Blockchain technology is an emerging technology, and cryptocurrency is the most well-known and successful blockchain application. With the development of the concept of blockchain technology, scientists and practitioners have found the potential of blockchain technology in the supply chain, which has led to much research on blockchain applications. There have been many reviews on the subject, but most of them are specific to particular supply chains and lack specific research on published papers. This paper addresses this research gap by examining, through qualitative analysis, the study finds that blockchain applications are moving in the direction of efficient automation. Appropriate consensus algorithms in different supply chain contexts will improve efficiency while reducing costs, while smart contracts play a huge role in security. Therefore, this paper explored articles related to the topic and explored articles in recent years by keyword combination.

With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD (LSTM-Transformer smart Ponzi schemes detection), which is a Ponzi scheme detection method that combines Long Short-Term Memory (LSTM) and Transformer considering the time-series transaction information of smart contracts as well as the global information. Based on the verified smart contract addresses, account features, and code features are extracted to construct a feature dataset, and the SMOTE-Tomek algorithm is used to deal with the imbalanced data classification problem. By comparing our method with the other four typical detection methods in the experiment, the LT-SPSD method shows significant performance improvement in precision, recall, and F1-score. The results of the experiment confirm the efficacy of the model, which has some application value in Ethereum Ponzi scheme smart contract detection.

Sarcasm detection is a complex and challenging task, particularly in the context of Chinese social media, where it exhibits strong contextual dependencies and cultural specificity. To address the limitations of existing metho... | Find, read and cite all the research you need on Tech Science Press