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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Hamel, Aleksandra Ursula Charlotte Zarcu, Bogdan-Cristian Csenteri, Andras-Gergely Pfliegler, Tamara Khan, Sajjad Svetinovic, Davor Vienna University of Economics and Business Information Systems and Operations Management Vienna Austria Khalifa University Center for Cyber-Physical Systems Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350304602

Using digital data gathering and analytics in healthcare brings benefits and risks to patients and practitioners. Smart Health information systems, such as Clinical Decision Support systems (CDSSs), consolidate data from various sources, utilizing artificial intelligence for decision support. However, machine learning models in CDSSs are vulnerable to various attacks, leading to incorrect predictions with severe consequences. This paper systematically investigates security and privacy threats related to CDSSs. First, we leverage the data flow and sequence diagrams to identify the critical use cases that might lead to security or privacy breaches. Second, we identify and classify threats imminent to the CDSSs using Security Cards and STRIDE. Lastly, the persona non-grata who pose a significant threat to the integrity of the CDSSs are identified. Implementing our method can assist teams in addressing security threats to CDSSs by considering their unique vulnerabilities. This research contributes to developing comprehensive security strategies for CDSSs. © 2023 IEEE.

关键词： Artificial intelligence

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Blockchain CBDC Security Threats Using STRIDE 5

Blockchain CBDC Security Threats Using STRIDE

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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Hans, Jaqueline Khan, Sajjad Svetinovic, Davor Information Systems and Operations Management Vienna University of Economics and Business Vienna Austria Center for Cyber-Physical Systems Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350339239

In strategic response to the increasing threats from crypto payment systems, cryptocurrencies, and stablecoins, central banks globally are contemplating the introduction of their digital currencies, termed Central Bank Digital Currencies (CBDCs). This trend, especially the emergence of CBDCs built on blockchain technology, instigates significant discourse on security and privacy concerns. This research paper delves into the potential security risks of blockchain-based CBDCs, distinguishing between permissionless and permissioned network architectures and token-based and account-based access mechanisms. We employed STRIDE, a threat modeling methodology, to elucidate these risks, on architectural constructs derived from CBDC proposals and use cases, identifying 39 distinct threats. Our study intends to enrich the ongoing dialogue on the evolution of blockchain-based CBDCs. The findings provide a robust foundation to guide and inform prudent design decisions by offering a detailed understanding of potential security and privacy risks, thereby contributing to developing more secure CBDCs. © 2023 IEEE.

关键词： Blockchain

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Threat Modeling for ML-based Topology Prediction in Vehicular Edge Computing Architecture

Threat Modeling for ML-based Topology Prediction in Vehicula...

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作者： Doan, Hong Hanh Paul, Audri Adhyas Zeindlinger, Harald Zhang, Yiheng Khan, Sajjad Svetinovic, Davor Vienna University of Economics and Business Information Systems and Operations Management Vienna Austria Center for Cyber-Physical Systems Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350304602

The Internet of Vehicles (IoV), a network that interlinks vehicles, infrastructure, and assorted entities, serves as a cornerstone for intelligent transportation systems and the emergence of smart cities. Within this context, edge computing has been identified as a critical solution for providing rapid and reliable data processing. Machine Learning (ML) techniques have become essential to IoV activities such as resource allocation and load balancing across mobile edge servers, typified by decentralized services that range from natural language processing to image recognition. The fusion of ML with edge computing within IoV architecture promises enhanced performance, efficiency, and safety. However, this amalgamation also creates challenges related to data privacy, cybersecurity, malfunctioning edge devices, inconsistent network connectivity, human errors, and malicious insiders. Consequently, this paper focuses on modeling security threats within an ML-based edge computing framework for the IoV. We analyze the system provided by the Linux Foundation Edge Akraino Project's Stable Topology Prediction blueprint by employing a hybrid threat modeling technique. Our strategy leverages STRIDE to elicit threats on distinct system elements like vehicle-to-vehicle communication networks, edge networks, and ML models. Subsequently, these threats are consolidated for a comprehensive view using an attack tree. © 2023 IEEE.

关键词： Machine learning

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Disentangling Spatio-Temporal Knowledge for Weakly Supervised Object Detection and Segmentation in Surgical Video

Disentangling Spatio-Temporal Knowledge for Weakly Supervise...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Liao, Guiqiu Jogan, Matjaz Koushik, Sai Eaton, Eric Hashimoto, Daniel A. University of Pennsylvania Pcaso Laboratory Department of Surgery United States University of Pennsylvania Department of Electrical and Systems Engineering United States University of Pennsylvania Department of Computer and Information Science United States

ISBN: (纸本)9798331510831

Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring extensive annotations of object masks, relying instead on coarse video labels indicating object presence. WSVOS in surgical videos is, however, more challenging due to the complex interaction of multiple transient objects, such as surgical tools moving in and out of the surgical field. In this scenario, state-of-the-art WSVOS methods struggle to learn accurate segmentation maps. We address this problem by introducing ViDeo Spatio-Temporal disentanglement Networks (VDST-Net), a framework to disentangle complex spatio-temporal object interactions using semi-decoupled knowledge distillation to predict high-quality class activation maps (CAMs). A teacher network is designed to help a temporal-reasoning student network resolve activation conflicts, as the student leverages temporal dependencies when specifics about object location and timing in the video are not provided. We demonstrate the efficacy of our framework on a challenging surgical video dataset where objects are, on average, present in less than 60% of annotated frames, and compare our method to state-of-the-art methods on surgical data and on a public dataset commonly used to benchmark WSVOS. Our method outperforms state-of-the-art techniques and generates accurate segmentation masks under video-level weak supervision. Our code is available at: https://***/PCASOlab/VDST-net. © 2025 IEEE.

关键词： Image segmentation

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Guest Editorial Enabling Technologies and systems for Industry 5.0: From Foundation Models to Foundation Intelligence

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IEEE Transactions on systems, Man, and Cybernetics: systems 2024年第11期54卷 6496-6499页

作者： Tang, Ying Tian, Yonglin Lin, Yilun Lv, Chen Fanti, Maria Pia Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Shanghai AI Laboratory Urban Computing Laboratory Shanghai200232 China Nanyang Technological University School of Mechanical and Aerospace Engineering Jurong West639798 Singapore Polytechnic University of Bari Department of Electrical and Information Engineering Bari70126 Italy

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The Efficient Cluster Head Selection for NarrowBand Internet of Things

The Efficient Cluster Head Selection for NarrowBand Internet...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Huang, Shih-Yun Lee, Shou-Yu Wu, Tin-Yu Chang, Yao-Chung Chao, Han-Chieh Tunghai University Department of Computer Science Taichung Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Taiwan National Taitung University Department of Computer Science and Information Engineering Taitung Taiwan National Dong Hwa University Department of Electrical Engineering Hualien Taiwan

ISBN: (纸本)9798350324174

The future of 5G will focus on massive machine-type communication (mMTC), and many applications will be based on this application and gradually mature. These applications include smart cities, e-health, and the Internet of Vehicles (IoV). In addition, the Narrowband Internet of things (NB-IoT) is a critical technology in mMTC. NB-IoT can bring many benefits, such as efficient bandwidth use and long-distance transmission. However, although NB-IoT has many advantages, issues such as throughput, energy consumption, and security still need to be solved. In order to effectively solve these problems, the choice of cluster heads is very important. Considering that the energy consumption of the CH will be more severe than that of other devices, which will also shorten the network;therefore, we will focus on the dynamic cluster head selection. To solve this problem, we introduced the concept of the pseudo-rigid body into this paper and proposed a method similar pseudo-rigid body to determine the CHs;at the same time, we also defined the maximum range of each cluster. The experimental results show that the proposed method can dynamically select the CH and increase the network lifetime. © 2023 IEEE.

关键词： Internet of things

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Identifying Appropriate Probability Density Function for Wind Speed Profiles of Two Coastal Cities in Pakistan for Better Wind Power Assessment 3

Identifying Appropriate Probability Density Function for Win...

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3rd International Conference on Emerging Trends in electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Abbas, Ghulam Ali, Arshad Zubair, Muhammad Mushtaq, Zohaib Farooq, Umar Khan, Irfan A. Department of Electrical Engineering The University of Lahore Lahore Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia College of Engineering and Technology University of Doha for Science and Technology Doha Qatar Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha Pakistan IEECE University of the Punjab Lahore Pakistan Lab Texas A&M University GalvestonTX77553-1675 United States

ISBN: (纸本)9798331504861

To assess the wind power potential of a specific wind site, a suitable distribution function that best suits the wind data needs to be known. In this study, we employ four probability density functions (PDFs), namely Weibull, Rayleigh, Log-Normal, and Gamma, on wind speed data obtained from two coastal cities in Pakistan, Gwadar and Karachi, to check which one is the best suited. Cities along the coastline of Pakistan offer greater wind speeds and, thus, greater wind power potential. The maximum likelihood technique is used to estimate the parameters of the density distributions. Three statistical measures—Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2)—are used to assess the goodness of fit for every distribution. These measures give a complete picture of every distribution's correctness in terms of reflecting the recorded wind speed patterns. Numerical results reveal that the Weibull distribution fits well with the wind speeds of the two coastal cities compared to the other three probability distributions, providing insightful analysis for the evaluation of wind resources and possible local energy generation. The MATLAB environment is used to compare the distributions and validate the effectiveness of the proposed methodology. ©2024 IEEE.

关键词： Windmill

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A Novel Internet of Medical Thing Cryptosystem Based on Jigsaw Transformation and Ikeda Chaotic Map

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computer systems Science & engineering 2023年第9期46卷 3017-3036页

作者： Sultan Almakdi Mohammed SAlshehri Yousef Asiri Mimonah Al Qathrady Anas Ibrar Jawad Ahmad Department of Computer Science Najran UniversityNajran61441Saudi Arabia Department of Information Systems Najran UniversityNajran61441Saudi Arabia Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image *** picture encryption is a crucial step in many cloud-based and healthcare *** this study,a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things(IoMT)and cloud storage.A disorganized three-dimensional map is the foundation of the proposed *** dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding *** suggested cryptosystem enhances the security of the delivered medical images by performing many *** validate the efficiency of the recommended cryptosystem,various medical image kinds are used,each with its unique *** measures are used to evaluate the proposed cryptosystem,which all support its robust *** simulation results confirm the supplied cryptosystem’s ***,it provides strong robustness and suggested protection standards for cloud service applications,healthcare,and *** is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998,which is near its most excellent value of 8,and a typical NPCR value of 99.62%,which is also near its extreme value of 99.60%.Moreover,the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.

关键词： Jigsaw transformation cryptosystem image encryption medical images Ikeda map chaotic system

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Signal Strength Based Ranging for Livestock Localization for Smart Farms 11

Signal Strength Based Ranging for Livestock Localization for...

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2023 IEEE 11th International Conference on information, Communication and Networks, ICICN 2023

作者： Tu, Chunling Du, Shengzhi Tong, Jigang Wang, Lushuo Plessis, Deon Du Tshwane University of Technology Department of Computer Systems Engineering Pretoria South Africa Tshwane University of Technology Department of Electrical Engineering Pretoria South Africa Tianjin University of Technology Electrical Engineering and Automation Tianjin China Tshwane University of Technology Department of Information Technology Pretoria South Africa

ISBN: (纸本)9798350314014

An intelligent livestock monitoring system has become increasingly popular for monitoring and managing animals in smart farms. Various technologies have been developed to localize the animals with wearable sensors. This paper addresses ranging from Received Signal Strength Indicator (RSSI). ESP32C3-Mini chips are used to construct the localization system, where both ear tags and base stations are built on these chips. Based on the distances captured by multiple base stations, triangulation is employed to estimate the position of the concerned tags. To improve the positioning accuracy, a weighted triangulation method is used. The advantage and distances of the proposed RSSI-based ranging method are discussed using real data. © 2023 IEEE.

关键词： Triangulation

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Innovative Machine Learning Approaches to Cardiovascular Disease Prediction

Innovative Machine Learning Approaches to Cardiovascular Dis...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Mehedi, Mithila Nishat, Nourin Nahar, Janifer Raasetti, Muniroopesh Shoaib, A.S.M. Ali, Basharat Pabel, Md Amran Hossen Rajshahi University of Engineering and Technology Department of Electrical and Computer Engineering Rajshahi6204 Bangladesh Lamar University Department of Management Information Systems TX United States Louisiana State University Department of Finance United States Lamar University Department of Electrical Engineering TX United States Wright State University Business Analytics and Insights DaytonOH United States

ISBN: (纸本)9798350369175

Cardiovascular diseases (CVDs) are a principal factor in health issues and death throughout the world, introducing extensive challenges to healthcare systems and impacting countless lives. This paper explores the application of various machine learning models to predict and monitor CVDs, leveraging data from wearable sensors and smart health devices. We analysed models featuring Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, Gradient Boosting, KNearest Neighbors, and Voting Classifiers. Our in-depth examination revealed that most models achieved remarkable accuracy, precision, recall, and F1-scores with the Voting Classifier. These models' high accuracy and straightforward implementation make them suitable for real-world clinical settings, where computational resources may be limited. By enabling early detection and continuous monitoring, these machine learning approaches can personalize healthcare, improve patient outcomes, and reduce healthcare costs. The integration of these advanced technologies promises to enhance cardiovascular disease management, providing timely interventions and better health monitoring, ultimately contributing to reduced incidence and severity of CVDs. © 2024 IEEE.

关键词： Logistic regression

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