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PurposeThis paper explores how task characteristics in terms of trigger type and task topic influence individual participation in community-based free/libre open source software (FLOSS) development by considering participation in individual tasks rather than entire projects.Design/methodology/approachA quantitative study was designed using choose tasks that were carried out via the email discourse on the developers' email fora in five FLOSS projects. Choice process episodes were selected as the unit of analysis and were coded for the task trigger and topic. The impact of these factors on participation (i.e. the numbers of participants and messages) was assessed by regression.FindingsThe results reveal differences in participation related to different task triggers and task topics. Further, the results suggest the mediating role of the number of participants in the relationships between task characteristics and the number of messages. The authors also speculate that project type serves as a boundary condition restricting the impacts of task characteristics on the number of participants and propose this relationship for future research.Research limitations/implicationsEmpirical support was provided to the important effects of different task characteristics on individual participation behaviors in FLOSS development tasks.Practical implicationsThe findings can help FLOSS participants understand participation patterns in different tasks and choose the types of tasks to attend to.Originality/valueThis research explores the impact of task characteristics on participation in FLOSS development at the task level, while prior research on participation in FLOSS development has focused mainly on factors at the individual and/or project levels.

Postural responses to similar perturbations of standing balance vary widely within and across subjects. Here, we identified two sources of variability and their interactions by combining experimental observations with computational modeling: differences in posture at perturbation onset across trials and differences in task-level goals across subjects. We first collected postural responses to unpredictable backward support-surface translations during standing in 10 young adults. We found that maximal trunk lean in postural responses to backward translations were highly variable both within subjects (mean of ranges = 28.3°) and across subjects (range of means = 39.9°). Initial center of mass (COM) position was correlated with maximal trunk lean during the response, but this relation was subject specific (R(2) = 0.29–0.82). We then used predictive simulations to assess causal relations and interactions with task-level goal. Our simulations showed that initial posture explains the experimentally observed intrasubject variability with a more anterior initial COM position increasing the use of the hip strategy. Differences in task-level goal explain observed intersubject variability with prioritizing effort minimization leading to ankle strategies and prioritizing stability leading to hip strategies. Interactions between initial posture and task-level goal explain observed differences in intrasubject variability across subjects. Our findings suggest that variability in initial posture due to increased sway as observed in older adults might increase the occurrence of less stable postural responses to perturbations. Insight in factors causing movement variability will advance our ability to study the origin of differences between groups and conditions. NEW & NOTEWORTHY Responses to perturbations of standing balance vary both within and between individuals. By combining experimental observations with computational modeling, we identified causes of observed kinematic variability in healthy young adults. First, we found that trial-by-trial differences in posture at perturbation onset explain most of the kinematic variability observed within subjects. Second, we found that differences in prioritizing effort versus stability explained differences in the postural response as well as differences in trial-by-trial variability across subjects.

This article presents an approach to efficiently control grippers/multifingered hands for dexterous manipulation according to a task, i.e., a predefined trajectory in the object space. The object motion is decomposed using a basis of predefined object motions equivalent to object-level coordinate couplings and leading to the definition of the task-level space. In the proposed approach, the decomposition of the motion in the task space is associated with a robust control design based on linear matrix inequalities (LMIs) and bilinear matrix inequalities (BMIs). Eigenvalue placement ensures the robustness of the system to geometric uncertainties and eigenvector placement decouples the system according to task specifications. A practical evaluation of the proposed control strategy is provided with a two-fingers' and six degrees-of-freedom robotic system manipulating an object in the horizontal plane. Results show a better trajectory tracking and the robustness of the control law according to geometric uncertainties and the manipulation of various objects.

Objectives Across the life span, deficits in executive functioning (EF) are associated with poor behavioral control and failure to achieve goals. Though EF is often discussed as one broad construct, a prominent model of EF suggests that it is composed of three subdomains: inhibition, set shifting, and updating. These subdomains are seen in both younger (YA) and older adults (OA), with performance deficits across subdomains in OA. Therefore, our goal was to investigate whether subdomains of EF might be differentially affected by age, and how these differences may relate to broader global age differences in EF. Methods To assess these age differences, we conducted a meta-analysis at multiple levels, including task level, subdomain level, and of global EF. Based on previous work, we hypothesized that there would be overall differences in EF in OA. Results Using 1,268 effect sizes from 401 articles, we found overall differences in EF with age. Results suggested that differences in performance are not uniform, such that variability in age effects emerged at the task level, and updating was not as affected by age as other subdomains. Discussion These findings advance our understanding of age differences in EF, and stand to inform early detection of EF decline.

This paper applies insights from different streams of value chain research to undertake a systematic analysis of various contemporary questions related to global value chains and their development implications. We exploit a detailed firm-level dataset that records the activities of 515 East African processing firms in three different value chains at the task level. By following a value chain mapping methodology, we first calculate the percentage of a chain’s total value-added that firms capture. After that, we separate and compare firms engaged to South–South and South–North value chains. We find that firms engaged with South–South chains tend to capture higher value-added shares while facing less entry barriers than firms predominantly engaged with South–North chains. This suggests that South–South value chains can provide a stepping-stone for firms to successfully participate in international markets.

Despite disagreement about how anterior cingulate cortex (ACC) supports decision making, a recent hypothesis suggests that activity in this region is best understood in the context of a task or series of tasks. One important task-level variable is average reward because it is both a known driver of effortful behaviour and an important determiner of the tasks in which we choose to engage. Here we asked how average task value affects reward-related ACC activity. To answer this question, we measured a reward-related signal said to be generated in ACC called the reward positivity (RewP) while participants gambled in three tasks of differing average value. The RewP was reduced in the high-value task, an effect that was not explainable by either reward magnitude or outcome expectancy. Average task value also affected cue-locked frontal midline theta (FMT), an ACC signal linked to cognitive control: FMT was enhanced for low-value cues relative to high-value cues. This effect was related to behaviour, as the more FMT a participant exhibited, the better they performed. These results suggest that ACC does not evaluate outcomes and cues in isolation, but in the context of the value of the current task.

Analyses of AI adoption focus on its adoption at the individual task level. What has received significantly less attention is how AI adoption is shaped by the fact that organisations are composed of many interacting tasks. AI adoption may, therefore, require system-wide change which is both a constraint and an opportunity. We provide the first formal analysis where multiple tasks may be part of a modular or non-modular system. We find that reliance on AI, a prediction tool, increases decision variation which, in turn, raises challenges if decisions across the organisation interact. Modularity, which leads to task independence rather than system-level inter-dependencies, softens that impact. Thus, modularity can facilitate AI adoption. However, it does this at the expense of synergies. By contrast, when there are mechanisms for inter-decision coordination, AI adoption is enhanced when there is a non-modular environment. Consequently, we show that there are important cases where AI adoption will be enhanced when it can be adopted beyond tasks but as part of a designed organisational system.

The multi-functional mixed-criticality automotive cyber-physical systems (ACPS) refer to multiple functions with several levels of criticality that can be run on a same platform, in which heterogeneous electronic control units (ECU) that are distributed on multiple network buses. Well-designed schedule policy for the system criticality of these systems can improve the system performance and reduce the deadline miss ratio. In this paper, we propose a scheduling algorithm called fairness and active multiple heterogeneous earliest finish time (FA_MHEFT) to meet the deadlines of high-criticality functions as soon as possible. FA_MHEFT actively adjusts the criticality of the system at the task level and reduces the delay of tasks in low-criticality functions when high-criticality scheduling is performed. With a more active scheduling policy, FA_MHEFT can reduce the overall makespan of the system and the deadline miss ratio (DMR) of the functions. Our experiments validate that FA_MHEFT can reduce the DMR of a high-criticality level function and improve system performance compared with other existing scheduling methods.

Recent advances in artificial intelligence and robotics have generated a robust debate about the future of work. An analogous debate occurred in the late nineteenth century when mechanization first transformed manufacturing. We analyze an extraordinary dataset from the late nineteenth century, the Hand and Machine Labor study carried out by the US Department of Labor in the mid-1890s. We focus on transitions at the task level from hand to machine production, and on the impact of inanimate power, especially of steam power, on labor productivity. Our analysis sheds light on the ability of modern task-based models to account for the effects of historical mechanization.

The present study used an established model of feedback (Hattie & Timperley, 2007) as a framework to explore which types and levels of feedback are most common in the upper primary classroom. Results demonstrate that feedback was predominantly directed toward the task level and that feed forward, information about the next steps for learning, was the least occurring feedback type in the classroom. Based upon research and findings, the authors propose a conceptual matrix of feedback that bridges research to practice with the aim of feedback being a driver to promote improvement.

Given a robotic manipulation task, decision on which robotic configuration (robotic system and necessary peripherals, including assistant tools, sensor systems, and so on.) to use and evaluating performance of the solution remains as an open, challenging but a meaningful problem. This work attempts to address this problem by defining the concept of task-level performance and developing an approach for systematic assessment based on a proposed task representation for performance quantification of different task plans and robotic configurations. Starting from productivity which is one of the main concern for manufacturers, this work introduces a methodology for quantitative assessment of any given robotic configuration and task handling. Such a method is useful for comparing the productivity of different robotic configurations and evaluating the worthiness of updated solution of a sub-task through observing improvements in productivity. Implemented and tested on a peg-in-hole task with the different level of difficulty through single-arm and dual-arm manipulation on a dual-arm robot, the methodology has demonstrated encouraging results. Finally, the connection of performance assessment from task-level to economy-level is presented. © 2019, Springer Nature B.V.

In recent years, deep reinforcement learning methods have achieved impressive performance in many different fields, including playing games, robotics, and dialogue systems. However, there are still a lot of restrictions here, one of which is the demand for massive amounts of sampled data. In this paper, a hierarchical meta-learning method based on the actor-critic algorithm is proposed for sample efficient learning. This method provides the transferable knowledge that can efficiently train an actor on a new task with a few trials. Specifically, a global basic critic, meta critic, and task specified network are shared within a distribution of tasks and are capable of criticizing any actor trying to solve any specified task. The hierarchical framework is applied to a critic network in the actor-critic algorithm for distilling meta-knowledge above the task level and addressing distinct tasks. The proposed method is evaluated on multiple classic control tasks with reinforcement learning algorithms, including the start-of-the-art meta-learning methods. The experimental results statistically demonstrate that the proposed method achieves state-of-the-art performance and attains better results with more depth of meta critic network.

While it has been consistently demonstrated that academic self-efficacy and performance are positively correlated in groups of students, little is known about whether individual students' academic self-efficacy levels align with their own performance abilities. At the same time, researchers contest whether self-efficacy should align with performance abilities to be of most benefit to students. In this study, we applied procedures used in the meta-cognitive calibration paradigm to investigate the alignment between academic self-efficacy and academic performance (i.e., self-efficacy calibration) in higher education. Undergraduate students (n = 207) completed five self-efficacy questionnaires with regard to academic performance outcomes in one subject over a semester (two written assignments, two exams, and the subject overall). Five corresponding grades were also collected. We calculated two types of self-efficacy calibration scores: self-efficacy accuracy (the deviation between self-efficacy and performance) and self-efficacy bias (the signed difference [i.e., valence]; over- and under-efficaciousness). Miscalibration of self-efficacy beliefs was prevalent, consistent with findings regarding meta-cognitive calibration. Under-efficaciousness was common at task level (for written assignments and exams), while over-efficaciousness was pronounced at domain level (for the subject overall). Self-efficacy exceeded performance for low-achievers, while it fell short of performance for high-achievers. A key finding was that self-efficacy bias predicted academic performance on similar subsequent tasks, with under-efficacious students performing better than accurate or over-efficacious students. Findings suggest self-efficacy is not a self-fulfilling prophecy; instead, over-efficacious students may experience negative impacts on academic self-regulation and performance.

In numerous safety critical applications, the use of high-reliability or radiation-tolerant equipment may not be a viable option due to the presence of several constraints (such as cost) and the need to utilize Commercial off-the Shelf (COTS) equipment. However, such equipment may not meet reliability requirements, and therefore certain appropriate measures need to be taken to enhance their reliability. In this paper, a fully software-based method is presented to increase the reliability of COTS equipment against transient faults. The reliability of COTS is increased by utilizing a task-level redundancy in operating system. The proposed method is evaluated using a software fault injection method and a full system prototype. The experimental results show that the proposed method increases the fault coverage up to 99.34%. Moreover, the proposed method can be used in embedded systems without any hardware, software, or information redundancy.

This chapter outlines optimization approaches for embedded systems. As this is multi-objective optimization problem, Pareto analysis is introduced. Scope of optimization is also discussed that includes power optimization, code level optimization, and task level optimization.

Pre-trained text-to-text transformers such as BART have achieved impressive performance across a range of NLP tasks. Recent study further shows that they can learn to generalize to novel tasks, by including task descriptions as part of the source sequence and training the model with (source, target) examples. At test time, these fine-tuned models can make inferences on new tasks using the new task descriptions as part of the input. However, this approach has potential limitations, as the model learns to solve individual (source, target) examples (i.e., at the instance level), instead of learning to solve tasks by taking all examples within a task as a whole (i.e., at the task level). To this end, we introduce Hypter, a framework that improves text-to-text transformer's generalization ability to unseen tasks by training a hypernetwork to generate task-specific, light-weight adapters from task descriptions. Experiments on ZEST dataset and a synthetic SQuAD dataset demonstrate that Hypter improves upon fine-tuning baselines. Notably, when using BART-Large as the main network, Hypter brings 11.3% comparative improvement on ZEST dataset., Comment: Accepted to ACL 2021. Camera-ready version. Code: https://github.com/INK-USC/hypter

It is currently unclear if damping plays a functional role in legged locomotion, and simple models often do not include damping terms. We present a new model with a damping term that is isolated from other parameters: that is, the damping term can be adjusted without retuning other model parameters for nominal motion. We systematically compare how increased damping affects stability in the face of unexpected ground-height perturbations. Unlike most studies, we focus on task-level stability: instead of observing whether trajectories converge towards a nominal limit-cycle, we quantify the ability to avoid falls using a recently developed mathematical measure. This measure allows trajectories to be compared quantitatively instead of only being separated into a binary classification of ‘stable' or ‘unstable'. Our simulation study shows that increased damping contributes significantly to task-level stability; however, this benefit quickly plateaus after only a small amount of damping. These results suggest that the low intrinsic damping values observed experimentally may have stability benefits and are not simply minimized for energetic reasons. All Python code and data needed to generate our results are available open source.