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Denoising graph neural network based on zero-shot learning for Gibbs phenomenon in high-order DG applications

Chinese Journal of Aeronautics 2025年第3期38卷 234-248页

作者： Wei AN Jiawen LIU Wenxuan OUYANG Haoyu RU Xuejun LIU Hongqiang LYU College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China

With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.

关键词： Computational fluid dynamics High-order discon tinuous Galerkin method Gibbs phenomenon Graph neural networks Zero-shot learning

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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AQRG: adaptive quantization reconstruction granularity for post-training quantization

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Neural Computing and Applications 2025年 1-17页

作者： Zhang, Wenbo Wang, Tianshuo Fu, Guohang Bao, Zhenshan College of Computer Science Beijing University of Technology Beijing China

Deploying models on resource-constrained edge devices remains always a critical challenge for the application of neural network. Quantization is one of the most popular methods to compress the model for meeting the performance limitations. As only a small amount of calibration data is required, post-training quantization (PTQ) is more suitable for protecting privacy than quantization-aware training(QAT). However, PTQ often causes substantial accuracy degradation when it goes below 4-bit, and previous PTQ works primarily focused on single reconstruction quantization granularity, either all layer-wise or all block-wise. Nevertheless, it is proved in our exploratory experiments that these schemes are sub-optimal. In this paper, we explore the relation of Hessian matrix trace and the inter-layer dependency which takes key role in the choice of quantization reconstruction granularity. Based on the discovery, we propose a novel hybrid reconstruction granularity quantization scheme AQRG, which adaptively adjusts quantization granularity guided by the Hessian matrix trace. In image classification and object detection, AQRG achieves better accuracy and robustness for calibration data size on several typical convolutional neural networks. In particular, our 4-bit weight 2-bit activation (W4A2) scheme in ResNet-18 achieved 65.06% accuracy on the ImageNet dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Prompt Learning for Few-Shot Question Answering via Self-Context Data Augmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 589-603页

作者： Qiu, Jian-Qiang Zhang, Chun-Yang Chen, C. L. Philip Fuzhou University College of Computer and Data Science Fuzhou350108 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China

Pretrained language models (PLMs) have shown remarkable performance on question answering (QA) tasks, but they usually require fine-tuning (FT) that depends on a substantial quantity of QA pairs. Therefore, improving the performance of PLMs in scenarios with only a small number of training examples, also known as a few-shot setting, is of great practical significance. Current mitigation strategies for the few-shot QA task largely rely on pretraining a QA task-specific language model from scratch, overlooking the potential of foundational PLMs to generate QA pairs, particularly in the few-shot setting. To address this issue, we propose a prompt-based QA data augmentation method aimed at automating the creation of high-quality QA pairs. It employs the PFT method, adapting the question generation process of PLMs to the few-shot setting. Additionally, we introduce a dynamic text filling training strategy. This strategy simulates the progressive human learning process, thereby alleviating overfitting of PLMs in the few-shot setting and enhancing their reasoning capability to tackle complex questions. Extensive experiments demonstrate that the proposed method outperforms existing approaches across various few-shot configurations. 2691-4581 © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://***/publications/rights/*** for more information.

关键词： Zero-shot learning

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Poor and rich squirrel algorithm-based Deep Maxout network for credit card fraud detection

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 123-134页

作者： Paul, Annu Paul, Varghese Department of Computer Science Alphonsa College Kottayam Kerala Pala India Department of Computer Science and Engineering Rajagiri School of Engineering and Technology Kerala Kochi India

This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation phase, transforming data using Yeo-Johnson (YJ) transformation. Then, the feature selection procedure is done by the Fisher score for creating the unique and significant features. Next, based on the selected textures, the data augmentation mechanism is done using the oversampling model. At last, the fraud detection is carried out by the Deep Maxout network, which is trained by the proposed PRSA optimisation algorithm, derived by integrating Poor and Rich Optimisation (PRO) and Squirrel Search Algorithm (SSA). The integration of parametric features of the PRSA algorithm effectively trained the classifier to update weights to generate the best solution by considering fitness measures. The proposed method achieved the best accuracy, sensitivity, and specificity measures of 0.96, 0.95 and 0.94, respectively. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： credit card data augmentation data transformation deep learning fraud detection

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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SVRM: Composing Various Network Service Fuzzing Corpus with One Single Model

SVRM: Composing Various Network Service Fuzzing Corpus with ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lin, Wenfeng Jiang, Zhiyuan Xu, Fangliang Su, Yunfei Li, Zhiwei Mao, Lingchu Tang, Chaojing College of Electronic Science and Technology College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Discovering vulnerabilities in network service is of great significance. Currently, coverage-guided fuzzing (CGF) is widely regarded as the most effective method. However, the efficiency of CGF depends on the quality of initial corpus. The initial corpus is a set of valid input examples used to initiate the fuzzing process. Constructing high-quality initial corpus typically requires manual efforts to understand the implementation details and corresponding protocol specifications, making it difficult to generalize across different protocol implementations. To generate high-quality corpus tailored to service under test (SUT), this paper proposes a protocol-independent smart generation method. The paper introduces a novel service communication model and utilizes active learning algorithms to automatically construct the model. By analyzing the minimum spanning tree of the model, we achieve automatic generation of high-quality corpus that adapts to the SUT. We conduct experiment by generating adaptive corpus for 6 targets of 6 different protocols in PROFUZZBENCH. Compared to the corpus provided by PROFUZZBENCH, the corpus generated by our system improve the state coverage of modern protocol fuzzers by 37.1% and discover known real protocol vulnerabilities at a speed 2.47x faster. © 2025 IEEE.

关键词： Auto Corpus Composition Communication Modeling Network Service Fuzzing

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JLOS: a Cooperative UAV-Based Optical Wireless Communication With Multi-Agent Reinforcement Learning

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1345-1356页

作者： Liu, Jiangang Luo, Hanjiang Tao, Hang Liu, Jiahong Zhou, Jiehan Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland

In maritime Internet of Things (IoT) systems, leveraging a swarm of Unmanned Aerial Vehicles (UAVs) and optical communication can achieve a variety of potential maritime missions. However, due to the high directionality of the optical beam and interference from the marine environment, the optical link via UAVs as relays is prone to interruption. To address this challenge, we propose a Joint Link Optimization Scheme (JLOS) that includes Wind Disturbance Resistance (WDR) and Adaptive Beamwidth Adjustment (ABA). In WDR, we first model the problem as a Partially Observed Markov Decision Process (POMDP), and then design a collaborative Multi-Agent Reinforcement Learning (MARL) approach to control a swarm of UAVs in windy conditions, to maintain mechanical stability and prevent link interruption. Furthermore, in ABA, to reduce uncertainties from control activities and environmental factors like sunlight and fog, we design an adaptive algorithm using distributed MARL. It adjusts beamwidth based on historical UAV locations and link Bit Error Ratio (BER) to improve communication reliability. Numerical simulations confirm its effectiveness in enhancing robust data transmission. © 2004-2012 IEEE.

关键词： Adaptive algorithms

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Automatic Positioning of Intelligent Mobile Robot Based on Improved Front-End Scanning Matching

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 559-572页

作者： Wang, Hong Wang, Yi-Gui Zhang, Jie Department of Computer and Software Engineering Shandong College of Electronic Technology Jinan250200 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China College of Agricultural Science and Technology Yana Royal Polytechnic University Chiang Mai50000 Thailand

Traditional autonomous navigation methods for mobile robots mainly rely on geometric feature-based LiDAR scan-matching algorithms, but in complex environments, this method is often affected due to the presence of moving objects, occlusions, and other interfering factors, resulting in a decrease in positioning accuracy. With the growing demand for robotics applications in logistics, security, exploration and other fields, the need for robust autonomous navigation and high-precision mapping in highly dynamic and complex environments is becoming more and more urgent. To solve this problem, an improved front-end scan matching algorithm based on Frequency Modulated Continuous Wave (FMCW) LiDAR is proposed in this paper. Firstly, by using the target Doppler velocity information provided by the FMCW LiDAR, we design a novel point cloud segmentation algorithm based on velocity clustering, which is able to effectively distinguish between stationary and moving objects, and avoid the dynamic interference affecting the position estimation. Secondly, we introduce the Gaussian Mixture Model Sampling Consistency (GMMSC) algorithm, which is more robust to reject the mis-matched pairs in the scanning matching process and improve the alignment accuracy. Finally, based on the residual high-quality matched pairs, we combine the classical ICP algorithm with the robust kernel function to further enhance the stability of the position estimation in the case of occlusion and local mismatch. The experimental results show that the proposed improved algorithm significantly improves the mapping capability of mobile robots in complex environments compared with the existing techniques. The average relative error of the improved front-end scanning matching algorithm is reduced by 40.6 % compared with the pre-improved one. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Mobile robots

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Monitoring COVID-19 environment: a real-time facial mask detection using YOLO models

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Research on Biomedical Engineering 2025年第1期41卷 1-18页

作者： Pandiyan, P. Thangaraj, Rajasekaran V, Balasubramanian V. K, Manavalasundaram S, Balakrishnan Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India

Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective measure for themselves and others. Coronavirus protection guidelines have been published by the World Health Organization (WHO). According to WHO standards, COVID-19 can be prevented by wearing a mask in public places and congested regions. In these places, it is very difficult to personally check to see if people are wearing face masks or not. Methods: The objective of this research work is to build a powerful, efficient, and real-time approach for detecting people not wearing masks. Three cutting-edge object identification models, namely YOLOv4, Tiny-YOLOv4, and YOLOv5, are employed in this study for the identification of masked faces. Result: The proposed YOLOv5 model is evaluated using real-time images captured using a smartphone or tablet. The test images include both single and multiple people with and without masks. The YOLOv5 model achieved recognition accuracy of 88.90% with an average detection speed of 0.0316 s per image, whereas the YOLOv4 and Tiny-YOLOv4 produced recognition accuracy of 82.24% and 74.80% with an average detection speed of 0.0530 s and 0.0541 s per image, respectively. Conclusion: The comparative performance suggests that the YOLOv5 model has a maximum recognition accuracy of 88.90% in face mask identification tasks compared to other models such as the YOLOv4 and Tiny-YOLOv4. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2025.

关键词： COVID-19

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