Low Power Blockchained E-Vote Platform for University Environment
Bibliographic record
Abstract
With the onset of the COVID-19 pandemic and the succession of its waves, the transmission of this disease and the number of deaths caused by it have been increasing. Despite the various vaccines, the COVID-19 virus is still contagious and dangerous for affected people. One of the remedies to this is precaution, and particularly social distancing. In the same vein, this paper proposes a remote voting system, which has to be secure, anonymous, irreversible, accessible, and simple to use. It therefore allows voters to have the possibility to vote for their candidate without having to perform the operation on site. This system will be used for university elections and particularly for student elections. We propose a platform based on a decentralized system. This system will use two blockchains communicating with each other: the public Ethereum blockchain and the private Quorum blockchain. The private blockchain will be institution-specific. All these blockchains send the necessary data to the public blockchain which manages different data related to the universities and the ministry. This system enables using encrypted data with the SHA-256 algorithm to have both security and information security. Motivated by the high energy consumption of blockchain and by the performance improvements in low-power, a test is performed on a low-power embedded platform Raspberry PI4 showing the possibility to use the Blockchain with limited resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".