The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Piyush Chauhan - One of the best experts on this subject based on the ideXlab platform.
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decentralized computation and communication Intensive Task scheduling algorithm for p2p grid
International Conference on Computer Modelling and Simulation, 2012Co-Authors: Piyush ChauhanAbstract:Most of earlier grid scheduling algorithms were based on centralized scheduler. Relying on central scheduler yields not only central point of failure, also, it is not possible because of scalability and political issues in present day gigantic grid systems. Hence, meta-schedulers came into limelight. However many authors recognizes limitations of hierarchical grid scheduling and proposed peer-to-peer (P2P) techniques, which have potential for grid scheduling. In this paper, new decentralized scheduling algorithm is proposed for P2P grid systems. In this method, an independent Task sovereignly selects a most suitable grid node based on local information of immediate neighbors. A vital feature of this method is that it can schedule both computation Intensive and communication Intensive Tasks to make grid system's workload balanced.
Ilsun You - One of the best experts on this subject based on the ideXlab platform.
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data Intensive Task scheduling for heterogeneous big data analytics in iot system
Energies, 2020Co-Authors: Liangyuan Wang, Jemal H Abawajy, Xiaolin Qin, Giovanni Pau, Ilsun YouAbstract:Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-Intensive services. Hence, the data center may host heterogeneous big data analysis Tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online Tasks in a short time. In this paper, we investigate the heterogeneous Task scheduling problem to reduce the global Task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the Task execution for heterogeneous Tasks respectively based on the data locality feature, which also indicate the relationship among the Tasks, data blocks and servers. We propose a heterogeneous Task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote Task execution and data migration, which could improve the data locality and reduce Task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall Task execution time. The algorithm also shows acceptable fairness for the heterogeneous Tasks.
Stacy L Fritz - One of the best experts on this subject based on the ideXlab platform.
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participants perspectives on the feasibility of a novel Intensive Task specific intervention for individuals with chronic stroke a qualitative analysis
Physical Therapy, 2013Co-Authors: Angela R Merlo, Ashley Goodman, Bruce A Mcclenaghan, Stacy L FritzAbstract:Background Evidence-based practice promotes patient-centered care, yet the majority of rehabilitative research fails to take patient perspectives into consideration. Qualitative research provides a unique opportunity for patients to express opinions and provide valuable insight on intervention processes. Objective The purpose of this study was to assess the feasibility of a novel, Intensive, Task-specific intervention from the patient's perspective. Design A phenomenological approach to qualitative inquiry was used. Methods Eight individuals with chronic stroke participated in an Intensive intervention, 3 hours per day for 10 consecutive days. Participants were interviewed twice regarding their impressions of the therapy, and a focus group was conducted with participants and family members. Data analysis included an analytical thematic approach. Results Five major themes arose related to the feasibility of the intervention: (1) a manageable amount of fatigue; (2) a difficult, yet doable, level of intensity; (3) a disappointingly short therapy duration; (4) enjoyment of the intervention; and (5) muscle soreness. Conclusions The findings suggest that participants perceived this novel and Intensive, Task-specific intervention as a feasible therapeutic option for individuals with chronic stroke. Despite the fatigue and muscle soreness associated with Intensive rehabilitation, participants frequently reported enjoying the therapy and stated disappointment with the short duration (10 days). Future research should include a feasibility trial of longer duration, as well as a qualitative analysis of the benefits associated with the intervention.
Kara Nowak - One of the best experts on this subject based on the ideXlab platform.
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effects of an Intensive Task specific rehabilitation program for individuals with chronic stroke a case series
Disability and Rehabilitation, 2010Co-Authors: Stephanie A Combs, Stephanie P Kelly, Rebecca Barton, Megan Ivaska, Kara NowakAbstract:Purpose. The purpose of this case series was to determine feasibility and evaluate changes in activity and participation outcomes in persons with chronic stroke after an Intensive, Task-specific rehabilitation program incorporating whole-body and client-centred interventions.Method. Participants with chronic stroke (N = 12) who were ambulatory and had at least minimal arm/hand function were recruited. The program included whole-body goal-focused activities, gait training and strengthening exercises for 4 h, 5 days per week for 2 weeks. Daily educational sessions and a home activities program were also included. Activity-based measures including the Wolf motor function test, Berg balance scale, timed up and go test and 6-min walk test and participation-based measures including the Stroke Impact Scale and Canadian Occupational Performance Measure were collected at pre-test, immediate post-test and 5-month retention.Results. The effect of the intervention on participation-based outcomes was much greater than...
Lester James V Miranda - One of the best experts on this subject based on the ideXlab platform.
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a deep learning approach based on stacked denoising autoencoders for protein function prediction
Computer Software and Applications Conference, 2018Co-Authors: Lester James V MirandaAbstract:Predicting protein functions is a fundamental Task with applications in medicine and healthcare. However, the accelerating pace of protein-discovery renders slow and expensive biochemical techniques unsustainable. Machine learning is suitable for such data-Intensive Task, but the presence of noise in protein datasets adds another level of difficulty. Hence, we propose a deep learning system based on a stacked denoising autoencoder that extracts robust features to improve predictive performance. We then feed the resulting features to a multilabel support-vector machine for classification. We evaluated on two protein benchmarks, and experimental results show that our system consistently produced the best performance against techniques that do not have a denoising or feature learning capability. This research demonstrates that learning robust representations from raw data can benefit the process of predicting protein functions.