Reduce Task Completion Time Research Articles

Effects of a Six-Month Multi-Ingredient Nutrition Supplement Intervention of Omega-3 Polyunsaturated Fatty Acids, vitamin D, Resveratrol, and Whey Protein on Cognitive Function in Older Adults: A Randomised, Double-Blind, Controlled Trial.

To investigate the impact of a six-month multi-ingredient nutrition supplement intervention (Smartfish®), containing omega-3 polyunsaturated fatty acids (PUFAs), vitamin D, resveratrol, and whey protein, on cognitive function in Irish older adults. Double-blind, randomised controlled trial (ClinicalTrials.gov: NCT02001831). A quantitative, mixed-model design was employed in which the dependent variable (cognitive function) was analysed with a between-subjects factor of group (placebo, intervention) and within-subjects factor of testing occasion (baseline, three-months, six-months). Community-based intervention including assessments conducted at University College Dublin, Ireland. Thirty-seven community-dwelling older adults (68-83 years; mean (x̄)= 75.14 years; standard deviation (SD)= 3.64; 18 males) with normal cognitive function (>24 on the Mini Mental State Examination) were assigned to the placebo (n= 17) or intervention (n= 20) via a block randomisation procedure. Daily consumption for six-months of a 200mL liquid juice intervention comprising 3000mg omega-3 PUFAs [1500mg docosahexaenoic acid (DHA) and 1500mg eicosapentaenoic acid (EPA)], 10μg vitamin D3, 150mg resveratrol and 8g whey protein isolate. The placebo contained 200mL juice only. A standardised cognitive assessment battery was conducted at baseline and follow-ups. Individual test scores were z-transformed to generate composite scores grouped into cognitive domains: executive function, memory, attention and sensorimotor speed. Motor imagery accuracy and subjective awareness of cognitive failures variables were computed from raw scores. A hierarchical statistical approach was used to analyse the data; first, by examining overall cognitive function, then by domain, and then by individual test scores. Using mixed between-within subjects, analyses of variance (ANOVAs), no significant differences in overall cognitive function or composite cognitive domains were observed between groups over time. The only significant interaction was for Stroop Color-Word Time (p< 0.05). The intervention group demonstrated reduced task completion time at three- and six-month follow-ups, indicating enhanced performance. The present nutrition intervention encompassed a multi-ingredient approach targeted towards improving cognitive function, but overall had only a limited beneficial impact in the older adult sample investigated. Future investigations should seek to establish any potential clinical applications of such targeted interventions with longer durations of supplementation, or in populations with defined cognitive deficits.

Improvement in human error by target predication in transmission control protocol/Internet protocol–based remote control system

In a robot teleoperation system, humans operate and respond by relying on the content displayed on the video screen. However, unavoidable delays, such as network transmission, cause asynchronous communication between humans and robot and further result in human operation errors such as redundant or duplicate operations or even wrong commands. Users of consumer electronics in daily life are different from users in large-scale industry as they may not be experts or even trained to use the robots leading to new challenges in developing home-used robots. In this study, a module-based remote control system is designed to illustrate the possible human operation errors caused by transmission delay of transmission control protocol/Internet protocol–based network and to provide a user-friendly framework for simulating solutions to improve human errors. This is followed by proposing two target prediction methods, namely the target area predictor and stop position predictor, to decrease the human errors. The experimental results show that target area predictor and stop position predictor effectively improve the performance by reducing task completion time and object trajectory errors by 60% and 36%, respectively.

De-centralised dynamic task scheduling using hill climbing algorithm in cloud computing environments

There are many tasks in cloud computing that should be executed by available resources to acquire high performance, reduce task completion time, increase utilisation of resources etc. Since task scheduling problem in cloud computing is an NP-hard problem, designing an efficient scheduling strategy to achieve the intentions listed above is challenging. Task scheduling is the process of allocating tasks to available resources such that performance metrics are improved. This paper proposes a dynamic scheduling algorithm that uses hill climbing algorithm. It tries to minimise completion time of tasks while maximising throughput and utilisation of resources. This algorithm allocates independent tasks to available resources to achieve load balance. The simulation results show that the algorithm can achieve load balance and reduces completion time of tasks.

De-centralised dynamic task scheduling using hill climbing algorithm in cloud computing environments

There are many tasks in cloud computing that should be executed by available resources to acquire high performance, reduce task completion time, increase utilisation of resources etc. Since task scheduling problem in cloud computing is an NP-hard problem, designing an efficient scheduling strategy to achieve the intentions listed above is challenging. Task scheduling is the process of allocating tasks to available resources such that performance metrics are improved. This paper proposes a dynamic scheduling algorithm that uses hill climbing algorithm. It tries to minimise completion time of tasks while maximising throughput and utilisation of resources. This algorithm allocates independent tasks to available resources to achieve load balance. The simulation results show that the algorithm can achieve load balance and reduces completion time of tasks.

Instructable Intelligent Personal Agent

Unlike traditional machine learning methods, humans often learn from natural language instruction. As users become increasingly accustomed to interacting with mobile devices using speech, their interest in instructing these devices in natural language is likely to grow. We introduce our Learning by Instruction Agent (LIA), an intelligent personal agent that users can teach to perform new action sequences to achieve new commands, using solely natural language interaction. LIA uses a CCG semantic parser to ground the semantics of each command in terms of primitive executable procedures defining sensors and effectors of the agent. Given a natural language command that LIA does not understand, it prompts the user to explain how to achieve the command through a sequence of steps, also specified in natural language. A novel lexicon induction algorithm enables LIA to generalize across taught commands, e.g., having been taught how to "forward an email to Alice," LIA can correctly interpret the command "forward this email to Bob." A user study involving email tasks demonstrates that users voluntarily teach LIA new commands, and that these taught commands significantly reduce task completion time. These results demonstrate the potential of natural language instruction as a significant, under-explored paradigm for machine learning.

"How being busy can increase motivation and reduce task completion time": Correction to Wilcox et al. (2016).

Reports an error in "How being busy can increase motivation and reduce task completion time" by Keith Wilcox, Juliano Laran, Andrew T. Stephen and Peter P. Zubcsek (Journal of Personality and Social Psychology, 2016[Mar], Vol 110[3], 371-384). In the article, the affiliation of the author Andrew T. Stephen was incorrectly listed in the byline and the author note. The author is affiliated with the University of Oxford. The author note paragraph "Andrew T. Stephen is now at the University of Oxford" should have been omitted. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2016-11945-002.) This research tests the hypothesis that being busy increases motivation and reduces the time it takes to complete tasks for which people miss a deadline. This effect occurs because busy people tend to perceive that they are using their time effectively, which mitigates the sense of failure people have when they miss a task deadline. Studies 1 and 2 show that when people are busy, they are more motivated to complete a task after missing a deadline than those who are not busy, and that the perception that one is using time effectively mediates this effect. Studies 3 and 4 show that this process makes busy people more likely to complete real tasks than people who are not busy. Study 5 uses data from over half a million tasks submitted by thousands of users of a task management software application to show that busy people take less time to complete a task after they miss a deadline for completing it. The findings delineate the conditions under which being busy can mitigate the negative effects of missing a deadline and reduce the time it takes to complete tasks. (PsycINFO Database Record

How being busy can increase motivation and reduce task completion time.

This research tests the hypothesis that being busy increases motivation and reduces the time it takes to complete tasks for which people miss a deadline. This effect occurs because busy people tend to perceive that they are using their time effectively, which mitigates the sense of failure people have when they miss a task deadline. Studies 1 and 2 show that when people are busy, they are more motivated to complete a task after missing a deadline than those who are not busy, and that the perception that one is using time effectively mediates this effect. Studies 3 and 4 show that this process makes busy people more likely to complete real tasks than people who are not busy. Study 5 uses data from over half a million tasks submitted by thousands of users of a task management software application to show that busy people take less time to complete a task after they miss a deadline for completing it. The findings delineate the conditions under which being busy can mitigate the negative effects of missing a deadline and reduce the time it takes to complete tasks. (PsycINFO Database Record

Energy efficient and robust allocation of interdependent tasks on mobile ad hoc computational grid

SummaryA mobile ad hoc computational grid is a distributed computing infrastructure that allows mobile nodes to share computing resources in a mobile ad hoc environment. Compared to traditional distributed systems such as grids and clouds, resource allocation in mobile ad hoc computational grids is not straightforward because of node mobility, limited battery power and an infrastructure‐less network environment. The existing schemes are either based on a decentralized architecture that results in poor allocation decisions or assume independent tasks. This paper presents a scheme that allocates interdependent tasks and aims to reduce task completion time and the amount of energy consumed in transmission of data. This scheme comprises two key algorithms: resource selection and resource allocation. The resource selection algorithm is designed to select nodes that remain connected for a longer period, whereas the resource assignment or allocation algorithm is developed to allocate interdependent tasks to the nodes that are accessible at the minimum transmission power. The scheme is based on a hybrid architecture that results in effective allocation decisions, reduces the communication cost associated with the exchange of control information, and distributes the processing burden among the nodes. The paper also investigates the relationship between the data transfer time and transmission energy consumption and presents a power‐based routing protocol to reduce data transfer costs and transmission energy consumption. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Electromyogram synergy control of a dexterous artificial hand to unscrew and screw objects

BackgroundDue to their limited dexterity, it is currently not possible to use a commercially available prosthetic hand to unscrew or screw objects without using elbow and shoulder movements. For these tasks, prosthetic hands function like a wrench, which is unnatural and limits their use in tight working environments. Results from timed rotational tasks with human subjects demonstrate the clinical need for increased dexterity of prosthetic hands, and a clinically viable solution to this problem is presented for an anthropomorphic artificial hand.MethodsInitially, a human hand motion analysis was performed during a rotational task. From these data, human hand synergies were derived and mapped to an anthropomorphic artificial hand. The synergy for the artificial hand is controlled using conventional dual site electromyogram (EMG) signals. These EMG signals were mapped to the developed synergy to control four joints of the dexterous artificial hand simultaneously.Five limb absent and ten able-bodied test subjects participated in a comparison study to complete a timed rotational task as quickly as possible with their natural hands (except for one subject with a bilateral hand absence), eight commercially available prosthetic hands, and the proposed synergy controller. Each test subject used two to four different artificial hands.ResultsWith the able-bodied subjects, the developed synergy controller reduced task completion time by 177% on average. The limb absent subjects completed the task faster on average than with their own prostheses by 46%. There was a statistically significant improvement in task completion time with the synergy controller for three of the four limb absent participants with integrated prostheses, and was not statistically different for the fourth.ConclusionsThe proposed synergy controller reduced average task completion time compared to commercially available prostheses. Additionally, the synergy controller is able to function in a small workspace and requires less physical effort since arm movements are not required. The synergy controller is driven by conventional dual site EMG signals that are commonly used for prosthetic hand control, offering a viable solution for people with an upper limb absence to use a more dexterous artificial hand to screw or unscrew objects.

Distributed Cognition in Software Design: An Experimental Investigation of the Role of Design Patterns and Collaboration

Software design is a knowledge intensive task that constitutes a critical part of the software development process. Using a controlled experiment involving software practitioners, this research examines two potentially useful mechanisms for improving the

Preliminary Articulable Probe Designs With RAVEN and Challenges: Image-Guided Robotic Surgery Multitool System

The primary focus of the vision systems in current minimally invasive surgery (MIS) surgical systems has been on the improvement of immersive experience through a static approach. One of the current limitations in an MIS robotic surgery is the limited field of view and restricted perspective due to the use of a sole rigid 3D endoscope. We seek to integrate a modular articulable imaging device and the teleoperated surgical robot, RAVEN. Another additional flexible imager can be helpful in viewing occluded surgical targets, giving increased visualization options. Two probe designs are proposed and tested to evaluate a robotized steering mechanism within the MIS robot framework. Both designs, a separate flexible imager and a fixed camera on a tool tip, did not show much improvement in reducing task completion time. The new system may have some potential in improved precise manipulation of surgical tools, which may offer safety benefits once the surgeon is trained. We have demonstrated feasibility of a novel MIS instrument imaging device to aid in viewing potentially occluded surgical targets. A new concept, a modular axis-shared articulable imaging probe located at the vicinity of a tool tip, is proposed for future evaluation. Full integration of the new flexible imaging device into the grasper of the RAVEN surgical robot is under study coordinated with clinicians.

Tactile Feedback Improves Performance in a Palpation Task: Results in a VR-Based Testbed

Robotic surgery provides many benefits such as reduced invasiveness and increased dexterity. This comes at the cost of no direct contact between surgeon and patient. This physical separation prevents surgeons from performing direct haptic exploration of tissues and organs, imposing exclusive reliance on visual cues. Current technology is not yet able to both measure and reproduce a realistic and complete sense of touch (interaction force, temperature, roughness, etc.). In this paper, we put forward a concept based on multimodal feedback consisting of the integration of different kinds of visual and tactile cues with force feedback that can potentially improve both the surgeon's performance and the patient's safety. We present a cost-effective tactile display simulating a pulsating artery that has been integrated into a haptic workstation to combine both tactile and force-feedback information. Furthermore, we investigate the effect of different feedback types, including tactile and/or visual cues, on the performance of subjects carrying out two typical palpation tasks: (1) exploring a tissue to find a hidden artery and (2) identifying the orientation of a hidden artery. The results show that adding tactile feedback significantly reduces task completion time. Moreover, for high difficulty levels, subjects perform better with the feedback condition combining tactile and visual cues. As a matter of fact, the majority of the subjects in the study preferred this combined feedback because redundant feedback reassures subjects in their actions. Based on this work, we can infer that multimodal haptic feedback improves subjects' performance and confidence during exploratory procedures.

Analysis and modeling of task scheduling in wireless sensor network based on divisible load theory

SUMMARYIn this paper, we propose a novel task scheduling algorithm (Divisible Task scheduling Algorithm for Wireless sensor networks (DTAW)) based on divisible load theory in heterogeneous wireless sensor networks to complete the tasks within the shortest possible time and reduce the sensors' energy‐consuming. In DTAW, the tasks are distributed to the wireless sensor network by the (SINK) on the basis of the processing and communication capacity of each sensor. After receiving the subtasks, the intracluster sensors carry out its tasks simultaneously and send the results to cluster head sequentially. By removing communication interference between each sensor, reduced task completion time and improved network resource utilization are achieved. Each cluster head simultaneously finishes sending fused data to the SINK after fusing the data obtained from intracluster sensors. In this way, the overlap between the task performing and communication phase would be much better. Simulation results are presented to demonstrate the impacts of different network parameters on the makespan and energy consumption. The results show that the algorithm enables to reasonably distribute tasks to each sensor and then effectively reduces the time‐consuming and energy‐consuming. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Validating pragmatic reuse tasks by leveraging existing test suites

SUMMARYTraditional industrial practice often involves the ad hoc reuse of source code that was not designed for that reuse. Such pragmatic reuse tasks play an important role in disciplined software development. Pragmatic reuse has been seen as problematic due to a lack of systematic support, and an inability to validate that the reused code continues to operate correctly within the target system. Although recent work has successfully systematized support for pragmatic reuse tasks, the issue of validation remains unaddressed. In this paper, we present a novel approach and tool to semi‐automatically reuse and transform relevant portions of the test suite associated with pragmatically reused code, as a means to validate that the relevant constraints from the originating system continue to hold, while minimizing the burden on the developer. We conduct a formal experiment with experienced developers, to compare the application of our approach versus the use of a standard IDE (the ‘manual approach’). We find that, relative to the manual approach, our approach: reduces task completion time; improves instruction coverage by the reused test cases; and improves the correctness of the reused test cases. Copyright © 2012 John Wiley &amp; Sons, Ltd.

A Comprehensive Study of Using 2D Barcode for Multi Robot Labelling and Communication

Multi robot is an emerging field in robotic research with surprisingly various open problems. Reports have been published stating the advantages of multi robot over conventional single robot, including reducing task completion time. One of the open problem as concentrated in this study is their communication. This study focuses on using a 2D QR barcode for robot labelling suitable for vision-based multi robot communication. The aim is to prove that QR barcode could be decoded in various distances as well as in different angles. Image sampling is done in controlled environment. A two-way ANOVA without repetition is used to analyze the image data in terms of successful QR decoding. ANOVA with significant level of critical α 0.05 is used and has justified that smallest variance at different distances and angular pose will give the best position in order to decode the respective QR barcode. Hence multi robot communication by means of labelling a QR label onto them has the possibilities for further research.

A Non-cooperative Game Algorithm for Task Scheduling in Wireless Sensor Networks

Scheduling tasks in wireless sensor networks is one of the most challenging problems. Sensing tasks should be allocated and processed among sensors in minimum times, so that users can draw prompt and effective conclusions through analyzing sensed data. Furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. But sensors may refuse to take pains to carry out the tasks due to the limited energy. To solve the potentially selfish problem of the sensors, a non-cooperative game algorithm (NGTSA) for task scheduling in wireless sensor networks is proposed. In the proposed algorithm, according to the divisible load theory, the tasks are distributed reasonably to every node from SINK based on the processing capability and communication capability. By removing the performance degradation caused by communications interference and idle, the reduced task completion time and the improved network resource utilization are achieved. Strategyproof mechanism which provide incentives to the sensors to obey the prescribed algorithms, and to truthfully report their parameters, leading to an effient task scheduling and execution. A utility function related with the total task completion time and tasks allocating scheme is designed. The Nash equilibrium of the game algorithm is proved. The simulation results show that with the mechanism in the algorithm, selfish nodes can be forced to report their true processing capability and endeavor to participate in the measurement, thereby the total time for accomplishing the task is minimized and the energy-consuming of the nodes is balanced.

Hierarchical state-abstracted and socially augmented Q-Learning for reducing complexity in agent-based learning

A primary challenge of agent-based policy learning in complex and uncertain environments is escalating computational complexity with the size of the task space (action choices and world states) and the number of agents. Nonetheless, there is ample evidence in the natural world that high-functioning social mammals learn to solve complex problems with ease, both individually and cooperatively. This ability to solve computationally intractable problems stems from both brain circuits for hierarchical representation of state and action spaces and learned policies as well as constraints imposed by social cognition. Using biologically derived mechanisms for state representation and mammalian social intelligence, we constrain state-action choices in reinforcement learning in order to improve learning efficiency. Analysis results bound the reduction in computational complexity due to state abstraction, hierarchical representation, and socially constrained action selection in agent-based learning problems that can be described as variants of Markov decision processes. Investigation of two task domains, single-robot herding and multirobot foraging, shows that theoretical bounds hold and that acceptable policies emerge, which reduce task completion time, computational cost, and/or memory resources compared to learning without hierarchical representations and with no social knowledge.

Research of Task Scheduling Algorithm Based on Parallel Computing

Through the research on the existing parallel computing technologies, this paper bas an in-depth research and analysis on the status, issues to be addressed and functional features of parallel computing task scheduling, and for the current problems existed, presents a solution. The program can better reflect the heterogeneity and dynamicity of the parallel resources, as far as possible ensure the reliability and stability of the selected resources, while reducing task completion time and meeting user requirements for service quality.

Assessment of Environmental Effects on Collaborative Haptic Guidance

This paper investigates the effect of environmental factors on user performance in a dual-user haptic guidance system. The system under study allows for interaction between both users, the trainee and the trainer, to collaboratively perform a common task in a shared virtual environment. User studies are carried out to experimentally evaluate the users' performance while following square and circular trajectories with two viewpoints of the environment (top view and front view), while the virtual manipulator tool moves in free motion or against forbidden-region virtual fixtures. The performance is measured and statistically evaluated against task completion time, tracking accuracy, and user energy exchange. The studies revealed that changing the environment geometry from a square to a circle results in reduced task completion time and tracking error. Changing the environment viewpoint from top to front decreases the task completion time in both geometries. Forbidden-region virtual fixtures increase energy exchange by both users and decrease task completion time while compromising the tracking performance in the square-following task. However, when visual feedback is removed in the presence of the fixtures, the square tracking performance improves. The results also indicate a strong relationship between user dominance and tracking error only when the experiment is time-limited.

Reducing Overshoot in Human-Operated Flexible Systems

Input shaping accomplishes vibration reduction by slightly increasing the acceleration and deceleration periods of the command. The increase in the deceleration period can lead to system overshoot. This paper presents a new class of reduced-overtravel input shapers that are designed to reduce shaper-induced overtravel from human-operator commands. During the development of these new shapers, an expression for shaper-induced overtravel is introduced. This expression is used as an additional constraint in the input-shaper design process to generate the reduced-overtravel shapers. Experiments from a portable bridge crane verify the theoretical predictions of improved performance. Results from a study of eight industrial bridge crane operators indicate that utilizing the new reduced-overtravel input shapers dramatically reduces task completion time, while also improving positioning accuracy.