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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]

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]

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]

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]

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]

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 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]

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]

This study aimed to investigate how different strategies of task constraint manipulation impact physical and tactical demands in small-sided and conditioned games (SSCG). Ten recreational U-17 soccer players participated in this study (16.89 ± 0.11 years). We used different strategies of task manipulation to design two 4 vs. 4 SSCG: Structural SSCG and Functional SSCG. In Structural SSCG, pitch format and goal sizes were manipulated, while in Functional SSCG, players were allowed to kick the ball twice and at least 5 passes to shoot at the opponent's goal. Players participated in four Structural and Functional SSCG, of five minutes duration with a two-minute interval in between. Players' physical performance and tactical behavior were assessed using the WIMU PROTM inertial device. Structural SSCG stimulated players to cover more distance in sprinting (p = 0.003) and high-speed running (p < 0.001). Regarding tactical behavior, Structural SSCG stimulated players to explore game space better (p < 0.001). Moreover, Functional SSCG stimulated players to be closer to the ball, decreasing the effective playing space (p = 0.008). We conclude that these strategies of task constraint manipulation impact physical and tactical demands of the game. [ABSTRACT FROM AUTHOR]

When managing a constellation of nanosatellites, one may leverage this structure to improve the mission's quality-of-service (QoS) by optimally distributing the tasks during an orbit. In this sense, this research proposes an offline energy-aware task scheduling problem formulation regarding the specifics of constellations, by considering whether the tasks are individual, collective, or stimulated to be redundant. By providing such an optimization framework, the idea of estimating an offline task schedule can serve as a baseline for the constellation design phase. For example, given a particular orbit, from the simulation of an irradiance model, the engineer can estimate how the mission value is affected by the inclusion or exclusion of individuals objects. The proposed model, given in the form of a multi-objective mixed-integer linear programming model, is illustrated in this work for several illustrative scenarios considering different sets of tasks and constellations. We also perform an analysis of the Pareto-optimal frontier of the problem, identifying the feasible trade-off points between constellation and individual tasks. This information can be useful to the decision-maker (mission operator) when planning the behavior in orbit. [ABSTRACT FROM AUTHOR]

Vehicular edge computing (VEC) is one of the prominent ideas to enhance the computation and storage capabilities of vehicular networks (VNs) through task offloading. In VEC, the resource-constrained vehicles offload their computing tasks to the local road-side units (RSUs) for rapid computation. However, due to the high mobility of vehicles and the overloaded problem, VEC experiences a great deal of challenges when determining a location for processing the offloaded task in real time. As a result, this degrades the quality of vehicular performance. Therefore, to deal with these above-mentioned challenges, an efficient dynamic task offloading approach based on a non-cooperative game (NGTO) is proposed in this study. In the NGTO approach, each vehicle can make its own strategy on whether a task is offloaded to a multi-access edge computing (MEC) server or a cloud server to maximize its benefits. Our proposed strategy can dynamically adjust the task-offloading probability to acquire the maximum utility for each vehicle. However, we used a best response offloading strategy algorithm for the task-offloading game in order to achieve a unique and stable equilibrium. Numerous simulation experiments affirm that our proposed scheme fulfills the performance guarantees and can reduce the response time and task-failure rate by almost 47.6% and 54.6%, respectively, when compared with the local RSU computing (LRC) scheme. Moreover, the reduced rates are approximately 32.6% and 39.7%, respectively, when compared with a random offloading scheme, and approximately 26.5% and 28.4%, respectively, when compared with a collaborative offloading scheme. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to study how changes in neural network architecture and its hyperparameters affect the results of biometric identification based on keystroke dynamics. The publicly available dataset of keystrokes was used, and the models with different parameters were trained using this data. Various neural network layers—convolutional, recurrent, and dense—in different configurations were employed together with pooling and dropout layers. The results were compared with the state-of-the-art model using the same dataset. The results varied, with the best-achieved accuracy equal to 82% for the identification (1 of 20) task. [ABSTRACT FROM AUTHOR]

Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations, such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. Most importantly, however, our algorithm achieves a worst-case speed-up of 160x over the latter approach. [ABSTRACT FROM AUTHOR]

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost [ABSTRACT FROM AUTHOR]

The interference between software components is increasing in safety-critical domains, such as autonomous driving. Low-criticality (LC) tasks, such as vehicle communication, may control high-criticality (HC) tasks, such as acceleration. In such cases, the LC task should also be considered as an HC task because the HC tasks relies on the LC task. However, the difficulty in guaranteeing these LC tasks is the catastrophic cost of computing resources, the electronic control unit in the domain of vehicles, required for every task. In this paper, we theoretically and practically provide safety-guaranteed and inexpensive scheduling for LC tasks by borrowing the computational power of neighbored systems in distributed systems, obviating the need for additional hardware components. As a result, our approach extended the schedulability of LC tasks without violating the HC tasks. Based on the deadline test, the compatibility of our approach with the task-level MC scheduler was higher than that of the system-level MC scheduler, such that the task-level had all dropped LC tasks recovered while the system-level only had 25.5% recovery. Conversely, from the worst-case measurement of violated HC tasks, the HC tasks were violated by the task-level MC scheduler more often than by the system-level MC scheduler, with 70.3% and 15.4% average response time overhead, respectively. In conclusion, under the condition that the HC task ratio has lower than 47% of the overall task systems at 80% of total utilization, the task-level approach with task migration has extensively higher sustainability on LC tasks. [ABSTRACT FROM AUTHOR]

Wrist velocity is an important risk factor for work-related musculoskeletal disorders in the elbow/hand, which is also difficult to assess by observation or self-reports. This study aimed to evaluate a new convenient and low-cost inertial measurement unit (IMU)-based method using gyroscope signals against an electrogoniometer for measuring wrist flexion velocity. Twelve participants performed standard wrist movements and simulated work tasks while equipped with both systems. Two computational algorithms for the IMU-based system, i.e., IMUnorm and IMUflex, were used. For wrist flexion/extension, the mean absolute errors (MAEs) of median wrist flexion velocity compared to the goniometer were <10.1°/s for IMUnorm and <4.1°/s for IMUflex. During wrist deviation and pronation/supination, all methods showed errors, where the IMUnorm method had the largest overestimations. For simulated work tasks, the IMUflex method had small bias and better accuracy than the IMUnorm method compared to the goniometer, with the MAEs of median wrist flexion velocity <5.8°/s. The results suggest that the IMU-based method can be considered as a convenient method to assess wrist motion for occupational studies or ergonomic evaluations for the design of workstations and tools by both researchers and practitioners, and the IMUflex method is preferred. Future studies need to examine algorithms to further improve the accuracy of the IMU-based method in tasks of larger variations, as well as easy calibration procedures. [ABSTRACT FROM AUTHOR]

The search algorithm based on symbiotic organisms' interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also an important factor. This paper contributed an adaptive Particle Swarm Optimisation (PSO) based task scheduling approach that reduces the task execution time, and increases throughput and Average Resource Utilization Ratio (ARUR). Moreover, an adaptive inertia weight strategy namely Linearly Descending and Adaptive Inertia Weight (LDAIW) is introduced. The proposed scheduling approach provides a better balance between local and global search leading to an optimized task scheduling. The performance of the proposed approach has been evaluated and compared against five renown PSO based inertia weight strategies concerning makespan and throughput. The experiments are then extended and compared the proposed approach against the other four renowned meta-heuristic scheduling approaches. Analysis of the simulated experimentation reveals that the proposed approach attained up to 10%, 12% and 60% improvement for makespan, throughput and ARUR respectively. [ABSTRACT FROM AUTHOR]

With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices' trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach. [ABSTRACT FROM AUTHOR]

Large interactive displays can provide suitable workspaces for learners to conduct collaborative learning tasks with visual information in co-located settings. In this research, we explored the use of these displays to support collaborative engagement and exploratory tasks with visual representations. Our investigation looked at the effect of four factors (number of virtual workspaces within the display, number of displays, position arrangement of the collaborators, and collaborative modes of interaction) on learners' knowledge acquisition, engagement level, and task performance. To this end, a user study was conducted with 72 participants divided into 6 groups using an interactive tool developed to support the collaborative exploration of 3D visual structures. The results of this study showed that learners with one shared workspace and one single display can achieve better user performance and engagement levels. In addition, the back-to-back position with learners sharing their view and control of the workspaces was the most favorable. It also led to improved learning outcomes and engagement levels during the collaboration process. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for Multi-Robot Task Allocation (MRTA) that introduces priority policies on preemptive task scheduling and considers dependencies between tasks, and tolerates faults. The approach is referred to as Multi-Robot Preemptive Task Scheduling with Fault Recovery (MRPF). It considers the interaction between running processes and their tasks for management at each new event, prioritizing the more relevant tasks without idleness and latency. The benefit of this approach is the optimization of production in smart factories, where autonomous robots are being employed to improve efficiency and increase flexibility. The evaluation of MRPF is performed through experimentation in small-scale warehouse logistics, referred to as Augmented Reality to Enhanced Experimentation in Smart Warehouses (ARENA). An analysis of priority scheduling, task preemption, and fault recovery is presented to show the benefits of the proposed approach. [ABSTRACT FROM AUTHOR]

Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent research into physiological markers of mental fatigue indicate that markers exist which extend across all individuals and all types of vigilance tasks. This suggests that it would be possible to build an EEG model which detects these markers and the subsequent vigilance decrement in any task (i.e., a task-generic model) and in any person (i.e., a cross-participant model). However, thus far, no task-generic EEG cross-participant model has been built or tested. In this research, we explored creation and application of a task-generic EEG cross-participant model for detection of the vigilance decrement in an unseen task and unseen individuals. We utilized three different models to investigate this capability: a multi-layer perceptron neural network (MLPNN) which employed spectral features extracted from the five traditional EEG frequency bands, a temporal convolutional network (TCN), and a TCN autoencoder (TCN-AE), with these two TCN models being time-domain based, i.e., using raw EEG time-series voltage values. The MLPNN and TCN models both achieved accuracy greater than random chance (50%), with the MLPNN performing best with a 7-fold CV balanced accuracy of 64% (95% CI: 0.59, 0.69) and validation accuracies greater than random chance for 9 of the 14 participants. This finding demonstrates that it is possible to classify a vigilance decrement using EEG, even with EEG from an unseen individual and unseen task. [ABSTRACT FROM AUTHOR]

The peg-in-hole task with object feature uncertain is a typical case of robotic operation in the real-world unstructured environment. It is nontrivial to realize object perception and operational decisions autonomously, under the usual visual occlusion and real-time constraints of such tasks. In this paper, a Bayesian networks-based strategy is presented in order to seamlessly combine multiple heterogeneous senses data like humans. In the proposed strategy, an interactive exploration method implemented by hybrid Monte Carlo sampling algorithms and particle filtering is designed to identify the features' estimated starting value, and the memory adjustment method and the inertial thinking method are introduced to correct the target position and shape features of the object respectively. Based on the Dempster–Shafer evidence theory (D-S theory), a fusion decision strategy is designed using probabilistic models of forces and positions, which guided the robot motion after each acquisition of the estimated features of the object. It also enables the robot to judge whether the desired operation target is achieved or the feature estimate needs to be updated. Meanwhile, the pliability model is introduced into repeatedly perform exploration, planning and execution steps to reduce interaction forces, the number of exploration. The effectiveness of the strategy is validated in simulations and in a physical robot task. [ABSTRACT FROM AUTHOR]

This study proposes a novel hybrid imitation learning (HIL) framework in which behavior cloning (BC) and state cloning (SC) methods are combined in a mutually complementary manner to enhance the efficiency of robotic manipulation task learning. The proposed HIL framework efficiently combines BC and SC losses using an adaptive loss mixing method. It uses pretrained dynamics networks to enhance SC efficiency and performs stochastic state recovery to ensure stable learning of policy networks by transforming the learner's task state into a demo state on the demo task trajectory during SC. The training efficiency and policy flexibility of the proposed HIL framework are demonstrated in a series of experiments conducted to perform major robotic manipulation tasks (pick-up, pick-and-place, and stack tasks). In the experiments, the HIL framework showed about a 2.6 times higher performance improvement than the pure BC and about a four times faster training time than the pure SC imitation learning method. In addition, the HIL framework also showed about a 1.6 times higher performance improvement and about a 2.2 times faster training time than the other hybrid learning method combining BC and reinforcement learning (BC + RL) in the experiments. [ABSTRACT FROM AUTHOR]

As the requirement for real-time data analysis increases, edge computing is being implemented to leverage the resources of edge devices to reduce system response times and decrease the latency. However, due to the resource constraints of edge clouds, edge servers are more prone to failures than other systems. Therefore, guaranteeing the reliability of services in edge clouds is critical. In this paper, we propose a fault-tolerant adaptive scheduling mechanism with dynamic quality of service (QoS) awareness (FASDQ), which extends the primary/backup (PB) model by applying QoS on demand to task copies. The aim of the method is to reduce the latency and achieve reliable service for tasks by changing the execution time of task copies. This paper also proposes a container resource-adaptive adjustment mechanism, which adjusts the timing of resources when the available resources cannot meet the task copy requirements. Finally, this paper reports the results of simulation experiments on the EdgeCloudSim platform to evaluate the difference in performance between FASDQ and other methods. The results show that the mechanism effectively reduces the execution time of task copies and outperforms other methods in terms of reliability and general resource utilization. [ABSTRACT FROM AUTHOR]

Virtual reality headsets, such as the HTC Vive, can be used to model objects, forces, and interactions between objects with high perceived realism and accuracy. Moreover, they can accurately track movements of the head and the hands. This combination makes it possible to provide subjects with precise quantitative feedback on their performance while they are learning a motor task. Juggling is a challenging motor task that requires precise coordination of both hands. Professional jugglers throw objects so that the arc peaks just above head height, and they time their throws so that the second ball is thrown when the first ball reaches its peak. Here, we examined whether it is possible to learn to juggle in virtual reality and whether the height and the timing of the throws can be improved by providing immediate feedback derived from the motion sensors. Almost all participants became better at juggling in the ~30 min session: the height and timing of their throws improved and they dropped fewer balls. Feedback on height, but not timing, improved performance, albeit only temporarily. [ABSTRACT FROM AUTHOR]

Computation offloading is a process that provides computing services to vehicles with computation sensitive jobs. Volunteer Computing-Based Vehicular Ad-hoc Networking (VCBV) is envisioned as a promising solution to perform task executions in vehicular networks using an emerging concept known as vehicle-as-a-resource (VaaR). In VCBV systems, offloading is the primary technique used for the execution of delay-sensitive applications which rely on surplus resource utilization. To leverage the surplus resources arising in periods of traffic congestion, we propose a hybrid VCBV task coordination model which performs the resource utilization for task execution in a multi-hop fashion. We propose an algorithm for the determination of boundary relay vehicles to minimize the requirement of placement for multiple road-side units (RSUs). We propose algorithms for primary and secondary task coordination using hybrid VCBV. Extensive simulations show that the hybrid technique for task coordination can increase the system utility, while the latency constraints are addressed. [ABSTRACT FROM AUTHOR]

As service latency and core network load relates to performance issues in the conventional cloud-based computing environment, the fog computing system has gained a lot of interest. However, since the load can be concentrated on specific fog computing nodes because of spatial and temporal service characteristics, performance degradation can occur, resulting in quality of service (QoS) degradation, especially for delay-sensitive services. Therefore, this paper proposes a prioritized task distribution scheme, which considers static as well as opportunistic fog computing nodes according to their mobility feature. Based on the requirements of offloaded tasks, the proposed scheme supports delay sensitive task processing at the static fog node and delay in-sensitive tasks by means of opportunistic fog nodes for task distribution. To assess the performance of the proposed scheme, we develop an analytic model for the service response delay. Extensive simulation results are given to validate the analytic model and to show the performance of the proposed scheme, compared to the conventional schemes in terms of service response delay and outage probability. [ABSTRACT FROM AUTHOR]

A sensor-based system using inertial magnetic measurement units and surface electromyography is suitable for objectively and automatically monitoring the lumbar load during physically demanding work. The validity and usability of this system in the uncontrolled real-life working environment of physically active workers are still unknown. The objective of this study was to test the discriminant validity of an artificial neural network-based method for load assessment during actual work. Nine physically active workers performed work-related tasks while wearing the sensor system. The main measure representing lumbar load was the net moment around the L5/S1 intervertebral body, estimated using a method that was based on artificial neural network and perceived workload. The mean differences (MDs) were tested using a paired t-test. During heavy tasks, the net moment (MD = 64.3 ± 13.5%, p = 0.028) and the perceived workload (MD = 5.1 ± 2.1, p < 0.001) observed were significantly higher than during the light tasks. The lumbar load had significantly higher variances during the dynamic tasks (MD = 33.5 ± 36.8%, p = 0.026) and the perceived workload was significantly higher (MD = 2.2 ± 1.5, p = 0.002) than during static tasks. It was concluded that the validity of this sensor-based system was supported because the differences in the lumbar load were consistent with the perceived intensity levels and character of the work tasks. [ABSTRACT FROM AUTHOR]

In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay. [ABSTRACT FROM AUTHOR]

Autonomous vehicle technology increasingly allows drivers to turn their primary attention to secondary tasks (e.g., eating or working). This dramatic behavior change thus requires new input modalities to support driver–vehicle interaction, which must match the driver's in-vehicle activities and the interaction situation. Prior studies that addressed this question did not consider how acceptance for inputs was affected by the physical and cognitive levels experienced by drivers engaged in Non-driving Related Tasks (NDRTs) or how their acceptance varies according to the interaction situation. This study investigates naturalistic interactions with a fully autonomous vehicle system in different intervention scenarios while drivers perform NDRTs. We presented an online methodology to 360 participants showing four NDRTs with different physical and cognitive engagement levels, and tested the six most common intervention scenarios (24 cases). Participants evaluated our proposed seven natural input interactions for each case: touch, voice, hand gesture, and their combinations. Results show that NDRTs influence the driver's input interaction more than intervention scenario categories. In contrast, variation of physical load has more influence on input selection than variation of cognitive load. We also present a decision-making model of driver preferences to determine the most natural inputs and help User Experience designers better meet drivers' needs. [ABSTRACT FROM AUTHOR]

UAVs often perform tasks that require flying close to walls or structures and in environments where a satellite-based location is not possible. Flying close to solid bodies implies a higher risk of collisions, thus requiring an increase in the precision of the measurement and control of the UAV's position. The aerodynamic distortions generated by nearby walls or other objects are also relevant, making the control more complex and further placing demands on the positioning system. Performing wall-related tasks implies flying very close to the wall and, in some cases, even touching it. This work presents a Near-Wall Positioning System (NWPS) based on the combination of an Ultra-wideband (UWB) solution and LIDAR-based range finders. This NWPS has been developed and tested to allow precise positioning and orientation of a multirotor UAV relative to a wall when performing tasks near it. Specific position and orientation control hardware based on horizontal thrusters has also been designed, allowing the UAV to move smoothly and safely near walls. [ABSTRACT FROM AUTHOR]

Manual inspection of workpieces in highly flexible production facilities with small lot sizes is costly and less reliable compared to automated inspection systems. Reinforcement Learning (RL) offers promising, intelligent solutions for robotic inspection and manufacturing tasks. This paper presents an RL-based approach to determine a high-quality set of sensor view poses for arbitrary workpieces based on their 3D computer-aided design (CAD). The framework extends available open-source libraries and provides an interface to the Robot Operating System (ROS) for deploying any supported robot and sensor. The integration into commonly used OpenAI Gym and Baselines leads to an expandable and comparable benchmark for RL algorithms. We give a comprehensive overview of related work in the field of view planning and RL. A comparison of different RL algorithms provides a proof of concept for the framework's functionality in experimental scenarios. The obtained results exhibit a coverage ratio of up to 0.8 illustrating its potential impact and expandability. The project will be made publicly available along with this article. [ABSTRACT FROM AUTHOR]

It is an essential capability of indoor mobile robots to avoid various kinds of obstacles. Recently, multimodal deep reinforcement learning (DRL) methods have demonstrated great capability for learning control policies in robotics by using different sensors. However, due to the complexity of indoor environment and the heterogeneity of different sensor modalities, it remains an open challenge to obtain reliable and robust multimodal information for obstacle avoidance. In this work, we propose a novel multimodal DRL method with auxiliary task (MDRLAT) for obstacle avoidance of indoor mobile robot. In MDRLAT, a powerful bilinear fusion module is proposed to fully capture the complementary information from two-dimensional (2D) laser range findings and depth images, and the generated multimodal representation is subsequently fed into dueling double deep Q-network to output control commands for mobile robot. In addition, an auxiliary task of velocity estimation is introduced to further improve representation learning in DRL. Experimental results show that MDRLAT achieves remarkable performance in terms of average accumulated reward, convergence speed, and success rate. Moreover, experiments in both virtual and real-world testing environments further demonstrate the outstanding generalization capability of our method. [ABSTRACT FROM AUTHOR]

This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size. [ABSTRACT FROM AUTHOR]

In this research, we focused on Human-Robot collaboration. There were two goals: (1) to develop and evaluate a real-time Human-Robot collaborative system, and (2) to achieve concrete tasks such as collaborative peg-in-hole using the developed system. We proposed an algorithm for visual sensing and robot hand control to perform collaborative motion, and we analyzed the stability of the collaborative system and a so-called collaborative error caused by image processing and latency. We achieved collaborative motion using this developed system and evaluated the collaborative error on the basis of the analysis results. Moreover, we aimed to realize a collaborative peg-in-hole task that required a system with high speed and high accuracy. To achieve this goal, we analyzed the conditions required for performing the collaborative peg-in-hole task from the viewpoints of geometric, force and posture conditions. Finally, in this work, we show the experimental results and data of the collaborative peg-in-hole task, and we examine the effectiveness of our collaborative system. [ABSTRACT FROM AUTHOR]

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment. [ABSTRACT FROM AUTHOR]

The aim of this study was (1) to evaluate the relative and absolute reliability of gait parameters during walking in single- and dual-task conditions in patients with axial spondyloarthritis (axSpA), (2) to evaluate the absolute and relative reliability of dual task effects (DTE) parameters, and (3) to determine the number of trials required to ensure reliable gait assessment, in patients with axSpA. Twenty patients with axSpa performed a 10-m walk test in single- and dual-task conditions, three times for each condition. Spatiotemporal, symmetry, and DTE gait parameters were calculated from foot-worn inertial sensors. The relative reliability (intraclass correlation coefficients-ICC) and absolute reliability (standard error of measurement-SEM and minimum detectable change-MDC) were calculated for these parameters in each condition. Spatiotemporal gait parameters showed good to excellent reliability in both conditions (0.59 < ICC < 0.90). The reliability of symmetry and DTE parameters was low. ICC, SEM, and MDC were better when using the mean of the second and the third trials. Spatiotemporal gait parameters obtained from foot-worn inertial sensors assessed in patients with axSpA in single- and dual-task conditions are reliable. However, symmetry and DTE parameters seem less reliable and need to be interpreted with caution. Finally, better reliability of gait parameters was found when using the mean of the 2nd and the 3rd trials. [ABSTRACT FROM AUTHOR]

Distribution mismatch caused by various resolutions, backgrounds, etc. can be easily found in multi-sensor systems. Domain adaptation attempts to reduce such domain discrepancy by means of different measurements, e.g., maximum mean discrepancy (MMD). Despite their success, such methods often fail to guarantee the separability of learned representation. To tackle this issue, we put forward a novel approach to jointly learn both domain-shared and discriminative representations. Specifically, we model the feature discrimination explicitly for two domains. Alternating discriminant optimization is proposed to obtain discriminative features with an l2 constraint in labeled source domain and sparse filtering is introduced to capture the intrinsic structures exists in the unlabeled target domain. Finally, they are integrated in a unified framework along with MMD to align domains. Extensive experiments compared with state-of-the-art methods verify the effectiveness of our method on cross-domain tasks. [ABSTRACT FROM AUTHOR]

The problem of revealing age-related distinctions in multichannel electroencephalograms (EEGs) during the execution of motor tasks in young and elderly adults is addressed herein. Based on the detrended fluctuation analysis (DFA), differences in long-range correlations are considered, emphasizing changes in the scaling exponent α. Stronger responses in elderly subjects are confirmed, including the range and rate of increase in α. Unlike elderly subjects, young adults demonstrated about 2.5 times more pronounced differences between motor task responses with the dominant and non-dominant hand. Knowledge of age-related changes in brain electrical activity is important for understanding consequences of healthy aging and distinguishing them from pathological changes associated with brain diseases. Besides diagnosing age-related effects, the potential of DFA can also be used in the field of brain–computer interfaces. [ABSTRACT FROM AUTHOR]

Manual carrying of heavy weight poses a major risk for work-related low back injury. Body-worn inertial sensors present opportunities to study the effects of ambulatory work tasks such as load carriage in more realistic conditions. An immediate effect of load carriage is reflected in altered gait kinematics. To determine the effects of load carriage mode and magnitude on gait parameters using body-worn angular rate gyroscopes, two laboratory experiments (n = 9 and n = 10, respectively) were conducted. Participants performed walk trials at self-selected speeds while carrying hand loads in two modes (two-handed side vs. anterior) at four load levels (empty-handed, 4.5 kg, 9.1 kg, and 13.6 kg). Six measures of postural sway and three measures of thoracic-pelvic coordination were calculated from data recorded by four body-worn gyroscopes for 1517 gait cycles. Results demonstrated that, after adjusting for relative walking speed, thoracic-pelvic sway, and movement coordination particularly in the coronal and transverse planes, characterized by gyroscope-based kinematic gait parameters, are systematically altered by the mode of load carriage and load magnitude. Similar trends were obtained for an anthropometrically homogenous (Expt-1) and diverse (Expt-2) sample after adjusting for individual differences in relative walking speed. Measures of thoracic-pelvic coordination and sway showed trends of significant practical relevance and may provide sufficient information to typify alterations in gait across two-handed side vs. anterior load carriage of different load magnitudes. This study contributes to understanding the effects of manual load carriage on thoracic-pelvic movement and the potential application of body-worn gyroscopes to measuring these gait adaptations in naturalistic work settings. [ABSTRACT FROM AUTHOR]

This study aimed to compare the dynamics of the prefrontal cortex (PFC), between adult and adolescent chess players, during chess-based problem-solving tasks of increasing level of difficulty, relying on the identification of changes in oxygenated hemoglobin (HbO2) and hemoglobin (HHb) through the functional near-infrared spectroscopy (fNIRS) method. Thirty male federated chess players (mean age: 24.15 ± 12.84 years), divided into adults and adolescents, participated in this cross-sectional study. Participants were asked to solve three chess problems with different difficulties (low, medium, and high) while changes in HbO2 and HHb were measured over the PFC in real-time with an fNIRS system. Results indicated that the left prefrontal cortex (L-PFC) increased its activation with the difficulty of the task in both adolescents and adults. Interestingly, differences in the PFC dynamics but not in the overall performance were found between adults and adolescents. Our findings contributed to a better understanding of the PFC resources mobilized during complex tasks in both adults and adolescents. [ABSTRACT FROM AUTHOR]

In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks' priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data. [ABSTRACT FROM AUTHOR]