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A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

computers, Materials & Continua 2023年第9期76卷 2959-2978页

作者： Muhammad Firdaus Harashta Tatimma Larasati Kyung-Hyune Rhee Department of Artificial Intelligence Convergence Pukyong National UniversityBusan48513Korea School of Electrical Engineering and Informatics Institut Teknologi BandungBandung40132Indonesia College of Information Technology and Convergence Division of Computer Engineering and AIPukyong National UniversityBusan48513Korea

The enormous volume of heterogeneous data fromvarious smart device-based applications has growingly increased a deeply interlaced cyber-physical *** order to deliver smart cloud services that require low latency with strong computational processing capabilities,the Edge Intelligence System(EIS)idea is now being employed,which takes advantage of Artificial Intelligence(AI)and Edge Computing technology(ECT).Thus,EIS presents a potential approach to enforcing future Intelligent Transportation Systems(ITS),particularly within a context of a Vehicular Network(VNets).However,the current EIS framework meets some issues and is conceivably vulnerable tomultiple adversarial attacks because the central aggregator server handles the entire ***,this paper introduces the concept of distributed edge intelligence,combining the advantages of Federated Learning(FL),Differential Privacy(DP),and blockchain to address the issues raised *** performing decentralized data management and storing transactions in immutable distributed ledger networks,the blockchain-assisted FL method improves user privacy and boosts traffic prediction ***,DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing *** model has been deployed in two strategies:First,DP-based FL to strengthen user privacy by masking the intermediate data during model ***,blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular *** simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget(ε)of 4.03,1.18,and 0.522,achieving model accuracy of 95.8%,93.78%,and 89.31%,respectively.

关键词： Edge intelligence federated learning differential privacy blockchain vehicular networks

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An Improved Deadlock Recovery Policy of Flexible Manufacturing Systems Based on Resource Flow Graphs

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IEEE Access 2024年 12卷 65202-65212页

作者： Tseng, Ching-Yun Chen, Ju-Chin Pan, Yen-Liang National Kaohsiung University of Science and Technology Department of Electronic Engineering Kaohsiung807618 Taiwan R.O.C. Air Force Academy Mathematics and Physics Division General Education Center Gangshan Kaohsiung820009 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung807618 Taiwan Chaoyang University of Technology Department of Aeronautical Engineering Taichung413310 Taiwan

The system deadlock problem of the flexible manufacturing system (FMS) needs to be solved urgently. Deadlocks may occur when resources are repeatedly requested by multiple working processes, where such competition causes a lack of resources, and the system cannot get into subsequent processes smoothly. In this paper, we build FMS by using a particular class of Petri Net (PN) model called a system of simple sequential processes with resources (S3PR) net, which features a high utilization rate of resources. When parts of resources are held by related processes and these processes wait for others to release resources to move on, a circular waiting situation occurs. Circular waiting is one of the conditions for system deadlocks occurring, where the system under this situation has a high probability of causing system deadlocks. The resources being held have to be released to break the waiting states. This paper develops a novel control transition-based deadlock recovery policy by combining graph theory and reachability graph analysis technique. The proposed approach firstly builds a subnet of the PN model called resource flow graph (RFG) and finds out all its hold and request circuits (HRCs), which may lead to a circular waiting situation. In addition, we design a new algorithm to obtain the minimal set of control transitions further. We introduce three PN models of FMS to demonstrate the proposed method. Through experimental results and comparison between related works, it is known that the proposed deadlock recovery uses fewer controllers and has the same performance. © 2013 IEEE.

关键词： Frequency modulation

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DDoS Attack Detection Using Deep-Learning Tool with Fused Features and Random Forest Classifier

DDoS Attack Detection Using Deep-Learning Tool with Fused Fe...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Aslam, Shabnam Mohamed Almutairi, Laila Fathima, Ruhi Prabha, S. Majmaah University Department of Information Technology Al Majmaah11952 Saudi Arabia Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India

ISBN: (纸本)9798331510022

Recently, a number of attempts and methodologies are created by the attacker to interrupt industry, disturb the significant infrastructures, and online services in order to create financial losses, and reputational damage. The Distributed Denial of Service (DDoS) attack is a common practice which aims to disturb a target server or a network by flooding it with excessive traffic with the help of botnet. When it is detected appropriately, necessary measures can be taken to reduce its impact. This study proposes a Deep-Learning (DL) tool to accurately detect the DDoS. The stages in the proposed scheme includes;data collection and resizing, implementing the chosen DL-scheme to extract the features, identification of the best DL-model for the chosen data and reducing its features using 50% dropout, feature-fusion (FF) and classification with three-fold cross-validation. The proposed DL-tool is verified using the FF with chosen classifiers and this approach help to achieve a DDoS detection accuracy of 100% when the Random Forest (RF) classifier based detection is implemented. © 2024 IEEE.

关键词： Bot (Internet)

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Smart Diaper Embedded with Fully Printed Sensors for Wireless Healthcare and Monitoring 5

Smart Diaper Embedded with Fully Printed Sensors for Wireles...

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5th IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2023

作者： Ali, Shawkat Khan, Arshad Bermak, Amine College of Science and Engineering Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

ISBN: (纸本)9781665457330

In this research, a real-time healthcare and monitoring wireless system is developed to monitor vital signs of a wearer i.e., temperature, respiration rate, and wetting volume on a smartphone. The smart diaper consists of two main units, a detachable electronic module and a disposable printed biosensors patch deployed on a commercial diaper. Custom sensors array was designed and fabricated in a single patch through printed electronics technology by using biocompatible materials on a thermal transfer substrate. The sensors patch was designed in such a way to accurately collect the vital signs data by placing each sensor at appropriate site. Individual sensors were characterized for their electrical and mechanical properties. The overall system was tested, and data was wirelessly transmitted to a smartphone via Bluetooth. The main purpose of the proposed smart diaper is to minimize the effort of caregiver and avoid infection, rashes, and discomfort. We believe this work will open a new window for the printed electronics systems for special needs, elderly, and infant healthcare. © 2023 IEEE.

关键词： Wetting

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A Design Study on Integrated Complex Coefficient Filters with Peripheral Circuits for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4322-4343页

作者： Al-Maliki, Shawqi Qayyum, Adnan Ali, Hassan Abdallah, Mohamed Qadir, Junaid Hoang, Dinh Thai Niyato, Dusit Al-Fuqaha, Ala Division College of Science and Engineering Doha34110 Qatar Information Technology University Lahore54000 Pakistan University of New South Wales Sydney Sydney2052 Australia Qatar University Department of Computer Science and Engineering College of Engineering Doha34110 Qatar University of Technology Sydney School of Electrical and Data Engineering Sydney2052 Australia Nanyang Technological University College of Computing and Data Science 639798 Singapore

Deep neural networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples - input samples that have been perturbed to force DNN-based models to make errors. As a result, adversarial machine learning (AdvML) has gained a lot of attention, and researchers have investigated these vulnerabilities in various settings and modalities. In addition, DNNs have also been found to incorporate embedded bias and often produce unexplainable predictions, which can result in antisocial AI applications. The emergence of new AI technologies that leverage large language models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing antisocial applications at scale. AdvML for social good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent prosocial applications. Regulators, practitioners, and researchers should collaborate to encourage the development of prosocial applications and hinder the development of antisocial ones. In this work, we provide the first comprehensive review of the emerging field of AdvML4G. This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating prosocial applications. Finally, we elaborate upon various challenges and open research issues that require significant attention from the research community. © 2020 IEEE.

关键词： Taxonomies

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Algebraic Adversarial Attacks on Integrated Gradients 23

Algebraic Adversarial Attacks on Integrated Gradients

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Simpson, Lachlan Costanza, Federico Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn School of Computer and Mathematical Sciences The University of Adelaide Australia School of Electrical and Mechanical Engineering The University of Adelaide Australia Defence Science & Technology Group Information Sciences Division Australia

ISBN: (纸本)9798331528041

Adversarial attacks on explainability models have drastic consequences when explanations are used to understand the reasoning of neural networks in safety critical systems. Path methods are one such class of attribution methods susceptible to adversarial attacks. Adversarial learning is typically phrased as a constrained optimisation problem. In this work, we propose algebraic adversarial examples and study the conditions under which one can generate adversarial examples for integrated gradients. Algebraic adversarial examples provide a mathematically tractable approach to adversarial examples. © 2024 IEEE.

关键词： Adversarial machine learning

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Tangentially Aligned Integrated Gradients for User-Friendly Explanations 32

Tangentially Aligned Integrated Gradients for User-Friendly ...

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32nd Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2024

作者： Simpson, Lachlan Costanza, Federico Millar, Kyle Cheng, Adriel Lim, Cheng-Chew Chew, Hong Gunn School of Electrical and Mechanical Engineering The University of Adelaide Australia School of Computer and Mathematical Sciences The University of Adelaide Australia Information Sciences Division Defence Science and Technology Group Australia

Integrated gradients is prevalent within machine learning to address the black-box problem of neural networks. The explanations given by integrated gradients depend on a choice of base-point. The choice of base-point is not a priori obvious and can lead to drastically different explanations. There is a longstanding hypothesis that data lies on a low dimensional Riemannian manifold. The quality of explanations on a manifold can be measured by the extent to which an explanation for a point lies in its tangent space. In this work, we propose that the base-point should be chosen such that it maximises the tangential alignment of the explanation. We formalise the notion of tangential alignment and provide theoretical conditions under which a base-point choice will provide explanations lying in the tangent space. We demonstrate how to approximate the optimal base-point on several well-known image classification datasets. Furthermore, we compare the optimal base-point choice with common base-points and three gradient explainability models. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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Automatic classification of normal/AD brain MRI slices using whale-algorithm optimized hybrid image features

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14237-14248页

作者： Kadry, Seifedine Jessy, V. Elizabeth Rajinikanth, Venkatesan Crespo, Rubén González Department of Applied Data Science Noroff University College Kristiansand4612 Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Innovation Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Technology Universidad Internacional de La Rioja La Rioja Logroño Spain

In recent years, the prevalence of Age-Related Illnesses (ARL) has been increasing among older individuals, and early recognition and treatment will result in better living conditions. It is well known that Alzheimer's Disease (AD) is among the ARD, and severe cases may result in dementia as well. It is the purpose of this study to propose a technique for distinguishing normal/AD brain MRI slices with improved accuracy utilizing the T2-modality. This scheme consists following phases: (i) Brain MRI collection and preprocessing, (ii) Deep feature extraction with the chosen scheme, (iii) Handcrafted feature extraction, (iv) Whale Algorithm (WA) based feature reduction and serial integration, and (v) binary classification using five-fold cross-validation. A total of 2000 MRI slices (1000 normal and 1000 AD class) are examined during this task using images collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI). This study confirms that the proposed scheme provides a classification accuracy of > 98% when applied with the K-Nearest Classifier. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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Cancer Cytoplasm Segmentation from Cell Image Based on Transfer Learning 24

Cancer Cytoplasm Segmentation from Cell Image Based on Trans...

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6th International Conference on Image, Video and Signal Processing, IVSP 2024

作者： Sultana, Rebeka Horibe, Hibiki Murakami, Tomoaki Shimizu, Ikuko Institute Of Global Innovation Research Tokyo University Of Agriculture And Technology Tokyo Japan Institute Of Agriculture Division Of Animal Life Science Tokyo University Of Agriculture And Technology Tokyo Japan Institute Of Engineering Division Of Advanced Information Technology & Computer Science Tokyo University Of Agriculture And Technology Tokyo Japan

ISBN: (纸本)9798400716829

Image segmentation is one of the most popular approaches used in digital pathology. Most existing deep learning-based image segmentation models extract nuclear from the background in cell images. The nuclear of cancer cell images are irregular in shape and size, with unclear borders. Detecting cancer regions from cell images for detailed analysis depending on the diagnosis's purpose, including other regions, is also necessary. Because of the characteristics of cancer cell images, conventional segmentation models trained on existing datasets targeted to extract cell regions only fail to detect cancer cytoplasm in cell images. Therefore, in this research, H&E stained cancer cell images are labeled manually, and segmentation is performed by transfer learning using a conventional method to segment cancer cytoplasm in cell images. Experimental results confirm that the IoU and dice coefficient are improved by 14% and 2%, respectively, compared to the method without transfer learning. © 2024 Owner/Author.

关键词： Image segmentation

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