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Article
Computer Science, Artificial Intelligence
Zhenguang Liu et al.
Summary: Smart contract vulnerability detection has gained significant attention recently. Existing methods heavily rely on expert-defined rules, which are labor-intensive and non-scalable. This paper proposes a method using graph neural networks and expert knowledge for smart contract vulnerability detection. Experimental results show improved accuracy compared to state-of-the-art methods on different types of vulnerabilities.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Tao Zhang et al.
Summary: This paper analyzes the functional and non-functional requirements of CBDC design and reviews the literature on blockchain-based CBDC schemes. The analysis findings suggest that permissioned blockchain is more suitable for CBDC, and there are challenges in blockchain-based CBDC, such as performance, scalability, and cross-chain interoperability.
Review
Economics
Hicham Sadok et al.
Summary: This article discusses the impact of artificial intelligence (AI) on the credit analysis process in banks and other financing institutions. AI models, combined with increased computing power, provide new sources of information for credit assessments. The use of AI and big data can improve prediction accuracy and access to credit for underserved borrowers, but it also raises concerns regarding biases and regulatory issues. The establishment of a new generation of financial regulation is necessary to address these limitations.
COGENT ECONOMICS & FINANCE
(2022)
Article
Green & Sustainable Science & Technology
Nigang Sun et al.
Summary: Cryptocurrencies have the potential to drive global socioeconomic growth, but the lack of identity management among virtual asset service providers poses risks of abuse. Establishing identity systems on blockchains can help VASPs enhance identity management and combat cryptocurrency crimes to achieve sustainability goals.
Article
Physics, Multidisciplinary
Zhuoming Gu et al.
Summary: This paper investigates the detection of abnormal transaction amounts in cryptocurrency exchanges. By collecting exchange data and conducting correlation analysis, the most important factors influencing transaction amounts are identified, and a prediction model based on deep learning is developed. The model calculates the deviation between predicted and actual transaction amounts for abnormal transaction amount detection. A case study reveals abnormal transaction amounts related to policy changes, industry events, and illegal activities.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Wenyu Zhang et al.
Summary: The study introduces a novel multi-stage ensemble model with enhanced outlier adaptation in credit scoring, using a local outlier factor algorithm and bagging strategy. A new dimension-reduced feature transformation method and a stacking-based ensemble learning method are proposed to improve predictive power and feature interpretability.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
Yannan Li et al.
Summary: Monero offers high anonymity for users and transactions, but lacks user accountability, which is crucial to combat criminal activities in cryptocurrency transactions. This paper introduces Traceable Monero, a new cryptocurrency that aims to strike a balance between user anonymity and accountability. By overlaying Monero with tracing mechanisms, Traceable Monero ensures security without significantly impacting transaction efficiency.
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING
(2021)
Article
Computer Science, Information Systems
Edwin Ayisi Opare et al.
Article
Business
Xiaojun Ma et al.
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2018)
Article
Computer Science, Artificial Intelligence
Andreas Ziegler et al.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2014)
Article
Economics
Gert Loterman et al.
INTERNATIONAL JOURNAL OF FORECASTING
(2012)
