Annotation of Real-World Data for Artificial Intelligence Systems : 9th International Workshop, ARDUOUS 2025, Bologna, Italy, October 25-26, 2025, Proceedings (Communications in Computer and Information Science)

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Annotation of Real-World Data for Artificial Intelligence Systems : 9th International Workshop, ARDUOUS 2025, Bologna, Italy, October 25-26, 2025, Proceedings (Communications in Computer and Information Science)

Tonkin, Emma L. (EDT)/ Tourte, Gregory J. L. (EDT)/ Yordanova, Kristina (EDT)

ウェブストア価格 ¥17,710（本体¥16,100）
Springer Nature Switzerland AG（2025/11発売）
外貨定価 US$ 84.99
ポイント 161pt

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製本 Paperback:紙装版/ペーパーバック版
言語 ENG
商品コード 9783032091161

Full 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.

.-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.