Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Protecting Elections by Recounting Ballots
Authors: Edith Elkind, Jiarui Gan, Svetlana Obraztsova, Zinovi Rabinovich, Alexandros A. Voudouris
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a detailed analysis of the computational complexity of the algorithmic problems faced by the attacker and the defender. Our main results are summarized in Table 1. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Oxford 2School of Computer Science and Engineering, Nanyang Technological University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Algorithms are described in natural language. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no information on public dataset availability for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve data partitioning or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details for running experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details, hyperparameters, or training configurations. |