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arXiv 2024年

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

We propose an uplink over-the-air aggregation (OAA) method for wireless federated learning (FL) that simultaneously trains multiple models. To maximize the multi-model training convergence rate, we derive an upper bound on the optimality gap of the global model update, and then, formulate an uplink joint transmit-receive beamforming optimization problem to minimize this upper bound. We solve this problem using the block coordinate descent approach, which admits low-complexity closed-form updates. Simulation results show that our proposed multi-model FL with fast OAA substantially outperforms sequentially training multiple models under the conventional single-model approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Exploring Relationships among Bitcoin's Market Price, Energy Consumption and Carbon Dioxide Emissions: A Machine Learning Approach 1

Exploring Relationships among Bitcoin's Market Price, Energy...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Jamali, Aadil Ali, Najma Imtiaz Brohi, Imtiaz Ali Kanasro, Nadeem Ahmed Murad, Muhammad Umar Jamali, Asad Ali University of Sindh IMCS Jamshoro Pakistan Government College University Dept. of Computer Science Hyderabad Pakistan Istanbul Aydin University Dept. of Software Engineering Istanbul Turkey University of Essex Dept. of Economics Colchester United Kingdom

ISBN: (纸本)9798350373042

The cryptocurrency boom has resulted in a substantial rise in energy usage and carbon emissions. The mining process of Bitcoin is notorious for its energy intensive process because it requires huge computational power. The carbon footprint of Bitcoin mining is a growing concern that's why we have to analyze the number of CO2 emissions resulting from the mining procedure. In this study, we have utilized machine learning techniques to examine the number of CO2 emissions generated by Bitcoin mining, analyzed the correlations between Bitcoin price, electricity consumption, and CO2 emissions, and pinpoint the factors during the process of Bitcoin mining that leads to CO2 emissions. We gathered global data set on Bitcoin electricity consumption, prices, and CO2 emissions. We trained supervised machine learning model (Regression) in Python to analyze and investigate the data. Our research findings have great implications for policymakers and researchers worried about the impact of the latest technologies on the environment, and in future Bitcoin policies and practices;they should prioritize sustainability and reduce the carbon footprint of mining operations. In the end, our research will provide valuable insights into the environmental impact of Bitcoin mining, highlight the factors that cause CO2 emissions and underscored the need for further research and action to reduce the carbon footprint of cryptocurrencies. © 2024 IEEE.

关键词： Carbon footprint

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Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated Learning

Uplink Over-the-Air Aggregation for Multi-Model Wireless Fed...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Chong Zhang Min Dong Ben Liang Ali Afana Yahia Ahmed Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

关键词： Training Upper bound Accuracy Array signal processing Federated learning Wireless networks Simulation Uplink Optimization Convergence

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Masked Face Detection and Recognition Using a Unified Feature Extractor

Masked Face Detection and Recognition Using a Unified Featur...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Aroobah Iftikhar Arslan Shaukat Rimsha Tariq Dept of Computer and Software Engineering College of EME National University of Sciences and Technology (NUST) Islamabad Pakistan Dept of Computer Engineering Information Technology University Lahore Pakistan

The research field of computer vision has recently taken an interest in the active computer vision problem of masked face recognition due to the COVID-19 pandemic. The use of face masks as a preventative measure against the spread of COVID-19 has presented a new challenge for the technology of face recognition. Masked Face Recognition (MFR) is a specific type of facial occlusion issues that obstruct vital facial features such as the mouth, nose, or chin. The purpose of research on Masked Face Detection and Recognition is to fine-tune a pre-trained model that can more accurately recognize masked faces and detect whether the person is wearing a mask or not, which can be advantageous in various applications, like security and surveillance, healthcare, retail, law enforcement, the workplace, and social media. This paper proposes an approach to enhance the performance of the single neural network architecture as a unified model capable of both detection and recognition of masked face images by achieving 99% and 98% respectively, on the MFR2 dataset. Also, a detection model is proposed that is evaluated on publicly available datasets for the detection of masked faces, which are the MDMFR dataset, the Kaggle face mask detection dataset, and the Facedatahybrid dataset. The findings of this research offer valuable insights into the potential of a pre-trained network with transfer learning to make an efficient masked face detection and recognition system and pave the way for future research in this area.

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A Comparative Analysis of Data Resampling Techniques for Addressing Class Imbalance in Mosquito Classification

A Comparative Analysis of Data Resampling Techniques for Add...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Reshma Pise Abhishek Singh Chauhan Bharati Ainapure Aditya Shenolkar Leena Deshpande Shivraj Jadhav Computer Engineering Dept. Vishwakarma University Pune India CE (Software Engineering) Dept. Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Mosquitoes, as vectors of life-threatening diseases such as dengue, Zika, and West Nile virus, present a significant public health challenge. Precise and rapid identification of mosquito species is crucial for early detection and prevention of these diseases. Deep learning techniques have proven to be highly effective for automatic visual insect classification tasks. However, the imbalanced distribution of mosquito species within training datasets severely impacts the algorithm’s classification performance. This study investigates data sampling solutions for learning from imbalanced mosquito images dataset. We transfer-learn InceptionV3, a pre-trained convolutional neural network to classify images of two vector mosquito species Aedes Aegypti and Anopheles stephensi. The effectiveness of these data sampling solutions is evaluated and compared in terms of their impact on the performance of CNN classifier.

关键词： Training Visualization Technological innovation Accuracy Sensitivity Prevention and mitigation Vectors Convolutional neural networks Viruses (medical) Diseases

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Methodology for the Study of Defects in Technical Structures at Construction Sites 23

Methodology for the Study of Defects in Technical Structures...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Snitsar, Liliya R. Iskakov, Kazizat T. Zhartybaeva, Makpal G. Suleimenov, Bakhytzhan K. Boranbaev, Samat A. L.N. Gumilyov Eurasian National University Dept. of Computer and Software Engineering Nur-Sultan Kazakhstan

ISBN: (纸本)9781665498043

The article deals with the problem of analyzing existing approaches to the survey of construction sites. It is given an explanation of the relevance of the study of objects by non-destructive diagnostic methods. In practice, the use of one of the methods of non-destructive testing of construction objects - the ultrasonic method is shown. The mathematical approach to solving the problems is also described in the article, which presents the basic formulas used in solving the problem. Authors also characterized the available non-destructive testing devices and their technical characteristics. The results and algorithms of various methods for solving the problem are presented. An experimental study is described using one of the considered devices. The process of preparation for measurements and installation of sensors is described in detail. There were given the results of experimental studies in laboratory conditions and on the construction site. According to the results of the experiments, conclusions were made for each of them. © 2022 IEEE.

关键词： Nondestructive examination

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Optimization of Processing Power by Improving the Performance of Application Service 23

Optimization of Processing Power by Improving the Performanc...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Tileubergenov, Nurken Romanchikova, Svetlana N. Zhumadillayeva, Ainur K. L.N. Gumilyov Eurasian National University Dept. of 'Computer and Software Engineering' Nur-Sultan Kazakhstan

ISBN: (纸本)9781665498043

This article discusses the problems of optimizing computing power by improving the performance of web applications. The shared responsibility model for the Azure cloud is considered and the area of responsibility of the user and the provider of cloud services for such models as IaaS, PaaS and SaaS is shown. The article discusses methods for optimizing loops and provides the results of performance tests in Microsoft Visual Studio using Performance Profiler and C# language. Methods for optimizing such constructs as algebraic expressions are described and ways to increase performance from caching are considered. Optimization methods are described when using the String type. The article also looks at how boxing/unboxing objects of reference and value types improve application performance. The practical steps given in the article are aimed at writing safe and efficient code. Please, note that the tests and optimization designs carried out and described in the article will help to write code that will not cause poor application performance in the future. These steps will help to avoid buying software to detect optimization problems and, accordingly, significantly reduce material costs for the entire project as a whole. © 2022 IEEE.

关键词： Computing power

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Algorithms and Technology for Using a Hardware Complex for Diagnosing Defects in Construction Piles

Algorithms and Technology for Using a Hardware Complex for D...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Snitsar, Liliya L.N. Gumilyov Eurasian National University Dept. Of 'Computer And Software Engineering' Nur-Sultan Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

In this paper, theoretical and experimental studies were carried out on the diagnostics of building structures, namely, piles using the pile diagnostics device (PDS-MG4). The task of the study is to determine the depth of pile driving and identify defects in piles to localize them. A technical problem and a methodology for solving the problem are formulated based on engineering and technical methods for interpreting the data of the device used. A technique for solving one-dimensional inverse problems for the propagation equations of acoustic and seismic waves is presented. © 2022 IEEE.

关键词： Piles

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Multifaceted Discrete Emotion Recognition from EEG Physiological Signals via Machine Learning Techniques

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

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Emerging Technology (INCET), International Conference for

作者： Amriti Gupta Kunal Nayyar Harshit Chopra Neha Gahlan Divyashikha Sethia Dept. of Software Engineering Delhi Technological University Dept. of Electronics & Communication Engineering Delhi Technological University Dept. of Computer Engineering Delhi Technological University

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Emotions, intricate mental states shaped by neu-rophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. The Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. This paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. This research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. The exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested.

关键词： Emotion recognition Accuracy Sleep Cognitive processes Machine learning Data collection Brain modeling

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EvoSL: A Large Open-Source Corpus of Changes in Simulink Models & Projects 26

EvoSL: A Large Open-Source Corpus of Changes in Simulink Mod...

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26th ACM/IEEE International Conference on Model Driven engineering Languages and Systems, MODELS 2023

作者： Shrestha, Sohil Lal Boll, Alexander Chowdhury, Shafiul Azam Kehrer, Timo Csallner, Christoph University of Texas at Arlington Computer Science & Eng. Dept. ArlingtonTX76019 United States University of Bern Software Engineering Group Bern3012 Switzerland

ISBN: (纸本)9798350324808

Having readily available corpora is crucial for performing replication, reproduction, extension, and verification studies of existing research tools and techniques. MAT-LAB/Simulink is a de-facto standard tool in several safety-critical industries for system modeling and analysis, compiling models to code, and deploying code to embedded hardware. There is no commonly used corpus for large-scale model change studies because there is no readily available corpus. EvoSL is the first large corpus of Simulink projects that includes model and project changes and allows redistribution. EvoSL is available under a permissive open-source license and contains its collection and analysis tools. Using a subset of EvoSL, we replicated a case study of model changes on a single closed-source industrial project. © 2023 IEEE.

关键词： Simulink

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