With versatility and complexity of computer systems, warning and errors are inevitable. To effectively monitor system’s status, system logs are critical. To detect anomalies in system logs, deep learning is a promising way to go. However, abnormal system logs in the real world are often difficult to collect, and effectively and accurately categorize the logs is an even time-consuming project. Thus, the data incompleteness is not conducive to the deep learning for this practical application. In this paper, we put forward a novel semi-supervised dual branch model that alleviate the need for large scale labeled logs for training a deep system log anomaly detector. Specifically, our model consists of two homogeneous networks that share the same parameters, one is called weak augmented teacher model and the other is termed as strong augmented student model. In the teacher model, the log features are augmented with small Gaussian noise, while in the student model, the strong augmentation is injected to force the model to learn a more robust feature representation with the guidance of teacher model provided soft labels. Furthermore, to further utilize unlabeled samples effectively, we propose a flexible label screening strategy that takes into account the confidence and stability of pseudo-labels. Experimental results show favorable effect of our model on prevalent HDFS and Hadoop Application datasets. Precisely, with only 30% training data labeled, our model can achieve the comparable results as the fully supervised version.

Pedestrians are among the most vulnerable road users, with significant risks arising at intersections due to potential conflicts with vehicular traffic. With advancements in deep learning, many approaches have sought to address this issue by understanding pedestrian intentions based on their behaviors. However, at intersections, a pedestrian’s crash risk is highly influenced by their crossing direction, yet no prior work has specifically focused on predicting crossing directions, leaving a critical gap in ensuring pedestrian safety in these high-risk areas. This study introduces a novel camera-invariant framework for predicting pedestrian crossing directions at intersections using keypoints and trajectory data extracted from CCTV footage. To address challenges posed by varying intersection geometries and camera perspectives, we developed a global coordinate system that standardizes spatial features. The framework leverages Transformer-based models, Graph Convolutional Networks (GCNs), and a hybrid Transformer+GCN approach to extract spatial and temporal features from the pedestrian behaviors. Our proposed framework with Transformer-based model achieved accuracy of 94.10% and F1-Score of 92.35%, demonstrating its effectiveness in capturing pedestrian intentions across diverse scenarios. With its demonstrated high accuracy and effectiveness, our framework predicts pedestrians’ crossing directions, which not only can enhance pedestrian safety by reducing potential conflicts with vehicular traffic at intersections but also can be applied to signal optimization systems to improve traffic flow. Furthermore, the geometric-invariant model ensures that the system is easily transferable across intersections. It reduces the burden of collecting more data and training it from several camera perspectives. The implementation of this work is publicly available at https://github.com/Kimyounggun99/CrossingDirectionPrediction.

ChatGPT embarks on a new era of artificial intelligence and will revolutionize the way we approach intelligent traffic safety systems. This paper begins with a brief introduction about the development of large language models (LLMs). Next, we exemplify using ChatGPT to address key traffic safety issues. Furthermore, we discuss the controversies surrounding LLMs, raise critical questions for their deployment, and provide our solutions. Moreover, we propose an idea of multi-modality representation learning for smarter traffic safety decision-making and open more questions for application improvement. We believe that LLM will both shape and potentially facilitate components of traffic safety research.