Adaptive resource prefetching with spatial–temporal and topic information for educational cloud storage systems

Huang, Qionghao; Huang, Changqin; Huang, Jin; Fujita, Hamido

doi:10.1016/j.knosys.2019.05.034

Cited by 15 publications

(6 citation statements)

References 43 publications

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“…Each user's local storage stores some resources, which can be pictures, audio, video, etc. [13][14][15]. Each resource can be shared through the Internet "O2O mode" to users who need this resource.…”

Section: Resource Sharing Model Of Distancementioning

confidence: 99%

Remote English Teaching Resource Sharing Based on Internet O2O Model

Hou

2022

Scientific Programming

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Aiming at the problems of low success rate, delay, and high communication cost in distance English teaching resource sharing, this paper puts forward a method of distance English teaching resource sharing based on Internet O2O mode. Based on the model of distance English teaching resource sharing, this paper designs four processes: query, reply, resource substitution, and resource sharing optimization. Experimental results show that the proposed method can achieve high success rate of resource sharing, low latency, communication cost, and high transmission efficiency. Therefore, it is an effective method.

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Section: Resource Sharing Model Of Distancementioning

confidence: 99%

Remote English Teaching Resource Sharing Based on Internet O2O Model

Hou

2022

Scientific Programming

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“…Topic models aim to automatically cluster words and expressions patterns for characterizing documents ( Xun et al, 2017 ; Lee and Kang, 2018 ; Hu et al, 2019 ). Recently, to understand the hidden semantics of spatial entities, many researchers applied topic models in the spatio-temporal data mining domain ( Zheng et al, 2017 ; Huang et al, 2019 ; Huang et al, 2020 ). For instance, Zhao et al discovered representative and interpretable human activity patterns from transit data automatically by a spatio-temporal topic model ( Zhao et al, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

Towards Semantically-Rich Spatial Network Representation Learning via Automated Feature Topic Pairing

et al. 2021

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Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.

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“…With the development of adaptive learning technologies in the world, Chinese scientists have also contributed to their research mainly in the field of computer science. They have examined such issues as: adaptive resource prefetching with spatial-temporal and topic information for educational cloud storage [17], adaptive testing [36], complex adaptive systems theory [37], providing an adaptive online course in accordance with the knowledge of the learner [35] and others.…”

Section: Meta-analysis Of the Papers On E-learningmentioning

confidence: 99%

Current State and Development Trends of E-Learning in China

Osadcha

Osadchyi

Spirin

2021

ITLT

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The emergence of new e-learning technologies requires a rethinking of their implementation in higher education. To fill this gap, this study analyzes the experience of using and creating e-learning tools in Chinese higher education institutions over the past 5 years. The survey found that the most common educational technologies in Chinese higher education are blended learning, collaborative learning, modified classes, micro-learning and adaptive learning, as well as e-learning tools such as mobile technology and mass open online courses. A survey of university faculty and students explored the practicalities of using e-learning tools in Chinese higher education. The results of the analysis of 20 responses from teachers and 16 responses from undergraduate and postgraduate students showed the following: the teachers have the opportunity to organize e-learning at their university, blended learning is mainly implemented in the learning process, teachers are not limited in choosing e-learning tools: video tools, messengers, social networks and e-learning tools such as Articulate 360 and Adapt. It has been proven that video lectures with a higher level of teacher expression were better than those that had a normal level of teacher expression and only audio, in terms of improving the level of students’ emotional and learning satisfaction. Accordingly, the teacher's facial expression plays a key role in teaching students online. A number of tasks have been proposed that will contribute to the development of e-learning in Chinese higher education. The authors emphasize that in order to develop e-learning in Chinese higher education, it is necessary to pay attention to the following tasks: understanding and studying trends in the ICT development in education; targeting e-learning strategies at improving learning interests; constant monitoring and updating of software and hardware of higher educational institutions for introduction of new technologies in higher education; development and distribution of platforms with simple software interfaces for creating distance learning courses; development of information resources.

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Adaptive resource prefetching with spatial–temporal and topic information for educational cloud storage systems

Cited by 15 publications

References 43 publications

Remote English Teaching Resource Sharing Based on Internet O2O Model

Remote English Teaching Resource Sharing Based on Internet O2O Model

Towards Semantically-Rich Spatial Network Representation Learning via Automated Feature Topic Pairing

Current State and Development Trends of E-Learning in China

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