检索结果-内蒙古大学图书馆

Corporate Fraud Detection in Rich-yet-Noisy Financial Graph

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Wang, Shiqi Zhang, Zhibo Fang, Libing Nguyen, Cam-Tu Li, Wenzhon School of Artificial Intelligence Nanjing University Jiangsu Nanjing China School of Computer Science and Technology Nanjing University Jiangsu Nanjing China School of Management and Engineering Nanjing University Jiangsu Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China

Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to effectively integrate rich interactions in the company network. To close this gap, we collect 18-year financial records in China to form three graph datasets with fraud labels. We analyze the characteristics of the financial graphs, highlighting two pronounced issues: (1) information overload: the dominance of (noisy) non-company nodes over company nodes hinders the message-passing process in Graph Convolution Networks (GCN);and (2) hidden fraud: there exists a large percentage of possible undetected violations in the collected data. The hidden fraud problem will introduce noisy labels in the training dataset and compromise fraud detection results. To handle such challenges, we propose a novel graph-based method, namely, Knowledge-enhanced GCN with Robust Two-stage Learning (KeGCNR), which leverages Knowledge Graph Embeddings to mitigate the information overload and effectively learns rich representations. The proposed model adopts a two-stage learning method to enhance robustness against hidden frauds. Extensive experimental results not only confirm the importance of interactions but also show the superiority of KeGCNR over a number of strong baselines in terms of fraud detection effectiveness and robustness. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph embeddings

FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Wang, Xiaoqin Ma, Xusen Hou, Xianxu Ding, Meidan Li, Yudong Chen, Junliang Chen, Wenting Peng, Xiaoyang Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China AIAC Xi’an Jiaotong-Liverpool University China Tsinghua University China The Hong Kong Polytechnic University Hong Kong City University of Hong Kong Hong Kong Sun Yat-sen University China

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in various tasks. However, effectively evaluating these MLLMs on face perception remains largely unexplored. To address this gap, we introduce FaceBench, a dataset featuring hierarchical multi-view and multi-level attributes specifically designed to assess the comprehensive face perception abilities of MLLMs. Initially, we construct a hierarchical facial attribute structure, which encompasses five views with up to three levels of attributes, totaling over 210 attributes and 700 attribute values. Based on the structure, the proposed FaceBench consists of 49,919 visual question-answering (VQA) pairs for evaluation and 23,841 pairs for fine-tuning. Moreover, we further develop a robust face perception MLLM baseline, Face-LLaVA, by training with our proposed face VQA data. Extensive experiments on various mainstream MLLMs and Face-LLaVA are conducted to test their face perception ability, with results also compared against human performance. The results reveal that, the existing MLLMs are far from satisfactory in understanding the fine-grained facial attributes, while our Face-LLaVA significantly outperforms existing open-source models with a small amount of training data and is comparable to commercial ones like GPT-4o and Gemini. The dataset will be released at https://***/CVI-SZU/FaceBench. Copyright © 2025, The Authors. All rights reserved.

关键词：

Semi-supervised software Defect Prediction Using Task-Driven Dictionary Learning

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2016年第6期25卷 1089-1096页

作者： CHENG Ming WU Guoqing YUAN Mengting WAN Hongyan School of Computer Wuhan University State Key Laboratory of Software Engineering Wuhan University

We present a semi-supervised approach for software defect prediction. The proposed method is designed to address the special problematic characteristics of software defect datasets, namely, lack of labeled samples and class-imbalanced data. To alleviate these problems, the proposed method features the following components. Being a semi-supervised approach, it exploits the wealth of unlabeled samples in software systems by evaluating the confidence probability of the predicted labels, for each unlabeled sample. And we propose to jointly optimize the classifier parameters and the dictionary by a task-driven formulation, to ensure that the learned features(sparse code) are optimal for the trained classifier. Finally, during the dictionary learning process we take the different misclassification costs into consideration to improve the prediction performance. Experimental results demonstrate that our method outperforms several representative stateof-the-art defect prediction methods.

关键词： software defect prediction Task-driven dictionary learning Cost-sensitive Semi-supervised learning Sparse representation

OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

学校读者我要写书评

暂无评论

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

A Robust key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

学校读者我要写书评

暂无评论

IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

A Survey of Adversarial Examples in computer Vision:Attack,Defense,and Beyond

学校读者我要写书评

暂无评论

Wuhan University Journal of Natural Sciences 2025年第1期30卷 1-20页

作者： XU keyizhi LU Yajuan WANG Zhongyuan LIANG Chao School of Computer Science Wuhan UniversityWuhan 430072HubeiChina National Engineering Research Center for Multimedia Software(NERCMS) Wuhan UniversityWuhan 430072HubeiChina Key Laboratory of Multimedia and Network Communication Engineering Hubei ProvinceWuhan UniversityWuhan 430072HubeiChina School of Cyber Science and Engineering Wuhan UniversityWuhan 430072HubeiChina

Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision ***,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input *** this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial ***,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.

关键词： computer vision adversarial examples adversarial attack adversarial defense

STAR-RIS and UAV Combination in MEC Networks: Simultaneous Task Offloading and Communications

学校读者我要写书评

暂无评论

IEEE Transactions on Communications 2025年

作者： Xiao, Han Hu, Xiaoyan Wang, Wenjie Su, Zhou Wong, Kai-Kit Yang, Kun Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China University College London Department of Electronic and Electrical Engineering Torrington Place LondonWC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou215163 China University of Essex School of Computer Science and Electronic Engineering Essex United Kingdom

This paper explores a simultaneous tasks offloading and communications (STOC) scheme in mobile edge computing (MEC) networks, supported by the combination of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Different from the traditional MEC schemes, the proposed scheme concurrently considers the computation and communication capabilities of the MEC networks, which is actually more practical in reality. Specifically, an optimization problem is devised to maximize the weighted sum of the minimum computed task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. This non-convex problem with strong couplings among variables is challenging to solve directly. Then, a novel alternating optimization method is proposed, leveraging the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme, which demonstrate that the proposed scheme supported by STAR-RIS and UAV outperforms five benchmark schemes in terms of performance gain. It is important to note that the proposed scheme offers a feasible and realistic way for the implementations of STOC in practical MEC networks. © 1972-2012 IEEE.

关键词： Convex optimization

Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Cao, Hongye Feng, Fan Huo, Jing Yang, Shangdong Fang, Meng Yang, Tianpei Gao, Yang National Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Department of Electrical Engineering City University of Hong Kong Hong Kong School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

Model-based offline Reinforcement Learning (RL) constructs environment models from offline datasets to perform conservative policy optimization. Existing approaches focus on learning state transitions through ensemble models, rollouting conservative estimation to mitigate extrapolation errors. However, the static data makes it challenging to develop a robust policy, and offline agents cannot access the environment to gather new data. To address these challenges, we introduce Model-based Offline Reinforcement learning with AdversariaL data augmentation (MORAL). In MORAL, we replace the fixed horizon rollout by employing adversaria data augmentation to execute alternating sampling with ensemble models to enrich training data. Specifically, this adversarial process dynamically selects ensemble models against policy for biased sampling, mitigating the optimistic estimation of fixed models, thus robustly expanding the training data for policy optimization. Moreover, a differential factor is integrated into the adversarial process for regularization, ensuring error minimization in extrapolations. This data-augmented optimization adapts to diverse offline tasks without rollout horizon tuning, showing remarkable applicability. Extensive experiments on D4RL benchmark demonstrate that MORAL outperforms other model-based offline RL methods in terms of policy learning and sample efficiency. © 2025, CC BY.

关键词： Adversarial machine learning

Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning