Sign Language Resources - Lexical Resources
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PhD researcher in Sign Language Processing at CVSSP, University of Surrey. Working on computer vision, NLP, and sign language linguistics.
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As part of the ML Reproducibility Challenge 2022 (MLRC), we conducted an independent reproducibility study of Joint Multisided Exposure Fairness for Recommendation — a method addressing exposure fairness for both users and items in recommender systems.
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Work from my time at the AEgIS experiment at CERN has been published in Nuclear Instruments and Methods in Physics Research Section A. The paper describes a scintillating fiber detector developed to monitor ortho-positronium (o-Ps) formation and decay, used in the context of antihydrogen production experiments.
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Published in Nuclear Instruments and Methods in Physics Research Section A, 2022
Development of a scintillating fiber detector system for monitoring positronium formation and decay at the AEgIS antimatter experiment at CERN.
Recommended citation: Rienäcker, B., Brusa, R.S., Caravita, R., Mariazzi, S., Penasa, L., Pino, F., Ranum, O.A., Nebbia, G. (2022). "A fiber detector to monitor ortho-Ps formation and decay." NIM-A. 1027, 166275. https://doi.org/10.1016/j.nima.2021.166275
Published in ML Reproducibility Challenge 2022 · ReScience, 2023
Reproducibility study of joint multisided exposure fairness for recommendation systems, as part of the ML Reproducibility Challenge 2022.
Recommended citation: Hu, A., Ranum, O., Pozrikidou, C., Zhou, M. (2023). "Reproducibility Study of Joint Multisided Exposure Fairness for Recommendation." ML Reproducibility Challenge 2022. https://openreview.net/forum?id=A0Sjs3IJWb-
Published in LREC-COLING 2024 · Sign Language Workshop, 2024
High-resolution motion capture dataset of 2,000 signs across ASL and NGT, with semi-automatic phonetic annotation that matches or exceeds expert accuracy.
Recommended citation: Ranum, O., Otterspeer, G., Andersen, J., Belleman, R., Roelofsen, F. (2024). "3D-LEX v1.0." LREC-COLING 2024 Sign Language Workshop. pp. 290–301. https://aclanthology.org/2024.signlang-1.33/
Published in GRaM Workshop @ ICML 2024, 2024
A novel spatio-temporal multi-view benchmark for isolated sign recognition of NGT. SE(2)-equivariant Temporal-PONITA improves multi-view recognition by 8–22% over the SL-GCN baseline.
Recommended citation: Ranum, O., Wessels, D., Otterspeer, G., Bekkers, E., Roelofsen, F., Andersen, J. (2024). "The NGT200 Dataset." GRaM Workshop @ ICML 2024. https://openreview.net/forum?id=idkNzTC67X
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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