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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Symbolic Regression Using Genetic Programming with Chaotic Method-Based Probability Mappings 13th

Symbolic Regression Using Genetic Programming with Chaotic ...

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13th International Conference on Frontier Computing, FC 2023

作者： Cao, Pu Pei, Yan Li, Jianqiang Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu965-8580 Japan Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819993413

In this study, we propose a novel pre-learning approach for genetic programming (GP) that aims to investigate the effect of the probability of being selected for each operator. Furthermore, we present a technique that combines chaos theory and searches for a relatively good possibility mapping for each operator using one-dimensional chaotic mapping. We conducted several sets of comparative experiments on real-world data to test the viability of the proposal. These experiments included comparisons with conventional GP, examination of the impact of various chaotic mappings on the proposed algorithm, and implementation of different optimization strategies to find the relative optimal probability mapping. The experimental results demonstrate that the proposed method can achieve better results than conventional GP in the tested dataset, without considering the total quantitative calculation amount. Through statistical tests, it has been proven that the proposed method is significantly different from the conventional method. However, the discussion regarding the circumstances under which the proposed method can obtain better results when the total calculation amount is limited is not yet fully explored due to the small-scale nature of the experiments. Our future studies will focus on improving and fully discussing this idea. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic programming

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Improving Oil Spill Detection in Marine Environments Through Deep Learning Approaches 4

Improving Oil Spill Detection in Marine Environments Through...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Shanmukh, M. Phani Priya, S. Baghavathi Madeswaran, Tamilselvi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Department of Information Technology Nizwa Oman

ISBN: (纸本)9798350343670

Oil spills represent a major environmental threat, particularly in marine ecosystems. Timely detection and segmentation of oil spills from satellite imagery is crucial for directing rapid response and remediation *** research concludes with an insights by developing a deep learning approach for automated detection and segmentation of oil spills from synthetic aperture radar (SAR) *** research implements and compares three algorithms PSPNET,DeepLapV3 and fully convolutional networks (FCN) with U-Net for semantic segmentation of SAR *** the networks are trained on a dataset of SAR images with pixel-wise labels indicating oil spill regions. This research also evaluates the segmentation performance using common metrics like intersection-over-union (IoU) and pixel-wise accuracy. Our results indicate that both FCN and U-Net can effectively detect and delineate oil spill regions, with U-Net demonstrating slightly higher IoU scores and with and accuracy of 95%. The deep learning models generalize well to unseen SAR images. Overall, this work demonstrates the feasibility of deep convolutional neural networks for automated analysis of SAR imagery to enable rapid oil spill response at marine environment. © 2024 IEEE.

关键词： Deep Learning Algorithms Oil Spill Detection Rapid Oil Spill Response Semantic Segmentation Synthetic Aperture Radar (SAR) Imagery

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Target Detection Algorithm for Drone Aerial Images based on Deep Learning

Target Detection Algorithm for Drone Aerial Images based on ...

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2024 International Conference on Distributed systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Liu, Tao Zhang, Bohan Heilongjiang Institute of Technology College of Computer Science and Technology Harbin China China University of Geosciences School of Information Engineering Wuhan China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan China

ISBN: (纸本)9798350375442

In recent years, the rapid development of drones has brought tremendous changes to many fields. From refined management in agriculture and forestry to aerial surveying in urban planning, the popularity of UAVs provides efficient and accurate data support for various industries, promoting social intelligence and technological progress. Meanwhile, deep learning methods represented by computer vision provide powerful tools for research such as image processing and object recognition. The research results of HE-YOLOX-ASFF (Highly Efficient You Only Look Once eXtend with Adaptive Spatial Feature Fusion) algorithm for target detection in UAV aerial images in this article can help improve the recognition accuracy and efficiency of drone remote sensing images, and apply it to fields such as smart agriculture, urban management, and environmental monitoring. At present, detection technologies for large-scale targets have achieved good detection results in clear backgrounds. However, drone aerial images often have complex backgrounds, small target volumes, limited available features, and high target density, making them prone to occlusion. Due to the above reasons, existing target detection algorithms have low detection accuracy for weak and small targets in aerial images under complex backgrounds, and are prone to missed and false detections. Therefore, the detection method of weak aerial targets in complex backgrounds remains a challenging issue. The experimental results show that in identifying pedestrians and bicycles, the parameter values are relatively high, at 33.3 and 65.1, respectively, while the FPS (frames per second) is relatively low, at 63 and 38, respectively. This indicates that the algorithm requires more parameters to process these targets and may affect processing speed. © 2024 IEEE.

关键词： Drones

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Blockchain-Driven Framework for Fake Product Detection 2nd

Blockchain-Driven Framework for Fake Product Detection

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Shah, Richa Shah, Het Obaidat, Mohammad S. Jadav, Nilesh Kumar Gupta, Rajesh Tanwar, Sudeep Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat382481 India King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The Amity University UP Noida201301 India

ISBN: (纸本)9783031689048

Blockchain has a vast range of applications and has been acknowledged in this current era. It has solved various issues related to security, tracking, immutability, etc., and also verifies transactional information without relying on centralized third-party systems. One of the most significant drawbacks of the online retail industry is that they showcase fake products in the market. It seems that the product is authentic, but in reality, it is a counterfeit retail product. To overcome this situation, in this paper, we propose a decentralized blockchain-based fake product identification framework for manufacturing industries. We designed a blockchain smart contract for the proposed framework to identify whether the product is authentic or not. The proposed framework uses both the QR code and the serial number for the identification of fake products along with supply chain tracing. The performance of the proposed framework is evaluated using the transaction and execution cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Smart contract

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QUALYST: Data Quality Assessment System for Thailand Open Government Data 20

QUALYST: Data Quality Assessment System for Thailand Open Go...

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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government Data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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Cross-Scale Bilevel Aggregation for Multi-exposure Fusion via Conditional Generative Adversarial Network 9th

Cross-Scale Bilevel Aggregation for Multi-exposure Fusion v...

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9th International Conference on Internet of Things, ICIOT 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Wang, Longchun Yu, Mali Zhang, Hai Yang, Taojun Leng, Qingming Dong, Xiwei Guo, Jingjuan Wang, Guangxing School of Computer and Big Data Science Jiujiang University Jiujiang332005 China School of Information Management Jiangxi University of Finance and Economics Nanchang330013 China Jiujiang Engineering Research Center for Collaborative Cyber Security Protection Jiujiang332005 China School of Electronic and Information Engineering Jiujiang University Jiujiang332005 China

ISBN: (纸本)9783031770029

The aim of multi-exposure fusion (MEF) is to generate a high-dynamic-range-like image from images captured by common cameras under different exposure settings. The existing generative adversarial network (GAN)-based MEF methods have achieved fair performance. However, when addressing extremely exposed images, these existing GAN-based MEF methods still face numerous limitations, such as detail loss and unnatural visual effects, partly due to their inability to sufficiently aggregate multiscale context information. To address these limitations, we propose a cross-scale bilevel aggregation-based conditional generative adversarial network (CBA-cGAN). The generator contains two subnetworks: a cross-scale aggregation GhostNetV2 (CSA-GhostNetV2) for capturing and integrating multiscale context information with long-range dependencies and a U-shaped local network (ULN) for extracting local features. To fully aggregate short-range and multiscale long- range dependencies, we propose the use of bilevel aggregation in CSA-GhostNetV2: intrablock and interblock aggregation. The intrablock aggregation scheme based on dilated convolution and GhostBlockV2 is designed to aggregate multiscale context information and long-range relationships. The interblock aggregation scheme is designed to balance local and global contextual information during the fusion process by combining features acquired from CSA-GhostNetV2 and the ULN. Additionally, to better align the visual effect with human perception, we take the average of the input images for the discriminator. We compare the performance of the proposed method with that of traditional and deep learning methods on two publicly available datasets in terms of six objective metrics. Extensive experiments are conducted to demonstrate that the proposed CBA-cGAN outperforms the existing state-of-the-art methods in retaining local details while preserving overall visual effects. © The Author(s), under exclusive license to Springer Nature Switzerla

关键词： Generative adversarial networks

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Deep Learning with Image Classification Based Secure CPS for Healthcare Sector

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computers, Materials & Continua 2022年第8期72卷 2633-2648页

作者： Ahmed S.Almasoud Abdelzahir Abdelmaboud Faisal S.Alsubaei Manar Ahmed Hamza Ishfaq Yaseen Mohammed Abaker Abdelwahed Motwakel Mohammed Rizwanullah Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil Asir62529Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Computer Science Community CollegeKing Khalid UniversityMahayil Asir62529Saudi Arabia

Cyber-Physical System(CPS)involves the combination of physical processes with computation and communication *** recent advancementsmade in cloud computing,Wireless Sensor Network(WSN),healthcare sensors,*** to develop CPS as a proficient model for healthcare applications especially,home patient *** several techniques have been proposed earlier related to CPS structures,only a handful of studies has focused on the design of CPS models for health care ***,the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data *** overcome the challenges,the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS(DLIDIC-SCPS)model for healthcare *** aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare ***,data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network(FDNN)***,Multiple Share Creation(MSC)approach is used to create several shares of medical image so as to accomplish ***,blockchain is employed as a distributed data storage entity to create a ledger that provides access to the *** image classification,Inception v3 with Fuzzy Wavelet Neural Network(FWNN)is utilized that diagnose the disease from the applied medical ***,Salp Swarm Algorithm(SSA)is utilized to fine tune the parameters involved in WNN model,thereby boosting its classification performance.A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS *** simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security,privacy,and image classification outcomes over recent state-of-the-art techniques.

关键词： Cyber Physical systems healthcare cybersecurity deep learning share creation image classification

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Metaheuristics with Machine Learning Enabled information Security on Cloud Environment

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computers, Materials & Continua 2022年第10期73卷 1557-1570页

作者： Haya Mesfer Alshahrani Faisal S.Alsubaei Taiseer Abdalla Elfadil Eisa Mohamed K.Nour Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The increasing quantity of sensitive and personal data being gathered by data controllers has raised the security needs in the cloud *** computing(CC)is used for storing as well as processing ***,security becomes important as the CC handles massive quantity of outsourced,and unprotected sensitive data for public *** study introduces a novel chaotic chimp optimization with machine learning enabled information security(CCOML-IS)technique on cloud *** proposed CCOML-IS technique aims to accomplish maximum security in the CC environment by the identification of intrusions or anomalies in the *** proposed CCOML-IS technique primarily normalizes the networking data by the use of data conversion and min-max *** by,the CCOML-IS technique derives a feature selection technique using chaotic chimp optimization algorithm(CCOA).In addition,kernel ridge regression(KRR)classifier is used for the detection of security issues in the *** design of CCOA technique assists in choosing optimal features and thereby boost the classification performance.A wide set of experimentations were carried out on benchmark datasets and the results are assessed under several *** comparison study reported the enhanced outcomes of the CCOML-IS technique over the recent approaches interms of several measures.

关键词： information security cloud computing intrusion anomalies data mining feature selection classification

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