Petri nets are suitable for modeling and analysis of business processes. However, the lack of data concepts often makes Petri-net-based models excessively large and difficult to analyze especially when process logic is sensitive to changes of process attribute values. Emergency treatment processes are a typical example of this situation. To solve the aforementioned problems, this paper proposes a new hierarchical Petri net model for modeling and verification of emergency treatment processes. The hierarchical Petri net model includes three separate but closely related models, i.e., a business process logic net, a business process semantic net, and a set of case models. Business process logic nets are used to model the task dependencies disregarding semantic information. Business process semantic nets introduce data concepts to business process logic nets to model semantic information such as process attributes or conditions of sequence flows. Case models are used to model the practical routes of specific business instances. Based on the three models, a formal verification algorithm of an emergency treatment process is presented. Finally, the hierarchical modeling and verification methods are validated by an emergency treatment process of highways under snow/ice weather conditions.

Malware detection is of great significance for maintaining the security of information systems. Malware obfuscation techniques and malware variants are increasingly emerging, but their samples and API (application programming interface) sequences are difficult to obtain. This poses difficulties for the development of malware variant detection models. To address this issue in this paper, we first generated a malware variant dataset using the obfuscation technique based on the disassembly and decompilation of malware. Then, an API call dataset of these malware variants was constructed through sandboxing. Compared to similar work, the malware variants and their obfuscated API call sequences generated in this paper were all runnable. After that, taking a public API call sequence dataset of obfuscation-free malware as input, a BERT (bidirectional encoder representation from transformers) pretrained model for malware detection was constructed. To enhance the ability of this pretrained model to handle obfuscation and variants, in this paper, we used adversarial training to improve the robustness and generalization of the detection model under obfuscation. As the experimental results show, the proposed scheme can improve the classification performance of malware variants under obfuscation. The accuracy of the malware variant classification was close to that of the unobfuscated case.

Knowledge tracing predicts students’ future performance based on their historical performance data, which is significant for students’ learning resource recommendation, learning path prediction, and other aspects. Students’ knowledge mastery, learning ability, and question difficulty all influence the performance metrics of knowledge tracing. This paper proposes a deep knowledge tracing model that integrates temporal causal inference and the PINN (Physics-Informed Neural Network) model. The model first uses the temporal causality model to explores the causal relationships between students’ knowledge points, which is then combined with the deep learning-based knowledge tracing model for prediction. Next, it treats the logical model as a ’physical model’, adds a loss term, considers the confounding factors caused by students’ answer preferences, and adjusts students’ learning ability through backdoors to obtain more accurate predictions. In the public education datasets ASSISTment2012 and ASSISTchall, the predictive performance of the TLPKT-PINN model is superior to some classical models and LPKT. From the experimental results, we can conclude that considering the degree of mastery of causal knowledge points and adjusting the loss term can improve the accuracy of predicting student grades.

Program deadlock detection plays an important role in ensuring the stability of software systems. In view of the high efficiency and low false alarm rate, dynamic deadlock analysis methods are widely studied. However, existing dynamic analysis methods use lock graph or its extended variants to model program behavior. Since there are information loss in existing lock graphs, deadlock false positives still exist. To solve the problem, a novel 2-stage dynamic deadlock analysis method is proposed. In the first stage, a Petri net model of the program is constructed by analyzing program running traces. Then, unfolding-based Petri net dead marking detection methods are used to identify potential program deadlocks. In the second stage, based on Petri net unfolding techniques, a deterministic program scheduling scheme that can be used for deadlock replay is derived for each potential deadlock. Each successfully replayed program deadlock is a real deadlock. It is shown that the proposed deadlock detection and replay method can eliminate more false positives compared to general dynamic methods, and the deadlock replay scheme is deterministic and easy to understand compared with existing random scheduling strategies.