A Novel Heuristics for Validating Pairwise Transactions on Cryptocurrencies
Bibliographic record
Abstract
Cryptocurrencies especially bitcoin has received a lot of attention in the past year. The popularity has increased the volume of transactions in an unprecedented way. The time to complete a simple pairwise transaction from one virtual wallet to other takes time in proof-of-work where transactions are added into the Blockchain. At the same time, validating a pairwise transaction is very significant in this new technology as a payment option. Through this study, we propose a heuristic approach for validating pairwise transactions on cryptocurrencies. Our heuristic simulate all entities sending and receiving transactions among themselves. We use SHA256 algorithm to enhance our solution for pairwise transactions, creating a local Blockchain of transactions that has been used in the development of various Blockchain systems. For our experiments, we ran simulations in-file and in-memory with 2 million transactions in 290.39 and 5.34 seconds respectively. Our peak transactions per second was 6.887 using a file persistence version and 374.255 with in-memory version. Based on our experiments we conclude that is possible to improve the number of transactions processed per second increasing the size of the block as well as avoiding access to the file during the simulation. We also present some of our results implementing our algorithm in a parallel environment and hence show better performance.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".