“I Got Some Free Time”: Investigating Task-execution and Task-effort Metrics in Mobile Crowdsourcing Tasks

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

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In-Lecture Media Use and Academic Performance: Investigating Demographic and Intentional Moderators

South African Computer Journal ◽

10.18489/sacj.v30i1.434 ◽

2018 ◽

Vol 30 (1) ◽

Cited By ~ 4

Author(s):

Douglas A. Parry ◽

Daniel B. Le Roux

Keyword(s):

Academic Performance ◽

University Students ◽

Digital Media ◽

Empirical Investigation ◽

Media Use ◽

Significant Negative Correlation ◽

Task Execution ◽

Communication Processes ◽

Undergraduate University Students ◽

And Task

The growing prevalence of continuous media use among university students in lecture environments has potential for detrimental effects. In this study we investigate the relationships between in-lecture media use and academic performance. Previous studies have shown that students frequently engage with digital media whilst in university lectures. Moreover, multitasking imposes cognitive costs detrimental to learning and task execution. We propose, accordingly, that the constant distractions created by digital media, interrupt the thought and communication processes of students during lectures and, subsequently, obstruct their ability to learn. To test this proposition we conducted a survey-based empirical investigation of digital media use and academic performance among undergraduate university students. A significant negative correlation was found between the number of in-lecture media use instances and academic performance. Furthermore, this effect was found to be pervasive independent of individual demographic factors and the intention with which a medium was used.

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Manipulation and Task Execution by Humanoids

Humanoid Robotics: A Reference ◽

10.1007/978-94-007-7194-9_31-1 ◽

2017 ◽

pp. 1-23

Author(s):

Kensuke Harada ◽

Máximo A. Roa

Keyword(s):

Task Execution ◽

And Task

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Optimal Task Partition with Delay Requirement in Mobile Crowdsourcing

Wireless Communications and Mobile Computing ◽

10.1155/2019/5216495 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12

Author(s):

Linbo Zhai ◽

Hua Wang ◽

Xiaole Li

Keyword(s):

Task Assignment ◽

Complex Task ◽

Process Data ◽

High Quality ◽

Mobile Crowdsourcing ◽

Mobile Worker ◽

Single Worker ◽

Complicated Task ◽

Task Partition ◽

And Task

Mobile crowdsourcing takes advantage of mobile devices such as smart phones and tablets to process data for a lot of applications (e.g., geotagging for mobile touring guiding monitoring and spectrum sensing). In this paper, we propose a mobile crowdsourcing paradigm to make a task requester exploit encountered mobile workers for high-quality results. Since a task may be too complex for a single worker, it is necessary for a task requester to divide a complex task into several parts so that a mobile worker can finish a part of the task easily. We describe the task crowdsourcing process and propose the worker arrival model and task model. Furthermore, the probability that all parts of the complicated task are executed by mobile workers is introduced to evaluate the result of task crowdsourcing. Based on these models, considering computing capacity and rewards for mobile workers, we formulate a task partition problem to maximize the introduced probability which is used to evaluate the result of task crowdsourcing. Then, using a Markov chain, a task partition policy is designed for the task requester to realize high-quality mobile crowdsourcing. With this task partition policy, the task requester is able to divide the complicated task into precise number of parts based on mobile workers’ arrival, and the probability that the total parts are executed by mobile workers is maximized. Also, the invalid number of task assignment attempts is analyzed accurately, which is helpful to evaluate the resource consumption of requesters due to probing potential workers. Simulations show that our task partition policy improves the results of task crowdsourcing.

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Creating a Truly Conscious AI – A Novel Approach

10.31234/osf.io/w5kq8 ◽

2019 ◽

Author(s):

Catarina Cunha

Keyword(s):

Visual Recognition ◽

Medical Diagnostics ◽

Healthcare Education ◽

Process Automation ◽

Electronic Trading ◽

Task Execution ◽

Daily Lives ◽

Novel Approach ◽

Level Performance ◽

And Task

The relatively new field of artificial intelligence (AI), which is defined as intelligence performed by machines, is crucial for progress in many disciplines in today's society, including medical diagnostics, electronic trading, robotic process automation in finance, healthcare, education, transportation and many more. However, until now, AIs were only capable of performing very specific tasks such as low-level visual recognition, speech recognition, coordinated motor control, and pattern detection. What we still need to achieve is a form of everyday human-level performance that is based on common sense, where AI are able to carry out adaptable planning and task execution and possess meaning-based natural language capabilities and generation. These are considered to be “conscious” or “creative” activities that are naturally part of our daily lives and which we execute without great mental effort. Developing conscious AI will allow us to gain knowledge and further our understanding about how consciousness works. In order to develop conscious and creative AI, machines must be self-aware; however, we hypothesize that current AI developments are skipping the most important step which will lead to AGIs: introspection (self-analysis and awareness).

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Quantifying differences between passive and task-evoked intrinsic functional connectivity in a large-scale brain simulation

10.1101/250894 ◽

2018 ◽

Author(s):

Antonio Ulloa ◽

Barry Horwitz

Keyword(s):

Computational Model ◽

Resting State ◽

Short Term Memory ◽

Brain Activity ◽

Brain Regions ◽

Intrinsic Activity ◽

Task Execution ◽

Short Term ◽

Visual Short Term Memory ◽

And Task

AbstractEstablishing a connection between intrinsic and task-evoked brain activity is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from non-task neural elements while giving us the effects of task execution on non-task regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step towards elucidating the intrinsic versus task-evoked connection. Here we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our Large-Scale Neural Modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of ten subjects performing passive fixation (PF), passive viewing (PV) and delay match-to-sample (DMS) tasks. We used the simulated BOLD fMRI time-series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain functional connectivity during task execution. Our results represent a step towards establishing a connection between intrinsic and task-related brain activity.Author SummaryStudies of resting-state conditions are popular in neuroimaging. Participants in resting-state studies are instructed to fixate on a neutral image or to close their eyes. This type of study has advantages over traditional task-based studies, including its ability to allow participation of those with difficulties performing tasks. Further, a resting-state neuroimaging study reveals intrinsic activity of participants’ brains. However, task-related brain activity may change this intrinsic activity, much as a stone thrown in a lake causes ripples on the water’s surface. Can we measure those activity changes? To answer that question, we merged a computational model of visual short-term memory (task regions) with an anatomical model incorporating major connections between brain regions (non-task regions). In a computational model, unlike real data, we know how different regions are connected and which regions are doing the task. First, we simulated neuronal and neuroimaging activity of both task and non-task regions during three conditions: passive fixation (baseline), passive viewing, and visual short-term memory. Then, applying graph theory to the simulated neuroimaging of non-task regions, we computed differences between the baseline and the other conditions. Our results show that we can measure changes in non-task regions due to brain activity changes in task-related regions.

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A - 12Aging, Initial Planning, and Task Execution

Archives of Clinical Neuropsychology ◽

10.1093/arclin/acy061.12 ◽

2018 ◽

Vol 33 (6) ◽

pp. 703-794

Author(s):

K Brown ◽

M Schmitter-Edgecombe

Keyword(s):

Task Execution ◽

And Task

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Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

Sensors ◽

10.3390/s19224856 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4856 ◽

Cited By ~ 1

Author(s):

Helen Harman ◽

Keshav Chintamani ◽

Pieter Simoens

Keyword(s):

Smart Home ◽

Smart Environments ◽

State Change ◽

Smart Environment ◽

Task Execution ◽

Estimation Task ◽

Planning Framework ◽

Robot Assistance ◽

Iot Devices ◽

And Task

By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices into the robot’s onboard planner increases the computational load, and thus can delay the execution of a task. Moreover, tasks may be frequently replanned due to the unanticipated actions of humans. Our framework aims to mitigate these inadequacies. In this paper, we propose a continual planning framework, which incorporates the sensing and actuation capabilities of IoT devices into a robot’s state estimation, task planing and task execution. The robot’s onboard task planner queries a cloud-based framework for actuators, capable of the actions the robot cannot execute. Once generated, the plan is sent to the cloud back-end, which will inform the robot if any IoT device reports a state change affecting its plan. Moreover, a Hierarchical Continual Planning in the Now approach was developed in which tasks are split-up into subtasks. To delay the planning of actions that will not be promptly executed, and thus to reduce the frequency of replanning, the first subtask is planned and executed before the subsequent subtask is. Only information relevant to the current (sub)task is provided to the task planner. We apply our framework to a smart home and office scenario in which the robot is tasked with carrying out a human’s requests. A prototype implementation in a smart home, and simulator-based evaluation results, are presented to demonstrate the effectiveness of our framework.

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