Autonomous driving has made significant progress in both academia and industry, including performance improvements in perception tasks and the development of end-to-end autonomous driving systems. However, the safety and robustness assessment of autonomous driving has not received sufficient attention. Current evaluations of autonomous driving are typically conducted in natural driving scenarios. However, accidents often occur in edge cases, also known as safety-critical scenarios. These safety-critical scenarios are difficult to collect, and there is currently no clear definition of what constitutes a safety-critical scenario. In this work, we explore the safety and robustness of autonomous driving in safety-critical scenarios. First, we provide a definition of safety-critical scenarios, including static traffic scenarios such as adversarial attack scenarios and natural distribution shifts, as well as dynamic traffic scenarios such as accident scenarios. Then, we develop an autonomous driving test framework to comprehensively evaluate autonomous driving systems, encompassing not only the assessment of perception modules but also system-level evaluations. Our work systematically constructs a safety verification process for autonomous driving, providing technical support for the industry to establish standardized test framework.

With the rise of AI in education, knowledge tracing (KT) has become important for modeling students’ knowledge from interaction data. However, existing methods still face three major challenges, including limited modeling of personalized exercise–concept relations, low robustness to noisy interactions, and inefficient training due to suboptimal data selection. To address these issues, we propose R 2 GCurL, a novel KT framework with two key designs. First, we recast KT as a graph classification problem and construct dynamic graphs from student responses, enabling the model to capture structural relations between exercises and concepts for more personalized KT. Second, we introduce a data-centric curriculum learning strategy based on dynamic graph entropy. Under our definition, pairwise dynamic graph entropy measures graph-transition continuity, where larger values indicate stronger structural similarity. Its sequence-level aggregation is used to derive a structure-aware difficulty signal for sample scheduling. On top of this, an RL-based scheduler further adapts batch selection based on model feedback and is especially beneficial under noisier and more unstable training regimes. Theoretical analysis shows that R 2 GCurL has lower computational complexity than existing graph-based KT models. Extensive experiments on five real-world datasets confirm its effectiveness, robustness, and generalizability, including as a plug-and-play enhancement for sequence-based KT models.

Graph neural networks (GNNs) have shown ad-vantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on heterophilic graphs, where the linked nodes have dissimilar features and different class labels, and the semantically related nodes might be multi-hop away. To address this limitation, this paper presents GraphRARE, a general framework built upon node relative entropy and deep reinforcement learning, to strengthen the expressive capability of GNNs. An innovative node relative entropy, which considers node features and structural similarity, is used to measure mutual information between node pairs. In addition, to avoid the sub-optimal solutions caused by mixing useful information and noises of remote nodes, a deep reinforcement learning-based algorithm is developed to optimize the graph topology. This algorithm selects informative nodes and discards noisy nodes based on the defined node relative en-tropy. Extensive experiments are conducted on seven real-world datasets. The experimental results demonstrate the superiority of GraphRARE in node classification and its capability to optimize the original graph topology.

As microservices architectures become increasingly prevalent, they introduce significant operational challenges due to the complexities in service interactions and fault propagation. These architectures often conceal the origins of faults due to intricate inter-service communications, making fault localization both critical and challenging. Addressing these difficulties, this paper introduces a novel fault localization method that leverages synergies between domain prior knowledge, ensemble learning, and graph-based modeling. Our approach models microservices as a graph, with services as nodes and their interactions as edges, illuminating complex dependencies and enhancing the depth of data analysis. The method integrates expert knowledge with a unique blend of multi-class decision trees and strategy models derived from a knowledge base, enabling effective de-tection of diverse patterns and anomalies. Additionally, a meta-learner refines the outputs from base models using a weighted decision-making process, significantly improving the accuracy and robustness of fault detection. Compared to traditional models, including graph neural networks, our approach sub-stantially reduces model complexity and enhances adaptability to evolving service patterns. It demonstrates superior scalability and real-time processing capabilities, offering a robust solution to the challenges of fault localization in dynamic microservice environments.

Knowledge tracing models based on deep learning can achieve impressive predictive performance by leveraging attention mechanisms. However, there still exist two challenges in attentive knowledge tracing (AKT): First, the mechanism of classical models of AKT demonstrates relatively low attention when processing exercise sequences with shifting knowledge concepts (KC), making it difficult to capture the comprehensive state of knowledge across sequences. Second, classical models do not consider stochastic behaviors, which negatively affects models of AKT in terms of capturing anomalous knowledge states. This article proposes a model of AKT, called Enhancing Locality for Attentive Knowledge Tracing (ELAKT), that is a variant of the deep KT model. The proposed model leverages the encoder module of the transformer to aggregate knowledge embedding generated by both exercises and responses over all timesteps. In addition, it uses causal convolutions to aggregate and smooth the states of local knowledge. The ELAKT model uses the states of comprehensive KCs to introduce a prediction correction module to forecast the future responses of students to deal with noise caused by stochastic behaviors. The results of experiments demonstrated that the ELAKT model consistently outperforms state-of-the-art baseline KT models.

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided.

Reinforcement Learning, a common framework for AI planing or decision making, is regarded as an effective framework fro planning students' learning sequences. However, previous research efforts rely on simulation data or students and few of them have verified effectiveness of their algorithms with teaching students in real educational environments. Also, the previous research is hard to using in real task such as online course or real classroom because of the strong assumptions and restrictions. In this paper, We propose a new deep reinforcement learning framework for instructional sequencing that can recommend students with personalized learning exercises in both MOOCs and classrooms. Based on our ATC model, is used to trace students learning process and Deep Reinforcement Learning Agent is used to induce efficient l earning sequences for students adaptively. Both simulation and experiment in classrooms confirm the effectiveness of our method.

Exception handling is an important built-in feature of many modern programming languages such as Java. It allows developers to deal with abnormal or unexpected conditions that may occur at runtime in advance by using try-catch blocks. Missing or improper implementation of exception handling can cause catastrophic consequences such as system crash. However, previous studies reveal that developers are unwilling or feel it hard to adopt exception handling mechanism, and tend to ignore it until a system failure forces them to do so. To help developers with exception handling, existing work produces recommendations such as code examples and exception types, which still requires developers to localize the try blocks and modify the catch block code to fit the context. In this paper, we propose a novel neural approach to automated exception handling, which can predict locations of try blocks and automatically generate the complete catch blocks. We collect a large number of Java methods from GitHub and conduct experiments to evaluate our approach. The evaluation results, including quantitative measurement and human evaluation, show that our approach is highly effective and outperforms all baselines. Our work makes one step further towards automated exception handling.

In the field of microservice, accurate indicator prediction is very important, which is helpful for service monitoring and anomaly detection. In many cases, it is difficult to accurately predict by the indicator itself, and other related indicators need to be imported to help predict. In traditional multi-indicator predicting, the related indicators are known or the amount is small, which is relatively easy to obtain. But there are many service indicators and the relationship between the indicators is constantly changing, so new methods need to be used to quickly and accurately find the related indicators in the mass of indicators. We combine Granger causality test and Attention LSTM time series prediction model to quickly find related indicators in microservice scenarios and participate in prediction. The experimental results show that our method can effectively improve the accuracy of indicator prediction.

With the advent of Massive Online Open Courses (MOOCs), the data scale of student learning behavior and knowledge mastery has significantly increased. In order to effectively and efficiently analyze these datasets and present on-the-fly intelligent tutoring to online learners, it is necessary to improve existing learning analytics tools in a parallel and automatic way. One of the most common tools is Bayesian Knowledge Tracing (BKT) that can model temporal progress of online learners and evaluate their mastery of course knowledge. Current implementation of BKT is mostly based on single machine, which leads to slow execution performance during its Expectation Maximization(EM) algorithm for parameter fitting. Although there are a few parallel implementations for EM algorithm, they don't support automatic initial BKT parameter tuning to ensure the correct convergence of the EM iteration. Therefore, this paper presents a new parallel BKT open source tool based on the Spark computational framework with the method of automatic tuning of initial parameters. This tool improves traditional knowledge tracing systems using a parallel EM algorithm with the capabilty of automatically choosing initial parameters. Experimental result demonstrates that our tool can achieve fast execution speed and greatly improve the accuracy of training parameters on both different sizes of simulated data and real educational data sets.