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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.

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.

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.

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.

The key to the success of tasks in promoting L2 is adopting a proper ordering of tasks. This research was done in pursuit of achieving two goals by utilizing Robinson’s (2010) SSARC (stabilize, simplify, automatize, reconstruct, and complexify) model. The first goal included probing the power of executing the non-complex without spatial reasoning and single listening task and the complex without spatial reasoning and dual listening task ahead of the high-complex spatial reasoning and dual listening task in executing the high-complex spatial reasoning and dual task. Probing the agreement between hypothetically defined task complexity and students’ thoughts on task difficulty was the second goal. To achieve its goals, this research adopted the relative comparison group and correlational designs. Participants of this research were thirty-two female undergraduate students from a non-profit university in Tehran. They were put into high-proficiency groups based on how they did the Oxford Placement Test. Participants of group one executed the high-complex task as the last task in non-complex, complex, and high-complex order and then gave their view of the difficulty level of tasks, while group two participants executed the same task as the first one. The results of the independent samples t-test, one sample t-test, and Spearman’s rho correlation disapproved the statistically significant power of executing preceding lower-complexity tasks in participants’ performance on the high-complex listening comprehension task and the agreement between the way participants think of task difficulty and task complexity has been defined theoretically. Accordingly, executing non-complex and complex listening tasks ahead of the high-complex listening task is not an instrumental means for forwarding how to execute the high-complex tasks and participants’ view of task difficulty cannot be a proper benchmark for determining the cognitive complexity of tasks. What was found by this research is instrumental to the selection and ordering of tasks for L2 classes and learners.

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.

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.

Using a novel gradual onset continuous performance task (gradCPT), recent research has uncovered a brain network of the sustained attention ability, demonstrating marked individual differences. Yet much about the cognitive processes that support performance on the gradCPT remains unknown. Here, we tested the importance of response inhibition and perceptual discrimination in the gradCPT. Participants monitored a continuous stream of natural scenes from two categories—cities and mountains—with a 9:1 ratio. In separate task blocks, they responded either to the frequent or the rare, yielding a response rate of either 90% or 10%. Performance was much worse, and declined more significantly over time, when the required response rate was higher. To test the role of stimulus onset, separate task blocks presented the scenes either gradually, with adjacent scenes blending into each other (gradCPT), or abruptly, with a single scene visible at a time (abruptCPT). Despite its increased complexity, the gradCPT yielded better performance than the abruptCPT, contradicting the perceptual complexity hypothesis and suggesting a detrimental role of the automaticity of responses to rhythmic stimuli in sustained attention. Further bolstering the role of response inhibition in the gradCPT, participants with superior inhibitory function, as assessed by the “stop-signal” task, did better on the gradCPT. These findings show that response inhibition contributes to the ability to sustain attention, especially in tasks that require frequent and repetitive responses as in assembly-line jobs.