Survey of latest technologies on Decentralized applications using Blockchain
Bibliographic record
Abstract
In recent years, Websites and Web applications have been playing a significant role in everyday life. In the past decade, the number of websites progressed from three million to more than 1.7 billion. The majority of contributions to this number are produced by CMS (content management systems). Current content delivery and management services has many issues in securing data, and are vulnerable to cyberthreats. Hence, one of the methods to create apps transparent and flexible is by implementing Deoentralized applications (Dapps). This paper describes the major security issues of cms and propose an efficient scheme to build a cms dapp on the most familiar blockchain platform-Ethereum. The main Blockchain has 2 categories: partially DApp and fully DApp for the software architectures in DApps. The full and partial Dapps have their own advantages and disadvantages. Here in this paper, a software architecture for full and partial DApp focusing on simulating asset transactions for comparing both the DApps efficiency has been proposed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".