Tonkin / Tourte / Yordanova Annotation of Real-World Data for Artificial Intelligence Systems

9th International Workshop, ARDUOUS 2025, Bologna, Italy, October 25–26, 2025, Proceedings
Erscheinungsjahr 2025
ISBN: 978-3-032-09117-8
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 203 Seiten

Reihe: Communications in Computer and Information Science

This volume constitutes the refereed proceedings of 9th International Workshop on Annotation of Real-World Data for Artificial Intelligence Systems, ARDUOUS 2025, held in Bologna, Italy, during October 25–26, 2025.

The 11 full papers included in this book were carefully reviewed and selected from 16 submissions. These papers have been categorized into the following themes: Automating Ergonomics: Scalable AI for Technical Hand Grip Classification; Activity Recognition; Annotation of Textual Data; Relation Extraction from Real-World Unstructured Text in the Domain of Dementia.

Zielgruppe

Research

Autoren/Hrsg.

Tonkin, Emma L.

Tourte, Gregory J. L.

Yordanova, Kristina

Inhaltsverzeichnis

.-Automating Ergonomics: Scalable AI for Technical Hand Grip Classification.
.-Automating Ergonomics: Scalable AI for Technical Hand Grip Classification.
.-Recommendations for Datasets Creation Process for Human Motion Generation in Industrial Simulations.
.-Toppled Realities: Challenges in Generation and Validation of Synthetic Data.
.-Activity recognition.
.-Retrieval-based Annotation for Multi-Channel Time Series Data of Human Activities.
.-Towards Standardized Dataset Creation for Human Activity Recognition: Framework, Taxonomy, Checklist, and Best Practices.
.-Towards Practical, Best Practice Video Annotation to Support Human Activity Recognition.
.-Annotation of textual data.
.-Large Language Models Rival Human Performance in Historical Labeling.
.-Annotation and label validation of upper-tier tribunal decisions in immigration law.
.-Span-Level Domain-Specific Annotated Student Feedback Pilot Dataset.
.-Relation Extraction from Real-World Unstructured Text in the Domain of Dementia.
.-Relation Extraction from Real-World Unstructured Text in the Domain of Dementia.
.-Assessing privacy-friendly local open-source voice annotation for participants with Parkinson’s disease.