In recent years, the maturation of emerging technologies such as Virtual Reality, Digital Twins and Blockchain has accelerated the realization of the metaverse. As a virtual world independent of the real world, the metaverse will provide users with a variety of virtual activities which bring great convenience to society. In addition, the metaverse can facilitate digital twins, which offers transformative possibilities for the industry. Thus, the metaverse has attracted the attention of the industry, and a huge amount of capital is about to be invested. However, the development of the metaverse is still in its infancy and little research has been undertaken so far. We describe the development of the metaverse. Next, we introduce the architecture of the socially beneficial metaverse (SB-Metaverse) and we focus on the technologies that support the operation of SB-Metaverse. In addition, we also present the applications of SB-Metaverse. Finally, we discuss several challenges faced by SB-Metaverse which must be addressed in the future.

In modern software systems, log anomaly detection is a key technology to ensure system stability and reliability. The existing log-based anomaly detection methods have make significant development, but they fail to provide high detection accuracy when processing semantic information, especially in terms of semantic noise and the need for task-specific semantic embeddings. To address these issues, we propose a novel anomaly detection method called Contrast-Enhanced Log Anomaly Detection (CELA). The CELA model first enriches the content of log events using large language models and further optimizes the pretrained BERT model through contrastive learning techniques, ensuring that the event representations extracted from logs more accurately reflect potential anomalous patterns. On this basis, we have developed an anomaly detection model based on LSTM, which can effectively learn and identify anomalies from rich event representations. Evaluated on two public log datasets, the CELA model demonstrated superior performance compared to existing methods.

In the question-answering system, users frequently express multiple intents within a single utterance. The majority of intent recognition models tend to either primarily address single-intent scenarios or simply aggregate the overall intent context vectors of all tokens, neglecting the integration of multi-intent information. However, current methods suffer from slow inference speed, limited generalization capability, and the relatively independent treatment of multi-intent recognition tasks, leading to suboptimal model performance. In this paper, we design a novel model based on BERT, employing a non-autoregressive approach for the task of multi-intent recognition. This model achieves enhanced speed and accuracy. Additionally, this model introduces a global slot-intent interaction layer, simulating interactions between multiple intents and slots within an utterance. Experimental results on a series of benchmarking datasets demonstrate that this model outperforms the state-of-the-art methods, improving both the effectiveness and efficiency of the overall system.

Social perception recommendation systems can effectively alleviate the user cold start problem by leveraging the side information of social networks. However, most social perception recommendation systems treat user relations as independently existing entities for learning, thereby overlooking potential connections between relations. Additionally, as the number of relations increases, it inevitably imposes a significant computational burden on the servers. To address these issues, we propose the Social perception recommendation based on relational clustering(SPRRC). SPRRC conducts relational clustering of social networks and projects knowledge graphs, effectively mining information about the similarity between relations. First, we cluster relationships in item knowledge graphs and social networks through unsupervised learning. After that, we use local weighted smoothing to aggregate the map information of the clustered items and social network virtual subgraphs respectively, and use the attention networks to learn the representation of users and items in the interactive bipartite graph. Finally, a large number of experiments have verified the high accuracy of our method, compared with the latest methods.

Anomaly detection is a critical aspect of industrial production processes. Most of the self-supervised training system utilise Convolutional Neural Networks (CNNs) for feature extraction. However, these methods often exhibit poor detection performance on small structural anomalies due to the limited receptive field of CNN. The prompt learning method has been demonstrated to be an effective means of enhancing the global information extraction abilities of the model. However, this approach necessitates the input of a significant number of manual prompts. To address these issues, we propose a Memory And Prompt Learning Based Network (MPLNet) for anomaly detection. MPLNet obtains the differences information between normal and detected images by comparing their features and then uses these differences to generate prompts. By automatically generating prompts, it reduces the workload of manually entering prompts in prompt learning. At the same time, it effectively enhances the model’s ability to extract and use global information. Extensive experiments have shown that the proposed MPLNet achieves state-of-the-art anomaly detection performance on the widely used and challenging MVTec AD dataset and MVTec AD-3D dataset. Index Terms—Anomaly detection, U-Net, Transformer, Prompt, artificial intelligence System

The paper provides a novel educational model on the Internet called CSCL, Computer Supported Cooperative Learning. It introduces the current models of education, overviews the whole system, elaborates the process of realization, discusses its implementation using Web technology, and finally, it provides some idea of the future of the CSCL model.