Annotation of Real-World Data for Artificial Intelligence Systems
Description
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.
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Content
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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.
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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.
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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.
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-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.
