A Payment Channel Based Hybrid Decentralized Ethereum Token Exchange
Bibliographic record
Abstract
Traditional centralized token exchange (CEX) is criticized for its security and privacy issues, since crypto currency users are required to surrender their private keys to the exchange. In contrast, decentralized token exchange (DEX) solves this issue by introducing additional trading gas fee and latency to the system. Hybrid decentralized token exchange (HEX) has been proposed to combine the advantages of CEX and DEX. However, existing HEX is still suffering from two issues. The first issue is that it is unfriendly for a trader who needs to exchange tokens frequently within a certain period of time, due to the fact that it is time-consuming and expensive. The second issue is the potential network congestion in Ethereum caused by excessive simultaneous transactions from the exchange. In this paper, we propose a payment channel based HEX, which extends existing solutions by adding a new payment channel layer to benefit frequent traders and alleviate network congestion.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".