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Distributed Meta-Learning for Context Based on Culture Heritage Collection Data in an Immersive Reality System
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0001-5924-5457
Stockholm University, Faculty of Humanities, Department of Slavic and Baltic Studies, Finnish, Dutch, and German, German.ORCID iD: 0000-0003-0390-7487
2022 (English)In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 210, p. 70-77Article in journal (Refereed) Published
Abstract [en]

This article proposes a new workflow for Culture Heritage (CH) Collection Data in which distributed meta-learning with context-based fusion is required. The multidisciplinary research outlines a new paradigm for collaborative creation of a context- based data fusion CH workflow. Whose criterion restricts the search space to the cultural sector to include multiple other CH object types, texts and predominantly analyzes visualizations of CH object metadata. The approach promotes user-driven content creation and offsets economic models, thereby rewarding creators and performers. In response to these challenges, we propose a framework for bringing about massive and real-time presence and awareness on the Internet through an Internet of Things infrastructure to connect objects, context and intangible assets. We enable an online virtual world that incorporates proximity of pervasive information, objects, people, processes, data and places. Finally, we investigate some new ways to achieve immersive experiences via distributed meta-learning computing and point out the necessity to do more with regard to collaborative interaction creation.

Place, publisher, year, edition, pages
2022. Vol. 210, p. 70-77
Keywords [en]
Distributed Data processing, Creative technologies, Distributed system, Immersive participation, IoT
National Category
Computer and Information Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-211923DOI: 10.1016/j.procs.2022.10.121Scopus ID: 2-s2.0-85144819070OAI: oai:DiVA.org:su-211923DiVA, id: diva2:1714308
Available from: 2022-11-29 Created: 2022-11-29 Last updated: 2024-10-14Bibliographically approved

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Rahmani Chianeh, RahimTienken, Susanne

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