Traces and logs record the behaviors and interactions between microservices, making them indispensable for diagnosing system anomalies. However, the intricate and flexible architecture of microservices presents significant challenges for automated anomaly detection. Existing approaches often struggle to capture the internal dynamics and correlations among microservices, which limits their ability to comprehensively detect anomalies arising from synchronous or asynchronous calls. Therefore, we categorize three prevalent anomaly paradigms in microservice systems: intra-service, inter-service, and joint anomalies. To address these challenges, we propose a novel unsupervised learning framework for dual-view anomaly detection, named TraceHG. Specifically, it constructs the trace graph composed of logs, response times, and traces. Besides, TraceHG utilizes hypergraph transformation to establish two views, providing comprehensive information in microservices. Furthermore, we introduce a dual-view framework that leverages both a hypergraph-view and a graph-view encoders to explicitly learn associations among microservices. These two views, employing graph and HGNNs, capture representations of internal dynamics, invocations, and their associated contexts in microservices. By constructing two minimized hyperspheres and measuring the distances from their centers in the latent space, we identify anomalies based on the anomaly scores. Extensive experiments on public benchmark datasets demonstrate that the proposed framework outperforms several state-of-the-art baselines in microservice anomaly detection.

Logs are valuable resources that record the health status of systems. Analyzing logs to uncover and investigate abnormal behaviors has become an essential approach of ensuring system security. However, some potential anomalies may be missed when performing log anomaly detection, and even a seemingly insignificant abnormal behavior can lead to a series of severe anomalies or continuous negative impact on the performance of systems. Therefore, the reliability and security of systems are facing significant challenges. To address this issue, in this study, we propose TransFlowLog, an Encoder-Decoder architecture-based approach for log anomaly detection. It utilizes the Transformer Encoder, a state-of-the-art sequence modeling technique, to comprehensively understand contextual relationships using self-attention mechanism. Moreover, we introduce the Interflow Decoder, which considers information exchange between channels in embedded log sequences. The Interflow Decoder enhances the features encoded by the Transformer Encoder in both sequence and channel dimensions, thereby capturing interdependence between different channels. Comparative experiments conducted on three real-world datasets demonstrate the effectiveness of the proposed method, as it achieves higher F1-score and reduces the number of false negatives.

To address the issue that point cloud data is often incomplete and difficult to obtain, we propose a point cloud completion method to improve the PoinTr method based on feature enhancement. In dataset preprocessing, the farthest point of the original point cloud is sampled to obtain the central point coordinates. Our method constructs an MLP network, where the local information of these central points is obtained and the location embedding is performed. Combining network and SENet network, the local features of the point cloud are extracted and enhanced, and the location embedding and local features are added to obtain the point proxies of the original point cloud. Afterward, our method predicts the missing part of the point cloud by using an Encoder to model the relationship between the point cloud structure information and points, and then using a Decoder to learn the relationship between the missing and existing parts of the point cloud and reconstruct the missing point cloud. Our method also modifies the attention mechanism to make the features more global and enhance the network expression. Finally, the point cloud is refined, and is realized by predicting multiple points around each point of the coarse point cloud through the FoldingNet network, and the final output is the complete point cloud. Experimental results show that the proposed method can not only reduce the performance overhead, but also improve the effects of point cloud completion.

Abstract 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.

Peking opera is one of the national cultural heritages in China. However, it is difficult for people to learn the gestures in Peking opera performance, which limits the spread of this traditional culture. To address this issue, we propose a Peking opera gesture training system using infrared sensors. Specifically, we build a character avatar for demonstrating the gestures in Peking opera in the proposed system. Based on the data collected by infrared sensors, a method for calculating gesture similarity is proposed and is applied for the training of Peking opera gestures, which allows natural interactions and provides interactive feedback for user gestures. We conducted multiple experiments to verify the feasibility and effectiveness of the training system. The experimental results showed that the proposed system can overcome the difficulties in the traditional learning process of Peking opera gestures, which helps users to achieve the goal of learning standard Peking opera gestures. The proposed training system greatly eases the learning of Peking opera gestures, adding vitality into the culture of traditional Peking opera.