With the surge of academic papers, it has become a common practice to recommend papers based on authors’ research interests. Existing methods focus on leveraging author–paper research interactions to mine authors’ research interests with coauthorship networks. However, sparse research interactions would pose a huge challenge to distinguish research interests of authors. Fortunately, inter-dependent knowledge across papers provides rich potential heterogeneous connections for author–paper interactions, offering much insights for learning authors’ research interests. Therefore, we propose a meta-relation–guided knowledge coupling approach for paper recommendation. Specifically, we construct a meta-relation–guided heterogeneous graph architecture to depict the numerous inter-dependencies among authors and papers, thereby exploring complex author–paper interactions. First, a meta-relation–aware heterogeneous graph encoder is developed to extract relational structure which maintains the relation-specific representation of authors’ research interest and papers’ research relatedness. Then, a cross-meta-path attention network is designed to aggregate the characteristics of different meta-relations and obtain research features of authors and papers. Finally, a self-supervised data augmentation architecture is constructed to mine and preserve local and global graph structure information, acquiring papers with high relevance to author’s research interests through training loss. Numerous experiments are conducted on two real academic datasets, effectively demonstrating the superiority of our proposed model and validating its effectiveness in paper recommendation.

Designing an efficient and secure authentication scheme is an significant means to ensure the security of IoT systems. Hundreds of authentication schemes tailored for IoT environments have been proposed in recent years, and regrettably, many of them were soon found to have succumbed to security vulnerabilities. In an effort to investigate the underlying reason for this, Wang et al. (at TIFS’23) recently analyzed the vulnerability of authentication schemes from the perspective of provable security. However, we observe that some authentication schemes with sound security proofs and heuristic security analysis are also not resistant to certain attacks, and even those that have been improved several times are still not immune. To explore the deep-seated reasons for security vulnerabilities in IoT authentication schemes, we divide security attacks into explicit and implicit attacks and find that many authentication schemes exhibit security under explicit attacks but are rendered vulnerable under implicit attacks. Further, we propose the relationship between the design goals of security attributes of authentication schemes and implicit attacks, analyze the vulnerability of three typical authentication schemes under implicit attacks, and find that only the security attributes capable of resisting the strongest implicit attacks are secure. Finally, we offer some specific suggestions on how to achieve the security attribute goals.

News web videos exhibit several characteristics, including a limited number of features, noisy text information, and error in near-duplicate keyframes (NDK) detection. Such characteristics have made the mining of the events from news web videos a challenging task. In this paper, a novel framework is proposed to better group the associated web videos to events. First, the data preprocessing stage performs feature selection and tag relevance learning. Next, multiple correspondence analysis is applied to explore the correlations between terms and events with the assistance of visual information. Cooccurrence and visual near-duplicate feature trajectory induced from NDKs are combined to calculate the similarity between NDKs and events. Finally, a probabilistic model is proposed for news web video event mining, where both visual temporal information and textual distribution information are integrated. Experiments on the news web videos from YouTube demonstrate that the integration of visual temporal information and textual distribution information outperforms the existing methods in the news web video event mining.