Blockchain-based Lightweight Transaction Process Modeling and Development
Bibliographic record
Abstract
Recently, blockchain systems are being applied in various application fields by combining blockchain with existing legacy systems. In particular, the cryptocurrency payment transaction system to support digital financial transactions is emerging as an important issue. Nevertheless, the development and valuation of blockchain-based cryptocurrency transactions and application services are fluctuating. With the advent of the Untact era due to Covid-19 recently, the commercialization of cryptocurrency is becoming more focused. In addition, as technical constraints for the spread of commercialization, there are problems of reaching a fast consensus in a large-scale blockchain network, consuming excessive energy for calculation, and storing the entire blockchain for verification. We propose a lightweight blockchain transaction process modeling to overcome these problems and to enhance blockchain applicability in an application environment where computing resources are weak. In addition, we propose a lightweight transaction-based blockchain application model optimized for areas with weak computing and network resources such as vending machines and ATMs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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".