HUGO-CS: A Hybrid-Labeled, Uncertainty-Aware, General-Purpose, Observational Dataset for Cold Spray

Cold spraying is an increasingly common approach for repairing and manufacturing components due to its solid-state manufacturing capabilities. However, process optimization remains difficult due to many interdependent parameters and the lack of large-scale, machine-readable data to support modeling. While the scientific literature contains many relevant experiments, results are inconsistently reported (often in tables and figures) and use non-uniform units, limiting utilization at scale. To address these limitations, this work presents HUGO-CS, a literature-derived dataset of 4,383 cold-spray experiments with 144 features from 1,124 sources, exceeding the previous largest dataset (137 samples) by 30x. With completely manual extraction requiring an average of 91 minutes per document, this work designs and leverages a Hybrid-labeled, Uncertainty-aware, General-purpose, Observational extraction framework, called HUGO, to support this extraction. HUGO combines automated LLM-based labeling with targeted manual label refinement to handle this experimental result extraction process from scientific literature. To balance labeling efficiency with extraction accuracy, HUGO introduces a Hierarchical Risk Mitigation (HRM) to route LLM outputs with a high risk of potential errors for manual review, while retaining low-risk records as auto-labeled. Lastly, HUGO post-processing consolidates categorical descriptors, maps reported feedstock chemistries into structured continuous compositions, and normalizes units across sources. Of the 4,383 reported experiments, 1,765 are hand-labeled, providing a high-quality labeled subset for benchmarking, error analysis, and higher-fidelity data points. All code to replicate this work, along with the complete HUGO-CS dataset, are released under a CC-BY license at https://github.com/sprice134/HUGO.

KRONE: Hierarchical and Modular Log Anomaly Detection

Log anomaly detection is crucial for uncovering system failures and security risks. Although logs originate from nested component executions with clear boundaries, this structure is lost when they are stored as flat sequences. As a result, state-of-the-art methods risk missing true dependencies within executions while learning spurious ones across unrelated events. We propose KRONE, the first hierarchical anomaly detection framework that automatically derives execution hierarchies from flat logs for modular multi-level anomaly detection. At its core, the KRONE Log Abstraction Model captures application-specific semantic hierarchies from log data. This hierarchy is then leveraged to recursively decompose log sequences into multiple levels of coherent execution chunks, referred to as KRONE Seqs, transforming sequence-level anomaly detection into a set of modular KRONE Seq-level detection tasks. For each test KRONE Seq, KRONE employs a hybrid modular detection mechanism that dynamically routes between an efficient level-independent Local-Context detector, which rapidly filters normal sequences, and a Nested-Aware detector that incorporates cross-level semantic dependencies and supports LLM-based anomaly detection and explanation. KRONE further optimizes hierarchical detection through cached result reuse and early-exit strategies. Experiments on three public benchmarks and one industrial dataset from ByteDance Cloud demonstrate that KRONE achieves consistent improvements in detection accuracy, F1-score, data efficiency, resource efficiency, and interpretability. KRONE improves the F1-score by more than 10 percentage points over prior methods while reducing LLM usage to only a small fraction of the test data.