System Design of QR Code-Based Deterministic Cryptocurrency Wallet
Bibliographic record
Abstract
This study addresses a critical challenge in cryptocurrency adoption: managing complex seed phrases for wallet access. Traditional methods require users to store seed phrases securely, leading to potential asset loss due to human error or improper storage. This study develops a QR code-based deterministic wallet solution to replace the reliance on conventional seed phrases, with the aim of simplifying authentication and improving security. The development method integrates React with Next.js framework, and Solidity for smart contract development. Key features include authentication via QR code scan/upload, deterministic wallet generation from a combination of QR code and password, and interoperability with external wallets (MetaMask/Phantom). The results demonstrate successful NFT verification on Etherscan and seed phrase compatibility when imported to third-party platforms. This study offers a practical and innovative solution to improve user experience and security in crypto wallet management, potentially driving wider adoption. Abstrak Penelitian ini membahas tantangan penting dalam adopsi mata uang kripto: pengelolaan frase awal yang kompleks untuk akses dompet. Metode tradisional mengharuskan pengguna untuk menyimpan frase awal dengan aman, yang menyebabkan potensi kerugian aset akibat kesalahan manusia atau penyimpanan yang tidak tepat. Penelitian ini mengembangkan solusi dompet deterministik berbasis kode QR untuk menggantikan ketergantungan pada seed phrase konvensional, dengan tujuan menyederhanakan autentikasi dan meningkatkan keamanan. Metode pengembangan mengintegrasikan React dengan framework Next.js, dan Solidity untuk pengembangan kontrak pintar. Fitur utama meliputi autentikasi via pemindaian/unggahan kode QR, pembuatan dompet deterministik dari kombinasi kode QR dan kata sandi, serta interoperabilitas dengan dompet eksternal (MetaMask/Phantom). Hasil pengujian membuktikan keberhasilan verifikasi NFT di Etherscan dan kompatibilitas seed phrase saat diimpor ke platform pihak ketiga. Penelitian ini menawarkan solusi praktis dan inovatif untuk meningkatkan pengalaman pengguna serta keamanan dalam manajemen dompet kripto, berpotensi mendorong adopsi lebih luas. Kata kunci: QR code; blockchain; NFT; Wallet Crypto
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".