visual memory, alertness and concentration ability under time pressure. The study confirmed the significant role of the train driver's cogni- tive functioning in the analysis and perception of safety-critical signals. The use of an eye-tracking method has delivered results that are in agreement with studies based on other methods. This pro- vides a sound basis for the continuation of research using eye- tracking in the assessment of rail transport safety. In the future, the research should be extended to include an analysis of the effect of demographic and situational variables (types of signalling devices and signage) and a broader model of the relationship between cog- nitive functioning and the driver's visual attention. This will provide data that is crucial for the prevention of railway incidents and the development of training plans for train drivers.

During reading, readers never fixate on all words in the text; shorter words sometimes gain zero fixation and skipped by the reader. Relying on E-Z Reader Model, this research hypothesized that a similar skipping effect also exists for a second language. The current study examined word skipping rates in EFL (English as a Foreign Language) with 75 EFL learners by using eye tracking methodology. The results showed that word skipping was affected by EFL reading proficiency significantly and articles (a, an, the) were skipped more than content words. Furthermore, more skilled learners were observed to have less fixation count and skipped more words during reading while less skilled learners employed more fixations and skipped less words. Eye tracking as a novel method to observe learner development and progress in EFL reading was also discussed. References Altarriba, J., Kroll, J. F., Sholl, A.. & Rayner, K. (1996). 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Internet of Things (IoT) is regarded as a next-generation wave of Information Technology (IT) after the widespread emergence of the Internet and mobile communication technologies. IoT supports information exchange and networked interaction of appliances, vehicles and other objects, making sensing and actuation possible in a low-cost and smart manner. On the other hand, cyber-physical systems (CPS) are described as the engineered systems which are built upon the tight integration of the cyber entities (e.g., computation, communication, and control) and the physical things (natural and man-made systems governed by the laws of physics). The IoT and CPS are not isolated technologies. Rather it can be said that IoT is the base or enabling technology for CPS and CPS is considered as the grownup development of IoT, completing the IoT notion and vision. Both are merged into closed-loop, providing mechanisms for conceptualizing, and realizing all aspects of the networked composed systems that are monitored and controlled by computing algorithms and are tightly coupled among users and the Internet. That is, the hardware and the software entities are intertwined, and they typically function on different time and location-based scales. In fact, the linking between the cyber and the physical world is enabled by IoT (through sensors and actuators). CPS that includes traditional embedded and control systems are supposed to be transformed by the evolving and innovative methodologies and engineering of IoT. Several applications areas of IoT and CPS are smart building, smart transport, automated vehicles, smart cities, smart grid, smart manufacturing, smart agriculture, smart healthcare, smart supply chain and logistics, etc. Though CPS and IoT have significant overlaps, they differ in terms of engineering aspects. Engineering IoT systems revolves around the uniquely identifiable and internet-connected devices and embedded systems; whereas engineering CPS requires a strong emphasis on the relationship between computation aspects (complex software) and the physical entities (hardware). Engineering CPS is challenging because there is no defined and fixed boundary and relationship between the cyber and physical worlds. In CPS, diverse constituent parts are composed and collaborated together to create unified systems with global behaviour. These systems need to be ensured in terms of dependability, safety, security, efficiency, and adherence to real‐time constraints. Hence, designing CPS requires knowledge of multidisciplinary areas such as sensing technologies, distributed systems, pervasive and ubiquitous computing, real-time computing, computer networking, control theory, signal processing, embedded systems, etc. CPS, along with the continuous evolving IoT, has posed several challenges. For example, the enormous amount of data collected from the physical things makes it difficult for Big Data management and analytics that includes data normalization, data aggregation, data mining, pattern extraction and information visualization. Similarly, the future IoT and CPS need standardized abstraction and architecture that will allow modular designing and engineering of IoT and CPS in global and synergetic applications. Another challenging concern of IoT and CPS is the security and reliability of the components and systems. Although IoT and CPS have attracted the attention of the research communities and several ideas and solutions are proposed, there are still huge possibilities for innovative propositions to make IoT and CPS vision successful. The major challenges and research scopes include system design and implementation, computing and communication, system architecture and integration, application-based implementations, fault tolerance, designing efficient algorithms and protocols, availability and reliability, security and privacy, energy-efficiency and sustainability, etc. It is our great privilege to present Volume 21, Issue 3 of Scalable Computing: Practice and Experience. We had received 30 research papers and out of which 14 papers are selected for publication. The objective of this special issue is to explore and report recent advances and disseminate state-of-the-art research related to IoT, CPS and the enabling and associated technologies. The special issue will present new dimensions of research to researchers and industry professionals with regard to IoT and CPS. Vivek Kumar Prasad and Madhuri D Bhavsar in the paper titled "Monitoring and Prediction of SLA for IoT based Cloud described the mechanisms for monitoring by using the concept of reinforcement learning and prediction of the cloud resources, which forms the critical parts of cloud expertise in support of controlling and evolution of the IT resources and has been implemented using LSTM. The proper utilization of the resources will generate revenues to the provider and also increases the trust factor of the provider of cloud services. For experimental analysis, four parameters have been used i.e. CPU utilization, disk read/write throughput and memory utilization. Kasture et al. in the paper titled "Comparative Study of Speaker Recognition Techniques in IoT Devices for Text Independent Negative Recognition" compared the performance of features which are used in state of art speaker recognition models and analyse variants of Mel frequency cepstrum coefficients (MFCC) predominantly used in feature extraction which can be further incorporated and used in various smart devices. Mahesh Kumar Singh and Om Prakash Rishi in the paper titled "Event Driven Recommendation System for E-Commerce using Knowledge based Collaborative Filtering Technique" proposed a novel system that uses a knowledge base generated from knowledge graph to identify the domain knowledge of users, items, and relationships among these, knowledge graph is a labelled multidimensional directed graph that represents the relationship among the users and the items. The proposed approach uses about 100 percent of users' participation in the form of activities during navigation of the web site. Thus, the system expects under the users' interest that is beneficial for both seller and buyer. The proposed system is compared with baseline methods in area of recommendation system using three parameters: precision, recall and NDGA through online and offline evaluation studies with user data and it is observed that proposed system is better as compared to other baseline systems. Benbrahim et al. in the paper titled "Deep Convolutional Neural Network with TensorFlow and Keras to Classify Skin Cancer" proposed a novel classification model to classify skin tumours in images using Deep Learning methodology and the proposed system was tested on HAM10000 dataset comprising of 10,015 dermatoscopic images and the results observed that the proposed system is accurate in order of 94.06\% in validation set and 93.93\% in the test set. Devi B et al. in the paper titled "Deadlock Free Resource Management Technique for IoT-Based Post Disaster Recovery Systems" proposed a new class of techniques that do not perform stringent testing before allocating the resources but still ensure that the system is deadlock-free and the overhead is also minimal. The proposed technique suggests reserving a portion of the resources to ensure no deadlock would occur. The correctness of the technique is proved in the form of theorems. The average turnaround time is approximately 18\% lower for the proposed technique over Banker's algorithm and also an optimal overhead of O(m). Deep et al. in the paper titled "Access Management of User and Cyber-Physical Device in DBAAS According to Indian IT Laws Using Blockchain" proposed a novel blockchain solution to track the activities of employees managing cloud. Employee authentication and authorization are managed through the blockchain server. User authentication related data is stored in blockchain. The proposed work assists cloud companies to have better control over their employee's activities, thus help in preventing insider attack on User and Cyber-Physical Devices. Sumit Kumar and Jaspreet Singh in paper titled "Internet of Vehicles (IoV) over VANETS: Smart and Secure Communication using IoT" highlighted a detailed description of Internet of Vehicles (IoV) with current applications, architectures, communication technologies, routing protocols and different issues. The researchers also elaborated research challenges and trade-off between security and privacy in area of IoV. Deore et al. in the paper titled "A New Approach for Navigation and Traffic Signs Indication Using Map Integrated Augmented Reality for Self-Driving Cars" proposed a new approach to supplement the technology used in self-driving cards for perception. The proposed approach uses Augmented Reality to create and augment artificial objects of navigational signs and traffic signals based on vehicles location to reality. This approach help navigate the vehicle even if the road infrastructure does not have very good sign indications and marking. The approach was tested locally by creating a local navigational system and a smartphone based augmented reality app. The approach performed better than the conventional method as the objects were clearer in the frame which made it each for the object detection to detect them. Bhardwaj et al. in the paper titled "A Framework to Systematically Analyse the Trustworthiness of Nodes for Securing IoV Interactions" performed literature on IoV and Trust and proposed a Hybrid Trust model that seperates the malicious and trusted nodes to secure the interaction of vehicle in IoV. To test the model, simulation was conducted on varied threshold values. And results observed that PDR of trusted node is 0.63 which is higher as compared to PDR of malicious node which is 0.15. And on the basis of PDR, number of available hops and Trust Dynamics the malicious nodes are identified and discarded. Saniya Zahoor and Roohie Naaz Mir in the paper titled "A Parallelization Based Data Management Framework for Pervasive IoT Applications" highlighted the recent studies and related information in data management for pervasive IoT applications having limited resources. The paper also proposes a parallelization-based data management framework for resource-constrained pervasive applications of IoT. The comparison of the proposed framework is done with the sequential approach through simulations and empirical data analysis. The results show an improvement in energy, processing, and storage requirements for the processing of data on the IoT device in the proposed framework as compared to the sequential approach. Patel et al. in the paper titled "Performance Analysis of Video ON-Demand and Live Video Streaming Using Cloud Based Services" presented a review of video analysis over the LVS \& VoDS video application. The researchers compared different messaging brokers which helps to deliver each frame in a distributed pipeline to analyze the impact on two message brokers for video analysis to achieve LVS & VoS using AWS elemental services. In addition, the researchers also analysed the Kafka configuration parameter for reliability on full-service-mode. Saniya Zahoor and Roohie Naaz Mir in the paper titled "Design and Modeling of Resource-Constrained IoT Based Body Area Networks" presented the design and modeling of a resource-constrained BAN System and also discussed the various scenarios of BAN in context of resource constraints. The Researchers also proposed an Advanced Edge Clustering (AEC) approach to manage the resources such as energy, storage, and processing of BAN devices while performing real-time data capture of critical health parameters and detection of abnormal patterns. The comparison of the AEC approach is done with the Stable Election Protocol (SEP) through simulations and empirical data analysis. The results show an improvement in energy, processing time and storage requirements for the processing of data on BAN devices in AEC as compared to SEP. Neelam Saleem Khan and Mohammad Ahsan Chishti in the paper titled "Security Challenges in Fog and IoT, Blockchain Technology and Cell Tree Solutions: A Review" outlined major authentication issues in IoT, map their existing solutions and further tabulate Fog and IoT security loopholes. Furthermore, this paper presents Blockchain, a decentralized distributed technology as one of the solutions for authentication issues in IoT. In addition, the researchers discussed the strength of Blockchain technology, work done in this field, its adoption in COVID-19 fight and tabulate various challenges in Blockchain technology. The researchers also proposed Cell Tree architecture as another solution to address some of the security issues in IoT, outlined its advantages over Blockchain technology and tabulated some future course to stir some attempts in this area. Bhadwal et al. in the paper titled "A Machine Translation System from Hindi to Sanskrit Language Using Rule Based Approach" proposed a rule-based machine translation system to bridge the language barrier between Hindi and Sanskrit Language by converting any test in Hindi to Sanskrit. The results are produced in the form of two confusion matrices wherein a total of 50 random sentences and 100 tokens (Hindi words or phrases) were taken for system evaluation. The semantic evaluation of 100 tokens produce an accuracy of 94\% while the pragmatic analysis of 50 sentences produce an accuracy of around 86\%. Hence, the proposed system can be used to understand the whole translation process and can further be employed as a tool for learning as well as teaching. Further, this application can be embedded in local communication based assisting Internet of Things (IoT) devices like Alexa or Google Assistant. Anshu Kumar Dwivedi and A.K. Sharma in the paper titled "NEEF: A Novel Energy Efficient Fuzzy Logic Based Clustering Protocol for Wireless Sensor Network" proposed a a deterministic novel energy efficient fuzzy logic-based clustering protocol (NEEF) which considers primary and secondary factors in fuzzy logic system while selecting cluster heads. After selection of cluster heads, non-cluster head nodes use fuzzy logic for prudent selection of their cluster head for cluster formation. NEEF is simulated and compared with two recent state of the art protocols, namely SCHFTL and DFCR under two scenarios. Simulation results unveil better performance by balancing the load and improvement in terms of stability period, packets forwarded to the base station, improved average energy and extended lifetime.

Cognition plays a fundamental role in most software engineering activities. This article provides a taxonomy of cognitive concepts and a survey of the literature since the beginning of the Software Engineering discipline. The taxonomy comprises the top-level concepts of perception, attention, memory, cognitive load, reasoning, cognitive biases, knowledge, social cognition, cognitive control, and errors, and procedures to assess them both qualitatively and quantitatively. The taxonomy provides a useful tool to filter existing studies, classify new studies, and support researchers in getting familiar with a (sub) area. In the literature survey, we systematically collected and analysed 311 scientific papers spanning five decades and classified them using the cognitive concepts from the taxonomy. Our analysis shows that the most developed areas of research correspond to the four life-cycle stages, software requirements, design, construction, and maintenance. Most research is quantitative and focuses on knowledge, cognitive load, memory, and reasoning. Overall, the state of the art appears fragmented when viewed from the perspective of cognition. There is a lack of use of cognitive concepts that would represent a coherent picture of the cognitive processes active in specific tasks. Accordingly, we discuss the research gap in each cognitive concept and provide recommendations for future research.

AbstractOur visual environment impacts multiple aspects of cognition including perception, attention and memory, yet most studies traditionally remove or control the external environment. As a result, we have a limited understanding of neurocognitive processes beyond the controlled lab environment. Here, we aim to study neural processes in real-world environments, while also maintaining a degree of control over perception. To achieve this, we combined mobile EEG (mEEG) and augmented reality (AR), which allows us to place virtual objects into the real world. We validated this AR and mEEG approach using a well-characterised cognitive response—the face inversion effect. Participants viewed upright and inverted faces in three EEG tasks (1) a lab-based computer task, (2) walking through an indoor environment while seeing face photographs, and (3) walking through an indoor environment while seeing virtual faces. We find greater low frequency EEG activity for inverted compared to upright faces in all experimental tasks, demonstrating that cognitively relevant signals can be extracted from mEEG and AR paradigms. This was established in both an epoch-based analysis aligned to face events, and a GLM-based approach that incorporates continuous EEG signals and face perception states. Together, this research helps pave the way to exploring neurocognitive processes in real-world environments while maintaining experimental control using AR.

Background and Aim Aging people can suffer from cognitive impairments with a range of symptoms, including memory, perception, and difficulty in solving problems called Alzheimer’s disease (AD). The early detection of Mild Cognitive Impairment (MCI), which can develop AD, plays a major role in the management of patients to slow the decline in cognitive function, as treatments are effective at an early stage of the disease course. For this purpose, advanced computer technologies can provide a tool for the early detection of AD and prediction of disease progression. This article presents a serious game, including 16 mini-games that aimed at detecting AD or MCI in the mild stage. Based on gamification techniques and machine learning (ML), by overcoming the limitations of traditional tests. This gamified cognitive tool, entitled AlzCoGame, evaluates the main cognitive domains considered to be the most pertinent indicators in diagnosing cognitive impairments: working memory, episodic memory, executive functions, Visio-spatial orientation, concentration, and attention. Results and Conclusion Six predictive ML models have been implemented using the AlzCoGame dataset. We used the K-fold cross-validation and classification metrics to validate the model's performance. Based on the results of the pilot study, the best overall performance was obtained by the RF classifier with average Sensitivity = 0.89, Specificity = 0.93, Accuracy = 0.92, F1-Score = 0.91, and ROC = 0.91. We can deduce that including machine learning techniques and serious games could help improve certain aspects of the clinical diagnosis of cognitive impairment. Moreover, clinical trials are required to prove the impact of this gamified program on cognitive skills and evaluate usability measures.

Cognitive science is an interdisciplinary and scientific study of the mind and its processes. Examining the nature, the tasks, and the functions of cognition takes place. Cognitive scientists study behaviour and intelligence, with a focus on how nervous systems represent, process, and transform information. Mental faculties of cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. The typical analysis of cognitive science spans many levels of organization, starting from learning and decision to logic and planning; from neural circuitry to modular brain organization. The fundamental concept of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures. The paper describes how the study of Cognitive science plays a vital role in building a successful Human- Computer Interaction system. Further, it also explains that why cognitive science is involved in this vast topic called Human-Computer Interaction.