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ICT Paradox: Cost Efficiency of Web Based Learning
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作者 Tety Elida 《Chinese Business Review》 2011年第3期233-238,共6页
Indonesia government in this term Directorate General of Higher Education providing grants for ICT infrastructttres supplied through all bequest competitions. The kinds and amount of the grants are various which can b... Indonesia government in this term Directorate General of Higher Education providing grants for ICT infrastructttres supplied through all bequest competitions. The kinds and amount of the grants are various which can be used to provide hardware to make ICT based teaching materials. The government issued a huge amount of funds, therefore, it must be balanced with an optimal utilization. This research aims to analyze cost efficiency of dual mode web-based learning in Indonesia. The object of this research is four higher educations in Indonesia. The analysis is done by comparing the average cost per student and the average cost per subject among 4 institutions. The result showed that there are institutions that issued higher cost compared with the others in producing the same learning media. 展开更多
关键词 cost efficiency cost of learning ICT paradox
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Leveraging reinforcement learning for dynamic traffic control:A survey and challenges for field implementation 被引量:1
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作者 Yu Han Meng Wang Ludovic Leclercq 《Communications in Transportation Research》 2023年第1期8-20,共13页
In recent years,the advancement of artificial intelligence techniques has led to significant interest in reinforcement learning(RL)within the traffic and transportation community.Dynamic traffic control has emerged as... In recent years,the advancement of artificial intelligence techniques has led to significant interest in reinforcement learning(RL)within the traffic and transportation community.Dynamic traffic control has emerged as a prominent application field for RL in traffic systems.This paper presents a comprehensive survey of RL studies in dynamic traffic control,addressing the challenges associated with implementing RL-based traffic control strategies in practice,and identifying promising directions for future research.The first part of this paper provides a comprehensive overview of existing studies on RL-based traffic control strategies,encompassing their model designs,training algorithms,and evaluation methods.It is found that only a few studies have isolated the training and testing environments while evaluating their RL controllers.Subsequently,we examine the challenges involved in implementing existing RL-based traffic control strategies.We investigate the learning costs associated with online RL methods and the transferability of offline RL methods through simulation experiments.The simulation results reveal that online training methods with random exploration suffer from high exploration and learning costs.Additionally,the performance of offline RL methods is highly reliant on the accuracy of the training simulator.These limitations hinder the practical implementation of existing RL-based traffic control strategies.The final part of this paper summarizes and discusses a few existing efforts which attempt to overcome these challenges.This review highlights a rising volume of studies dedicated to mitigating the limitations of RL strategies,with the specific aim of enhancing their practical implementation in recent years. 展开更多
关键词 Reinforcement learning Road traffic control learning cost Transferability Sim-to-real transfer
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Influences of misprediction costs on solar flare prediction 被引量:3
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作者 HUANG Xin WANG HuaNing DAI XingHua 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS 2012年第10期1956-1962,共7页
The mispredictive costs of flaring and non-flaring samples are different for different applications of solar flare prediction.Hence,solar flare prediction is considered a cost sensitive problem.A cost sensitive solar ... The mispredictive costs of flaring and non-flaring samples are different for different applications of solar flare prediction.Hence,solar flare prediction is considered a cost sensitive problem.A cost sensitive solar flare prediction model is built by modifying the basic decision tree algorithm.Inconsistency rate with the exhaustive search strategy is used to determine the optimal combination of magnetic field parameters in an active region.These selected parameters are applied as the inputs of the solar flare prediction model.The performance of the cost sensitive solar flare prediction model is evaluated for the different thresholds of solar flares.It is found that more flaring samples are correctly predicted and more non-flaring samples are wrongly predicted with the increase of the cost for wrongly predicting flaring samples as non-flaring samples,and the larger cost of wrongly predicting flaring samples as non-flaring samples is required for the higher threshold of solar flares.This can be considered as the guide line for choosing proper cost to meet the requirements in different applications. 展开更多
关键词 flares: forecasting sun: magnetic field cost sensitive learning
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