Automatic Report Generation(ARG), which aims to automatically provide observations on images, is challenged by the lack of coherence between multiple scenes and precise description of multiple lesions. In order to explore the task of multi-scene multi-lesion report generation(MSMLRG) in one shot, in this paper, we introduce a multi-scene multi-lesion report generation framework to extract the scene-report alignment relation and scene-topic relation. Specifically, we design a scene-report feature aligner to achieve fine-grained alignment of different lesion in different scenes in images and reports, and incorporate a topic-aware module to help generate a topic text vocabulary for different scenes. Our framework has been successfully experimented on several automatic report generation models, and performs well on automatic evaluation metrics. The framework for one-shot report generation during multi-scene not only fills the gap of multi-scene image report generation, but also effectively improves the accuracy and consistency of diagnostic reports.

Automated Report Generation (ARG) aims to automatically provide observations on images based on images, but faces difficulties due to the lack of graphic correspondence between multi-scenes and precise descriptions of multi-scene medical knowledge topics. To explore the task of multi-scene multi-lesion report generation (MSMLRG) and to address the above difficulties, this study proposes a recurrent scene topic-aware (RSTA) report generation model. We simulate the process of report writing by ear, nose, and throat (ENT) specialists with an innovative combination of the scene topic-aware module and recurrent generation module architectures. The scene-report feature aligner is used to realize the fine-grained alignment of images and different lesions in different scenes in the report, and incorporates a topic-aware module to help generate the topic text vocabulary for different scenes. The recurrent module includes an initialization statement generation module and a recurrent paragraph generation module to ensure coherent multi-sentence report generation for multiple complex images of endoscopy. Our approach has been successfully experimented on existing single-scene public datasets and multi-scene datasets using nasal endoscopy as an example, with excellent performance on natural language generation (NLG) and clinical efficacy (CE) metrics.

Timely detection of performance regression issues is critical to ensuring the stability and user experience of software systems. Traditional methods often rely on high-quality annotated data or data distribution assumptions, which cannot effectively adapt to performance changes in dynamic workload environments. To solve this problem, we propose DynamicRegress, a performance regression detection method based on Siamese network and semi-supervised learning. DynamicRegress integrates multi-dimensional key performance indicators (KPIs) with workload context to accurately characterize system states and detect performance regressions in real time. By employing a dual weight-shared LSTM network, DynamicRegress reduces training complexity while retaining strong feature extraction capabilities. Data augmentation and a weighted loss function are incorporated to enhance the learning of minority regression cases, mitigating the class imbalance issue. Additionally, a semi-supervised learning strategy generates high-quality pseudo-labels to expand the training dataset, effectively addressing the challenge of limited labeled data. Experiments on production data from a top-tier global cloud service provider demonstrate that DynamicRegress achieves a superior F1 Score of 0.958 (outperforming the best baseline method by 0.282) while maintaining a low detection latency of 0.006 seconds per KPI pair. DynamicRegress provides a robust adaptive solution for performance regression detection in dynamic and complex software systems, and we have made the code publicly available to facilitate further research.

With the booming of large-scale network devices, anomaly detection on multivariate time series (MTS), such as a combination of CPU utilization, average response time, and network packet loss, is important for system reliability. Although a collection of learning-based approaches have been designed for this purpose, our study shows that these approaches suffer from long initialization time for sufficient training data. Our previously proposed JumpStarter model stands as a MTS anomaly detection method characterized by its brief initialization time and commendable detection performance. However, it suffers from high computational cost and inappropriateness for periodic MTS. In this paper, we propose VersaGuardian, which introduces the Dynamic Mode Decomposition technique to MTS anomaly detection for diverse types of MTS in a rapidly initialized, computationally efficient manner. With real-world MTS datasets collected from three companies, our results show that VersaGuardian achieves an average F1 score of 94.42%, significantly outperforming the popular anomaly detection algorithms, with a much shorter initialization time of 20 minutes and detection time of 15.28 milliseconds.

Microservices improve the scalability and flexibility of monolithic architectures to accommodate the evolution of software systems, but the complexity and dynamics of microservices challenge system reliability. Ensuring microservice quality requires efficient failure diagnosis, including detection and triage. Failure detection involves identifying anomalous behavior within the system, while triage entails classifying the failure type and directing it to the engineering team for resolution. Unfortunately, current approaches reliant on single-modal monitoring data, such as metrics, logs, or traces, cannot capture all failures and neglect interconnections among multimodal data, leading to erroneous diagnoses. Recent multimodal data fusion studies struggle to achieve deep integration, limiting diagnostic accuracy due to insufficiently captured interdependencies. Therefore, we proposeUniDiag, which leverages temporal knowledge graphs to fuse multimodal data for effective failure diagnosis.UniDiagapplies a simple yet effective stream-based anomaly detection method to reduce computational cost and a novel microservice-oriented graph embedding method to represent the state of systems comprehensively. To assess the performance ofUniDiag, we conduct extensive evaluation experiments using datasets from two benchmark microservice systems, demonstrating its superiority over existing methods and affirming the efficacy of multimodal data fusion. Additionally, we have publicly made the code and data available to facilitate further research.