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Existing multifactorial particle swarm optimisation (MFPSO) algorithms only explore a relatively narrow area between the inter-task particles. Meanwhile, these algorithms use a fixed inter-task learning probability throughout the evolution process. However, the parameter is problem dependent and can be various at different stages of the evolution. In this work, the authors devise an inter-task learning-based information transferring mechanism to replace the corresponding part in MFPSO. This inter-task learning mechanism transfers the searching step by using a differential term and updates the personal best position by employing an inter-task crossover. By this mean, the particles can explore a broad search space when utilising the additional searching experiences of other tasks. In addition, to enhance the performance on problems with different complementarity, they design a self-adaption strategy to adjust the inter-task learning probability according to the performance feedback. They compared the proposed algorithm with the state-of-the-art algorithms on various benchmark problems. Experimental results demonstrate that the proposed algorithm can transfer inter-task knowledge efficiently and perform well on the problems with different complementarity.

With the increasing number of services and industries including nuclear, chemical, aerospace, and automotive sectors in cyber-physical systems (CPSs), systems are being severely overloaded. CPSs comprises mixed-critical tasks which are of either safety-critical (high) or non-safety critical (low). In traditional task scheduling, most of the existing scheduling algorithms provide poor performance for high-criticality tasks when the system experiences overload and do not show explicit separation among different criticality tasks to take advantage of using cloud resources. Here, we propose a framework to schedule the mixed-criticality tasks by analyzing their deadlines and execution times which leverage the performance of parallel processing through OpenMP. The proposed framework introduces a machine learning-based prediction for a task offloading in the cloud. Moreover, it illustrates to execute a selected number of low-criticality tasks in the cloud while the high-criticality tasks are run on the local processors during the system overload. As a result, the high-criticality tasks meet all their deadlines and the system achieves a significant improvement in the overall execution time and better throughput. In addition, the experimental results employing OpenMP show the effectiveness of using the partitioned scheduling over the global scheduling method upon multiprocessor systems to achieve the tasks isolation.

An underlying hypothesis for broad transfer from cognitive training is that the regional brain signals engaged during the training task are related to the transfer tasks. However, it is unclear whether the brain activations elicited from a specific cognitive task can generalize to performance of other tasks, esp. in normal aging where cognitive training holds much promise. In this large dual‐site functional magnetic resonance imaging (fMRI) study, we aimed to characterize the neurobehavioral correlates of task‐switching in normal aging and examine whether the task‐switching‐related fMRI‐blood‐oxygen‐level‐dependent (BOLD) signals, engaged during varieties of cognitive control, generalize to other tasks of executive control and general cognition. We therefore used a hybrid blocked and event‐related fMRI task‐switching paradigm to investigate brain regions associated with multiple types of cognitive control on 129 non‐demented older adults (65–85 years). This large dataset provided a unique opportunity for a data‐driven partial least squares–correlation approach to investigate the generalizability of multiple fMRI‐BOLD signals associated with task‐switching costs to other tasks of executive control, general cognition, and demographic characteristics. While some fMRI signals generalized beyond the scanned task, others did not. Results indicate right middle frontal brain activation as detrimental to task‐switching performance, whereas inferior frontal and caudate activations were related to faster processing speed during the fMRI task‐switching, but activations of these regions did not predict performance on other tasks of executive control or general cognition. However, BOLD signals from the right lateral occipital cortex engaged during the fMRI task positively predicted performance on a working memory updating task, and BOLD signals from the left post‐central gyrus that were disengaged during the fMRI task were related to slower processing speed in the task as well as to lower general cognition. Together, these results suggest generalizability of these BOLD signals beyond the scanned task. The findings also provided evidence for the general slowing hypothesis of aging as most variance in the data were explained by low processing speed and global low BOLD signal in older age. As processing speed shared variance with task‐switching and other executive control tasks, it might be a possible basis of generalizability between these tasks. Additional results support the dedifferentiation hypothesis of brain aging, as right middle frontal activations predicted poorer task‐switching performance. Overall, we observed that the BOLD signals related to the fMRI task not only generalize to the performance of other executive control tasks, but unique brain predictors of out‐of‐scanner performance can be identified. This dual‐site functional magnetic resonance imaging (fMRI) study (n = 129) uses multivariate partial least squares–correlation analysis to examine the relationships between fMRI brain activations from task‐switching and performance on other tasks of executive control functions and general cognition. We found that the brain activations from this fMRI task can predict performance on a broad range of cognitive tasks.

Although deep learning algorithms have achieved significant progress in a variety of domains, they require costly annotations on huge datasets. Self-supervised learning (SSL) using unlabeled data has emerged as an alternative, as it eliminates manual annotation. To do this, SSL constructs feature representations using pretext tasks that operate without manual annotation, which allows models trained in these tasks to extract useful latent representations that later improve downstream tasks such as object classification and detection. The early methods of SSL are based on auxiliary pretext tasks as a way to learn representations using pseudo-labels, or labels that were created automatically based on the dataset’s attributes. Furthermore, contrastive learning has also performed well in learning representations via SSL. To succeed, it pushes positive samples closer together, and negative ones further apart, in the latent space. This paper provides a comprehensive literature review of the top-performing SSL methods using auxiliary pretext and contrastive learning techniques. It details the motivation for this research, a general pipeline of SSL, the terminologies of the field, and provides an examination of pretext tasks and self-supervised methods. It also examines how self-supervised methods compare to supervised ones, and then discusses both further considerations and ongoing challenges faced by SSL.

There is a growing body of research focused on developing and evaluating behavioral training paradigms meant to induce enhancements in cognitive function. It has recently been proposed that one mechanism through which such performance gains could be induced involves participants’ expectations of improvement. However, no work to date has evaluated whether it is possible to cause changes in cognitive function in a long-term behavioral training study by manipulating expectations. In this study, positive or negative expectations about cognitive training were both explicitly and associatively induced before either a working memory training intervention or a control intervention. Consistent with previous work, a main effect of the training condition was found, with individuals trained on the working memory task showing larger gains in cognitive function than those trained on the control task. Interestingly, a main effect of expectation was also found, with individuals given positive expectations showing larger cognitive gains than those who were given negative expectations (regardless of training condition). No interaction effect between training and expectations was found. Exploratory analyses suggest that certain individual characteristics (e.g., personality, motivation) moderate the size of the expectation effect. These results highlight aspects of methodology that can inform future behavioral interventions and suggest that participant expectations could be capitalized on to maximize training outcomes.

Multitasking, such as listening while balancing, relies on integrated processing in the sensory, cognitive, and motor systems; systems that often decline with age. Hearing loss is linked to increased risks of both falls and cognitive decline. Improving cognitive processing through executive function (EF) training may support balance, especially in older adults with hearing loss. This randomized controlled study conducted across age groups and hearing abilities, examined the effects of a 12-week EF training program on postural outcomes (center of pressure (COP)) using an auditory-cognitive-postural dual-task paradigm. Sixty-five participants including middle-aged adults with normal hearing (MA; n = 19), older adults with normal hearing (OA; n = 23), and older adults with hearing loss who used hearing aids (OAHL; n = 23) were randomly assigned within each age group to an EF training condition or a control condition. Primary outcome measures were auditory-cognitive reaction time on an auditory 2-back working memory task and postural measures (COP path length variability), which were collected in single- and dual-task conditions. Secondary analyses examined whether sensory, cognitive, and mobility performance, as evaluated by baseline standardized assessments, predicted training-related outcomes. Across MA, OA, and OAHL groups, cognitive performance generally improved following EF training and transfer of these training effects were observed during experimental postural tasks and auditory-cognitive tasks, but differed depending on age, pure-tone hearing thresholds, and cognitive abilities. Specifically, for postural outcomes, performance improved after training, but only for older adults with better hearing, while those with poorer hearing at any age did not improve. For auditory-cognitive task performance, older adults with the poorest hearing and cognition benefited the most from training. EF training may support balance and cognition in older adults, although its benefits for balance may be limited by severe hearing loss, underscoring the value of early intervention. Registry Name: ClinicalTrials.gov. Registration/Trial number: NCT05418998. Trial URL: https://clinicaltrials.gov/ct2/show/NCT05418998.

PurposeThis paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task allocation strategies mainly with respect to the problem application, constraints, objective functions and uncertainty handling methods.Design/methodology/approachThis paper briefs the introduction of multi-robot dynamic task allocation problem and discloses the challenges that exist in real-world dynamic task allocation problems. Numerous task allocation strategies are discussed in this paper, and it establishes the characteristics features between them in a qualitative manner. This paper also exhibits the existing research gaps and conducive future research directions in dynamic task allocation for multiple mobile robot systems.FindingsThis paper concerns the objective functions, robustness, task allocation time, completion time, and task reallocation feature for performance analysis of different task allocation strategies. It prescribes suitable real-world applications for variant task allocation strategies and identifies the challenges to be resolved in multi-robot task allocation strategies.Originality/valueThis paper provides a comprehensive review of dynamic task allocation strategies and incites the salient research directions to the researchers in multi-robot dynamic task allocation problems. This paper aims to summarize the latest approaches in the application of exploration problems.

Consumer eHealth interventions are of a growing importance in the individual management of health and health behaviors. However, a range of access, resources, and skills barriers prevent health care consumers from fully engaging in and benefiting from the spectrum of eHealth interventions. Consumers may engage in a range of eHealth tasks, such as participating in health discussion forums and entering information into a personal health record. eHealth literacy names a set of skills and knowledge that are essential for productive interactions with technology-based health tools, such as proficiency in information retrieval strategies, and communicating health concepts effectively. We propose a theoretical and methodological framework for characterizing complexity of eHealth tasks, which can be used to diagnose and describe literacy barriers and inform the development of solution strategies. We adapted and integrated two existing theoretical models relevant to the analysis of eHealth literacy into a single framework to systematically categorize and describe task demands and user performance on tasks needed by health care consumers in the information age. The method derived from the framework is applied to (1) code task demands using a cognitive task analysis, and (2) code user performance on tasks. The framework and method are applied to the analysis of a Web-based consumer eHealth task with information-seeking and decision-making demands. We present the results from the in-depth analysis of the task performance of a single user as well as of 20 users on the same task to illustrate both the detailed analysis and the aggregate measures obtained and potential analyses that can be performed using this method. The analysis shows that the framework can be used to classify task demands as well as the barriers encountered in user performance of the tasks. Our approach can be used to (1) characterize the challenges confronted by participants in performing the tasks, (2) determine the extent to which application of the framework to the cognitive task analysis can predict and explain the problems encountered by participants, and (3) inform revisions to the framework to increase accuracy of predictions. The results of this illustrative application suggest that the framework is useful for characterizing task complexity and for diagnosing and explaining barriers encountered in task completion. The framework and analytic approach can be a potentially powerful generative research platform to inform development of rigorous eHealth examination and design instruments, such as to assess eHealth competence, to design and evaluate consumer eHealth tools, and to develop an eHealth curriculum.

This paper evaluates how an increase in the supply of skilled labor affects task assignmentwithin and between occupations. Guided by a simple theoretical framework, we exploitdetailed information about individual workers’ tasks from multiple surveys to examine theimpact of a twofold rise in the share of university graduates in the French workforce between1991 and 2013. Our identification strategy uses variation in the change in the graduateshare across local labor markets. We find that higher average educational attainment isassociated with more routine, fewer cognitive and fewer social tasks within occupationsand with fewer routine, more cognitive and more social tasks across occupations. Cet article évalue l’impact d’une croissance de l’offre de travail qualifié sur l’allocation des travailleurs aux tâches, au sein et entre les professions. Guidé par un cadre théorique simple, nous exploitons des informations détaillées sur les tâches exercées par les travailleurs, mesurées dans les enquêtes afin d’évaluer l’impact d’un doublement de la part des diplômés du supérieur entre 1991 et 2013. Notre stratégie d’identification s’appuie sur la variation de l’évolution de cette part de diplômés entre les marchés locaux du travail. Nos résultats démontrent qu’une hausse du niveau de diplôme cause plus de tâches dites de routine, moins de tâches dites cognitives et moins de tâches dites sociales au sein des professions mais moins de tâches routinières, plus de tâches cognitives et sociales entre professions .

Using a wireless single channel EEG device, we investigated the feasibility of using short-term frontal EEG as a means to evaluate the dynamic changes of mental workload. Frontal EEG signals were recorded from twenty healthy subjects performing four cognitive and motor tasks, including arithmetic operation, finger tapping, mental rotation and lexical decision task. Our findings revealed that theta activity is the common EEG feature that increases with difficulty across four tasks. Meanwhile, with a short-time analysis window, the level of mental workload could be classified from EEG features with 65%-75% accuracy across subjects using a SVM model. These findings suggest that frontal EEG could be used for evaluating the dynamic changes of mental workload.