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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Huang, Chenyu Tan, Weimin Shi, Jiaxing Xing, Zhen Yan, Bo Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (纸本)9781728163277

Recently, unsupervised image denoising methods learning from paired noisy samples have received increasing attention. These methods build on the idea that the mean of multiple noisy images of the same scene is the ideal clean image. However, these methods ignore the effect of Aleatoric uncertainty in the noisy image (e.g., pixels deviating from the expected distribution). The presence of Aleatoric uncertainty causes degradation of the reconstructed target pixels, resulting in high uncertainty for these pixels (i.e., low confidence), which in turn leads to sub-optimal denoising results. To address this problem, we propose a novel uncertainty-aware unsupervised image denoising method named Uncer2Natural (U2N). It dynamically predicts the Aleatoric uncertainty for each noisy sample and produces satisfactory denoising results by reducing the effect of Aleatoric uncertainty. Extensive experimental results show that U2N outperforms state-of-the- art unsupervised image denoising methods in terms of both quantitative metrics and qualitative visual quality. © 2023 IEEE.

关键词： Pixels

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Motion Matters: Difference-based Multi-scale Learning for Infrared UAV Detection

Motion Matters: Difference-based Multi-scale Learning for In...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： He, Ruian Zhou, Shili Cheng, Ri Sun, Yuqi Tan, Weimin Yan, Bo Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798350302493

Unmanned Aerial Vehicle (UAV) detection in the wild is a challenging task due to the presence of background noise and the varying size of the object. To address these obstacles, we propose a novel learning framework for robust UAV detectors, which we call Difference-based Multi-scale Learning (DML). We argue that motion information matters in UAV detection because of the low recognition in one frame. Our method utilizes the frame difference of multiple previous frames, extracting motion information and blocking background noise. We also fuse multiple spatial-temporal scales for training and inferencing, enabling fusion from different sources. In addition, to better evaluate the performance of UAV detection in different scales, we propose Multi-Scale Average Precision (MSAP) metric to aggregate the detection accuracy over multiple scales. Through extensive experiments, we demonstrate that our proposed approach improves the detection accuracy of baseline models. Notably, we achieve SOTA performance in the 3rd Anti-UAV Challenge, with 2nd place in Track 2 and 4th place in Track 1. © 2023 IEEE.

关键词： Antennas

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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PVDM-YOLOv8l: a solution for reliable pedestrian and vehicle detection in autonomous vehicles under adverse weather conditions

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Multimedia Tools and Applications 2024年 1-26页

作者： Tahir, Noor Ul Ain Zhang, Zuping Asim, Muhammad Iftikhar, Sundas A. Abd El-Latif, Ahmed School of Computer Science and Engineering Central South University Changsha410083 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Gianforte School of Computing Montana State University Bozeman59717 United States Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown remarkable advancements, accurately identifying pedestrians and vehicles in adverse weather conditions remains a challenging task. Adverse weather introduces image quality degradation, leading to issues such as low contrast, reduced visibility, blurred edges, false detection, misdetection of tiny objects, and other impediments that further complicate the accuracy of detection. This paper introduces a novel Pedestrian and Vehicle Detection Model under adverse weather conditions, denoted as PVDM-YOLOv8l. In our proposed model, we first incorporate the Swin-Transformer method, which is designed for global extraction of feature of small objects to identify in poor visibility, into the YOLOv8l backbone structure. To enhance detection accuracy and address the impact of inaccurate features on recognition performance, CBAM is integrated between the neck and head networks of YOLOv8l, aiming to gather crucial information and obtain essential data. Finally, we adopted the loss function Wise-IOU v3. This function was implemented to mitigate the adverse effects of low-quality instances by minimizing negative gradients. Additionally, we enhanced and augmented the DAWN dataset and created a custom dataset, named DAWN2024, to cater to the specific requirements of our study. To verify the superiority of PVDM-YOLOV8l, its performance was compared against several commonly used object detectors, including YOLOv3, YOLOv3-tiny, YOLOv3-spp, YOLOv5, YOLOv6, and all the versions of YOLOv8 (n, m, s, l, and x) and some traditional models. The experimental results demonstrate that our proposed model achieved a 6.6%, 5.4%, 6%, and 5.1% improvement in precision, recall, F1-score and mean Average Precision (mAP) on the custom DAWN2024 dataset. This substantial improvement in accuracy ind

关键词： Vehicle detection

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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CDA-GNN: A Chain-driven Accelerator for Efficient Asynchronous Graph Neural Network 24

CDA-GNN: A Chain-driven Accelerator for Efficient Asynchrono...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Yu, Hui Zhang, Yu He, Ligang He, Donghao Li, Qikun Zhao, Jin Liao, Xiaofei Jin, Hai Gu, Lin Liu, Haikun National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798400706011

Asynchronous Graph Neural Network (AGNN) has attracted much research attention because it enables faster convergence speed than the synchronous GNN. However, existing software/hardware solutions suffer from redundant computation overhead and excessive off-chip communications for AGNN due to irregular state propagations along the dependency chains between vertices. This paper proposes a chain-driven asynchronous accelerator, CDA-GNN, for efficient AGNN inference. Specifically, CDA-GNN proposes a chain-driven asynchronous execution approach into novel accelerator design to regularize the vertex state propagations for fewer redundant computations and off-chip communications and also designs a chain-aware data caching method to improve data locality for AGNN. We have implemented and evaluated CDA-GNN on a Xilinx Alveo U280 FPGA card. Compared with the cutting-edge software solutions (i.e., Dorylus and AMP) and hardware solutions (i.e., BlockGNN and FlowGNN), CDA-GNN improves the performance of AGNN inference by an average of 1,173x, 182.4x, 10.2x, and 7.9x and saves energy by 2,241x, 242.2x, 12.4x, and 8.9x, respectively. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph neural networks

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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Adaptive Attribute-Based Honey Encryption: A Novel Solution for Cloud Data Security

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computers, Materials & Continua 2025年第2期82卷 2637-2664页

作者： Reshma Siyal Muhammad Asim Long Jun Mohammed Elaffendi Sundas Iftikhar Rana Alnashwan Samia Allaoua Chelloug School of Computer Science and Engineering Central South UniversityChangsha410083China EIAS LAB College of Computer and Information Sciencesand Center of Excellence in Quantum and Intelligent ComputingPrince Sultan UniversityRiyadh11586Saudi Arabia Gianforte School of Computing Montana State UniversityBozemanMT 59717USA Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.

关键词： Cybersecurity data security cloud storage hadoop encryption and decryption privacy protection attribute-based honey encryption

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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

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computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir Irfan Mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia Centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

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Digital Twin, Metaverse and Smart Cities in a Race to the Future 24

Digital Twin, Metaverse and Smart Cities in a Race to the Fu...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Zainab, Hijab E Bawanay, Narmeen Zakaria Jinnah University for Women Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

In the dynamic landscape of urban development, the synergy of the Metaverse, Digital Twins, and Smart Cities emerges as a potent catalyst for transformation. The Metaverse, a vibrant virtual space, collaborates with cutting-edge technologies, including Virtual Reality (VR), Augmented Reality (AR), the Internet of Things (IoT), and Artificial Intelligence (AI), crafting immersive experiences. Simultaneously, Digital Twins, serving as virtual replicas of physical entities, instigating an industrial revolution, while Smart Cities harness advanced technologies for sustainable urban living. This paper explores the applications of converging these technologies across diverse industries, showcasing how Digital Twins reshape conventional practices and amplify the concept of smart cities through the immersive realm of the Metaverse. However, the exploration unveils challenges, particularly in data privacy, interoperability, and ethical considerations. The study advocates for a balanced and informed approach, emphasizing that the symbiotic relationship between Digital Twins and the Metaverse should steer the evolution of Smart Cities toward security, innovation, and interconnected urban spaces. © 2023 IEEE.

关键词： Digital Twins Immersive Experiences Metaverse Smart Cities Sustainable Urban Living Urban Development

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