Computing offloading of mobile devices (MDs) through cloud is a greatly effective way to solve the problem of local resource constraints. However, cloud servers are usually located far away from MDs leading to a long response time. To this end, edge cloud servers (ECSs) provide a shorter response time due to being closer to MDs. In this paper, we propose a computing offloading game for MDs and ECSs. We prove the existence of a Stackelberg equilibrium in the game. In addition, we propose two algorithms, F‐SGA and C‐SGA, for delay‐sensitive and compute‐intensive applications, respectively. Moreover, the response time is reduced by F‐SGA, which makes decisions quickly. An optimal decision is obtained by C‐SGA, which achieves the equilibrium. Both algorithms above proposed can adjust the computing resource and utility of system users according to parameters control in computing offloading. The simulation results show that the game significantly saves the computing resources and response time of both the MD and the ECSs during the computing offloading process.

In the domain of natural language processing (NLP), a primary challenge pertains to the process of Chinese tokenization, which remains challenging due to the lack of explicit word boundaries in written Chinese. The existing tokenization methods often treat each Chinese character as an indivisible unit, neglecting the finer semantic features embedded in the characters, such as radicals. To tackle this issue, we propose a novel token representation method that integrates radical-based features into the process. The proposed method extends the vocabulary to include both radicals and original character tokens, enabling a more granular understanding of Chinese text. We also conduct experiments on seven datasets covering multiple Chinese natural language processing tasks. The results show that our method significantly improves model performance on downstream tasks. Specifically, the accuracy of BERT on the BQ Croups dataset was enhanced to 86.95%, showing an improvement of 1.65% over the baseline. Additionally, the BERT-wwm performance demonstrated a 1.28% enhancement, suggesting that the incorporation of fine-grained radical features offers a more efficacious solution for Chinese tokenization and paves the way for future research in Chinese text processing.