Cognitive-Caching: Cognitive Wireless Mobile Caching by Learning Fine-Grained Caching-Aware Indicators

Hao, Yixue; Chen, Min; Cao, Donggang; Zhao, Wenlai; Petrov, Ivan; Antonenko, Vitaly; Smeliansky, R.

doi:10.1109/mwc.001.1900273

Cited by 16 publications

(2 citation statements)

References 12 publications

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“…In the process of combining fine-grained parallel computing programming into the construction of a model of college students' mental health education, we also paid attention to some research works in the field related to mental health education. According to the literature [11], psychological education refers to the theoretical guidance about the comprehensive development of human beings and based on respecting the law of students' growth and success and the law of psychological development, psychological principles and methods are infiltrated into the whole process of education in colleges and universities, to realize the organic integration of "cultivating the mind" and "cultivating morality" and finally cultivate new people who can integrate into the society. In the end, we can cultivate new people who can integrate into the society.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Zhu

Xue

2022

Mathematical Problems in Engineering

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Mental health and mental health problems of college students are becoming more and more obvious, and there is more and more negative news caused by psychological problems, and society from all walks of life has given high attention to this problem. Given the new situations and new problems, how to keep up with the times and reform and innovate in the content, method, and path of psychological education in colleges and universities is an important work of ideological and political education in colleges and universities. Because fine-grained category information can provide rich semantic clues, fine-grained parallel computing techniques are widely used in tasks such as sensitive feature filtering, medical image classification, and dangerous goods detection. In this study, we adopt a fine-grained parallel computing programming approach and propose a multiobjective matrix regular optimization algorithm that can simultaneously perform the joint square root, low-rank, and sparse regular optimization for bilinear visual features, which is used to stabilize the higher-order semantic information in bilinear features, improve the generalization ability of features, and apply it to the construction of mental health education models for college students to promote the construction of mental health education bases, improve mental health education network platform, and strengthen the construction of mental health education data platform. A new practical aspect has been added to the abstract. The saliency-guided data augmentation method in this study is an improvement on random data augmentation but reduces the randomness in the data augmentation process and significantly improves the results. The best result belongs to SCutMix data augmentation, which improves by 1.9% compared to the baseline network.

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Section: Introductionmentioning

confidence: 99%

Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Zhu

Xue

2022

Mathematical Problems in Engineering

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“…In this case, the student must endure a long transmission delay and the system will cost more backhaul link load. Then, the key of MEC based online education is the caching strategy 4,5 …”

Section: Introductionmentioning

confidence: 99%

Mobile edge computation induced caching strategy for huge online education with college teachers and students

2020

Internet Technology Letters

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Via wireless network, mobile edge computation (MEC) provides computation ability to make user feel better. However, caching strategy restricts the performance of MEC. For instance, MEC requires to decide which course should be cached in huge asynchronous online education with college teachers and students. In order to solve the caching problem in asynchronous online education, we put the computation ability to network edge, then make the access points can analysis independently. In access points, we use a reduced support vector regression (rSVR) model to predict the popularity of local data. The popularity is used to improve the hit rate of the caching files. The rSVR model is learnt offline. Different SVR, rSVR is learnt on a small retained subset. Thus, learning rSVR is much faster and the rSVR's model is smaller than SVR's, which is helpful to improve the caching process. A real‐world asynchronous online learning dataset is used to verify effectiveness of our framework. The experimental results show that rSVR predicts popularity for local data is better than previous ones and our caching framework only requires near 50% storage space to maintain the same cache hit rate.

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Priority-MECE: A Mobile Edge Cloud Ecosystem Based on Priority Tasks Offloading

Xiong

Zhang

et al. 2022

Mobile Netw Appl

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Cognitive-Caching: Cognitive Wireless Mobile Caching by Learning Fine-Grained Caching-Aware Indicators

Cited by 16 publications

References 12 publications

Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Construction of a Mental Health Education Model for College Students Based on Fine-Grained Parallel Computing Programming

Mobile edge computation induced caching strategy for huge online education with college teachers and students

Priority-MECE: A Mobile Edge Cloud Ecosystem Based on Priority Tasks Offloading

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