ElectS: Advanced E-Voting System with Facial Recognition
Bibliographic record
Abstract
Traditional elections can be costly, time-consuming, and logistically complex in economically constrained countries such as Sri Lanka. While manual result counting causes various problems regarding delays and manipulation, voters, particularly the elderly and those living abroad, often face difficulties with the traditional process. Furthermore, the use of unverifiable sources for candidate data allows for the free flow of false information. This work presents ElectS, a web-based e-voting system that addresses these limitations. We developed ElectS utilizing the MERN stack; it employs facial characteristics and hand verifi-cation to accurately authenticate voters and prevent fraud. To facilitate wiser choices, the system also presents open applicant profiles. our study demonstrates ElectS's u ccess, including sub-stantially lowered election costs, improved accessibility, faster and more accurate results, and enhanced democratic integrity. ElectS aims to develop the future of elections in underdeveloped countries by prioritizing voter transparency, accountability, and the fundamental right to vote.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".