Yumeng Hou

Biographie

Yumeng Hou is a Chinese-speaking researcher at the Laboratory for Experimental Museology, EPFL. Yumeng received her MSc in Computer (data) Science and is now pursuing her PhD under the supervision of Prof. Sarah Kenderdine. Her current research involves intangible cultural heritage, computational archives, embodied knowledge, Chinese martial arts, and cultural AI.Before joining EMPLUS, Yumeng was a professional product manager, data analyst, and data storyteller.

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https://people.epfl.ch/yumeng.hou?lang=fr

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Publications associées (9)

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This research, within the framework of computational archives, inspects a novel approach to representing intangible knowledge in traditional martial arts. The methodology presents a unity of ontological modeling, semantic annotation, and feature-based machine learning to display, (re-)interpret, and transmit embodied knowledge using multimodal digital archives.

Zentrum für Informationsmodellierung - Austrian Centre for Digital Humanities, University of Graz2023

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Unlocking a multimodal archive of Southern Chinese martial arts through embodied cues

Yumeng Hou, Sarah Irene Brutton Kenderdine, Fadel Mamar Seydou

Purpose: Despite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have created new avenues to open up cultural data resources, yet mainly as apparatuses for well-annotated and object-based collections. Hence, there is a pressing need for empowering the representation of intangible expressions, particularly embodied knowledge within its cultural context. To address this issue, the authors propose to inspect the potential of machine learning methods to enhance archival knowledge interaction with intangible cultural heritage (ICH) materials. - Design/methodology/approach: This research adopts a novel approach by combining movement computing with knowledge-specific modelling to support retrieving through embodied cues, which is applied to a multimodal archive documenting the cultural heritage (CH) of Southern Chinese martial arts. - Findings: Through experimenting with a retrieval engine implemented using the Hong Kong Martial Arts Living Archive (HKMALA) datasets, this work validated the effectiveness of the developed approach in multimodal content retrieval and highlighted the potential for the multimodal's application in facilitating archival exploration and knowledge discoverability. - Originality/value: This work invents knowledge-specific encoding approach through a deep-learning workflow. The article underlines that the convergence of algorithmic reckoning and content-centred design holds promise for transforming the paradigm of archival interaction, thereby augmenting data access to (I)CH materials.

2023

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Encoding the Whole-of-Environment Knowledge: A computational experiment with the Hong Kong Martial Arts Living Archive

Yumeng Hou, Sarah Irene Brutton Kenderdine

AMPS2022

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