I request the withdrawal of this paper because serious concerns have been identified regarding the originality of the manuscript, and the authors suspect that the fifth author may have engaged in plagiarism, including the possible use of unpublished results from a manuscript affiliated with Donghua University
Financial fraud refers to the act of obtaining financial benefits through dishonest means. Such behavior not only disrupts the order of the financial market but also harms economic and social development and breeds other illegal and criminal activities. With the popularization of the internet and online payment methods, many fraudulent activities and money laundering behaviors in life have shifted from offline to online, posing a great challenge to regulatory authorities. How to efficiently detect these financial fraud activities has become an urgent issue that needs to be resolved. Graph neural networks are a type of deep learning model that can utilize the interactive relationships within graph structures, and they have been widely applied in the field of fraud detection. However, there are still some issues. First, fraudulent activities only account for a very small part of transaction transfers, leading to an inevitable problem of label imbalance in fraud detection. At the same time, fraudsters often disguise their behavior, which can have a negative impact on the final prediction results. In addition, existing research has overlooked the importance of balancing neighbor information and central node information. For example, when the central node has too many neighbors, the features of the central node itself are often neglected. Finally, fraud activities and patterns are constantly changing over time, so considering the dynamic evolution of graph edge relationships is also very important.
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beihang University（北京航空航天大学）
Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in recent years, revealing the suspiciousness of nodes by aggregating their neighborhood information via different relations. However, few prior works have noticed the camouflage behavior of fraudsters, which could hamper the performance of GNN-based fraud detectors during the aggregation process. In this paper, we introduce two types of camouflages based on recent empirical studies, i.e., the feature camouflage and the relation camouflage. Existing GNNs have not addressed these two camouflages, which results in …
Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patterns
Department of Electrical Engineering（电气工程系）；Sharif University of Technology（谢里夫技术大学）；Department of Electrical and Electronic Engineering（电气与电子工程系）；Shahed University（沙赫德大学）
Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention
Columbia University（哥伦比亚大学）；University of Michigan（密歇根大学）；Wake Forest University（维克森林大学）；University of Minnesota（明尼苏达大学）；The University of Chicago（芝加哥大学）；Northeastern University（东北大学）
Amazon and other e-commerce sites must employ mechanisms to protect their millions of customers from fraud, such as unauthorized use of credit cards. One such mechanism is order fraud evaluation, where systems evaluate orders for fraud risk, and either "pass" the order, or take an action to mitigate high risk. Order fraud evaluation systems typically use binary classification models that distinguish fraudulent and legitimate orders, to assess risk and take action. We seek to devise a system that considers both financial losses of fraud and long-term customer satisfaction, which may be impaired…
The large variety of digital payment choices available to consumers today has been a key driver of e-commerce transactions in the past decade. Unfortunately, this has also given rise to cybercriminals and fraudsters who are constantly looking for vulnerabilities in these systems by deploying increasingly sophisticated fraud attacks. A typical fraud detection system employs standard supervised learning methods where the focus is on maximizing the fraud recall rate. However, we argue that such a formulation can lead to sub-optimal solutions. The design requirements for these fraud models require…
Reinforcement-Guided Hyper-Heuristic Hyperparameter Optimization for Fair and Explainable Spiking Neural Network-Based Financial Fraud Detection
organization= Department of Computer Science \& Engineering, Khulna University of Engineering \& Technology (KUET) , city= Khulna , postcode= 9203 , country= Bangladesh；organization= Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= USA；organization= Department of Computer Science, American International University-Bangladesh (AIUB) , city= Dhaka , postcode= 1229 , country= Bangladesh
University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；Columbia University（哥伦比亚大学）
Machine learning models have widely been used in fraud detection systems. Most of the research and development efforts have been concentrated on improving the performance of the fraud scoring models. Yet, the downstream fraud alert systems still have limited to no model adoption and rely on manual steps. Alert systems are pervasively used across all payment channels in retail banking and play an important role in the overall fraud detection process. Current fraud detection systems end up with large numbers of dropped alerts due to their inability to account for the alert processing capacity. I…
Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision
Zhejiang University of Technology（浙江工业大学）；Binjiang Institute of Artificial Intelligence, ZJUT（浙江工业大学滨江人工智能研究院）；Science and Technology on Communication Information Security Control Laboratory（通信信息安全控制技术实验室）
Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud Mitigation using Machine Learning Algorithms
Computer Engineering, University of Central Florida, Orlando, Florida, USA
A Machine Learning Driven Website Platform and Browser Extension for Real-time Scoring and Fraud Detection for Website Legitimacy Verification and Consumer Protection
University of Central Arkansas（中阿肯色大学）；University of Texas at El Paso（德克萨斯大学埃尔帕索分校）
VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection
National University of Singapore（新加坡国立大学）；Shopee Singapore（Shopee新加坡）；Zhejiang University（浙江大学）；University of Michigan（密歇根大学）；Beijing Institute of Technology（北京理工大学）
We demonstrate the use of Adaptive Stress Testing to detect and address potential vulnerabilities in a financial environment. We develop a simplified model for credit card fraud detection that utilizes a linear regression classifier based on historical payment transaction data coupled with business rules. We then apply the reinforcement learning model known as Adaptive Stress Testing to train an agent, that can be thought of as a potential fraudster, to find the most likely path to system failure -- successfully defrauding the system. We show the connection between this most likely failure pat…
Advanced Financial Fraud Detection Using GNN-CL Model
Columbia University（哥伦比亚大学）；Rutgers University（罗格斯大学）；College of William & Mary（威廉与玛丽学院）；Stevens Institute of Technological（史蒂文斯理工学院）；University of California, Davis（加利福尼亚大学戴维斯分校）
A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
University of Melbourne（墨尔本大学）；Trinity College Dublin（都柏林三一学院）；Indian Institute of Technology（印度理工学院）；Birla Institute of Technology and Science Pilani（比拉理工学院和科学学院）；South Asian University（南亚大学）；New York University Abu Dhabi (NYUAD)（纽约大学阿布扎克分校）；NYUAD Research Institute（NYUAD研究机构）
LLM-based Personalized Portfolio Recommender: Integrating Large Language Models and Reinforcement Learning for Intelligent Investment Strategy Optimization
University of Electronic Science and Technology of China（电子科技大学）；Ross School of Business, University of Michigan（密歇根大学罗斯商学院）；School of Humanities and Sciences, Stanford University（斯坦福大学人文与科学学院）
Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding
Columbia University（哥伦比亚大学）；University of Michigan（密歇根大学）；University of California, Irvine（加利福尼亚大学尔湾分校）
Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may not be robust to domain uncertainties and changes. In this paper we present a unified framework {\em PEORL} that integrates symbolic planning with hierarchical reinforcement learning (HRL) to cope with decision-making in a dynamic environment with uncertainties. Symbolic plans ar…
Machine learning applications in medical imaging are frequently limited by the lack of quality labeled data. In this paper, we explore the self training method, a form of semi-supervised learning, to address the labeling burden. By integrating reinforcement learning, we were able to expand the application of self training to complex segmentation networks without any further human annotation. The proposed approach, reinforced self training (ReST), fine tunes a semantic segmentation networks by introducing a policy network that learns to generate pseudolabels. We incorporate an expert demonstrat…
National Research Council of Italy（意大利国家研究委员会）；Institute of Cognitive Sciences and Technologies（认知科学与技术研究所）；University of Plymouth（普利茅斯大学）
A common view on the brain learning processes proposes that the three classic learning paradigms -- unsupervised, reinforcement, and supervised -- take place in respectively the cortex, the basal-ganglia, and the cerebellum. However, dopamine outbursts, usually assumed to encode reward, are not limited to the basal ganglia but also reach prefrontal, motor, and higher sensory cortices. We propose that in the cortex the same reward-based trial-and-error processes might support not only the acquisition of motor representations but also of sensory representations. In particular, reward signals mig…
Reinforcement learning is an appropriate and successful method to robustly perform low-level robot control under noisy conditions. Symbolic action planning is useful to resolve causal dependencies and to break a causally complex problem down into a sequence of simpler high-level actions. A problem with the integration of both approaches is that action planning is based on discrete high-level action- and state spaces, whereas reinforcement learning is usually driven by a continuous reward function. However, recent advances in reinforcement learning, specifically, universal value function approx…
University of Exeter Medical School（埃克塞特大学医学院）；NIHR South West Peninsula Applied Research Collaboration（英国国家健康研究所西南半岛应用研究合作组织）；University of Exeter Institute of Data Science and Artificial Intelligence（埃克塞特大学数据科学与人工智能研究所）
Background and motivation: Combining Deep Reinforcement Learning (Deep RL) and Health Systems Simulations has significant potential, for both research into improving Deep RL performance and safety, and in operational practice. While individual toolkits exist for Deep RL and Health Systems Simulations, no framework to integrate the two has been established. Aim: Provide a framework for integrating Deep RL Networks with Health System Simulations, and to ensure this framework is compatible with Deep RL agents that have been developed and tested using OpenAI Gym. Methods: We developed our framewor…
Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System
Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences（约瑟夫·雷塞尔智能与安全工业自动化中心，萨尔茨堡应用科学大学）；Salzburg University of Applied Sciences（萨尔茨堡应用科学大学）；Paris Lodron University of Salzburg（萨尔茨堡帕里斯·洛德隆大学）
Korea University（高丽大学）；Deakin University（迪肯大学）；University of Oulu（奥卢大学）
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. Integrating this with burgeoning unmanned aerial vehicle (UAV) assisted non-terrestrial networks will be a disruptive solution for beyond 5G systems provisioning large scale three-dimensional connectivity. In this article, we study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation and a mobile high-altitude platform (HAP) such as a fixed-wing UAV. To maximize the end-to-end data rat…
University of Alberta（阿尔伯塔大学）；Tsinghua University（清华大学）
Episodic memory is a psychology term which refers to the ability to recall specific events from the past. We suggest one advantage of this particular type of memory is the ability to easily assign credit to a specific state when remembered information is found to be useful. Inspired by this idea, and the increasing popularity of external memory mechanisms to handle long-term dependencies in deep learning systems, we propose a novel algorithm which uses a reservoir sampling procedure to maintain an external memory consisting of a fixed number of past states. The algorithm allows a deep reinforc…
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving
Center for Applied Autonomous Sensor Systems (AASS)（应用自主传感器系统中心）；Örebro University（奥雷布罗大学）；Computer Vision Laboratory（计算机视觉实验室）；Linköping University（利德斯霍尔大学）
Robotics Research Center, College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院机器人研究中心）
It is still an open and challenging problem for mobile robots navigating along time-efficient and collision-free paths in a crowd. The main challenge comes from the complex and sophisticated interaction mechanism, which requires the robot to understand the crowd and perform proactive and foresighted behaviors. Deep reinforcement learning is a promising solution to this problem. However, most previous learning methods incur a tremendous computational burden. To address these problems, we propose a graph-based deep reinforcement learning method, SG-DQN, that (i) introduces a social attention mec…
Texas A&M University（得克萨斯农工大学）；US Army Research Laboratory（美国陆军研究实验室）；Columbia University（哥伦比亚大学）
This paper investigates how to efficiently transition and update policies, trained initially with demonstrations, using off-policy actor-critic reinforcement learning. It is well-known that techniques based on Learning from Demonstrations, for example behavior cloning, can lead to proficient policies given limited data. However, it is currently unclear how to efficiently update that policy using reinforcement learning as these approaches are inherently optimizing different objective functions. Previous works have used loss functions, which combine behavior cloning losses with reinforcement lea…
Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search
Graduate School of Science and Technology, Keio University（Keio大学科学技术研究生院）；Graduate School of Information Science and Electrical Engineering, Kyushu University（九州大学信息科学与电气工程研究生院）；RIKEN Center for Computational Science（理化学研究所计算科学中心）
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for its own sake rather than to directly solve problems. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchical-DQN (h-DQN), a framework to integrate hierarchical value functions, operating at different temporal scales, …
Traffic congestion in metropolitan areas is a world-wide problem that can be ameliorated by traffic lights that respond dynamically to real-time conditions. Recent studies applying deep reinforcement learning (RL) to optimize single traffic lights have shown significant improvement over conventional control. However, optimization of global traffic condition over a large road network fundamentally is a cooperative multi-agent control problem, for which single-agent RL is not suitable due to environment non-stationarity and infeasibility of optimizing over an exponential joint-action space. Moti…
Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Technion - Israel Institute of Technology（以色列理工学院）
The challenge of mapping indoor environments is addressed. Typical heuristic algorithms for solving the motion planning problem are frontier-based methods, that are especially effective when the environment is completely unknown. However, in cases where prior statistical data on the environment's architectonic features is available, such algorithms can be far from optimal. Furthermore, their calculation time may increase substantially as more areas are exposed. In this paper we propose two means by which to overcome these shortcomings. One is the use of deep reinforcement learning to train the…
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
The Chinese University of Hong Kong（香港中文大学）；School of Computer Science, Peking University（北京大学计算机学院）；SmartMore（智谱AI）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）
本文提出双层级框架 RL-GPT，通过慢智能体进行代码规划、快智能体结合强化学习执行低层动作，在 Minecraft 和 MineDojo 中实现高效率与 SOTA 表现。
TU Darmstadt（达姆施塔特工业大学）；Huazhong University of Science and Technology（华中科技大学）
Recent methods for reinforcement learning from images use auxiliary tasks to learn image features that are used by the agent's policy or Q-function. In particular, methods based on contrastive learning that induce linearity of the latent dynamics or invariance to data augmentation have been shown to greatly improve the sample efficiency of the reinforcement learning algorithm and the generalizability of the learned embedding. We further argue, that explicitly improving Markovianity of the learned embedding is desirable and propose a self-supervised representation learning method which integrat…
Integrating LTL Constraints into PPO for Safe Reinforcement Learning
School of Informatics, University of Edinburgh（信息学院，爱丁堡大学）；School of Computer Science, Faculty of Engineering, University of Sydney（计算机科学学院，工程学院，悉尼大学）；School of Engineering and Physical Sciences, Heriot-Watt University（工程与物理科学学院，赫瑞-瓦德大学）
Stanford University（斯坦福大学）；Woods Institute for the Environment, Stanford University（斯坦福大学伍兹环境研究所）；Institute for Computational and Mathematical Engineering, Stanford University（斯坦福大学计算与数学工程研究所）；Department of Biology, Stanford University（斯坦福大学生物系）；Department of Earth System Science, Stanford University（斯坦福大学地球系统科学系）
One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe this evacuation process as a Capacitated Vehicle Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep Algorithm). A central question is whether Deep Reinforcement Learning provides sufficient extra routing efficiency to accommodate increased social dist…
Beyond On-Policy Exploration: Integrating External Policy Rollouts for Reinforcement Learning in Diffusion Language Models
Seoul National University（首尔大学）；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔大学人工智能交叉项目）；Artificial Intelligence Institute, Seoul National University（首尔大学人工智能研究院）；Department of Intelligence and Information, Seoul National University（首尔大学智能与信息系）
