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In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software's effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation. [ABSTRACT FROM AUTHOR]

The objective of this work is to predict the impact of technology on employment demand by profession in Spain between 2023 and 2035. The evaluation of this effect involved the comparison of two scenarios: a trend scenario obtained by predicting the evolution of occupations in demand and a technological scenario anticipated in the case of technological progress. To accomplish this goal, a new approach was developed in the present study based on previous research. Thus, we estimated the proportion of jobs likely to be automated using a task-based approach. Each occupation was examined based on its components to determine the degree to which these tasks could be automated. The results suggest that technology may influence job demand but with low percentages (between 3% and 5% for both low- and high-qualified workers) in the long term. However, job losses are greater in absolute difference in low-skilled professions, where a great share of the labor force is engaged. [ABSTRACT FROM AUTHOR]

This article presents a dataset containing messages from the Digital Teaching Assistant (DTA) system, which records the results from the automatic verification of students' solutions to unique programming exercises of 11 various types. These results are automatically generated by the system, which automates a massive Python programming course at MIREA—Russian Technological University (RTU MIREA). The DTA system is trained to distinguish between approaches to solve programming exercises, as well as to identify correct and incorrect solutions, using intelligent algorithms responsible for analyzing the source code in the DTA system using vector representations of programs based on Markov chains, calculating pairwise Jensen–Shannon distances for programs and using a hierarchical clustering algorithm to detect high-level approaches used by students in solving unique programming exercises. In the process of learning, each student must correctly solve 11 unique exercises in order to receive admission to the intermediate certification in the form of a test. In addition, a motivated student may try to find additional approaches to solve exercises they have already solved. At the same time, not all students are able or willing to solve the 11 unique exercises proposed to them; some will resort to outside help in solving all or part of the exercises. Since all information about the interactions of the students with the DTA system is recorded, it is possible to identify different types of students. First of all, the students can be classified into 2 classes: those who failed to solve 11 exercises and those who received admission to the intermediate certification in the form of a test, having solved the 11 unique exercises correctly. However, it is possible to identify classes of typical, motivated and suspicious students among the latter group based on the proposed dataset. The proposed dataset can be used to develop regression models that will predict outbursts of student activity when interacting with the DTA system, to solve clustering problems, to identify groups of students with a similar behavior model in the learning process and to develop intelligent data classifiers that predict the students' behavior model and draw appropriate conclusions, not only at the end of the learning process but also during the course of it in order to motivate all students, even those who are classified as suspicious, to visualize the results of the learning process using various tools. Dataset: doi: 10.5281/zenodo.8092417. Dataset License: CC-BY-4.0 [ABSTRACT FROM AUTHOR]

This study intends to make a characterization of the main tasks and skills needed to face the Industry 4.0. Moreover, it gives special attention to the different company's hierarchical levels. To achieve the goals of this paper, a methodology based on Collaborative Decision Making method was used. Firstly, thirty participants were interviewed to understand their point of views. Next, they were split into Group Works to refine the results. Finally, a Group Concordance took place to create the final list of tasks and skills. The skills were organized into four categories: Technical, Social, Methodological and Personal. The results show different requirements in each level. In the Workers level, due to the presence of more technical tasks, eight of the top ten skills belong to the Technical category. In the Middle Managers level, there are tasks of both leading and analyzing nature and thus the top ten skills needed are split through the four categories. Finally, in the Top Managers level, due to the presence of mainly strategy tasks, the top ten skills are split between the Methodological and Social categories. To the authors' knowledge, this is one of the first studies to use a Collaborative Decision Making methodology to develop a set of tasks and skills that future work environments will demand on the different hierarchical levels of the organization. [ABSTRACT FROM AUTHOR]

In this paper, the authors have compared all of the currently available Apple MacBook Pro laptops, in terms of their usability for basic machine learning research applications (text-based, vision-based, tabular). The paper presents four tests/benchmarks, comparing four Apple Macbook Pro laptop versions: Intel based (i5) and three Apple based (M1, M1 Pro and M1 Max). A script in the Swift programming language was prepared, whose goal was to conduct the training and evaluation process for four machine learning (ML) models. It used the Create ML framework—Apple's solution dedicated to ML model creation on macOS devices. The training and evaluation processes were performed three times. While running, the script performed measurements of their performance, including the time results. The results were compared with each other in tables, which allowed to compare and discuss the performance of individual devices and the benefits of the specificity of their hardware architectures. [ABSTRACT FROM AUTHOR]

Concerning occupational safety, the aim of ergonomics as a scientific discipline is to study and adjust working conditions, worker equipment, and work processes from a psychological, physiological, and anatomical aspect instead of adapting the worker to the needs of the job. This paper will discuss and analyze the potential of the garment-embedded body posture tracking sensor and its usage as standard working equipment, which is meant to help correct improper and high-risk upper body positions during prolonged and static work activities. The analysis evaluation cross-reference is based on the Rapid Upper Limb Assessment ergonomics risk assessment tool. Signals generated by the wearable are meant to help the wearer and observer promptly-continuously detect and correct bad posture. The results show a positive progression of workers' body posture to reduce the ergonomic risks this research covers. It can be concluded that wearable technology and sensors would significantly contribute to the observer as the evaluation tool and the wearer to spot the risk factors promptly and self-correct them independently. This feature would help workers learn and improve the correct habits of correcting ergonomically incorrect body postures when performing work tasks. [ABSTRACT FROM AUTHOR]

This paper presents a method to include unmodeled dynamics of load or a robot's end-effector into algorithms for collision detection or general understanding of a robot's operation context. The approach relies on the application of a previously developed modification of the Dynamic Time Warping algorithm, as well as a universally applicable algorithm for identifying kinematic parameters. The entire process can be applied to arbitrary robot configuration, and it does not require identification of dynamic parameters. The paper addresses the two main categories of contact tasks with unmodelled dynamics, which are determined based on whether the external contact force has a consistent profile in the end effector or base coordinate. Conclusions for representative examples analysed in the paper are applicable to tasks such as load manipulation, press bending, and crimping for the first type of forces and applications such as drilling, screwdriving, snap-fit, bolting, and riveting assembly for the latter category. The results presented in the paper are based on realistic testing with measurements obtained from an industrial robot. [ABSTRACT FROM AUTHOR]

This paper presents the reverse priority impedance control of manipulators with reference to redundant robots of a given task. The reverse priority kinematic control of redundant manipulators is first expressed in detail. The motion in the joint space is derived following the opposite order compared with the classical task priority–based solution. Then the Cartesian impedance control is combined with the reverse priority impedance control to solve the reverse hierarchical impedance controlled, so that the Cartesian impedance behavior can be divided into the primary priority impedance control and the secondary priority impedance control. Furthermore, the secondary impedance control task will not disturb the primary impedance control task. The motion in the joint space is affected following the opposite order and working in the corresponding projection operators. The primary impedance control tasks are implemented at the end, so as to avoid the possible deformations caused by the singularities occurring in the secondary impedance control tasks. Hence, the proposed reverse priority impedance control of manipulator can achieve the desired impedance control tasks with proper hierarchy. In this paper, the simulation experiments of the manipulator will verify the proposed reverse priority control algorithm. [ABSTRACT FROM AUTHOR]

As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution for improving the efficiency of task execution on edge networks. However, the peculiarities of the edge networks, especially the random access of mobile devices, brings unpredictable challenges to the task offloading in a mobile edge network. In this paper, we propose a trajectory prediction model for moving targets in edge networks without users' historical paths which represents their habitual movement trajectory. We also put forward a mobility-aware parallelizable task offloading strategy based on a trajectory prediction model and parallel mechanisms of tasks. In our experiments, we compared the hit ratio of the prediction model, network bandwidth and task execution efficiency of the edge networks by using the EUA data set. Experimental results showed that our model is much better than random, non-position prediction parallel, non-parallel strategy-based position prediction. Where the task offloading hit rate is closed to the user's moving speed, when the speed is less 12.96 m/s, the hit rate can reach more than 80%. Meanwhile, we we also find that the bandwidth occupancy is significantly related to the degree of task parallelism and the number of services running on servers in the network. The parallel strategy can boost network bandwidth utilization by more than eight times when compared to a non-parallel policy as the number of parallel activities grows. [ABSTRACT FROM AUTHOR]

Partial domain adaptation (PDA) is a framework for mitigating the covariate shift problem when target labels are contained in source labels. For this task, adversarial neural network (ANN) methods proposed in the literature have been proven to be flexible and effective. In this work, we adapt such methods to tackle the more general problem of open-set domain adaptation (OSDA), which further allows the existence of target instances with labels outside the source labels. The aim in OSDA is to mitigate the covariate shift problem and to identify target instances with labels outside the source label space. We show that the effectiveness of ANN methods utilized in the PDA setting is hindered by outlier target instances, and we propose an adaptation for effective OSDA. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel two-stage transformer with GhostNet, which improves the performance of the small object detection task. Specifically, based on the original Deformable Transformers for End-to-End Object Detection (deformable DETR), we chose GhostNet as the backbone to extract features, since it is better suited for an efficient feature extraction. Furthermore, at the target detection stage, we selected the 300 best bounding box results as regional proposals, which were subsequently set as primary object queries of the decoder layer. Finally, in the decoder layer, we optimized and modified the queries to increase the target accuracy. In order to validate the performance of the proposed model, we adopted a widely used COCO 2017 dataset. Extensive experiments demonstrated that the proposed scheme yielded a higher average precision (AP) score in detecting small objects than the existing deformable DETR model. [ABSTRACT FROM AUTHOR]

Fog computing is one of the major components of future 6G networks. It can provide fast computing of different application-related tasks and improve system reliability due to better decision-making. Parallel offloading, in which a task is split into several sub-tasks and transmitted to different fog nodes for parallel computation, is a promising concept in task offloading. Parallel offloading suffers from challenges such as sub-task splitting and mapping of sub-tasks to the fog nodes. In this paper, we propose a novel many-to-one matching-based algorithm for the allocation of sub-tasks to fog nodes. We develop preference profiles for IoT nodes and fog nodes to reduce the task computation delay. We also propose a technique to address the externalities problem in the matching algorithm that is caused by the dynamic preference profiles. Furthermore, a detailed evaluation of the proposed technique is presented to show the benefits of each feature of the algorithm. Simulation results show that the proposed matching-based offloading technique outperforms other available techniques from the literature and improves task latency by 52% at high task loads. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted by a dynamical caching strategy in MEC. Furthermore, a two-level computing strategy called joint task offloading and service caching (JTOSC) is proposed to solve the optimized problem. The outer layer in JTOSC iteratively updates the service caching decisions based on the Gibbs sampling. The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading scheme. The simulation results indicate that the proposed strategy outperforms the other four comparison strategies in terms of maximum offloading delay, service cache hit rate, and edge load balance. [ABSTRACT FROM AUTHOR]

A method for determining the optimal position of a robotic task within a manipulator's workspace considering the minimum singularity free paths in joint space in order to achieve a high kinematic performance is presented. The selected performance criterion was the minimization of the joint velocities during task execution under a given end effector velocity. The proposed method is applied to place kinematic tasks for a UR-5 manipulator. Joint speed measurements are compared for the optimal and the "bad" task positions and the results show that at the optimal position, lower joint speeds are exerted during task execution. [ABSTRACT FROM AUTHOR]

Comparative researchers have heavily focused their studies of social cognition on species that live in large social groups, while neglecting other potential predictors of social cognition. African crested porcupines (Hystrix cristata) are relatively rare among mammals in that they are cooperative breeders that pair for life. Little is known about their social cognition, but they are good candidates for exploring cooperative behavior due to the need to coordinate behavior to cooperatively raise young. Cooperation, as defined in this study, is the process by which two or more participants perform independent actions on an object to obtain a reward for all parties. Humans are thought to outperform all other species in the frequency and magnitude of cooperative behaviors. Yet, only by studying a variety of species can researchers fully understand the likely selection pressures for cooperation, such as cooperative breeding. Here, we pilot tested the feasibility of the popular loose-string task with a mated pair of African crested porcupines, a task that required the porcupines to cooperatively pull ropes to access an out of reach platform baited with food rewards. Other species presented with this task were able to work together to receive rewards but did not always demonstrate understanding of the role of their partner. The porcupines achieved success but did not appear to coordinate their actions or solicit behavior from their partner. Thus, similar to other species, they may achieve success in this task without taking their partner's role into account. This study demonstrates that the loose string task can be used to assess cooperation in porcupines. However, further experiments are needed to assess the porcupine's understanding of their partner's role under this paradigm. [ABSTRACT FROM AUTHOR]

One of the proposed neural mechanisms involved in working memory is coupling between the theta phase and gamma amplitude. For example, evidence from intracranial recordings shows that coupling between hippocampal theta and cortical gamma oscillations increases selectively during working memory tasks. Theta-gamma phase-amplitude coupling can also be measured non-invasively through scalp EEG; however, EEG can only assess coupling within cortical areas, and it is not yet clear if this cortical-only coupling is truly memory-specific, or a more general phenomenon. We tested this directly by measuring cortical coupling during three different conditions: a working memory task, an attention task, and a passive perception condition. We find similar levels of theta-gamma coupling in all three conditions, suggesting that cortical theta-gamma phase-amplitude coupling is not a memory-specific signal, but instead reflects some other attentional or perceptual processes. Implications for understanding the brain dynamics of visual working memory are discussed. [ABSTRACT FROM AUTHOR]

Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments. In this paper, we propose to evaluate these measurements' reliability under cognitively demanding tasks using a near real-life setting. For this purpose, we selected 31 HRV features, extracted from data collected from 21 programmers performing code comprehension, and compared them across 18 different time frames, ranging from 3 min to 10 s. Statistical significance and correlation tests were performed between the features extracted using the larger window (3 min) and the same features extracted with the other 17 time frames. We paired these analyses with Bland–Altman plots to inspect how the extraction window size affects the HRV features. The main results show 13 features that presented at least 50% correlation when using 60-second windows. The HF and mNN features achieved around 50% correlation using a 30-second window. The 30-second window was the smallest time frame considered to have reliable measurements. Furthermore, the mNN feature proved to be quite robust to the shortening of the time resolution. [ABSTRACT FROM AUTHOR]

Several industrial pick-and-place applications, such as collaborative assembly lines, rely on visual tracking of the parts. Recurrent occlusions are caused by the manipulator motion decrease line productivity and can provoke failures. This work provides a complete solution for maintaining the occlusion-free line of sight between a variable-pose camera and the object to be picked by a 6R manipulator that is not wrist-partitioned. We consider potential occlusions by the manipulator as well as the operator working at the assembly station. An actuated camera detects the object goal (part to pick) and keeps track of the operator. The approach consists of using the complete set of solutions obtained from the derivation of the univariate polynomial equation solution to the inverse kinematics (IK). Compared to numerical iterative solving methods, our strategy grants us a set of joint positions (posture) for each root of the equation from which we extract the best (minimizing the risks of occlusion). Our analytical-based method, integrating collision and occlusion avoidance optimizations, can contribute to greatly enhancing the efficiency and safety of collaborative assembly workstations. We validate our approach with simulations as well as with physical deployments on commercial hardware. [ABSTRACT FROM AUTHOR]

Risk-taking is a fundamental aspect of life spanning diverse contexts. Despite many tests being readily available, the behavioral assessment of risk propensity in recreational contexts, where decisions are not necessarily motivated by monetary gains or losses, is not well represented. As the tower building task (TBT) shares features of recreational activities, we evaluated whether it could assess risk-taking in this type of scenario. In the TBT, participants use standard-size wooden blocks to build the tallest tower they can within a 10 min period. In the current study we (i) examined methodological modifications to the task to either promote or reduce risky behavior and (ii) tested possible associations between the TBT scores and those of two commonly used risk-taking evaluations: The Balloon Analogue Risk Task (BART) and the Sensation Seeking Scale (SSS). We found that limiting the number of permitted collapses decreased the willingness to take risks, whereas challenging participants to exceed a "record" height increased risk-taking. TBT scores of one of the conditions correlated with scores of the BART and the SSS, particularly with the subscale addressing recreational activities. These findings suggest that the TBT offers a potentially useful means of evaluating risk-taking behaviors akin to those of the recreational domain. [ABSTRACT FROM AUTHOR]

Pharmacophores are an established concept for the modelling of ligand–receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus has been put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here, we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest. [ABSTRACT FROM AUTHOR]

Background: Recently, it was reported that the extent of cortico-cortical functional connections can be estimated by the correlation coefficient based on electroencephalography (EEG) monitoring. We aimed to investigate whether the EEG correlation coefficient change with motor task activation can predict the functional outcomes of hemiparetic stroke patients. Methods: Sixteen post-stroke hemiparetic patients admitted to our rehabilitation ward were studied. On admission, EEG recording to calculate the correlation coefficient was performed at rest and during motor task activation. For the analysis of the EEG data, the program software FOCUS (NIHON KOHDEN, Japan) was used. The motor function of paretic limbs was evaluated with the Fugl–Meyer Assessment (FMA) on admission and 4 weeks after admission. Results: Significant increases in the correlation coefficient with motor task activation were noted in C3-F3 or C4-F4, C3-F7 or C4-F8, and F3-F7 or F4-F8 of the lesional hemisphere. Among them, the rate of the correlation coefficient change in F3-F7 or F4-F8 in the lesional hemisphere was significantly correlated with the rate of the upper-limb FMA score change. Conclusion: The extent of the EEG correlation coefficient change with motor task activation in F3-F7 or F4-F8 of the lesional hemisphere may help predict the motor functional outcomes of hemiparetic upper limbs after stroke. [ABSTRACT FROM AUTHOR]

This study aimed: (1) to evaluate the hand motor fatigability in people with spinal cord injury (SCI) and compare it with measurements obtained form an able-bodied population; (2) to compare the hand motor fatigability in people with tetraplegia and in people with paraplegia; and (3) to analyse if motor fatigability is different in people with SCI with and without clinical significant perceived fatigability. Materials and Methods: 96 participants with SCI (40 cervical and 56 thoracolumbar) and 63 able-bodied controls performed a simple hand isometric task to assess motor fatigability. The Fatigue Severity Scale was used for perceived fatigability evaluation. Results: The main results of this study can be summarized as follows: (1) the waning in muscle force (motor fatigability) during a fatiguing task is similar in controls and participants with SCI; (2) the motor fatigability is influenced by the maximal muscle force (measured at the beginning of the task); and (3) the perceived fatigability and the motor fatigability are largely independent in the individuals with SCI. Conclusion: Our findings suggest that the capability to maintain a prolonged effort is preserved in SCI, and this capacity depends on the residual maximal muscle force in people with SCI. [ABSTRACT FROM AUTHOR]

The short task methodology enhances the Bebras constructive environment, and provides an emotional context that triggers the convolution of initially biased mental models and corresponding emotional reactions into an unbiased set of conceptual models for informatics education. This provides the motivation of our research–to explore the process of pedagogical design of short informatics concept-based tasks from the standpoint of mindset formation, which allows one to build conceptual models for CT education. The aim of the research is to gain a conceptual understanding of what a short task is in the context of the global Bebras Challenge initiative. We explore the principles which should underlie the pedagogical design of short tasks for informatics education that scaffold CT. Exploration of a number of practical examples of the Bebras short tasks is the background of our research methodology. The results include an analysis of the structure of short tasks, focusing on the interaction of mental models, conceptual models, and heuristics inherent in the task design. The discussion provides a comprehensive insight into the issues of the short tasks in relation to CT and the Bebras environment. We conclude with recommendations for organizing an effective pedagogical design of a short task. [ABSTRACT FROM AUTHOR]

This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, eliminating the necessity for a repair step. That is, with respect to other quantum-inspired evolutionary algorithms, our proposed QiEA algorithm presents a new way of collapsing the quantum state that integrates the problem constraints in order to avoid later adjusting operations of the system to make it feasible. This results in lower computations and also faster convergence. We compare our proposed QiEA algorithm with three commonly used benchmark methods: the greedy algorithm, Hungarian method and Simplex, in five different case studies. The results show that the quantum approach presents better scalability and interesting properties that can be used in a wider class of assignment problems where the matching is not perfect. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise. [ABSTRACT FROM AUTHOR]

Featured Application: The influence of product handle material stiffness on tactile perception has been investigated using a sawing task. Deformable handles with appropriate stiffness resulted in significantly improved user tactile perception and higher subjective comfort rating compared to stiff handles. The results also suggest that the stiffness of product handle material has a higher influence on the tactile perception than the product handle size and shape. Findings provided in this research allow for improvement of product handle tactile perception and, hence, ergonomics. When considering product handle ergonomics, authors have focused on product handle sizes and shapes, while handle materials have been largely ignored. Authors have shown that handles coated with rubber foam were more comfortable than stiff handles. However, they did not provide detailed material properties, nor did they investigate different stiffnesses and their impact on tactile perception during grasping. Therefore, in this article, we investigated the influence of product interface material stiffness using a common wood sawing task with a saw handle made of hard plastic and 3D-printed deformable material with different stiffnesses. The results showed that user tactile perception can be improved significantly where the 3D-printed cellular density, and, hence material stiffness, has a significant influence on the resulting tactile perception. However, results have shown that the material stiffness must be determined appropriately to maintain the stability of the products in hands. The results also suggest that the product interface material had a greater influence on the reported overall comfort rating than the product handle shape in the sawing task. [ABSTRACT FROM AUTHOR]

Multi-purpose service robots must execute their tasks reliably in different situations, as well as learn from humans and explain their plans to them. We address these issues by introducing a knowledge representation scheme to facilitate skill generalization and explainability. This scheme allows representing knowledge of the robot's understanding of a scene and performed task. We also present techniques for extracting this knowledge from raw data. Such knowledge representation and extraction methods have not been explored adequately in previous research. Our approach does not require any prior knowledge or 3D models of the objects involved. Moreover, the representation scheme is easy to understand for humans. The system is modular so that new recognition or reasoning routines can be added without changing the basic architecture. We developed a computer vision system and a task reasoning module that works with our knowledge representation. The efficacy of our approach is demonstrated with two different tasks: hanging items on pegs and stacking one item on another. A formalization of our knowledge representation scheme is presented, showing how the system is capable of learning from a few demonstrations. [ABSTRACT FROM AUTHOR]

Cloud computing is one of the most commonly used infrastructures for carrying out activities using virtual machines known as processing units. One of the most fundamental issues with cloud computing is task scheduling. The optimal determination of scheduling criteria in cloud computing is a non-deterministic polynomial-time (NP)-complete optimization problem, and several procedures to manage this problem have been suggested by researchers in the past. Among these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm is recognized to produce optimal outcomes in a shorter time period for scheduling tasks in a heterogeneous environment. Literature shows that HEFT gives extraordinary results in terms of quality of schedule and execution time. However, in some cases, the average computation cost and selection of the first idle slot may not produce a good solution. Therefore, here we propose modified versions of the HEFT algorithm that can obtain improved results. In the rank generation phase, we implement different methodologies for calculating ranks, while in the processor selection phase, we modify the way of selecting idle slots for scheduling the tasks. This paper suggests enhanced versions of the HEFT algorithm under user-required financial constraints to minimize the makespan of a specified workflow submission on virtual machines. Our findings also suggest that enhanced versions of the HEFT algorithm perform better than the basic HEFT method in terms of lesser schedule length of the workflow problems running on various virtual machines. [ABSTRACT FROM AUTHOR]

Recovery of motor function following stroke requires interventions to enhance ipsilesional cortical activity. To improve finger motor function following stroke, we developed a movement task with visuomotor feedback and measured changes in motor cortex activity by electroencephalography. Stroke patients performed two types of movement task on separate days using the paretic fingers: a visuomotor tracking task requiring the patient to match a target muscle force pattern with ongoing feedback and a simple finger flexion/extension task without feedback. Movement-related cortical potentials (MRCPs) were recorded before and after the two motor interventions. The amplitudes of MRCPs measured from the ipsilesional hemisphere were significantly enhanced after the visuomotor tracking task but were unchanged by the simple manual movement task. Increased MRCP amplitude preceding movement onset revealed that the control of manual movement using visual feedback acted on the preparatory stage from motor planning to execution. A visuomotor tracking task can enhance motor cortex activity following a brief motor intervention, suggesting efficient induction of use-dependent cortical plasticity in stroke. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm that provides operational strategies for multiple heterogeneous mobile robot systems utilized in many real-world applications, such as deliveries, surveillance, search and rescue, monitoring, and transportation. Specifically, the authors focus on developing an algorithm that solves a min–max multiple depot heterogeneous asymmetric traveling salesperson problem (MDHATSP). The algorithm is designed based on a primal–dual technique to operate given multiple heterogeneous robots located at distinctive depots by finding a tour for each robot such that all the given targets are visited by at least one robot while minimizing the last task completion time. Building on existing work, the newly developed algorithm can solve more generalized problems, including asymmetric cost problems with a min–max objective. Though producing optimal solutions requires high computational loads, the authors aim to find reasonable sub-optimal solutions within a short computation time. The algorithm was repeatedly tested in a simulation with varying problem sizes to verify its effectiveness. The computational results show that the algorithm can produce reliable solutions to apply in real-time operations within a reasonable time. [ABSTRACT FROM AUTHOR]

Highlights: Increased postural task difficulty was associated with greater responses for pupillary response, posterior alpha power, and fronto-central theta/beta power ratio. Greater pupillary response correlated with lower posterior EEG alpha power during eyes occluded standing with and without dual-task. Lastly, a greater pupillary response was associated with lower CoP displacement in the anterior-posterior direction during dual-task eyes occluded. The pupillary response reflects mental effort (or cognitive workload) during cognitive and/or motor tasks including standing postural control. EEG has been shown to be a non-invasive measure to assess the cortical involvement of postural control. The purpose of this study was to understand the effect of increasing postural task difficulty on the pupillary response and EEG outcomes and their relationship in young adults. Fifteen adults completed multiple trials of standing: eyes open, eyes open while performing a dual-task (auditory two-back), eyes occluded, and eyes occluded with a dual-task. Participants stood on a force plate and wore an eye tracker and 256-channel EEG cap during the conditions. The power spectrum was analyzed for absolute theta (4–7 Hz), alpha (8–13 Hz), and beta (13–30 Hz) frequency bands. Increased postural task difficulty was associated with greater pupillary response (p < 0.001) and increased posterior region alpha power (p = 0.001) and fronto-central region theta/beta power ratio (p = 0.01). Greater pupillary response correlated with lower posterior EEG alpha power during eyes-occluded standing with (r = −0.67, p = 0.01) and without (r = −0.69, p = 0.01) dual-task. A greater pupillary response was associated with lower CoP displacement in the anterior–posterior direction during dual-task eyes-occluded standing (r = −0.60, p = 0.04). The pupillary response and EEG alpha power appear to capture similar cortical processes that are increasingly utilized during progressively more challenging postural task conditions. As the pupillary response also correlated with task performance, this measurement may serve as a valuable stand-alone or adjunct tool to understand the underlying neurophysiological mechanisms of postural control. [ABSTRACT FROM AUTHOR]

Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications, and in particular, safety-critical ones. In this work, we study the calibration of uncertainty prediction for regression tasks which often arise in real-world systems. We show that the existing definition for the calibration of regression uncertainty has severe limitations in distinguishing informative from non-informative uncertainty predictions. We propose a new definition that escapes this caveat and an evaluation method using a simple histogram-based approach. Our method clusters examples with similar uncertainty prediction and compares the prediction with the empirical uncertainty on these examples. We also propose a simple, scaling-based calibration method that preforms as well as much more complex ones. We show results on both a synthetic, controlled problem and on the object detection bounding-box regression task using the COCO and KITTI datasets. [ABSTRACT FROM AUTHOR]

In this work, we extend the topology-based framework and method for the quantification and classification of general resilient asynchronous complexity. We present the arbitrary resilient asynchronous complexity theorem, applied to decision tasks in an iterated delayed model which is based on a series of communicating objects, each of which mainly consists of the delayed algorithm. In order to do this, we first introduce two topological structures, delayed complex and reduced delayed complex, and build the topological computability model, and then investigate some properties of those structures and the computing power of that model. Our theorem states that the time complexity of any arbitrary resilient asynchronous algorithm is proportional to the level of a reduced delayed complex necessary to allow a simplicial map from a task's input complex to its output complex. As an application, we use it to derive the bounds on time complexity to approximate agreement with n + 1 processes. [ABSTRACT FROM AUTHOR]

Fitts' law is used as a performance measurement metric in human–computer interactions. The original formulation implied that movement time was identical for movements with the same value of the index of difficulty under varied movement amplitude and target width. An experiment was designed to test this implication. The result indicates that movement time is related to the index of difficulty when the amplitude is constant. Nowadays, most of the icons in applications are represented as two-dimensional targets. An object of equal width and height is a particular case of a two-dimensional target. This target area could be a factor in a Fitts task and impact the movement time, number of errors, and perceived difficulty. Therefore, the area could replace the target width in the formulation of the index of difficulty. The modified index of difficulty is easy to implement without the complexity of post-calculation. Researchers can design the index of difficulty before the empirical test. This research proposes a modified index of difficulty by varying the target's area and applying the square-root movement time model simultaneously, which results in an excellent performance with a higher R-square and satisfies the residual normality robustly than the traditional formulation of Fitts' law. [ABSTRACT FROM AUTHOR]

Vat photopolymerization (VP), a type of additive manufacturing process that cures resin to build objects, can emit potentially hazardous particles and gases. We evaluated two VP technologies, stereolithography (SLA) and digital light processing (DLP), in three separate environmental chambers to understand task-based impacts on indoor air quality. Airborne particles, total volatile organic compounds (TVOCs), and/or specific volatile organic compounds (VOCs) were monitored during each task to evaluate their exposure potential. Regardless of duration, all tasks released particles and organic gases, though concentrations varied between SLA and DLP processes and among tasks. Maximum particle concentrations reached 1200 #/cm3 and some aerosols contained potentially hazardous elements such as barium, chromium, and manganese. TVOC concentrations were highest for the isopropyl alcohol (IPA) rinsing, soaking, and drying post-processing tasks (up to 36.8 mg/m3), lowest for the resin pouring pre-printing, printing, and resin recovery post-printing tasks (up to 0.1 mg/m3), and intermediate for the curing post-processing task (up to 3 mg/m3). Individual VOCs included, among others, the potential occupational carcinogen acetaldehyde and the immune sensitizer 2-hydroxypropyl methacrylate (pouring, printing, recovery, and curing tasks). Careful consideration of all tasks is important for the development of strategies to minimize indoor air pollution and exposure potential from VP processes. [ABSTRACT FROM AUTHOR]

The aim of the research results presented in the article was to develop a method that enables the analysis and evaluation of deviations at the stage of implementing a construction investment in various construction sectors. The new method was based on the well-known Earned Value Method (EVM). It was assumed that the monitoring of costs and the constant control of the implementation time of construction projects, with the simultaneous analysis of the level of deviations, allows for the consistent management of the investment task. Moreover, it ensures—in the required time—the adoption of appropriate improvement actions, and also enables the quantification of risk during the planning of an investment. The article attempts to prove the validity of the assumption about the necessity of the cyclical verification of the level of deviations in construction projects budget and schedule. Such an approach provides numerous perceptible benefits; such as the precise determination of the size of possible losses; an immediate reaction to risk; and the possibility of making changes at every stage of implementing an investment. On the basis of the analysis of the author's own research carried out in three research groups of facilities (collective housing; hotels; and commercial and service facilities), an original attempt was made to determine the size of cost and time performance indexes of various investment tasks. The values of these performance indexes were determined, and are presented as quantile images in boxplots. [ABSTRACT FROM AUTHOR]

Change detection (CD) is in demand in satellite imagery processing. Inspired by the recent success of the combined transformer-CNN (convolutional neural network) model, TransCNN, originally designed for image recognition, in this paper, we present STDecoder-CD for change detection applications, which is a combination of the Siamese network ("S"), the TransCNN backbone ("T"), and three types of decoders ("Decoder"). The Type I model uses a UNet-like decoder, and the Type II decoder is defined by a combination of three modules: the difference detector, FPN (feature pyramid network), and FCN (fully convolutional network). The Type III model updates the change feature map by introducing a transformer decoder. The effectiveness and advantages of the proposed methods over the state-of-the-art alternatives were demonstrated on several CD datasets, and experimental results indicate that: (1) STDecoder-CD has excellent generalization ability and has strong robustness to pseudo-changes and noise. (2) An end-to-end CD network architecture cannot be completely free from the influence of the decoding strategy. In our case, the Type I decoder often obtained finer details than Types II and III due to its multi-scale design. (3) Using the ablation or replacing strategy to modify the three proposed decoder architectures had a limited impact on the CD performance of STDecoder-CD. To the best of our knowledge, we are the first to investigate the effect of different decoding strategies on CD tasks. [ABSTRACT FROM AUTHOR]

Mobile crowd sensing (MCS) is a new sensing paradigm that leverages participatory sensing data from mobile devices for accomplishing large-scale sensing tasks. Incentivizing device owners to contribute high-quality sensing data is a prerequisite for the success of MCS services. In this paper, we first propose a pre-contracting incentive mechanism that involves the participation of not only the device owners located in close proximity to Point of Interests (PoIs) but also the device owners that are going to pass through those locations. Furthermore, the quality of sensing data is guaranteed through the use of redundancy. In particular, sensing data from multiple device owners is processed and compared at an edge side (i.e., base station) so as to detect the measurement error at the proximity of data sources. Simulation results confirm that the proposed incentive mechanism is efficient in terms of improving the total utility. [ABSTRACT FROM AUTHOR]

The last three years have been a period of many challenges related to the dynamically changing conditions of the economic environment. Among these many changes, some of the most important for the further functioning of private and public entities are those related to the instability of the energy market. Rapidly rising energy prices increase the costs of implementing public tasks. They also greatly increase the search for innovative, energy-saving and environmentally friendly ways of performing municipal tasks. The main aim of the article is to present the concept of a smart village as an instrument for the implementation of public tasks in rural areas. The theoretical basis of the smart village concept is the basic point of reference. The implementation of the assumptions of the smart village concept in Poland gives municipalities the possibility of an innovative approach to the implementation of local public services. In addition, examples of good practices implemented by rural local communities that can act as models for other groups of residents are also included. It was essential, from the point of view of measurable effects, to identify potential limitations and hazards in the implementation of the smart village concept, which may be identified in the outermost regions. Analysis and critical literature review were used to achieve the article's goals. These methods are characteristic for review publications. At the beginning, we presented the theoretical foundations of the smart village concept. Secondly, we indicated how the smart village concept contributes to the improvement in public service delivery in rural areas. The authors demonstrated that there is no universal model for each unit. A smart village will implement solutions tailored to economic, social, cultural, and natural conditions. [ABSTRACT FROM AUTHOR]

In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models. [ABSTRACT FROM AUTHOR]

The proliferation of IoT applications has pushed the horizon of edge computing, which provides processing ability at the edge of networks. Task offloading is one of the most important issues in edge computing and has attracted continuous research attention in recent years. With task offloading, end devices can offload the entire task or only subtasks to the edge servers to meet the delay and energy requirements. Most existing offloading schemes are limited by the increasing complexity of task topologies, as considerable time is wasted for local/edge subtasks to wait for their precedent subtasks being executed at the edge/local device. This problem becomes even worse when the dependencies among subtasks become complex and the number of end-users increases. To address this problem, our key methodology is to exploit subtask duplications to reduce the inter-subtask delay and shorten the task completion time. Based on this, we propose a Duplication-based and Energy-aware Task Offloading scheme (DETO), which duplicates critical subtasks that have a large impact on the completion time and thus enhances the parallelism between local and edge computing. In addition, among numerous choices of subtask duplications, DETO evaluates the gain/cost ratio for each possible duplication and chooses the most efficient ones. As a result, the extra resource for duplications is greatly reduced. We also design a distributed DETO algorithm to support multi-user, multi-server edge computing. Extensive evaluation results show that DETO can effectively reduce the task completion time (by 12.22%) and improve the resource utilization (by 15.17%), in particular for multi-user edge computing networks. [ABSTRACT FROM AUTHOR]

Effective task scheduling in human-robot collaboration (HRC) scenarios is one of the great challenges of collaborative robotics. The shared workspace inside an industrial setting brings a lot of uncertainties that cannot be foreseen. A prior offline task scheduling strategy is ineffective in dealing with these uncertainties. In this paper, a novel online framework to achieve a resilient and reliable task schedule is presented. The framework can deal with deviations that occur during operation, different operator skills, error by the human or robot, and substitution of actors, while maintaining an efficient schedule by promoting parallel human-robot work. First, the collaborative job and the possible deviations are represented by AND/OR graphs. Subsequently, the proposed architecture chooses the most suitable path to improve the collaboration. If some failures occur, the AND/OR graph is adapted locally, allowing the collaboration to be completed. The framework is validated in an industrial assembly scenario with a Franka Emika Panda collaborative robot. [ABSTRACT FROM AUTHOR]

The generally unsupervised nature of autoencoder models implies that the main training metric is formulated as the error between input images and their corresponding reconstructions. Different reconstruction loss variations and latent space regularizations have been shown to improve model performances depending on the tasks to solve and to induce new desirable properties such as disentanglement. Nevertheless, measuring the success in, or enforcing properties by, the input pixel space is a challenging endeavour. In this work, we want to make use of the available data more efficiently and provide design choices to be considered in the recording or generation of future datasets to implicitly induce desirable properties during training. To this end, we propose a new sampling technique which matches semantically important parts of the image while randomizing the other parts, leading to salient feature extraction and a neglection of unimportant details. The proposed method can be combined with any existing reconstruction loss and the performance gain is superior to the triplet loss. We analyse the resulting properties on various datasets and show improvements on several computer vision tasks: illumination and unwanted features can be normalized or smoothed out and shadows are removed such that classification or other tasks work more reliably; a better invariances with respect to unwanted features is induced; the generalization capacities from synthetic to real images is improved, such that more of the semantics are preserved; uncertainty estimation is superior to Monte Carlo Dropout and an ensemble of models, particularly for datasets of higher visual complexity. Finally, classification accuracy by means of simple linear classifiers in the latent space is improved compared to the triplet loss. For each task, the improvements are highlighted on several datasets commonly used by the research community, as well as in automotive applications. [ABSTRACT FROM AUTHOR]

(1) Background: The development of a lightweight, easy-to-use system that measures the foot's plantar pressure is becoming an increasingly important area of research in physiotherapy. For further development of the smart sock system, a formative usability study was conducted, where the smart textile sock sensor system was used for self-correction during functional tasks; (2) Methods: Five athletes from the football school participated in the formative usability study. Athletes performed pre-defined functional tasks for self-correction when interacting with the smart textile sock system. Formative usability evaluation methods: effectiveness (task success rate, error rate), efficiency (time-based), satisfaction evaluated by System Usability Scale (SUS); (3) Results: Formative usability indicators: task completeness effectiveness ranged from 40% to 100% in the first- and second-stage tasks. Completed task efficiency time: Stage 1, from 4.2 s (SD 1.3) to 88.8 s (SD 19.8); Stage 2, from 7.2 s (SD 1.9) to 9.6 s (SD 2.1). Satisfaction was assessed by the SUS system user group with 76 points (SD 7.42), which indicates "good" satisfaction; (4) Conclusions: formative usability indicators showed the need for technical improvements to the smart textile sock pressure sensor system. The SUS results indicate "good" satisfaction with the smart textile sock pressure sensor system and its application. [ABSTRACT FROM AUTHOR]

According to the Theory of Basic Human Values, values are relatively stable, but not immutable, abstract goals which strongly influence peoples' lives. Since their relative stability, psychosocial research is attempting to understand the extent to which it is possible to induce a voluntary change in people's personal values. The main aim of this study was to systematically review the existing literature on experiments to induce a value change, also highlighting the theoretical perspectives used to develop the experimental tasks. We conducted a literature search of five databases (SCOPUS, ProQuest, PsycINFO, PubMed, and Web of Science). After the screening and the eligibility phase, we included a total of 14 articles (25 experiments). Most of these studies involved university students and adopted a pre-and post-test design, using different manipulation tasks. The results highlighted the possibility of inducing a voluntary value change, assessed in terms of mean levels and/or rank order. These findings provide new insights regarding the stability of values in the light of the Theory of Basic Human Values. The practical implications and future research directions are discussed. [ABSTRACT FROM AUTHOR]

In this study, we analyze how 9–10-year-old pupils work with equations, a central aspect of algebraic thinking in early grades and a cornerstone for more formal learning of algebra. Specifically, we seek: (a) to describe the main characteristics of the tasks that support algebraic thinking through a translation process from arithmetic word problems to algebraic language and vice versa, and (b) to identify how pupils refer to indeterminate quantities in these contexts and what meaning they give to them. The analysis focuses on the semantic congruence of the expressions proposed by them and on the dialogue they held during the translation process. We analyzed the oral discussion in the pools and the written responses to the problem that pupils posed. The results show that arithmetic word problems allow the indeterminate to become an object of thought for pupils, who represent it in multiple ways and refer to it when proposing equations that represent the structure of each problem. Another finding highlights that reflection on the interpretation of the equations supports the identification of two meanings associated with indeterminate quantities, namely, unknown and variable. [ABSTRACT FROM AUTHOR]

This study explores how drivers of an automated vehicle distribute their attention as a function of environmental events and driving task instructions. Twenty participants were asked to monitor pre-recorded videos of a simulated driving trip while their eye movements were recorded using an eye-tracker. The results showed that eye movements are strongly situation-dependent, with areas of interest (windshield, mirrors, and dashboard) attracting attention when events (e.g., passing vehicles) occurred in those areas. Furthermore, the task instructions provided to participants (i.e., speed monitoring or hazard monitoring) affected their attention distribution in an interpretable manner. It is concluded that eye movements while supervising an automated vehicle are strongly 'top-down', i.e., based on an expected value. The results are discussed in the context of the development of driver availability monitoring systems. [ABSTRACT FROM AUTHOR]

In Mixed-Criticality (MC) systems, multiple functions with different levels of criticality are integrated into a common platform in order to meet the intended space, cost, and timing requirements in all criticality levels. To guarantee the correct, and on-time execution of higher criticality tasks in emergency modes, various design-time scheduling policies have been recently presented. These techniques are mostly pessimistic, as the occurrence of worst-case scenario at run-time is a rare event. Nevertheless, they lead to an under-utilized system due to frequent drops of Low-Criticality (LC) tasks, and creation of unused slack times due to the quick execution of high-criticality tasks. Accordingly, this paper proposes a novel optimistic scheme, that introduces a learning-based drop-aware task scheduling mechanism, which carefully monitors the alterations in the behaviour of the MC system at run-time, to exploit the generated dynamic slacks for reducing the LC tasks penalty and preventing frequent drops of LC tasks in the future. Based on an extensive set of experiments, our observations have shown that the proposed approach exploits accumulated dynamic slack generated at run-time, by 9.84% more on average compared to existing works, and is able to reduce the deadline miss rate by up to 51.78%, and 33.27% on average, compared to state-of-the-art works. [ABSTRACT FROM AUTHOR]

In this paper, we propose a centralized task allocation and an alignment technique based on constraint table and alignment rules. For task allocation, a scoring scheme has to be set. The existing time-discounted scoring scheme has two problems; if the score is calculated based on arrival time, the agent who arrives in a task point first may finish the task late, and if the score is calculated based on end-time of the task, agents who have the same score may appear because of temporal constraints. Therefore, a modified time-discounted reward scheme based on both arrival and end-time is proposed. Additionally, an accumulated distance cost scheme is proposed for minimum fuel consumption. The constraint table made by tasks that are already aligned is also considered in scoring. For centralized task alignment based on the constraint table and alignment rules, a technique based on sequential greedy algorithm is proposed. Resolving conflicts on alignment is described in detail using constraint table and alignment rules, which are composed of four basic principles. We demonstrate simulations about task allocation and alignment for multi-agent with coupled constraints. Simple and complicated cases are used to verify the scoring schemes and the proposed techniques. Additionally, a huge case is used to show computational efficiency. The results are feasibly good when the constraints are properly set. [ABSTRACT FROM AUTHOR]

In daily life, we perform several tasks simultaneously, and it is essential to have adequate postural control to succeed. Furthermore, when performing two or more tasks concurrently, changes in postural oscillation are expected due to the competition for the attentional resources. The aim of this study was to evaluate and compare the center of pressure (CoP) behavior and the hemodynamic response of the prefrontal cortex during static postural standing while performing cognitive tasks of increasing levels of difficulty on a smartphone in young adults. Participants were 35 healthy young adults (mean age ± SD = 22.91 ± 3.84 years). Postural control was assessed by the CoP analysis (total excursion of the CoP (TOTEX CoP), displacements of the CoP in medial–lateral (CoP-ML) and anterior–posterior (CoP-AP) directions, mean total velocity displacement of CoP (MVELO CoP), mean displacement velocity of CoP in medial–lateral (MVELO CoP-ML) and anterior–posterior (MVELO CoP-AP) directions, and 95% confidence ellipse sway area (CEA)), the hemodynamic response by the oxyhemoglobin ([oxy-Hb]), deoxyhemoglobin ([deoxy-Hb]), and total hemoglobin ([total-Hb]) concentrations using a force plate and functional near-infrared spectroscopy (fNIR), respectively. The results showed that the difficult cognitive task while performing static postural standing caused an increase in all CoP variables in analysis (p < 0.05) and of [oxy-Hb] (p < 0.05), [deoxy-Hb] (p < 0.05) and [total-Hb] (p < 0.05) compared to the postural task. In conclusion, the increase in the cognitive demands negatively affected the performance of the postural task when performing them concurrently, compared to the postural task alone. The difficult cognitive task while performing the postural task presented a greater influence on postural sway and activation of the prefrontal cortex than the postural task and the easy cognitive task. [ABSTRACT FROM AUTHOR]