An Ontology-Based Augmented Reality Application Exploring Contextual Data of Cultural Heritage Sites
2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2016
In this study, we present a novel Augmented Reality (AR) application for providing contextual inf... more In this study, we present a novel Augmented Reality (AR) application for providing contextual information of cultural heritage sites. Whereas most location-based AR systems are designed to show simple information on a Point of Interest (POI) in the real world, the suggested application offers information related to various cultural heritages including tangible and intangible heritages linked to the POI. is is accomplished by a cultural heritage ontology that aggregates heterogeneous data and reorganizes information in themes and relationships. The users can browse information such as the person who created the POI and events that took place at the location as separate entities. We implemented the application for Injeongjeon and the vicinity of Changdeokgung palace and conducted user studies to determine how people explore and consume contextual information at the heritage site. We provide directions that will be useful for designing information to support heritage site visiting, and...
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Papers by Tamás Matuszka
by selecting the most informative samples from an unlabeled dataset. However, the traditional active learning process often demands extensive computational resources, hindering scalability and efficiency. In this paper, we address this critical issue by presenting a novel method designed to alleviate the computational burden associated with active learning on massive datasets. To achieve this goal, we introduce a simple, yet effective method-agnostic framework that outlines how to strategically choose and annotate data points, optimizing the process for efficiency while maintaining model performance. Through case studies, we demonstrate the effectiveness of our proposed method in reducing computational costs while maintaining or, in some cases, even surpassing baseline model outcomes. Code is available at https://github.com/aimotive/Compute-Efficient-Active-Learning
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