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[Subset] Unique Identification of 50,000+ Virtual Reality Users from Head & Hand Motion Data

Authors: Vivek Nair;

[Subset] Unique Identification of 50,000+ Virtual Reality Users from Head & Hand Motion Data

Abstract

A sub-sampled version of the main experiment from "Unique Identification of 50,000+ Virtual Reality Users from Head & Hand Motion Data," with up to 5,000 users instead of 50,000. Allows for a partial reproduction of the results with significantly fewer resources than running the main evaluation. To run, run C1.py, C2.py, and C3.py. After running C1.py, an extrapolation of the results will be printed in the terminal and saved as results.pdf. After running C2.py, the feature importance distribution will be printed in the terminal and saved as features.pdf. After running C3.py, the open-world evaluation results will be printed in the terminal. The following dependencies are required: PyTorch (torch) v1.13.1 pandas v1.5.2 tqdm v4.64.1 scikit-learn (sklearn) v1.2.0 NumPy (numpy) v1.24.0 LightGBM (lightgbm) v3.3.3 Joblib (joblib) v1.2.0 NetworkX (networkx) v3.0 Matplotlib (matplotlib) v3.6.2

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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