Web Intelligence

Pretrained transformer models have demonstrated excellent performance on complex tasks. To improve their inference efficiency, recent studies have introduced the multi-exit mechanism, which enables early exiting through multiple intermediate classifiers. However, the deep architectures of pretrained transformers cause severe gradient conflicts during multi-exit fine-tuning, leading to degraded shallow-exit accuracy and reduced early-exit efficiency. To address this issue, we propose Separate Reverse, a multi-exit training strategy specifically designed for pretrained transformer models. The method iteratively integrates reverse iterative optimization and hierarchical knowledge distillation from deeper to shallower exits, maintaining pretrained parameter integrity, enhances the representation capacity of shallow exits, and coordinates gradient updates across exits to achieve a balanced optimization between shallow and deep classifiers. Experiments on multiple GLUE benchmark datasets using BERT demonstrate that our method significantly improves shallow-exit accuracy, maintains main-exit performance, and accelerates inference for simple samples by a large margin.

The effectiveness of group and online learning environments has been widely recognized. Hence, supporting teachers monitoring of group activities in online learning environments is increasingly important. In this study, we propose algorithms for analyzing discussion transitions and their main themes. The first algorithm aims to help teachers quickly identify groups that require intervention. It uses entities and Wikipedia data in the discussion texts to create models that capture continuously changing discussion content over time. The second algorithm aims to instantaneously identify the discussion topic of the group prior to teacher intervention. From the Wikipedia categories obtained regarding discussion transition analysis, the algorithm selects those with the highest probability of being appropriate for the discussion text. To evaluate the effectiveness of the proposed algorithms, we tested them on the W2E dataset, which includes topics comprising multiple events, and an actual discussion dataset. The results confirmed that the algorithms detected the discussion transitions and main themes with high accuracies.

Blockchain is a decentralized, public, and distributed ledger designed to securely record and track transactions. It possesses the potential to transform various industries, including healthcare, supply chain management, and financial services, by enhancing transparency, efficiency, and trust. Despite its promise, blockchain development continues to face several challenges, particularly concerning security, scalability, and standardization. This paper provides a comprehensive analysis of blockchain technology, focusing on its quality of service (QoS), security mechanisms, and the latest frameworks and models shaping its evolution. Furthermore, it examines existing limitations and identifies key open research challenges that must be addressed for broader adoption. The findings suggest that blockchain can substantially improve operational efficiency and data integrity across multiple domains; however, realizing its full potential requires continued research and technological advancement to overcome current barriers.