ABSTRACT Nowadays, vehicular ad‐hoc networks (VANETs) offer increased convenience to drivers and enable intelligent traffic management. However, the public wireless transmission channel in VANETs brings challenges related to security vulnerabilities and privacy leakage, in addition, vehicles produced by different manufacturers may use different cryptosystems such as certificateless cryptosystems (CLCs) and identity‐based cryptosystems (IBC). To address privacy leakage during cross‐cryptosystem communication in VANETs, we propose a lattice‐based heterogeneous signcryption scheme named LHS‐C2I. The scheme facilitates secure multi‐cryptosystem bidirectional communication as CLC‐based vehicles to IBC‐based vehicles and IBC‐based vehicles to CLC‐based vehicles. The confidentiality and authenticity of LHS‐C2I help to prevent the users from privacy leakage during cross‐cryptosystem communication and to authenticate the message integrity and the sender's identity legitimacy. The proposed scheme is proven to achieve Indistinguishability under Chosen Ciphertext Attack (IND‐CCA2) and Existential Unforgeability against Adaptive Chosen Messages Attack (EUF‐CMA) within the random oracle model. Performance analysis demonstrates that LHS‐C2I outperforms existing schemes in terms of computational overhead, communication overhead, and overall security features. It is particularly well‐suited for scenarios requiring secure communication across different cryptosystems in VANETs.

ABSTRACT Large language models (LLMs) have transformed educational applications through personalized learning and intelligent tutoring systems. However, educational LLMs (EduLLMs) face significant deployment challenges due to their massive computational demands and autoregressive nature, particularly in resource‐constrained environments. This paper presents a novel approach for inference acceleration aimed at facilitating the deployment of EduLLMs on a commodity GPU. This technique substantially diminishes the memory footprint and the volume of data transfers between the CPU and GPU by strategically preloading critical neurons directly onto the GPU, thereby enabling rapid access. Concurrently, computations pertaining to non‐critical neurons are processed on the CPU. Implementation of our optimized approach, AccEduLLM, on a single NVIDIA RTX 3090 GPU using FP16 type, achieves 60.09 tokens/s, which is 8.32 faster than the previous work, while preserving the model's accuracy. The approach demonstrates particular effectiveness for variable‐length educational content like essays and textbooks.

Abstract While contemporary community‐based recommendation algorithms based on a single community structure are more capable of processing large datasets than ever, they lack recommendation precision. This article proposes a collaborative filtering recommendation algorithm that integrates community structure and user implicit trust. The algorithm first applies a method based on the Gaussian function to fill the matrix of item ratings of users to alleviate data sparsity. It then uses the trust matrix to obtain the asymmetric trust relationship of the trustor and trustee, based on which the degree of users' implicit trust is calculated. The users are divided into communities based on the implicit trust degree to determine the influence among users more accurately. The algorithm then predicts the target user's rating using the ratings of users in the community to generate recommendations. To verify the performance of the proposed algorithm, we compared the proposed algorithm with three contemporary algorithms under the same conditions using FilmTrust datasets. The recommendation accuracy as well as the mean absolute error and root mean square error values of the proposed algorithm were better than those of the other four algorithms by approximately 14% and 4%, respectively. The experimental results demonstrate that the proposed algorithm can achieve better recommendation efficiency than existing algorithms.