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The artifact consists of a virtual machine with all necessary software to execute the code accompanying in the paper's GitHub repository: https://github.com/itu-square/gauss-privug. The repository contains a proof-of-concept implementation of our inference engine. All the experiments in the paper are included here. For convenience, they are presented in a Jupyter notebook with further comments. The experiments generate all the evaluation plots in the paper. The password of the Zip file is: sefm_conference_2023
| selected citations These citations are derived from selected sources. 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). | 0 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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