Because of its many advantages, big data has been extending to various domains of science, health, education, and commerce. Despite its many applications, big data sharing typically suffers from some key issues, such as user control, lack of incentives, cost, and the right of data. This paper proposes a decentralized big data sharing prototype to improve the applications and services of big data. The method makes use of Ethereum blockchain and related technologies to systematically recommend the implementation guidelines. The research provides a detailed description of the design and implementation of each sublayer of a big data system. As the method is based on blockchain technology, the key technical points are properly addressed in each of the layers. For evaluation, relevant data were collected, and functional testing was performed. A comparison was performed about the sharing frequency and blockchain consensus performance of similar platforms. The dual mining node of the proposed prototype succeeded in processing 1366 blocks and 300 messages. A comparatively satisfactory file access time in the range of 10 m to 20 s and file transmission time between 100 m and 200 s were achieved. The results obtained show that this prototype can effectively verify the feasibility of the model, the layered architecture, and the related sharing mechanism. For the functional and performance testing, practical projects were implemented and evaluated. The promising results obtained testify that the research offers a theoretical background for innovative research in the domain and specialized guidelines for practical implementation.
With the rapid development of Internet technology and the popularity of 5G and broadband, online education in China, especially mobile online education, is in full swing. Based on the development status of online education in China, this paper analyzes the innovative application of learning attention discrimination based on head posture analysis in the development of online education mode of Internet thinking. Learning attention is an important factor of students’ learning efficiency, which directly affects students’ learning effect. In order to effectively monitor students’ learning attention in online teaching, a method of distinguishing students’ learning attention based on head posture recognition is proposed. In the tracking process, as long as the head angle of the current frame is close to the head angle of the key frame in a certain scale model, the visual angle apparent model can reduce the error accumulation in large-scale tracking. A Dynamic Bayesian Network (DBN) model is used to reason students’ Learning Attention Goal (LAG), which combines the relationships among multiple LAGs, multiple students’ positions, multicamera face images, and so on. We measure the head posture through the similarity vector between the face image and multiple face categories without explicitly calculating the specific head posture value. The test results show that the proposed model can effectively detect students’ learning attention and has a good application prospect.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.