Keeping pace with the times, this paper analyzes and interprets the concept, characteristics of Metaverse, and its influence on new media communication. Based on this, under the topological... Show More

Keeping pace with the times, this paper analyzes and interprets the concept, characteristics of Metaverse, and its influence on new media communication. Based on this, under the topological characteristics of information communication, this paper discusses the transformation of new media communication paradigm in the metaverse era and models the prediction of new media information dissemination in metaverse. In this paper, the multi-agent modeling method is used to analyze and compare the evolution results of each agent attribute under different parameter values. At the same time, based on the ideal social media network, the improved integrated immune control strategy is applied to the dynamic model of epidemic (SEIR) with latent period, and the spreading and evolution process of rumors before and after immunization is simulated. This research has both theoretical value and practical application value, which can provide a useful reference for the follow-up research of new media communication.

The prevalence of stress-related disorders has increased significantly in recent years, necessitating scalable methods to identify affected individuals. This paper proposes a novel approach... Show More

The prevalence of stress-related disorders has increased significantly in recent years, necessitating scalable methods to identify affected individuals. This paper proposes a novel approach utilizing large language models (LLMs), with a focus on OpenAI's generative pre-trained transformer (GPT-3) embeddings and machine learning (ML) algorithms to classify social media posts as indicative or not of stress disorders. The aim is to create a preliminary screening tool leveraging online textual data. GPT-3 embeddings transformed posts into vector representations capturing semantic meaning and linguistic nuances. Various models, including support vector machines, random forests, XGBoost, KNN, and neural networks, were trained on a dataset of >10,000 labeled social media posts. The top model, a support vector machine, achieved 83% accuracy in classifying posts displaying signs of stress.

In recent years, more and more data has been stored on the cloud to provide various services. These data often contain users' private information, which inevitably raises concerns about data... Show More

In recent years, more and more data has been stored on the cloud to provide various services. These data often contain users' private information, which inevitably raises concerns about data security. Encryption before outsourcing is a direct solution to mitigate these concerns. However, traditional encryption schemes such as block encryption make basic data services hard to support. Therefore, this paper proposes a secure and fast range query scheme for encrypted multi-dimensional data, called SFRQ. The scheme constructs a secure index over the ciphertexts of multi-dimensional data, utilizing the R-tree index, Bloom filter, and 0-1 encoding techniques. This secure index enables the cloud to provide fast range query services over the ciphertexts of multi-dimensional data. The authors have evaluated SFRQ through extensive experiments, which demonstrate its high efficiency. Additionally, the security analysis shows that no external entity, including the cloud, can obtain additional information during the entire query process.