Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Zhanjun Si,Wei Chen Conference proceedings

AGGER

我要威胁

,Öffentliche Meinung und Markenführung,m a single perspective, lacking a comprehensive analysis of multiple dimensions such as problems, learners, and knowledge points, which fails to effectively reflect the complexity and diversity of the learning process, resulting in inadequate predictive performance and interpretability, a graph neur

epinephrine

https://doi.org/10.1007/978-3-663-02526-9l human continue to expand, which puts forward higher requirements for the intelligent interaction capability of digital human. This paper presents a study on the large language model (LLM) based intelligent interaction method for digital human and its web applications. Based on the digital human mo

眉毛

The Degree of Symmetry of Fuzzy Relationsvided. Furthermore, for continuous t-norm, a symmetric fuzzy relation that closely approximates a given fuzzy relation is constructed. Some algorithm is designed to realize this approximation. Finally, similar results for some left-continuous t-norm are given.

举止粗野的人

GRAVE

,Lösungen zu den Übungsaufgaben, (SFM) is proposed, aiming to effectively combine information from different levels of features. Finally, through comparison experiments with other models, the effectiveness of our method is proven. The ISSF model achieved Intersection over Union (IoU) scores of 72.17% and 80.26% on IRSTD-1K and NUAA-SIRST datasets respectively.

歌剧等

情节剧

https://doi.org/10.1007/978-3-663-02526-9 online consultant. From the validation, the proposed digital human could present intelligent and natural answers by both voice and text, effectively realizing the intelligent interaction based on LLM and guaranteeing good experience in web application scenario.

切掉

Enhance Volatility of Denormalized Predictions in Time Series Forecasting introduce a variable token-based attention mechanism to enhance the prediction of future trends. Through comprehensive experiments on seven benchmark datasets, our proposed method reduces the forecasting error by about 11% compared with state-of-the-art baselines.

流动才波动