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A Two-Pronged Approach to Security of Discrete-Event Systems

Journal of Systems Science & Complexity 2025年第1期38卷 178-209页

作者： MATSUI Shoma RUDIE Karen CAI Kai Department of Electrical and Computer Engineering&the Ingenuity Labs Research Institute Queen's UniversityKingston K7L 3N6Canada Department of Core Informatics Osaka Metropolitan UniversityOsaka 545-0051Japan

This paper investigates a security problem of simultaneously addressing two types of attacks:Eavesdropping and *** authors model the target system as a discrete-event system(DES)with subsets of concealable events and protectable events,in order to make the proposed methodology applicable to various practical systems and employ two existing works of DES security:Degree of opacity and state ***,the authors consider that all protectable events are observable,and some observable events are *** addition,protectable events cannot be protected once they are *** such a constraint,the goal is to figure out which events to conceal and which transitions to protect so that the prescribed requirements of degree of opacity and state protection are *** this work the authors decide which events to conceal as all transitions of a given event label are concealed or not *** proposed problem formulation also requires a solution to only involve absolutely necessary protectable events in order for the system to avoid superfluous protection *** authors first examine a general version of our security problem with an intuitive algorithm to compute acceptable solutions,and then present a special version which results in a reduced computation time compared to the general version.

关键词： Degree of opacity discrete-event systems security state protection

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Simulating inexact DNNs for use in Custom Hardware Architectures

Simulating inexact DNNs for use in Custom Hardware Architect...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Danopoulos, Dimitrios Zervakis, Georgios Soudris, Dimitrios National Technical University of Athens School of Electrical and Computer Engineering Athens Greece University of Patras Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350318845

In response to the growing complexity of Deep Neural Network (DNN) models, the paradigm of approximate computing has emerged as a compelling approach to strike a balance between computational efficiency and model accuracy. Approximate computing involves intentionally allowing errors using inexact computations, exploiting the inherent resilience of many applications to such inaccuracies. While current state-of-the-art approaches leverage approximate multipliers for approximate DNN accelerators, there has been a notable difficulty in emulating such DNNs on common frameworks such as PyTorch, as they do not inherently support inexact arithmetic. This paper introduces a seamless PyTorch plugin, to evaluate inexact DNN models while having GPU acceleration to enable rapid emulation. Additionally, we support approximate-aware retraining and perform evaluation on several popular convolutional neural networks on Cifar10 and ImageNet datasets. © 2024 IEEE.

关键词： Deep neural networks

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RESIDENTIAL electrical STORAGE: FEASIBILITY STUDY FOR HYBRID RESIDENTIAL PV/BATTERY SYSTEMS IN CYPRUS 14

RESIDENTIAL ELECTRICAL STORAGE: FEASIBILITY STUDY FOR HYBRID...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Tillyros, Kypros Komodromos, Michael Lestas, Marios Christofides, Nicholas Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus Frederick Research Centre Nicosia Cyprus

ISBN: (纸本)9781837242689

This paper presents a simulation study on integrating grid-connected photovoltaic (PV) systems with a potential to implement battery storage in residential settings. It analyses the performance and feasibility of such systems in enhancing self-consumption and energy export. The simulation model considers factors like household energy demand profiles, and PV output profiles. Through this analysis, it is anticipated to offer insights for homeowners, policymakers, and industry stakeholders on achieving greater energy independence and resilience. Finally, findings show that the attempt to effectively utilise battery storage in a residence while aiming for the most economically feasible system heavily depends on the load demand. It was observed that no matter the profile of the building or the energy utilisation, there is no significant change in energy costs. However, the results manifested an upcoming trend for utilising energy that will prove beneficial for costumers. © The Institution of engineering & Technology 2024.

关键词： Battery storage

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Developing an Anti -Phishing Large Language Model: A Focus Group Study on Human, Technological, and Legal Factors 15

Developing an Anti -Phishing Large Language Model: A Focus G...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Thomopoulos, George A. Kiortsi, Panagiota Sigalas, Damianos Dumi Kosmopoulos, Dimitris Igglezakis, Ioannis Fidas, Christos University of Patras Department of Electrical and Computer Engineering Greece University of Thessaloniki Faculty of Law Aristotle Greece University of Patras Department of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350368833

The phishing problem poses a significant threat in modern information systems, putting both individuals and businesses at risk of financial and professional harm. Owing to social media's rapid development and widespread appeal, deception of this sort is hurting millions of people and growing more dangerous. The current work, as part of the AILA (Artificial Intelligence-driven Framework and Legal Advice Tools for Phishing Prevention and Mitigation in Information Systems) project, aims to specify and validate an AI-driven multifactor (human, technology and legal) anti-phishing data model, with the implementation of focus group studies. The findings assist to provide human, technology, and legislative user model endpoints that will be identified and discussed for explicit and implicit user modeling, which will guide the development of the corresponding AI-driven user modeling and profiling mechanisms. To this end a Large Language System is planned to be employed. © 2024 IEEE.

关键词： Phishing

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Introduction to Heterogeneous Robotic Systems in 5G and Beyond Networks

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IEEE WIRELESS COMMUNICATIONS 2023年第2期30卷 64-65页

作者： Lu, Huimin Ho, Pin-Han Khalfi, Bassem Verikoukis, Christos Kyushu Institute of Technology Japan Department of Electrical and Computer Engineering The University of Waterloo Canada Oregon State University CorvallisOR United States University of Patras Department of Computer Engineering & Informatics Greece

Remote intelligence in the application of robotics and the autonomous system relies heavily on seamless wireless connections. The 5G mobile network technology meets traditional manufacturing enterprises' application requirements for wireless networks based on robot transformation and upgrading, robot interconnection, and remote interactive applications in production. However, there exists many challenging 5G communication issues, such as different communication protocols in the system varies with different robots and no dis-ruptive changes to the physical layer.

关键词： Special issues and sections Robots 5G mobile communication Physical layer Autonomous systems Heterogeneous networks

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Classification of Arrhythmias 12-Lead ECG Signals Based on 1 Dimensional Convolutional Neural Networks

Classification of Arrhythmias 12-Lead ECG Signals Based on 1...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Kurniawan, Arief Triwibowo, Bayu Aditya Fandiantoro, Dion Hayu Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Engineering Indonesia

ISBN: (纸本)9798350364101

Heart disease is the highest cause of death in the world. Arrhythmia is an abnormality in the rhythm of the heartbeat. The heart beats too fast, too slow, or irregularly. Arrhythmias are not always dangerous, e.g., someone who does excessive activity has a faster heart rate. Then, a diagnosis is needed to classify arrhythmias. One method used is ECG (Electrocardiogram) signal analysis. The ECG signal consists of P, QRS Complex, and T waves. The morphology of the QRS is used for arrhythmia classification. Currently, cardiologists analyze ECG signals by observing directly. This method is depending on the level of expertise of the cardiologist. Previous research classified arrhythmias based on the QRS morphology from a single ECG lead. As 12-lead ECG devices have now become standard in ECG examinations because abnormalities can be observed from multiple angles. This study proposes the classification of arrhythmias in 12-lead ECG signals based on the morphology of QRS complex waves using a deep learning 1-dimensional Convolutional Neural Network. The output of deep learning is the classification of arrhythmias into four classes, namely: Normal, Right Bundle Branch Block, Premature Ventricular Contraction, and Atrial Premature Beat. The outcome of the proposed system is that each QRS segment is used as input for deep learning, which can improve classification performance compared to the classification carried out by each lead. The experimental results show the method can be done well, with an average Accuracy, Precision, Sensitivity, and F1-Score were 98.8%, 99.2%, 99.2%, and 99.2%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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UAV-Assisted Internet of Vehicles over Licensed and Unlicensed Spectrum: Architecture, Intelligent Resource Management, and Challenges

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IEEE Internet of Things Magazine 2023年第3期6卷 78-84页

作者： Su, Yuhan Liwang, Minghui Chen, Zhong Wang, Xianbin School of Electronic Science and Engineering Xiamen University China School of Informatics Xiamen University China Western University Department of Electrical and Computer Engineering London Canada

Benefited from their flexibility and on-demand deployment capability, unmanned aerial vehicles (UAVs) have emerged as critical aerial communication platforms in future Internet of Vehicles (IoV). However, limited spectrum resources can lead to unsatisfying data rate of IoV, which thus incur large latency, especially under congested IoV network conditions. Although UAVs and road side units (RSUs) can work within the same spectrum and increase spectral efficiency, mutual interference becomes unavoidable. To this end, this article develops a heterogeneous network architecture, in which a UAV-assisted IoV system coexists with a Wi-Fi system: the RSUs can properly occupy unlicensed spectrum to increase the capacity of the UAV-assisted IoV system while mitigating interference, without affecting the performance of the Wi-Fi system. A case study of resource management over licensed and unlicensed spectrum is investigated under the proposed architecture, where time and power are jointly optimized to maximize the sum user satisfaction of the system. We further provide an intelligent solution to tackle the problem in the considered case study. Simulations demonstrate that our proposed case can efficiently improve the sum user satisfaction of the system. Key challenges and opportunities for UAV-assisted IoV over licensed and unlicensed spectrum are discussed, while recommendable future research directions are investigated. © 2018 IEEE.

关键词： Wi-Fi

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Utilizing Edge Device and Streamlit for Comparative Evaluation of CLAHE-Based Underwater Image Enhancement Techniques 13

Utilizing Edge Device and Streamlit for Comparative Evaluati...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Amalia, Nurul Asmi Afira, Aida Ulfa, Maria Muchtar, Kahlil Universitas Syiah Kuala Department of Informatics Aceh Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Indonesia

ISBN: (纸本)9798350355079

This study presents an edge-based system for underwater image enhancement using CLAHE and its fusion with various techniques. Utilizing the NVIDIA Jetson Orin Nano and the Streamlit framework, the system processes images in real-time. The enhancement methods include CLAHE, Fusion of CLAHE and USM, Fusion of CLAHE and HEF, and Blending CLAHE with Percentile Techniques. Tested on real underwater images, these methods demonstrate significant improvements in UIQM and UCIQE scores while reducing LOE. This combination boosts image quality while maintaining natural lightness, making it suitable for marine exploration, underwater robotics, and environmental monitoring. A demo of our image enhancement web app can be found here: https://***/EDJxPKv1LSs. © 2024 IEEE.

关键词： Underwater imaging

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Equipment Parameter Investigation for a Proposed Power Conductor Hot Spot Identification system 10

Equipment Parameter Investigation for a Proposed Power Condu...

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10th International Conference on Modern Power Systems, MPS 2023

作者： Polycarpou, Alexis Komodromos, Michael Kalli, Kyriacos Argyrou, Maria C. Ioannou, Andreas Computer Engineering and Informatics Frederick University Frederick Research Center Deparment of Electrical Nicosia Cyprus Cyprus University of Technology PhOSLab Department of Electrical Engineering/Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350326826

Reliability, efficiency and continuity of power energy supplied is an area which receives increasing attention as the main infrastructure of power transmission and distribution systems in many countries is ageing. Hot spot related cable failures and power interruptions have a big financial impact on the power provider. The operational parameters of a method to identify in real time hot spots on cables is investigated in this paper, which can result to timely fault prevention. This can be achieved through exploiting the optical sensing capabilities of the existing optical fibre on-grid network. The resulting parameters aim to be advantageous for the system as they increase accuracy and will enable the Remote, automated, continuous, and real time monitoring of the grid infrastructure integrity. © 2023 IEEE.

关键词： Cables

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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

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Tsinghua Science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

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