Abstract In the field of multimodal sentiment analysis, it is an important research task to fully extract modal features and perform efficient fusion. Traditional sentiment classification models usually have the problems of insufficient interaction information, easy to be affected by visual or perceptual interference, and lack of stability. To address these issues, this paper proposes an interactive gated attention network for multimodal sentiment analysis. Firstly, the feature extraction network based on the pretraining model is constructed to obtain high-dimensional feature vectors. Secondly, the mutual feature vector generator is designed to generate interactive feature vectors with high levels. Furthermore, the gating vector generator is designed to highlight the differences in semantic features. Finally, attention of the interaction mechanism is introduced to focus on the important parts between features, and the feature enhancement and fusion are realized. Comprehensive experiments and analysis on the Twitter-15 and Twitter-17 datasets show that the proposed model is superior to a series of comparative models in information interaction, semantic enhancement, and feature fusion.

Translating configurations between different network devices is a common yet challenging task in modern network operations. This challenge arises in typical scenarios such as replacing obsolete hardware and adapting configurations to emerging paradigms like Software Defined Networking (SDN) and Network Function Virtualization (NFV). Engineers need to thoroughly understand both source and target configuration models, which requires considerable effort due to the complexity and evolving nature of these specifications. To promote automation in network configuration translation, we propose INTA, an intent-based translation framework that leverages Large Language Model (LLM) agents. The key idea of INTA is to use configuration intent as an intermediate representation for translation. It first employs LLMs to decompose configuration files and extract fine-grained intents for each configuration fragment. These intents are then used to retrieve relevant manuals of the target device. Guided by a syntax checker, INTA incrementally generates target configurations. The translated configurations are further verified and refined for semantic consistency. We implement INTA and evaluate it on real-world configuration datasets from the industry. Our approach outperforms state-of-the-art methods in translation accuracy and exhibits strong generalizability. INTA achieves an accuracy of 98.15% in terms of both syntactic and view correctness, and a command recall rate of 84.72% for the target configuration. The semantic consistency report of the translated configuration further demonstrates its practical value in real-world network operations.