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 ...
GitHub

TDAAD – Topological Data Analysis for Anomaly Detection

TDAAD is a Python package for unsupervised anomaly detection in multivariate time series using Topological Data Analysis (TDA). Website and documentation: https://irt-systemx.github.io/tdaad/ ...
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 ...
IEEE

A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection

Abstract: In this research, we proposed a novel anomaly detection system (ADS) that integrates federated learning (FL) with blockchain for resource-constrained IoT. The proposed system allows IoT ...