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Full-Text Articles in Social and Behavioral Sciences
Real-Time Management Of Groundwater Resource Based On Wireless Sensor Networks, Qingguo Zhou, Chong Chen, Gaofeng Zhang, Huaming Chen, Dan Chen, Yingnan Yan, Jun Shen, Rui Zhou
Real-Time Management Of Groundwater Resource Based On Wireless Sensor Networks, Qingguo Zhou, Chong Chen, Gaofeng Zhang, Huaming Chen, Dan Chen, Yingnan Yan, Jun Shen, Rui Zhou
Faculty of Engineering and Information Sciences - Papers: Part B
Groundwater plays a vital role in the arid inland river basins, in which the groundwater management is critical to the sustainable development of area economy and ecology. Traditional sustainable management approaches are to analyze different scenarios subject to assumptions or to construct simulation–optimization models to obtain optimal strategy. However, groundwater system is time-varying due to exogenous inputs. In this sense, the groundwater management based on static data is relatively outdated. As part of the Heihe River Basin (HRB), which is a typical arid river basin in Northwestern China, the Daman irrigation district was selected as the study area in this …
Towards Massive Data And Sparse Data In Adaptive Micro Open Educational Resource Recommendation: A Study On Semantic Knowledge Base Construction And Cold Start Problem, Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Fang Dong, Dongming Xu, Jun Shen
Towards Massive Data And Sparse Data In Adaptive Micro Open Educational Resource Recommendation: A Study On Semantic Knowledge Base Construction And Cold Start Problem, Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Fang Dong, Dongming Xu, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
Micro Learning through open educational resources (OERs) is becoming increasingly popular. However, adaptive micro learning support remains inadequate by current OER platforms. To address this, our smart system, Micro Learning as a Service (MLaaS), aims to deliver personalized OER with micro learning to satisfy their real-time needs.
Gscheduler: Optimizing Resource Provision By Using Gpu Usage Pattern Extraction In Cloud Environment, Zhuqing Xu, Fang Dong, Jiahui Jin, Junzhou Luo, Jun Shen
Gscheduler: Optimizing Resource Provision By Using Gpu Usage Pattern Extraction In Cloud Environment, Zhuqing Xu, Fang Dong, Jiahui Jin, Junzhou Luo, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
GPU-based clusters are widely chosen for accelerating a variety of scientific applications in high-end cloud environments. With their growing popularity, there is a necessity for improving the system throughput and decreasing the turnaround time for co-executing applications on the same GPU device. However, resource contention among multiple applications on a multi-tasked GPU leads to the performance degradation of applications. Previous works are not accurate enough to learn the characteristics of GPU application before execution, or cannot get such information timely, which may lead to misleading scheduling decisions. In this paper, we present GScheduler, a framework to detect and reduce interference …
Assisting Open Education Resource Providers And Instructors To Deal With Cold Start Problem In Adaptive Micro Learning: A Service Oriented Solution, Geng Sun, Tingru Cui, Dongming Xu, Huaming Chen, Shiping Chen, Jun Shen
Assisting Open Education Resource Providers And Instructors To Deal With Cold Start Problem In Adaptive Micro Learning: A Service Oriented Solution, Geng Sun, Tingru Cui, Dongming Xu, Huaming Chen, Shiping Chen, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Organizing Online Computation For Adaptive Micro Open Education Resource Recommendation, Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Dongming Xu, Jun Shen
Organizing Online Computation For Adaptive Micro Open Education Resource Recommendation, Geng Sun, Tingru Cui, Ghassan Beydoun, Shiping Chen, Dongming Xu, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
Our previous work, Micro Learning as a Service (MLaaS), aimed to deliver adaptive micro open education resources (OERs). However, relying solely on the offline computation, the recommendation lacks rationality and timeliness. It is also difficult to make the first recommendation to a new learner. In this paper we introduce the organization of the online computation of the MLaaS. It targets at solving the cold start problem due to the shortage of learner information and real-time updates of the learner-micro OER profile