ElectionBlock: An Electronic Voting System using Blockchain and Fingerprint Authentication
Bibliographic record
Abstract
Voting is the method of choice used to make a large number of democratic decisions amongst many groups of people. Regardless of whether the method is used in professional or casual scenarios, it provides a fair and efficient way to determine a decision based on the majority. In smaller groups, keeping track of voter decisions is not a difficult task, however, in situations where there are hundreds of thousands of voters, keeping a precise record of voter decisions becomes important and more difficult. The advancements in blockchain technology provide a potential solution to the record-keeping problem of contemporary voting procedures, as blockchain technology by design, excels in applications where multiple users are working on immutable data. In this paper we discuss the design and development of ElectionBlock, a voting system that provides its own blockchain, running on a centralized network of nodes, with the integration of a biometric scanner, to maintain vote integrity and distinguish between registered and unregistered voters. This scheme allows data immutability while providing the user with security and control over their ballot. Experimental results demonstrate the potential for scalability of the system to handle a high volume of votes from multiple servers while maintaining data integrity, performance, and security. This paper will address the considerations taken to develop and implement the centralized and independent blockchain network for use as a voting platform with the integration of biometrics for the purpose of enhanced user security.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".