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 Hierarchical classification learning aims to exploit the hierarchical relationship between data categories. Making full use of the hierarchical structure between class labels can effectively reduce the number of categories for each classification task and improve the accuracy of classification. For hierarchical feature selection, usually the more similar two labels are, the more features they share. However, existing hierarchical feature selection algorithms often ignore this. In addition, current hierarchical feature selection algorithms do not deeply consider the semantic structure between labels when exploiting label correlations. In this article, we propose a hierarchical feature selection based on instance correlation and label semantic structure. This algorithm expresses the correlation between instances with the help of Laplacian matrix. Then, the instance correlation is combined with the semantic relationship between labels in the hierarchical structure to construct a hierarchical feature selection model. To prove the effectiveness of the proposed algorithm, a large number of experiments are conducted on hierarchical datasets in different fields, and multiple hierarchical feature selection are compared. The experimental results demonstrate that the proposed algorithm has significant performance superiority.

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.

Label distribution learning (LDL) is a novel approach that outputs labels with varying degrees of description. To enhance the performance of LDL algorithms, researchers have developed different algorithms with mining label correlations globally, locally, and both globally and locally. However, existing LDL algorithms for mining local label correlations roughly assume that samples within a cluster share same label correlations, which may not be applicable to all samples. Moreover, existing LDL algorithms apply global and local label correlations to the same parameter matrix, which cannot fully exploit their respective advantages. To address these issues, a novel LDL method based on horizontal and vertical mining of label correlations (LDL-HVLC) is proposed in this paper. The method first encodes a unique local influence vector for each sample through the label distribution of its neighbor samples. Then, this vector is extended as additional features to assist in predicting unknown instances, and a penalty term is designed to correct wrong local influence vector (horizontal mining). Finally, to capture both local and global correlations of label, a new regularization term is constructed to constrain the global label correlations on the output results (vertical mining). Extensive experiments on real datasets demonstrate that the proposed method effectively solves the label distribution problem and outperforms the current state-of-the-art methods.

Summary Label distribution learning (LDL) is an emerging learning paradigm, which can be used to solve the label ambiguity problem. In spite of the recent great progress in LDL algorithms considering label correlations, the majority of existing methods only measure pairwise label correlations through the commonly used similarity metric, which is incapable of accurately reflecting the complex relationship between labels. To solve this problem, a novel label distribution learning method—based on high‐order label correlations (LDL‐HLC) is proposed. By virtue of the ‐regularization sparse reconstruction of the label space, the high‐order label correlations matrix is firstly obtained. Then, a new regular term can be constructed to fit the final prediction label distribution via the correction matrix. Furthermore, efficient classification performance and complete feature selection are guaranteed by common features learning via ‐regularization. Finally, the performance and effectiveness of the proposed algorithm are well illustrated through extensive experiments on 14 label distribution datasets and comparisons with some existing algorithms.

Abstract Multi‐label feature selection eliminates irrelevant and redundant features, and then improves the performance of multi‐label classification models. Most multi‐label feature selection algorithms assume that the training set contains logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances with respect to labels. To solve the problem, a multi‐label feature selection method based on relative entropy and fuzzy neighborhood mutual discriminant index is proposed. Firstly, logical labels are converted to label distribution through label enhancement. Secondly, the neighborhood and relative entropy are introduced into the label distribution, the label neighborhood similarity matrix is constructed to describe the similarity of samples under label space. Finally, the fuzzy neighborhood mutual discrimination index is used to combine the candidate features with the label neighborhood similarity matrix, which is used to judge the distinguishing ability of the candidate features. Comprehensive experiment of eight multi‐label datasets shows that the proposed algorithm has better classification performance than other compared algorithms.

Abstract In hierarchical classification learning, the feature space of data has high dimensionality and is unknown with emergent features. To solve the above problems, we propose an online hierarchical feature selection algorithm based on adaptive ReliefF. Firstly, ReliefF is adaptively improved via using the density information of instances around the target sample, making it unnecessary to prespecify parameters. Secondly, the hierarchical relationship between classes is used, and a new method for calculating the feature weight of hierarchical data is defined. Then, an online correlation analysis method based on feature interaction is designed. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between features in order to achieve the dynamic updating of feature redundancy. A large number of experiments verify the effectiveness of the proposed algorithm.

Abstract A community structure in a complex network often exhibits hierarchical characteristics. Current hierarchical community‐discovery algorithms generally consider a single node as a community during the initial stage. This approach leads to over‐fine clustering granularity, too‐deep clustering levels, and other issues. Therefore, this article proposes a hierarchical community‐discovery algorithm that combines the core nodes and the three‐order structure model. Between neighboring nodes, there is a first‐order structure. The core node is identified based on its influence, and the similarity between the core node and its neighboring nodes is defined as the second‐order structure. The nodes satisfying the second‐order structure are then formed into a friend circle. The similarity between friend circles is defined as the third‐order structure. According to this structure, the friend circles are construed as a hierarchical clustering tree (HCT) where one HCT represents a community. The HCT built by this algorithm has relatively fewer levels and exhibits a flat feature. Experimental results on both artificial and real networks show that the algorithm performs well on various indicators. Additionally, the algorithm exhibits near‐linear time complexity.

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.