Zobrazeno 1 - 10
of 711
pro vyhledávání: '"Liu Guanjun"'
Graph Neural Networks (GNNs) are widely used in financial fraud detection due to their excellent ability on handling graph-structured financial data and modeling multilayer connections by aggregating information of neighbors. However, these GNN-based
Externí odkaz:
http://arxiv.org/abs/2407.17333
Autor:
Zhang Zhengchuan, Viacheslav Tarelnyk, Ievgen Konoplianchenko, Liu Guanjun, Wang Hongyue, Du Xin, Ju Yao, Li Zongxi
Publikováno v:
Materials Research Express, Vol 10, Iss 3, p 036401 (2023)
The running-in coatings were formed on the surface of tin bronze QSn10-1 by electroerosive alloying (EEA) with soft antifriction materials such as silver, copper, Babbitt B83 and graphene oxide (GO). The mass transfer, surface roughness, coating thic
Externí odkaz:
https://doaj.org/article/babd71bb74914cd0a19670bc6ca43ca4
Rust relies on its unique ownership mechanism to ensure thread and memory safety. However, numerous potential security vulnerabilities persist in practical applications. New language features in Rust pose new challenges for vulnerability detection. T
Externí odkaz:
http://arxiv.org/abs/2401.01114
Autor:
Tian, Yue, Liu, Guanjun
How to obtain informative representations of transactions and then perform the identification of fraudulent transactions is a crucial part of ensuring financial security. Recent studies apply Graph Neural Networks (GNNs) to the transaction fraud dete
Externí odkaz:
http://arxiv.org/abs/2307.05121
Many machine learning methods have been proposed to achieve accurate transaction fraud detection, which is essential to the financial security of individuals and banks. However, most existing methods leverage original features only or require manual
Externí odkaz:
http://arxiv.org/abs/2307.05633
Deep reinforcement learning (DRL) performance is generally impacted by state-adversarial attacks, a perturbation applied to an agent's observation. Most recent research has concentrated on robust single-agent reinforcement learning (SARL) algorithms
Externí odkaz:
http://arxiv.org/abs/2307.00907
Autor:
Song, Jian, Liu, Guanjun
Many operations in workflow systems are dependent on database tables. The classical workflow net and its extensions (e.g., worflow net with data) cannot model these operations so that some related errors cannot be found by them. Recently, workflow ne
Externí odkaz:
http://arxiv.org/abs/2307.03685
Autor:
Zhou, Ziyuan, Liu, Guanjun
Multi-Agent Reinforcement Learning (MARL) has been widely applied in many fields such as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are vulnerable to adversarial perturbations on agent states. Robustness testing for a t
Externí odkaz:
http://arxiv.org/abs/2306.06136
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI) technique. However, current studies and applications need to address its scalability, non-stationarity, and trustworthiness. This paper aims to review methods and
Externí odkaz:
http://arxiv.org/abs/2305.10091