Microservices architecture has advantages such as independent development, independent deployment, scalability, and reusability. However, faults are inevitable during the operation of microservices systems. This paper introduces an anomaly detection approach based on multi-source data, which combines multi-level attention mechanisms and multi-scale convolutional neural networks. It designs feature extraction modules for different data sources, effectively capturing features of log data and KPI (Key Performance Indicator) data. The extracted features from different data sources are input in parallel to an attention network, where they are weighted and fused. Finally, the fused features are input into the anomaly detection model for detection. We deployed an open-source benchmark microservices system, TrainTicket [1], and injected various typical faults to validate our approach. Experimental results indicate that compared to existing approach, this approach can more accurately identify anomalies.

Sarcasm is a form of language used to convey implicit information contradicting the literal meaning of words, often observed on online social media platforms. Accurately detecting satirical or ironic expressions could significantly enhance sentiment analysis and opinion mining. For multi-modal data, capturing both inter- and intra-modal incongruities is crucial for this task. Recently, graph-based approaches to modeling incongruous features bet-ween image and text have made significant progress in this task. However, these methods rely on static networks to capture incongruous features, which makes them inflexible in adapting to diverse groups of text and image, or neglect important information due to inadequate use of text and image. To address these limitations, we propose a multi-modal sarcasm detection model based on the combination of Graph Convolutional Network and Dynamic Network. The graph convolutional network learns the incongruity of the three modal graphs and makes full use of the object-level information. The dynamic network dynamically captures the incongruity between the global-level image and the text and can flexibly adapt to different image and related text. At the same time, we generate augmented text to better utilize the text information. Extensive experiments demonstrate that our proposed method performs favorably against state-of-the-art approaches.