Reduce Task Completion Time Research Articles

Polyarchy and project performance in open, distributed forms of innovation

Although research on open collaboration for innovation has generally focused on the voluntary participation of individuals who are not strictly governed by formal authority concentrated at higher levels of organizational hierarchy, more recent studies highlight the importance of exploring how their tasks should be organized to enhance performance outcomes. This study extends this line of work by conceptualizing polyarchy as a decentralized decision-making structure that directs how core members of a project team screen a large collection of novel ideas generated by team members, and empirically examining whether and how polyarchy affects task completion time. In the context of online projects for open-source software development at GitHub, we find that a polyarchical decision-making structure reduces task completion time. Closer examination also shows that this positive effect on decision-making speed becomes stronger when tasks are explorative rather than exploitative in nature. Implications for open, distributed models of innovation and nontraditional forms of decision-making structures are discussed.

Multirobot Allocation In A Flexible Manufacturing System, Using Reinforcement Learning For Decision-Making, Case of Study

This paper presents a reinforcement learning approach for decision-making in assigning multiple robots in a flexible manufacturing system. The algorithm allows for learning the optimal action policy for assigning tasks to multiple robots, outperforming other methods. The case study involved three robots and four tasks, some requiring cooperation. The study demonstrates the effectiveness of the approach in comparison to other methods and the results indicate that the reinforcement learning approach increases production efficiency and reduces task completion time compared to traditional assignment methods.

Human–Robot Shared Control Based on Locally Weighted Intent Prediction for a Teleoperated Hydraulic Manipulator System

Human–robot shared control provides a promising opportunity to improve operational safety and efficiency for a teleoperated hydraulic manipulator system that works in unstructured environments, such as rescue response and underwater exploration. However, the poor dynamic of the heavy-duty manipulator (e.g., oscillation tendency and response delay) brings a challenging problem on intent prediction and effective assistance. To overcome this problem, a blended shared control method is proposed via locally weighted intent prediction. First, a task learning method from demonstration is presented by task-based action primitives with the Bayesian nonparametric clustering to extract subgoals of the remote arm trajectory, especially a locally weighted chattering cancellation method is proposed to reduce unintended action primitives due to velocity fluctuations. A dynamic intent prediction method is presented based on the empirical and real-time motion information by introducing an iterative transition matrix and a locally weighted dynamic angle, respectively. A prediction-based arbitration rule is finally established to blend the controllers of the human and robot seamlessly. Comparative tests in two typical scenes were carried out on a teleoperated hydraulic manipulator system. The results validate the proposed shared control method with fewer collision accidents and reduced task completion time, and subjective feelings on operation are also improved.

Design and Evaluation of a Silent Speech-Based Selection Method for Eye-Gaze Pointing

We investigate silent speech as a hands-free selection method in eye-gaze pointing. We first propose a stripped-down image-based model that can recognize a small number of silent commands almost as fast as state-of-the-art speech recognition models. We then compare it with other hands-free selection methods (dwell, speech) in a Fitts' law study. Results revealed that speech and silent speech are comparable in throughput and selection time, but the latter is significantly more accurate than the other methods. A follow-up study revealed that target selection around the center of a display is significantly faster and more accurate, while around the top corners and the bottom are slower and error prone. We then present a method for selecting menu items with eye-gaze and silent speech. A study revealed that it significantly reduces task completion time and error rate.

Load Balancing Algorithms for Hadoop Cluster in Unbalanced Environment.

Considering that in the process of job scheduling, the cluster load should be prebalanced rather than remedied when the load is seriously unbalanced; therefore, in this paper, the task scheduling flow of the Hadoop cluster is analyzed deeply. On the Hadoop platform, a self-dividing algorithm is proposed for load balancing. An intelligent optimization algorithm is used to solve load balance. A dynamic feedback load balancing scheduling method is proposed from the point of view of task scheduling. In order to solve the shortcoming of the fair scheduling algorithm, this paper proposes two ways to improve the resource utilization and overall performance of Hadoop. When the mapping task is completed and the tasks to be reduced are assigned, the task assignment is based on the performance of the nodes to be reduced. It gives full play to the advantages of the ant colony algorithm and the hive colony algorithm so that the fusion algorithm can better deal with load balance. Then, three existing scheduling algorithms are introduced in detail: single queue scheduling, capacity scheduling, and fair scheduling. On this basis, an improved task scheduling strategy based on genetic algorithm is proposed to allocate and execute application tasks to reduce task completion time. The experiment verifies the effectiveness of the algorithm. The LBNP algorithm greatly improves the efficiency of reducing task execution and job execution. The delay capacity scheduling algorithm can ensure that most tasks can achieve localization scheduling, improve resource utilization, improve load balance, and speed up job completion time.

Reliable Task Allocation for Time-Triggered IoT-WSN Using Discrete Particle Swarm Optimization

A wireless sensor network (WSN) constitutes of nodes that are used to sense a certain physical environment phenomena and sends these collected data to a remote node for further processing and decision making. With the proliferation of Internet of Things (IoT), WSN is evolving to be used as reliable, energy efficient, cost effective, and scalable network infrastructure for many IoT applications. To have a robust WSN with IoT (IoT-WSN), it is a challenge to allocate the tasks among different sensor nodes because the distribution of dependent tasks among wireless nodes must satisfy precedence and time constraints, minimize energy consumption, and prevent signal interference. The problem becomes more complex if a system requires high reliability in the event of link or node failures. This article presents an offline discrete particle swarm optimization algorithm for reliable task allocation (DPSO-TA) in IoT-WSN. DPSO-TA defines a utility function to optimize the task allocation problem by iteratively trying to improve the solution. The defined function is designed to satisfy goals, including saving energy, reducing task completion time, and minimizing the failure rates. A load balance mechanism is applied to make balancing of the tasks on several hosts. During the task allocation process, the frame replication and elimination for reliability (FRER) approach is used to support flow fault tolerance by replicating transmitted flows over redundant routes. DPSO-TA considers the periodicity of the time-trigged (TT) flows and applies a physical interference model to prevent signal interference on the transmitted flows through assigning them to conflict-free time slots. The analysis and simulation results show the optimization of DPSO-TA on timeliness, deadline missing ratio, failure rates, and energy efficiency comparing it with algorithms that allocate tasks to hosts that produce the minimum completion time or that use a traditional procedure to find the most suitable nodes during the task allocation process.

How Far Can We Go in Compute-less Networking: Computation Correctness and Accuracy

Emerging applications such as augmented reality and tactile Internet are compute-intensive and latency sensitive, which hampers their running in constrained end devices alone or in the distant cloud. The stringent requirements of such applications have given rise to the realization of edge computing, wherby computation is offloaded near to users. Compute-less networking is an extension of edge computing that aims to reduce computation and abridge communication by adopting in-network computing and computation reuse. Computation reuse aims to cache the result of computations and use them to perform similar tasks in the future and, therefore, avoid redundant calculations and optimize the use of resources. In this article, we focus on the correctness of the final output produced by computation reuse. Since the input might not be identical but similar, the reuse of previous computation raises questions about the accuracy of the final results. To this end, we implement a proof of concept to study and gauge the effectiveness and efficiency of computation reuse. We are able to reduce task completion time by up to 80 percent while ensuring high correctness. We further discuss open challenges and highlight future research directions.

An Efficient Computing Offloading Scheme Based on Privacy-Preserving in Mobile Edge Computing Networks

Computation offloading is an important technology to achieve lower delay communication and improve the experience of service (EoS) in mobile edge computing (MEC). Due to the openness of wireless links and the limitation of computing resources in mobile computing process, the privacy of users is easy to leak, and the completion time of tasks is difficult to guarantee. In this paper, we propose an efficient computing offloading algorithm based on privacy-preserving (ECOAP), which solves the privacy problem of offloading users through the encryption technology. To avoid the algorithm falling into local optimum and reduce the offloading user energy consumption and task completion delay in the case of encryption, we use the improved fast nondominated sorting genetic algorithm (INSGA-II) to obtain the optimal offloading strategy set. We obtain the optimal offloading strategy by using the methods of min-max normalization and simple additive weighting based on the optimal offloading strategy set. The ECOAP algorithm can preserve user privacy and reduce task completion time and user energy consumption effectively by comparing with other algorithms.

A Self-Learning Strategy for Task Offloading in UAV Networks

The edge computing paradigm has opened new opportunities for IoT devices, which can be used in novel applications involving heavy processing of data. Typical and common examples of IoT devices are the Unmanned Aerial Vehicles (UAVs), which are deployed for surveillance and environmental monitoring and are attracting increasing attention because of their ease deployment. However, their limited capacity, e.g., battery, forces the design of an edge-assisted solution, where heavy tasks are offloaded to the edge cloud. To solve the problem of task offloading from UAV to the closest edge computation, many proposals have appeared, mainly based on a Reinforcement Learning (RL) formulation. While these solutions successfully learn how to reduce task completion time in the UAV context, some limitations appear when these models are applied in real scenarios, given the memory-hungry nature of RL. To this end, we propose a simple yet effective formalization that still enables a learning process, but reduces the required information and the training time. Our evaluation results confirm our hypothesis, showing a marked improvement when compared to other RL-based strategies and deep learning-based solutions.

The effects of physical training versus combined action observation and motor imagery in conjunction with physical training on upper-extremity performance

Introduction Combined action observation and motor imagery training (AO+MI training), which involves motor imagery during action observation and physical training, has been attracting attention as an effective strategy for learning motor skills. However, little has been reported on the effects of AO+MI training. In the present study, we compared the effects of AO+MI training to the effects of physical training on upper-extremity performance. Materials and methods Ninety-six healthy participants were randomly assigned to either the control group or the experimental group. Sport stacking, which is often used to evaluate upper-extremity performance, was adopted for the task. The experiment was scheduled for three days. The training was 20 min per day. The control group performed only physical training, while the experimental group performed four 5-min AO+MI training sessions. Time taken to complete a sport stacking try (task completion time) was defined as the index of speed of upper-extremity performance and number of fallen cups as the index of its accuracy. The outcomes within each group and between the two groups were compared. Results Both AO+MI training and physical training showed reduced task completion time and increased number of fallen cups. There were no significant differences in the degree of changes between the groups. Conclusion Results from the present study showed that AO+MI training and physical training had almost the same influence on upper-extremity performance in the early stages of learning sport stacking. This result suggests that AO+MI training may be an effective and low-burden training method for participants.

EA-DFPSO: An intelligent energy-efficient scheduling algorithm for mobile edge networks

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals. Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays. Compute nodes are usually distributed in edge environments, enabling crucially efficient task scheduling among those nodes to achieve reduced processing time. Moreover, it is imperative to conserve edge server energy, enhancing their lifetimes. To this end, this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization (EA-DFPSO) that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time. The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks. Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.

Resource Allocation Using Particle Swarm Optimization Algorithm in Cloud Computing

Abstract: Cloud computing is now widely used in organisations and bussiness firms because of its on-demand accessibility of framework assets, web innovation, and pay-as-you-go principle. Despite of numerous advantages of cloud computing, such as availability and accessibility, it also has few significant drawbacks. The most fundamental issue is the resource management, where Cloud computing provides IT assets such as memory, network, storage, and so on based on a virtualization concept and a pay-as-you-go model. Much research has gone into the administration of these assets. The suggested framework employs the particle swarm optimization algorithm method for assigning and performing the tasks of an application. The proposed algorithm will likely reduce task completion time, cost by utilizing the maximum resources. The proposed system is evaluated using the cloudsim toolkit. Keywords: Particle swarm optimization algorithm, Resource allocation, Cloud computing.

Mobility-aware dynamic offloading strategy for C-V2X under multi-access edge computing

Multi-access edge computing (MEC) technology is envisioned as a promising paradigm to achieve the user needs of low-latency applications. Complex computation tasks are offloaded from resource-constrained devices to resource-rich devices to reduce task completion delays. However, given the high-speed mobility of vehicles, the traditional MEC network architecture is insufficient for the Internet of Vehicle (IoV) computing scenarios. Besides, many resource-rich idle vehicles can be used as mobile servers to perform computing tasks, extending the choices for vehicle users. To better integrate MEC and IoV technologies, we introduce a Cellular-V2X-based MEC offloading scenario, enabling MEC servers and vehicle nodes in the 5G cellular network environment to perform offloading cooperatively. For a typical delay-sensitive task, taking the Augmented Reality (AR) application as an example, we divide the task into multiple subtasks with linear correlation. A mobility-aware dynamic offloading algorithm (MADO) is proposed to minimize the impact of vehicular mobility on offloading. The algorithm is used to find an optimal resource allocation, which reduces the overhead of the entire offloading process. It continuously updates the resource selection and the offloading strategy with the vehicular location changes. Simulation results corroborate that our proposed MADO algorithm can effectively reduce task completion time, improve task completion success rate and adapt to a dynamic environment.

Bandwidth-Aware Rescheduling Mechanism in SDN-Based Data Center Networks

Recently, with the increase in network bandwidth, various cloud computing applications have become popular. A large number of network data packets will be generated in such a network. However, most existing network architectures cannot effectively handle big data, thereby necessitating an efficient mechanism to reduce task completion time when large amounts of data are processed in data center networks. Unfortunately, achieving the minimum task completion time in the Hadoop system is an NP-complete problem. Although many studies have proposed schemes for improving network performance, they have shortcomings that degrade their performance. For this reason, in this study, we propose a centralized solution, called the bandwidth-aware rescheduling (BARE) mechanism for software-defined network (SDN)-based data center networks. BARE improves network performance by employing a prefetching mechanism and a centralized network monitor to collect global information, sorting out the locality data process, splitting tasks, and executing a rescheduling mechanism with a scheduler to reduce task completion time. Finally, we used simulations to demonstrate our scheme’s effectiveness. Simulation results show that our scheme outperforms other existing schemes in terms of task completion time and the ratio of data locality.

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks.

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

Operation of button feedback design within TV remote control applications for middle‐aged and older adult

AbstractTV remote control interfaces have gradually changed from traditional physical buttons to touch screens. However, vision, touch, and even learning abilities can become impacted with increasing age, resulting in an impaired ability to operate these interfaces and issues such as an increase in mistouches on the device and an increase in searching time. Fifty middle‐aged and older adult between the ages of 45 and 64 were recruited to investigate whether changes to the design of buttons and sound effects influence the time and error rates in interface task operations. The results showed that buttons whose background colors changed when pressed reduced task completion time, whereas buttons whose outlines thickened did not. In addition, on/off keys should be designed with reference to current remote control buttons and styled in a similar manner. Finally, sound feedback design may be added in the channel selection function to reduce operation time and error rates.

Simulation-Based Arthroscopic Skills Using a Spaced Retraining Schedule Reduces Short-Term Task Completion Time and Camera Path Length

To investigate whether acquiring basic knee arthroscopic skills via a spaced retraining schedule could prevent skills deterioration and achieve further skills improvement. In the learning phase, 16 residents with no previous hands-on experience in practicing arthroscopic skills were asked to perform basic arthroscopic tasks on a simulator until they attained perfect scores in each task. Immediately after completing the learning phase, a pretest was performed to assess their performance. Next, they were randomly assigned into 2 groups. The spaced retraining group, which undertook a spaced repetitive training phase with a fixed-time interval, returned on days 2, 4 and 6 to repeat the same tasks for 20 minutes per day, whereas the control group did nothing. On day 7, all participants performed a posttest. A 2× 2 mixed analysis of variance model was used for statistical analysis. Significant differences between the 2 groups were found in task completion time (P= .003) and camera path length (P= .043) but not cartilage injury (P= .186). Residents in the spaced retraining group decreased their task completion time (163.2 ± 23.9 seconds) whereas the task time in the control group increased (351.3 ± 25.5 seconds). The same pattern was found with the camera path length. Implementing a spaced retraining schedule in 1 week resulted in a reduced task completion time and camera path length but no significant reduction in cartilage injury. It appears that introducing a spaced retraining schedule to retain arthroscopic skills acquired through massed learning may be advantageous. In consideration of the training time available to residents and the trend toward massed learning, this spaced retraining schedule may offer a cost-effective and convenient way for residents to maintain and improve their basic arthroscopic skills with no significant increase in time invested.

Put That Needle There

Informed by modern imaging techniques, current medical navigation systems support physicians during a variety of interventions, such as needle-based operations. During these, an abundance of information is often displayed on monitors placed in positions that are uncomfortable for the operator to view. In this article, we address these issues with the concept and prototype of a customized flexible display that is placed on the patient’s body to provide the essential information at just the right location. We present an empirical evaluation comparing the flexible display against a control condition using a standard interventional monitor setup and an additional condition that combines both. Our results show that the flexible display significantly reduces task load while improving overall usability. Furthermore, we found indications that the flexible display reduces task completion time while also observing a negative effect on accuracy, which needs to be balanced carefully.

Comparison of spatial and temporal interaction techniques for 3D audio trajectory authoring

With the popularity of immersive media, developing usable tools for content development is important for the production process. In the context of 3D audio production, user interfaces for authoring and editing 3D audio trajectories enable content developers, composers, practitioners, and recording and mixing engineers to define how audio sources travel in time. However , common interaction techniques in 3D audio production tools can make the workflow of this task tedious and difficult to accomplish. This study investigates this problem by classifying the atomic tasks (spatially and temporally) of a general composite task of authoring 3D audio trajectories and then evaluating different interaction techniques across these tasks. Common graphical user interfaces were compared with input devices having varying degrees-of-freedom for spatial atomic tasks in order to investigate the effect of direct manipulation and integrality of interaction techniques. Continuous and discrete interaction techniques were compared for temporal tasks in order to investigate the effect of direct manipulation. Results suggest that interaction techniques with high degrees of integrality and direct manipulation reduce task completion time compared to standard GUI techniques. The design of temporal tasks can create a visual bias, and discrete-time controls can be a suitable method for traversing a small number of control points. These results and further observations provide directions on the study of interaction technique design for 3D audio tools, which in turn should improve workflows of 3D audio content creation.

A Heuristic Approach to Overcome Architectural Barriers Using a Robotic Wheelchair.

The Mobility Enhancement roBotic (MEBot) wheelchair was developed to improve the safety and accessibility of wheelchair users when facing architectural barriers. MEBot uses pneumatic actuators attached to its frame and six wheels to provide curb ascending/descending for heights up to 20.3 cm. To improve MEBot's application, this study used a heuristic approach with power wheelchair users to evaluate and improve the MEBot application at different curb heights. Wheelchair users were trained on MEBot's features to operate its curb ascending/descending application. Three trials were carried out with wheelchair users ascending and descending three curbs of different height. Quantitative variables were analyzed to improve the sequential steps to ascend/descend curbs. Additionally, the application's effectiveness and efficiency were measured by the number of completed tasks, change in seat angle, and task completion time. Results showed that participants completed each trial and applied alternative strategies to traverse different curb heights. Furthermore, results suggested the combination and/or re-arrangement of steps to reduce task completion time. MEBot demonstrated its effectiveness to ascend/descend different curb heights with a heterogeneous participant sample. Future work will incorporate participant's most efficient strategies to improve the ascending/ascending process and the efficiency of the MEBot application.