Quesma Releases OTelBench: Independent Benchmark Reveals Frontier LLMs Struggle with Real-World SRE Tasks

New benchmark shows top LLMs achieve only 29% pass rate on OpenTelemetry instrumentation, exposing the gap between ...
IEEE

Anomaly Detection in ECG Signal Using Unsupervised Learning

Abstract: An essential diagnostic tool for finding irregularities in the rhythm and function of the heart is the electrocardiogram (ECG) signal. After obtaining the ECG signals from the adjacent ...
GitHub

Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

07.2025: Dinomaly has been integrated in Intel open-edge Anomalib in v2.1.0. Great thanks to the contributors for the nice reproduction and integration. Anomalib is a comprehensive library for ...
IEEE

Advancing Anomaly Detection: Non-Semantic Financial Data Encoding With Large Language Models

Abstract: Detecting anomalies in general ledger data is of utmost importance to ensure the trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms ...
GitHub

Steel Defect Detection with YOLOv8

The dataset is already organized in YOLO format in the steel_dataset/ directory. If you need to reorganize from original format, see utility/reorganize_dataset.py. steel-defect-detection/ ├── ...