IntelliChain: An Intelligent and Adaptive Framework for Decentralized Applications on Public Blockchain Technologies: An NFT Marketplace Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Non-fungible tokens (NFTs), attracting interest from a variety of audiences including collectors and traders, saw transactions exceeding $50 billion in 2022. The inherent features of blockchain technology–distributed, immutable, and transparent–make it an ideal platform for verifying ownership of digital assets. Despite these advantages, the high computational and transaction costs of networks, which utilizes proof of work pose significant challenges. To overcome these, alternative public blockchains have been developed, each offering unique benefits for NFT marketplaces. Choosing the right blockchain platform is crucial but complex. In our study, we introduce a prototype NFT marketplace optimized for scalability and efficiency, capable of rapidly handling a large volume of NFT transactions. We also conducted a comparative analysis of various public blockchains to identify the most cost-effective and reliable options for NFT exchanges. Further, we developed two predictive models to enhance decision-making around transaction fees and error management, thus improving cost-efficiency and reliability. We also propose a self-adaptive mechanism that allows for dynamic switching between blockchain platforms, enhancing the flexibility, and overall performance of the marketplace. Our contributions are integrated into IntelliChain, a self-adaptive framework designed to predict optimal transaction fees, reduce errors, and adapt to changing conditions like network stability and fee structures, bolstering efficiency, and reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it