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An Integrated Approach To Mitigating Systemic Cyber Risk In Autonomous Electric Vehicles

An Integrated Approach To Mitigating Systemic Cyber Risk In ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Quazi Mamun Zhenni Pan Shigeru Shimamoto School of Computing Mathematics and Engineering Charles Sturt University Global Center for Science and Engineering Faculty of Science and Engineering Waseda University School of Fundamental Science and Engineering Faculty of Science and Engineering Waseda University

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Integrating autonomous vehicles (AVs) within our transportation ecosystem presents exciting possibilities and introduces intricate challenges. Systemic failures due to cyberattacks on AVs could have far-reaching consequences, impacting safety and financial stability. This paper emphasizes the interdependence of stakeholders, particularly within the financial and insurance sectors. We propose a proactive approach, focusing on three objectives: (1) Systemic Risk Mitigation, (2) Premortem Analysis with a focus on cascading failures, and (3) Integration of Cybersecurity Standards tailored for autonomous electric vehicles (EVs). By addressing EV-specific vulnerabilities in charging infrastructure and telematics systems, we aim to significantly enhance the security of safety-critical cyber-physical systems, strengthen the resilience of interconnected industries, and contribute to broader economic stability.

关键词： Industries Protocols Stability criteria Transportation Electric vehicles Stakeholders Computer security Autonomous vehicles Standards Resilience

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Trusted Cloud Service Framework for Cloud computing Security 1

引用

6th International Conference on Innovative computing and Communication, ICICC 2023

作者： Sasmitha Suresh, A. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu SRM Nagar Kattankulathur 603203 India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Cloud computing is the pooling of several adaptive computing resources including servers, networks, storages, and services to provide users with convenient and timely access. IAM is a virtual server which manages user and resource rights and authorization. To control who has entry to what and how, IAM rules are collections of permissions that may be applied to either users or cloud resources. Cloud computing is an easily managed technology in which the apps we use are issued by a mysterious node that will never claim ownership but actually has full control over the application. The proliferation of personal computers and mobile devices across all industries has made secure data storage an urgent priority. There has been a recent explosion in the number of large and small firms investing in data infrastructure. Plus, they’re shelling out a lot of cash to keep the data secure. Using this approach to cloud computing significantly reduces the user’s dependence on hardware and software combinational models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Personal computers

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Feature Reduction of Relational Oil Drilling Data Before Propositionalization and Harmonization by Measuring Relational Data Missingness

Feature Reduction of Relational Oil Drilling Data Before Pro...

引用

ASEAN Australian engineering Congress, AAEC 2022

作者： Ting, Clement Pek Wen Then, Patrick Hang Hui Centre for Digital Futures School of Information and Communications Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Kuching Malaysia

ISBN: (纸本)9789819955466

Knowledge Discovery in Database (KDD) is a process undertaken by many data scientists who aim to extract knowledge from previously collected, secondary data. KDD is difficult when the scientist must analyze big data without prior due to the amount of knowledge required to understand and select relevant features for propositionalization, which is the conversion of raw relational data (stored in several tables) to propositional data (stored as one table) which is more compatible with established feature selection and data mining techniques. In this paper, we describe the Database Attribute Health Feature Reduction process (DAHFR) which is used as a filter-method feature selection metric for Feature Selection before Propositionalization (FSbP) to reduce the scope of features for observation, analysis, harmonization, and propositionalization before data mining. We employ FSbP using DAHFR during the KDD of multi-sourced oil drilling data which is contained in about 20,000 features in about 700 tables in 1 database per source. Each database follows an identical schema but has varying feature-wide missingness and semantic patterns which makes effective propositionalization difficult. DAHFR is a semi-automated process which allows the user to measure relational missingness of each feature for feature reduction and compare missingness patterns across databases. DAHFR is an improvement to existing feature selection processes because it requires minimum initial knowledge of the database’s business rules and could be used with relational data without the need for direct data observation nor prior propositionalization. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates 17

Feed-Forward Probabilistic Error Cancellation with Noisy Rec...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Kurosawa, Leo Saito, Yoshiyuki Lee, Xinwei Yan, Xinjian Xie, Ningyi Cai, Dongsheng Shin, Jungpil Asai, Nobuyoshi University of Aizu School of Computer Science and Engineering Aizu-Wakamatsu Fukushima Japan Singapore Management University School of Computing and Information Systems Singapore University of Tsukuba School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9798331530471

Probabilistic Error Cancellation (PEC) aims to improve the accuracy of expectation values for observables. This is accomplished using the probabilistic insertion of recovery gates, which correspond to the inverse of errors. However, the inserted recovery gates also induce errors. Thus, it is difficult to obtain accurate expectation values with PEC since the estimator of PEC has a bias due to noise induced by recovery gates. To address this challenge, we propose an improved version of PEC that considers the noise resulting from gate insertion, called Feed-Forward PEC (FFPEC). FFPEC provides an unbiased estimator of expectation values by cancelling out the noise induced by recovery gates. We demonstrate that FFPEC yields more accurate expectation values compared to the conventional PEC method through analytical evaluations. Numerical experiments are used to evaluate analytical results. © 2024 IEEE.

关键词： Inverse problems

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Estimating the State of Health of Lithium Ion Batteries using Neural Networks 24

Estimating the State of Health of Lithium Ion Batteries usin...

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24th International Middle East Power System Conference, MEPCON 2023

作者： Elsabbagh, Belal Farrag, Mohamed Misr International University Faculty of Computer Science Cairo Egypt Glasgow Caledonian University School of Computing Engineering and Built Environment Glasgow United Kingdom

ISBN: (纸本)9798350358469

With the world steadily transitioning to use electric vehicles, a new problem arises as for how batteries can be better maintained. Their maintenance requires a fully featured battery management system that can optimize the usage of these batteries for longer lifespans. One of the functionalities that could be useful is predicting the state of health of a lithium-ion battery. This paper explains how a model can be built to predict the Remaining Useful Life (RUL) of a battery by estimating the State of Health (SoH) of the battery using a neural network with given resistance measurements of a lithium-ion battery. © 2023 IEEE.

关键词： Lithium-ion batteries

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Deep Learning for Cross-Selling Health Insurance Classification

Deep Learning for Cross-Selling Health Insurance Classificat...

引用

Green Energy, computing and Sustainable Technology (GECOST), International Conference on

作者： Jasmin Z. K. Chu Joel C. M. Than Hudyjaya Siswoyo Jo Faculty of Engineering Computing and Science (PG) Swinburne University of Technology Sarawak Kuching Sarawak Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Sarawak Malaysia

In the ever-evolving landscape of insurance services, the strategic cross-selling of health insurance to existing customers stands as a pivotal endeavor. This study delves into this domain, aiming to harness the potential of machine learning and deep learning methodologies to identify prospects for health insurance adoption within the existing customer base, irrespective of their current insurance holdings. To achieve this goal, this study conducts various experiments employing different machine learning and deep learning algorithms, such as TabPFN, Graph Convolutional Network (GCN), LightGBM, Hist Gradient Boosting and XGBoost. Data preprocessing involves filtering out empty data and normalizing the remaining data. The dataset is then subjected to various optimization methods, including Arithmetic Optimization Algorithm (AOA), Gradient-Based Optimization (GBO), and Sine Cosine Algorithm (SCA), to enhance the model's performance, ensuring the development of a reliable and trustworthy predictive model for identifying customers likely to purchase health insurance. Notably, AOA consistently demonstrates strong performance in LightGBM with an increment of 9.34%, XGBoost with an increment of 8.66%, and TabPFN with an increment of 10.48%, establishing it as the optimal method for enhancing model performance across different models. Moreover, an additional discovery in this study emphasizes TabPFN's notable superiority over other algorithms, showcasing a substantial improvement in accuracy from 52.62% to 63.10%. This substantial enhancement underscores TabPFN's exceptional efficacy specifically in managing tabular data.

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Semantic Querying of Integrated Raster and Relational Data: A Virtual Knowledge Graph Approach 17

Semantic Querying of Integrated Raster and Relational Data: ...

引用

17th International Rule Challenge and 7th Doctoral Consortium @ RuleM+RR, RuleML+RR-Companion 2023

作者： Ghosh, Arka Šimkus, Mantas Calvanese, Diego Department of Computing Science Umeå Universitet Umeå Sweden Research Centre for Knowledge and Data Faculty of Engineering Free University of Bozen-Bolzano Bolzano Italy

Ontology-based data access (OBDA) facilitates access to heterogeneous data sources through the mediation of an ontology (e.g. OWL), which captures the domain of interest and is connected to data sources through a declarative mapping. In our study, large, heterogeneous earth observational (EO) data, known as raster data, and geometrical data, known as vector data, are considered as (heterogeneous) data sources. Raster data represent, e.g., Earth's natural phenomena, such as surface temperature, elevation, or air pollution, as multidimensional arrays. In contrast, vector data depict, e.g., locations, networks, or regions on Earth, using geometries. Domain experts, such as earth scientists and GIS practitioners, still struggle to undertake advanced studies by querying large raster and vector data in an integrated way because, unlike relational data, they come in diverse formats and different data structures. In our approach to integration, we use a geospatial extension of an RDBMS to represent vector data as relational data, and a domain-agnostic array DBMS to handle raster data. Our aim is to extend the OBDA paradigm to effectively deal with relational, vector, and raster data in a combined way, while leveraging the built-in capabilities of data management tools relevant to each type of data. We also plan to develop techniques to calculate on the fly for each user query posed over the ontology an optimal query plan that exploits, at best, the query processing capabilities of each tool, while limiting costly data transfer operations between tools. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Semantics

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GENSIMO - A Generic Framework for Modelling Social Insurance Systems 25th

GENSIMO - A Generic Framework for Modelling Social Insuranc...

引用

25th International Workshop on Multi-Agent-Based Simulation, MABS 2024

作者： de Haan, Fjalar J. Thompson, Jason School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Parkville Australia Melbourne Centre for Data Science The University of Melbourne Parkville Australia Department of Psychiatry Faculty of Medicine Dentistry and Health Sciences The University of Melbourne Parkville Australia

ISBN: (纸本)9783031880162

We report on the design, development and prototype testing of gensimo (GENeric Social Insurance MOdeling), a free and open-source software framework for modelling and simulation of social insurance systems. We discuss the conceptual and software design considerations of the framework in general before demonstrating how it works in detail by setting it up to model a particular social insurance scheme. We then present how we used this prototype set up to model the impact of a policy intervention on the workload of the insurer. While gensimo is under active development and we are a while away from serious verification and validation, this prototype testing shows realistic results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Software testing

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An Individual-Based Model In Maintaining Herd Immunity: Case On Malaysia Pertussis Data

An Individual-Based Model In Maintaining Herd Immunity: Case...

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2022 International Conference on Digital Transformation and Intelligence, ICDI 2022

作者： Ja'ffar, Halimatul Sa'adiah Labadin, Jane King, Tiong Wei Lian, Cynthia Kon Mui Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology Samarahan Malaysia Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia

ISBN: (纸本)9798350397000

Public health controls the re-emergence of infectious diseases by achieving herd immunity establishment in the population. This paper aims to consolidate two factors affecting herd immunity namely individuals' trust in public health and vaccines as well as wane of immunity in individuals into one epidemiological model in maintaining herd immunity. The model formulation adopts Individual-Based Model (IBM) approach to address the heterogeneity effects of individuals' attributes. The individuals' vaccination decision is modelled with imitation dynamics based on game theory. The model simulations use the real parameters obtained for Pertussis in Malaysia. By using different probability of individuals acquire trust, the simulation results are validated with the real Pertussis prevalence where the Root-Mean-Square-Percentage-Error (RMSPE) is used as the accuracy measurement metric. Around 70 percent to 80 percent of individuals must acquire trust towards public health and vaccines so that the simulation results become more fitted to the real Pertussis prevalence. In addition, the modified herd immunity threshold formula is derived from the model and is analyzed through parameter sensitivity analysis. Comparing to the general formula of herd immunity threshold, the modified herd immunity threshold formula provides a higher threshold for the establishment of herd immunity due to additional parameters included in the formula. The disruption of herd immunity because of the wane of immunity in individual would be countered by the vaccination of individuals who trust in public health and vaccines, hence establishing the herd immunity in the population. (Abstract) © 2022 IEEE.

关键词： Vaccines

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Machine Learning and OpenCV: A Powerful Combination for Driver Drowsiness Detection

Machine Learning and OpenCV: A Powerful Combination for Driv...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Siva Kumar, A. Gopirajan, P.V. Ramasamy, Jayaraj Jackson, Beulah School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur Chennai603203 India School Of Computing Srm Institute Of Science And Technology Department Of Computational Intelligence Kattankulathur Campus Chennai603203 India Botho University Faculty Of Engineering And Technology Department of IT Botswana Vel Tech Rangarajan Dr. Sagunthala R & D Institute Of Science And Technology Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

Driver fatigue is a significant contributor to road accidents worldwide, and there is a need for efficient driver drowsiness detection systems to prevent such accidents. Computer vision techniques like OpenCV have recently been utilized to develop such strategies. This proposed work investigates the use of machine learning models and OpenCV Python package for driver drowsiness detection. The proposed system uses a camera to continuously capture the driver's face and applies image processing techniques to identify drowsiness. The system detects blinks by monitoring the driver's eyes and measuring the frequency and duration of eyelid closures. The real-time experiments conducted with the proposed method showed high accuracy in detecting drowsiness, and the system can be implemented in various settings, including cars and heavy vehicles. Ultimately, the system has some potential to reduce accidents caused by driver fatigue, thereby enhancing road safety. © 2024 IEEE.

关键词： Support vector machines

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