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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Jaouhar Fattahi Baha Eddine Lakdher Mohamed Mejri Ridha Ghayoula Feriel Sghaier Laila Boumlik Department of Computer Science and Software Engineering Laval University Quebec Canada DArS DGSSI MCN Québec Canada Faculty of Science University of Monastir Monastir Tunisia Faculty of Engineering University of Moncton New Brunswick Canada Carthage National Engineering School Carthage University Tunis Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

In forensic investigations, facial recognition techniques serve as critical tools for identifying and apprehending suspects. In this study, we investigate the impact of Convolutional Neural Networks (CNNs) parallelization on the performance of facial recognition models within forensic contexts. Through experiments, we demonstrate the potential benefits of parallelization in enhancing model accuracy and robustness. Leveraging a reduced dataset, we employ augmentation techniques to expand the diversity of training samples. Our findings highlight the advantages of CNN parallelization in achieving superior recognition outcomes. Nevertheless, we identify constraints linked to excessive parallelization, which may induce model overfitting.

关键词： Training Navigation Face recognition Forensics Robustness System identification Convolutional neural networks Information technology Faces Research and development

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Enhancing Energy Efficiency in WSNs With Hybrid LEACH-D and ANN

Enhancing Energy Efficiency in WSNs With Hybrid LEACH-D and ...

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Multimedia University engineering Conference (MECON)

作者： Muhammad Salman Qamar Ihsan ul Haq Muhammad Fahad Munir Mardeni Bin Roslee Adnan Anwar Awan Athar Waseem Department of Electrical and Computer Engineering International Islamic University Islamabad Pakistan Centre for Wireless Technology Faculty of Engineering Multimedia University Cyberjaya Malaysia Department of Computer Science and Software Engineering School of Engineering California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

This paper explores strategies to improve the energy efficiency and operational longevity of Wireless Sensor Networks (WSNs) through the optimization of clustering techniques and energy efficient routing protocols. The proposed methodology is precisely crafted to optimize energy consumption (EC) while preserving the overall effectiveness of WSNs. LEACH (Low Energy Adaptive Cluster Hierarchy) is a cluster-based protocol where non-cluster head nodes deactivate their RF until their allocated time slots. Nevertheless, a drawback of traditional LEACH is its random rotation of local Cluster Heads (CH), leading to unequal cluster distribution. To overcome this issue, we proposed a novel hybrid technique called LEACH-D protocol and Artificial Neural Network (ANN). The LEACH-D algorithm enhances the duration of transmission tasks while ensuring uniform battery energy consumption. On the other hand, the ANN plays a pivotal role in coordinating optimal routing decisions and CH placement, refining data aggregation and transmission mechanisms to restrain idle listening. The findings indicate that the suggested approach manifests as substantial reductions in energy consumption and prolonged network lifespan of WSN.

关键词： Wireless sensor networks Energy consumption Simulation Artificial neural networks Data aggregation Energy efficiency Magnetic heads Routing protocols Batteries Optimization

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Database Scaling on Kubernetes 3

Database Scaling on Kubernetes

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Perera, H.C.S. De Silva, T.S.D. Wasala, W.M.D.C. Rajapakshe, R.M.P.R.L. Kodagoda, N. Samaratunge, Udara Srimath S. Jayanandana, H.H.N.C. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe10115 Sri Lanka

ISBN: (纸本)9781665408622

Kubernetes is a hot topic in the field of software engineering and Distributed Computing. When compared to previous methods, the principle underlying Kubernetes, which is containerization, has altered how applications are created and delivered. However, when considering the state, particularly the databases, with Kubernetes, there is a scalability and data synchronization barrier. The most frequently used approach is to host the database outside of Kubernetes and maintain connectivity with the cluster. Kubernetes inherent capabilities are sufficient for hosting databases. But that requires high domain knowledge to do the configurations and maintain the databases on Kubernetes. The purpose of this research is to fulfil that gap by introducing a solution for managing highly available databases on Kubernetes. The solution is limited to managing PostgreSQL databases on Kubernetes using auto-scaling. A novel algorithm is proposed for auto-scaling, as previous algorithms do not take database requests into account when determining the scaling need. The drawbacks of data synchronization and auto-scaling will be solved in this research, and the end user will be able to access the service without interruption for the majority of the time, as the final solution makes the database cluster highly available for the service layer. © 2021 IEEE.

关键词： Database systems

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Adaptive Graph Structure Learning Neural Rough Differential Equations for Multivariate Time Series Forecasting

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IEEE Transactions on Big Data 2025年

作者： Su, Yuming Ma, Tinghuai Rong, Huan Wahab, Mohamed Magdy Abdel Nanjing University of Information Science & Technology The School of Cyber Science and Engineering School of Computer Science Nanjing210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang222005 China Nanjing University of Information Science & Technology School of Software Nanjing210044 China Nanjing University of Information Science & Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Cairo University Faculty of Science Cario 12613 Egypt

Multivariate time series forecasting has extensive applications in urban computing, such as financial analysis, weather prediction, and traffic forecasting. Using graph structures to model the complex correlations among variables in time series, and leveraging graph neural networks and recurrent neural networks for temporal aggregation and spatial propagation stage, has shown promise. However, traditional methods' graph structure node learning and discrete neural architecture are not sensitive to issues such as sudden changes, time variance, and irregular sampling often found in real-world data. To address these challenges, we propose a method called Adaptive Graph structure Learning neural Rough Differential Equations (AGLRDE). Specifically, we combine dynamic and static graph structure learning to adaptively generate a more robust graph representation. Then we employ a spatio-temporal encoderdecoder based on Neural Rough Differential Equations (Neural RDE) to model spatio-temporal dependencies. Additionally, we introduce a path reconstruction loss to constrain the path generation stage. We conduct experiments on six benchmark datasets, demonstrating that our proposed method outperforms existing state-of-the-art methods. The results show that AGLRDE effectively handles aforementioned challenges, significantly improving the accuracy of multivariate time series forecasting. © 2015 IEEE.

关键词： Graph neural networks

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Federated Distillation: A Survey

arXiv

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arXiv 2024年

作者： Li, Lin Gou, Jianping Yu, Baosheng Du, Lan Yi, Zhang Tao, Dacheng The School of Computer Science and Communication Engineering Jiangsu Key Laboratory of Security Tech. for Industrial Cyberspace Jiangsu University Zhenjiang212013 China The College of Computer and Information Science College of Software Southwest University Chongqing400715 China The School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia The Faculty of Information Technology Monash University ClaytonVIC Australia The School of Computer Science Sichuan University Chengdu610065 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Federated Learning (FL) seeks to train a model collaboratively without sharing private training data from individual clients. Despite its promise, FL encounters challenges such as high communication costs for large-scale models and the necessity for uniform model architectures across all clients and the server. These challenges severely restrict the practical applications of FL. To address these limitations, the integration of knowledge distillation (KD) into FL has been proposed, forming what is known as Federated Distillation (FD). FD enables more flexible knowledge transfer between clients and the server, surpassing the mere sharing of model parameters. By eliminating the need for identical model architectures across clients and the server, FD mitigates the communication costs associated with training large-scale models. This paper aims to offer a comprehensive overview of FD, highlighting its latest advancements. It delves into the fundamental principles underlying the design of FD frameworks, delineates FD approaches for tackling various challenges, and provides insights into the diverse applications of FD across different scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Explainable Artificial Intelligence (XAI) techniques for energy and powersystems: Review, challenges and opportunities

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Energy and AI 2022年第3期9卷 193-205页

作者： R.Machlev L.Heistrene M.Perl K.Y.Levy J.Belikov S.Mannor Y.Levron The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion—Israel Institute of TechnologyHaifa 3200003Israel Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia School of Technology Pandit Deendayal Energy UniversityGandhinagar 382007GujaratIndia

Despite widespread adoption and outstanding performance, machine learning models are considered as ‘‘blackboxes’’, since it is very difficult to understand how such models operate in practice. Therefore, in the powersystems field, which requires a high level of accountability, it is hard for experts to trust and justify decisionsand recommendations made by these models. Meanwhile, in the last couple of years, Explainable ArtificialIntelligence (XAI) techniques have been developed to improve the explainability of machine learning models,such that their output can be better understood. In this light, it is the purpose of this paper to highlight thepotential of using XAI for power system applications. We first present the common challenges of using XAI insuch applications and then review and analyze the recent works on this topic, and the on-going trends in theresearch community. We hope that this paper will trigger fruitful discussions and encourage further researchon this important emerging topic.

关键词： Power Energy Neural network Deep-learning Explainable artificial intelligence XAI

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Beyond Data, Towards Sustainability: A Sydney Case Study on Urban Digital Twins

arXiv

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arXiv 2024年

作者： Sohail, Ammar Shen, Bojie Cheema, Muhammad Aamir Ali, Mohammed Eunus Ulhaq, Anwaar Babar, Muhammad Ali Qureshi, Asama Faculty of Information Technology Monash University Australia Department of Computer Science and Engineering BUET Bangladesh School of Engineering and Technology Central Queensland University Australia Centre for Research on Engineering Software Technologies University of Adelaide Australia

As urban areas grapple with unprecedented challenges stemming from population growth and climate change, the emergence of urban digital twins offers a promising solution. This paper presents a case study focusing on Sydney's urban digital twin, a virtual replica integrating diverse real-time and historical data, including weather, crime, emissions, and traffic. Through advanced visualization and data analysis techniques, the study explores some applications of this digital twin in urban sustainability, such as spatial ranking of suburbs and automatic identification of correlations between variables. Additionally, the research delves into predictive modeling, employing machine learning to forecast traffic crash risks using environmental data, showcasing the potential for proactive interventions. The contributions of this work lie in the comprehensive exploration of a city-scale digital twin for sustainable urban planning, offering a multifaceted approach to data-driven decision-making. © 2024, CC BY.

关键词： Risk analysis

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A Secured and Optimized Clustering Approch in Vehicular Ad-hoc Network

A Secured and Optimized Clustering Approch in Vehicular Ad-h...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Imran Khan Fazlullah Khan Asad Ali Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Zhejiang China Department of Computer Science University of Engineering and Technology Peshawar KPK Pakistan Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Vehicular ad hoc networks (VANETs) take vital role in intelligent transportation systems, but they face challenges due to dynamic network topology, impacting communication efficiency, especially with increasing active nodes. Security and privacy concerns are paramount, necessitating authentication to ensure only legitimate vehicles access the network. Secure vehicular clustering offers a solution, yet the number of clusters affects network dependability and efficiency. To tackle this, we present a secure clustering mechanism using Bat optimization algorithm, mimicking echolocation behavior to optimize clusters. We introduce an authentication technique for cluster heads to admit approved members, enhancing vehicular authentication. Simulations using MATLAB evaluate our approach, showing improved performance in end-to-end delay, overhead, and cluster head count compared to similar methods.

关键词： Privacy Network topology Authentication Vehicular ad hoc networks Security Vehicle dynamics Optimization Intelligent transportation systems Faces Matlab

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Compact NSGA-II for Multi-objective Feature Selection*

arXiv

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arXiv 2024年

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada

Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other post-processing task conducted after feature selection. In this regard, we define feature selection as a multi-objective binary optimization task with the objectives of maximizing classification accuracy and minimizing the number of selected features. In order to select optimal features, we have proposed a binary Compact NSGA-II (CNSGA-II) algorithm. Compactness represents the population as a probability distribution to enhance evolutionary algorithms not only to be more memory-efficient but also to reduce the number of fitness evaluations. Instead of holding two populations during the optimization process, our proposed method uses several Probability Vectors (PVs) to generate new individuals. Each PV efficiently explores a region of the search space to find non-dominated solutions instead of generating candidate solutions from a small population as is the common approach in most evolutionary algorithms. To the best of our knowledge, this is the first compact multiobjective algorithm proposed for feature selection. The reported results for expensive optimization cases with a limited budget on five datasets show that the CNSGA-II performs more efficiently than the well-known NSGA-II method in terms of the hypervolume (HV) performance metric requiring less memory. The proposed method and experimental results are explained and analyzed in detail. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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PID Controller Machine Learning Algorithm Applied to the Mathematical Model of Quadrotor Lateral Motion 6

PID Controller Machine Learning Algorithm Applied to the Mat...

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6th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2021

作者： Kucherov, Dmytro Kozub, Andrei Tkachenko, Valerii Rosinska, Galina Poshyvailo, Olexii Faculty of Cybersecurity Computer and Software Engineering National Aviation University Department of Computerized Control Systems Kyiv Ukraine National Space Facilities Control and Test Center State Space Agency of Ukraine Kyiv Ukraine

ISBN: (纸本)9781665438223

The paper discusses the problem of automatic tuning of the PID controller. The auto-tuning algorithm of the PID controller based on one machine learning method, which is equivalent to the steepest descent, is proposed. A feature of the proposed approach is the vector adjustment of the PID controller parameters, where the angular direction of descent is taken into account at first, and the step sizes are made if taking into account the increased accuracy of the approximation near the optimum point. Modeling performed in the Matlab environment confirms the effectiveness of the proposed approach. © 2021 IEEE.

关键词： Three term control systems

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