Knowledge tracing is a significant research area in educational data mining, aiming to predict future performance based on students’ historical learning data. In the field of programming, several challenges are faced in knowledge tracing, including inaccurate exercise representation and limited student information. These issues can lead to biased models and inaccurate predictions of students’ knowledge states. To effectively address these issues, we propose a novel programming knowledge tracing model named GPPKT (Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing), which enhances performance by using knowledge graphs and personalized answer sequences. Specifically, we establish the associations between well-defined knowledge concepts and exercises, incorporating student learning abilities and latent representations generated from personalized answer sequences using Variational Autoencoders (VAE) in the model. This deep knowledge tracing model employs Long Short-Term Memory (LSTM) networks and attention mechanisms to integrate the embedding vectors, such as exercises and student information. Extensive experiments are conducted on two real-world programming datasets. The results indicate that GPPKT outperforms state-of-the-art methods, achieving an AUC of 0.8840 and an accuracy of 0.8472 on the Luogu dataset, and an AUC of 0.7770 and an accuracy of 0.8799 on the Codeforces dataset. This demonstrates the superiority of the proposed model, with an average improvement of 9.03% in AUC and 2.02% in accuracy across both datasets.

China’s low-elevation coastal zone (LECZ) is characterized by multiple hazards and high impacts. How to quantitatively portray the spatiotemporal characteristics of the exposed population to multi-hazards in the LECZ is an important subject of risk reduction. In this study, the overall characteristics, spatial patterns, and main impact hazard in the LECZ from 1990 to 2020 were investigated using a multi-hazard population exposure model, spatial autocorrelation method, and principal component analysis (PCA) method. The results show that among the four hazards (earthquake, tropical cyclones (TCs), flood, and storm surge), TCs cover the largest area, accounting for 90.1% of the total LECZ area. TCs were also the hazard with the largest average annual growth rate of the exposed population (2.36%). The central region of China’s LECZ is the cluster of exposed populations and the main distribution area with the largest increase in exposed populations. Therefore, the central region is a hotspot for multi-hazard risk management. Additionally, flood contributes the most to the multi-hazard population exposure index; thus, flood is a key hazard of concern in the LECZ. This study identifies the hotspot areas and priority hazards of multi-hazard exposed populations in the LECZ and provides important policy recommendations for multi-hazard risk management in the LECZ, which is important for LECZ to enhance the resilience of hazards.

Named Data Networking (NDN) presents a promising alternative to TCP/IP, but its access control design poses challenges for cybersecurity. Addressing this, the paper introduces the Security Verification Framework for NDN Access Control (SVF-NDN). This framework employs formal analysis to assess access control schemes, evaluating their resilience against cyberattacks. SVF-NDN verifies five crucial security properties-deadlock freedom, data availability, key authentication, data leakage protection, and data access protection. Implemented using the PAT model checking tool, the framework focuses on a data encryption-based NDN access control. Uncovering vulnerabilities such as node key pair faking and data leakage, two enhancement methods are proposed and evaluated. Recognizing the potential compromise of Access Control Manager (ACM), an innovative solution is presented. Additionally, four algorithms streamline the automatic updating of formal models. Results indicate SVF-NDN’s efficacy in fortifying access control against cyber threats, offering valuable insights for bolstering NDN security.

The Internet of Things (IoT) generates vast amounts of data from numerous applications. However, since wireless channels are the primary means of communication, IoT networks are vulnerable to several security threats, which can compromise their security and privacy. To address these issues, various user authentication protocols have been proposed. Thus, it is still a challenge to provide multi-granularity verifications for different authentications of the IoT. In this paper, we propose a multi-granularity formal framework of user authentication for the IoT (MFF-IoT). Our framework builds different formal models (specification language HLPSL models, process algebra CSP models, Timed CSP models, and timed automata) to complete multi-granularity formal verification. By using both coarse-grained and fine-grained modeling, we can balance the tradeoff between model complexity and verification accuracy. Specifically, our fine-grained models provide a more detailed representation of the framework’s behavior and enable us to perform timing-related probability analysis. As these formal models can be implemented by model-checking tools (AVISPA, PAT with C#, and UPPAAL), important properties and features can be analyzed and verified. We also propose several algorithms for better formal model building and evaluate our framework with a case study to show its practicality and effectiveness.