Artificially Intelligent Electronic Money
Bibliographic record
Abstract
Electronic money or e-Cash is becoming increasingly popular as the preferred strategy for making purchases, both on and offline. Several unique attributes of e-Cash are appealing to customers, including the convenience of always having “cash-on-hand” without the need to periodically visit the ATM, the ability to perform peer-to-peer transactions without an intermediary, and the peace of mind associated in conducting those transactions privately. Equally important is that paper money provides customers with an anonymous method of payment, which is highly valued by many individuals. Although anonymity is implicit with fiat money, it is a difficult property to preserve within e-Cash schemes. In this article, we investigate several artificial intelligence (AI) approaches for improving performance and privacy within a previously proposed e-Cash scheme called PUF-Cash.PUF-Cash utilizes physical unclonable functions for authentication and encryption operations between Alice, the Bank, and multiple trusted third parties. The AI methods select a subset of the TTPs and distribute withdrawal amounts to maximize the performance and privacy associated with Alice's e-Cash tokens. Simulation results show the effectiveness of the various AI approaches using a large test-bed architecture.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".