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A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

Digital Communications and Networks 2024年第1期10卷 126-134页

作者： Naiyu Wang Wenti Yang Xiaodong Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power UniversityChina Department of Mathematics&Computer Science Fayetteville State UniversityFayettevilleNCUSA Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ07030USA Department of Computer Science and Engineering Qatar UniversityDohaQatar

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic ***,concerns have been raised over the security and privacy of the tons of traffic and vehicle *** this regard,Federated Learning(FL)with privacy protection features is considered a highly promising ***,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global *** existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same *** this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of *** improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving ***,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice *** security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

关键词： Federated learning Blockchain Privacy-preservation Homomorphic encryption Internetof vehicles

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An Ontology-based Three-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation 28th

An Ontology-based Three-Stage Approach to Medical Text Class...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Abdollahi, Mahdi Gao, Xiaoying Mei, Yi Ghosh, Shameek Li, Jinyan Narag, Michael School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Founder & CTO Medius Health Sydney Australia Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

The document classification (DC) task assigns predefined classes to unlabeled documents using trained models. In the medical field, DC is crucial for tasks like categorizing risk factors and classifying electronic health records. This paper addresses challenges in medical document analysis, such as the prevalence of abbreviations and acronyms. Existing classification performance in medical documents is suboptimal. The paper introduces novel feature engineering methods leveraging domain-specific knowledge to enhance classification performance. Results indicate that the Three-Stage approach surpasses related works, showcasing improved medical document classification performance. © 2024 The Authors.

关键词： Particle swarm optimization (PSO)

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Multi-view stereo based on adaptive expansion propagation 7

Multi-view stereo based on adaptive expansion propagation

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7th International Conference on computer Information Science and Application Technology, CISAT 2024

作者： Nie, Zixuan Fu, Haohai Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin132022 China Changchun Institute of Technology School of Computer Technology and Engineering Changchun130012 China

ISBN: (纸本)9798350375107

Through computer vision and image processing techniques, a set of images from a scene can be reconstructed in 3D to recover a 3D model of the scene, in which dense reconstruction is a crucial part, and most existing algorithms cannot effectively recover weak texture regions in dense reconstruction. In response to this issue, this article proposes a hypothesis propagation strategy based on adaptive extension. In the hypothesis propagation stage of PatchMatch MVS, an adaptive mechanism for region propagation is introduced, and an update counter is set to regulate the expansion of the sampling area. This ensures that there are enough excellent hypotheses for sampling in this algorithm iteration, and the quality of sampling hypotheses is not limited by fixed sampling pixel positions, thereby improving the probability of excellent hypotheses being sampled in the sampling space. It is demonstrated in the ETH3D dataset that our method reconstructs models with higher integrity, and fewer voids and smoother surfaces in the reconstruction of weakly textured regions. © 2024 IEEE.

关键词： 3D reconstruction

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Detection of Orange Juice Concentration based on the Absorption of Visible Light 3

Detection of Orange Juice Concentration based on the Absorpt...

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3rd Conference on Biomedical Photonics and Cross-Fusion, BPC 2024

作者： Chengxin, Mao Lin, Gui Yu, Peng Zi Yi, Wang Di Sen, Shang Ying, Wu Qin School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic University Shanghai201209 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China

ISBN: (纸本)9781510682849

This paper presents a system for detecting orange juice concentration based on the principle of solution absorption of visible light. The system efficiently detects orange juice concentration using visible light communication technology. It utilizes a LED to emit modulated light signals at specific frequencies. By leveraging the differences in absorption and scattering characteristics of orange juice solutions at varying concentrations, the system can determine the concentration of the juice. It features low cost, high sensitivity, and strong anti-interference capabilities, offering significant application value. The system primarily consists of an Arbitrary Waveform Generator (AWG), LED light source, lamp cover, silicon photo diode (PD), and oscilloscope. After passing through the orange juice solution, the specific frequency light signals are received by the silicon photo diode and converted into electrical signals. The oscilloscope measures the peak-to-peak data of the light signals, translating it into signal power levels to obtain the concentration information of the orange juice solution. The raw data are fitted with linear and quadratic functions, yielding R2 values of 0.91547 and 0.99546, respectively. This system establishes a correlation between the concentration of orange juice and the signal power level converted from received light, based on the absorption and scattering characteristics of the juice solution at specific frequencies. Experimental results demonstrate that the system can accurately detect different concentrations of orange juice solution, showing good linear fit and broad application prospects, suitable for quality monitoring and real-time detection in orange juice production processes. © 2024 SPIE

关键词： Citrus fruits

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Real-Time Notifier for Blockchain Events 22

Real-Time Notifier for Blockchain Events

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22nd RoEduNet Conference: Networking in Education and Research, RoEduNet 2023

作者： Tanase-Paris, Cristian Carabas, Costin Tapus, Nicolae University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science and Engineering Department Bucharest Romania

ISBN: (纸本)9798350393651

Blockchain technology gained much traction in the last few years. These decentralized databases offer security, immutability, and scalability across various applications. Decentralized applications generate vast amounts of data, known as events, that are recorded on the blockchain and are public to anyone. Some people may see opportunities for financial gains in these events and would like to know when they occur. This paper proposes a solution to process and deliver those events as real-time alerts to the users. It uses existing technologies such as message queues, multithreading, and asynchronous processing and integrates them into a scalable architecture. The results we achieved in this paper show that for an evenly distributed network traffic, which does not entirely consists of transaction bursts, the proposed solution offers reliability, efficiency, and a suitable delivery time to those wishing to integrate it into their projects. With time, this solution, or improved architectures, may form the basis of the following big-data architectures for processing blockchain events. © 2023 IEEE.

关键词： Blockchain

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Mining Erasable Itemsets from a Product Database with Quantitative Components 24

Mining Erasable Itemsets from a Product Database with Quanti...

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11th Multidisciplinary International Social Networks Conference, MISNC 2024

作者： Hong, Tzung-Pei Chen, Da Huang, Wei-Ming Tsai, Yu-Chuan Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Taiwan Department of Computer Science and Engineering Department of Electrical and Control China Steel Inc. China Library and Information Center National University of Kaohsiung Taiwan

ISBN: (纸本)9798400717550

Erasable itemset mining is one of the most well-known methods in data mining for optimizing limited materials. After mining erasable itemsets, the manager can rearrange the production plan effectively. However, in real applications, traditional mining methods neglect some critical factors, such as costs or quantities of components forming a product. This thesis thus considers mining erasable itemsets from a product database with quantitative components. An erasable profit-based itemset mining (EPIM) algorithm is proposed to mine erasable itemsets with low profits by analyzing the relationships between products and components. In experiments, a synthetic dataset was used to compare the performance in execution speed and memory usage under various threshold settings. It can also be easily generalized to find erasable itemsets satisfying the other criteria with a quantitative product database. © 2024 ACM.

关键词： Mine safety

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A novel fractional physics-informed neural networks method for solving the time-fractional Huxley equation

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Neural Computing and Applications 2024年第30期36卷 19097-19119页

作者： Shi, Jieyu Yang, Xiaozhong Liu, Xinlong School of Control and Computer Engineering North China Electric Power University Beijing102206 China Institute of Information and Computing School of Mathematics and Physics North China Electric Power University Beijing102206 China

The neural network methods in solving differential equations have significant research importance and promising application prospects. Aimed at the time-fractional Huxley (TFH) equation, we propose a novel fractional physics-informed neural networks (fPINNs) method. By integrating the physical information of the TFH equation into neural networks, the fPINNs are trained as a precise approximation model for solving the TFH equation. The fPINNs method involves calculating the Caputo fractional derivative using the L1 formula and estimating the integer-order derivative through the chain rule. The Adam algorithm is employed to optimize two kinds of loss functions constructed by hard and soft constraints, respectively. Through an analysis of the impact of fPINNs parameters on training results, we identify the optimal parameter configuration for solving both one-dimensional and two-dimensional (2D) TFH equations. Numerical examples validate the efficiency and robustness of the fPINNs method in solving TFH equations. Furthermore, numerical simulation of the 2D TFH problem demonstrates the method’s practical applicability to engineering problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Numerical methods

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Research on Grayscale Images Flame Detections Based on Colorazion Algorithm and YOLOv8

Research on Grayscale Images Flame Detections Based on Color...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Liu, Peng Sun, Zhenfu Zhou, Biao Wang, Hongyu Qu, Haozhen Institute of Energy and Electrical Engineering Changchun University of Science and Technology Changchun China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798350378214

Flame detection algorithms are crucial for real-time fire monitoring using surveillance cameras. Current flame detection algorithms perform excellently on color cameras;however, many night vision cameras can only capture grayscale images, making flame detection less effective in such scenarios. Traditional flame detection algorithms rely heavily on color features, especially the red and yellow hues of flames. Conversely, grayscale images lack color information, making these algorithms ineffective. To address this limitation, we propose a novel flame detection algorithm that integrates the DeOldify model with the you only look once(YOLOv8) model. DeOldify can effectively add color to grayscale images. YOLOv8 is a powerful object detection model that excels in identifying various objects within an image. This algorithm leverages DeOldify's colorization capabilities and YOLOv8's powerful object detection abilities to enhance flame detection accuracy in grayscale images. This algorithm has been tested multiple times on public datasets, converting original images to grayscale and then colorizing them to construct the dataset. The results show that, compared to grayscale images, this algorithm improves the mean average precision (mAP@0.5) by 19.7% on different validation sets, by 4% on different training sets, and by 12.9% on grayscale images captured by night vision cameras. By comparing the training results of models such as YOLOv8n and YOLOv8s, the colorized images improve the model's performance. This demonstrates the high generalizability of this algorithm. © 2024 IEEE.

关键词： Premixed flames

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A Distributed Automatic Domain-Specific Multi-Word Term Recognition Architecture using Spark Ecosystem 22

A Distributed Automatic Domain-Specific Multi-Word Term Reco...

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22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023

作者： Truica, Ciprian-Octavian Istrate, Neculai-Ovidiu Apostol, Elena-Simona University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science and Engineering Department Bucharest Romania

ISBN: (纸本)9798350341270

Automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem;(2) perform an in-depth analysis of our architecture in terms of accuracy and scalability;(3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language Processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets. © 2023 IEEE.

关键词： Ecosystems

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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