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With the rapid development of network and communication techniques, the teaching forms have become diversified. To enhance the education experience and improve the teaching environment, an increasing number of educational institutions have adopted virtual simulation technology. A typical teaching mechanism is to exploit Virtual Reality (VR) technology, which affords participants an immersive experience. Unquestionably, such a VR-based mode is highly approved. However, the performance of this technology requires further optimization. On one hand, for VR 360° video, the current intraframe decision cannot adapt to rapid response demands. On the other hand, the generated data size is considerably large and fast computation may not be realized, depending on the local VR device. Therefore, this study proposes an improved teaching mechanism empowered by edge computing–driven VR, called VE4T, that involves two parts. First, an intraframe decision algorithm for VR 360° videos is devised to realize the rapid responses. Second, an edge computing framework is proposed to offload some tasks to an edge server for computation, where a task scheduling strategy is developed to check whether a task needs to be offloaded. Finally, experiments are performed using a practical teaching scenario with some VR devices. The obtained results demonstrate that VE4T is more efficient than existing mechanisms.

Computer network security (CNS) evaluation is a comprehensive analysis and assessment of a network system's security to identify potential vulnerabilities and threats, ensuring the system complies with relevant security standards. The evaluation includes the effectiveness of technical measures such as firewalls, intrusion detection, access control, and encryption, to ensure the confidentiality, integrity, and availability of data, ultimately enhancing overall security defenses. The evaluation of CNS is a multi-attribute group decision-making (MAGDM) problem. Recently, both the CoCoSo method and the information entropy approach have been applied to solve MAGDM challenges. Type-2 Neutrosophic Sets (T2NSs) are utilized to represent uncertain data during the network security evaluation process. In this study, the CoCoSo method is adapted for MAGDM with T2NSs. Furthermore, the type-2 Neutrosophic Number with CoCoSo (T2NN-CoCoSo) approach based on T2NN is developed for MAGDM. Finally, a numerical example is provided to demonstrate the application of the T2NN-CoCoSo approach in CNS evaluation. The key contributions of this study include: (1) the development of a MAGDM method using the T2NN-CoCoSo approach with T2NSs, and (2) the proposal of a novel MAGDM approach for CNS evaluation using the T2NN-CoCoSo method. [ABSTRACT FROM AUTHOR]