论文标题
一种新的高性能方法，用于近似模式匹配，以用于基于区块链的无牙代币（NFTS）中的窃检测
A New High-Performance Approach to Approximate Pattern-Matching for Plagiarism Detection in Blockchain-Based Non-Fungible Tokens (NFTs)
论文作者
论文摘要
我们正在使用基于NDFA的方法显着提高了与其他现有的相似性测量值相比，我们提出了一种快速而创新的方法，用于对窃检测进行近似模式匹配。我们在基于区块链的无牙代币（NFTS）的背景下概述了方法的优势。我们在几种现实世界中介绍，正式化，讨论和测试我们所提出的方法，并使用窃检测中常用的不同相似性度量，并且在整个测试范围内观察到显着的吞吐量增强，几乎没有妥协检测过程的准确性。我们得出的结论是，我们的方法适合且足以执行用于窃检测的近似模式匹配，并概述了未来改进的研究方向。
We are presenting a fast and innovative approach to performing approximate pattern-matching for plagiarism detection, using an NDFA-based approach that significantly enhances performance compared to other existing similarity measures. We outline the advantages of our approach in the context of blockchain-based non-fungible tokens (NFTs). We present, formalize, discuss and test our proposed approach in several real-world scenarios and with different similarity measures commonly used in plagiarism detection, and observe significant throughput enhancements throughout the entire spectrum of tests, with little to no compromises on the accuracy of the detection process overall. We conclude that our approach is suitable and adequate to perform approximate pattern-matching for plagiarism detection, and outline research directions for future improvements.
