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A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 Pandemic 16

A Review and Implementation of Object Detection Models and O...

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16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022

作者： Gogou, Ioanna C. Koutsomitropoulos, Dimitrios A. University of Patras Dept. of Computer Engineering and Informatics Patras Greece

ISBN: (数字)9781665498104

ISBN: (纸本)9781665498104

Convolutional Neural Networks (CNN) are commonly used for the problem of object detection thanks to their increased accuracy. Nevertheless, the performance of CNN-based detection models is ambiguous when detection speed is considered. To the best of our knowledge, there has not been sufficient evaluation of the available methods in terms of the speed/accuracy trade-off in related literature. This work assesses the most fundamental object detection models on the Common Objects in Context (COCO) dataset with respect to this trade-off, their memory consumption, and computational and storage cost. Next, we select a highly efficient model called YOLOv5 to train on the topical and unexplored dataset of human faces with medical masks, the Properly-Wearing Masked Faces Dataset (PWMFD), and analyze the benefits of specific optimization techniques for real-time medical mask detection: transfer learning, data augmentations, and a Squeeze-and-Excitation attention mechanism. Using our findings in the context of the COVID-19 pandemic, we propose an optimized model based on YOLOv5s using transfer learning for the detection of correctly and incorrectly worn medical masks that surpassed more than two times in speed (69 frames per second) the state-of-the-art model SE-YOLOv3 on the PWMFD dataset while maintaining the same level of mean Average Precision (67%). © 2022 IEEE.

关键词： Object detection

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An Indicative Demanding Teaching Scenario for Primary education with the Support of a Multi-layer Fog Computing Architecture 26

An Indicative Demanding Teaching Scenario for Primary Educat...

引用

26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Giannou, Vasiliki Mamalis, Basilis University of West Attica Dept. of Informatics and Computer Engineering Greece

ISBN: (纸本)9781450398541

Fog computing is a new, modern type of computing that distributes some of the storage, estimation, and processing constraints of cloud computing closer to the edge of the network. The impact of Cloud and Fog Computing in an educational context is now more important than ever. This paper presents innovative designed services of fog computing techniques in the educational sector. An original educational scenario is described, based on innovative fog computing techniques and the appropriate use of online collaborative learning tools. The aim of the paper is to propose an appropriate fog-based architecture to support education in Primary and Secondary education as well as to provide appropriate recommendations and present some indicative education scenarios implemented based on the use of cloud and fog computing. The proposed scenario is properly documented, while specific implementation issues are presented and analyzed. © 2022 ACM.

关键词： Fog computing

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Don't Look Up: The Cost of Attention to Stimulus Phrases in Mobile Text Entry Evaluations 26

Don't Look Up: The Cost of Attention to Stimulus Phrases in ...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Komninos, Andreas Tsiouma, Angeliki Gogoulou, Georgia Garofalakis, John University of Patras Computer Engineering & Informatics Dept. Patras Greece

ISBN: (纸本)9781450398541

Transcription tasks have been long used as the de-facto evaluation method in mobile text entry research. Evaluations use memorable phrase sets, in order to prevent participants from devoting more attention to the stimulus phrase than the bare minimum. We present evidence from an eye-tracking study, demonstrating that the attention devoted to the stimulus phrase is much higher than might be expected. In fact, attention to the stimulus phrase takes up almost 50% of participant attention spent outside the keyboard area, and overall 25% of participant attention throughout any single transcription task. We explore a modification to the transcription task aimed at reducing this level of visual attention, without finding any statistically significant differences. These findings raise important questions on the continued use of the transcription task as the mainstream evaluation method for mobile text entry research. © 2022 ACM.

关键词： Eye tracking

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Prototyping a Digital Twin System for Environmental education 26

Prototyping a Digital Twin System for Environmental Educatio...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Komninos, Andreas Tsigkas, Georgios Computer Engineering & Informatics Dept. University of Patras Patras Greece

ISBN: (纸本)9781450398541

Our time is characterized by big sprawling urban complexes that a majority of the human population chooses to live where interactions with the environment seem to be vanishing from our day to day lives. Our relationship with the planet's natural ecosystem is paramount to our survival and well-being, a relationship that begins to form during our childhood years. Considering the above we propose a novel system which aims to give the ability to children to interact with the natural environment even when they have no access to it, from the classrooms in which they spend considerable parts of their lives. Specifically the proposed system is based on the technology of the Digital Twin, a core pillar of the 4th Industrial Revolution, and is comprised of a smart birdhouse and an electronic Digital Twin. The smart birdhouse can be deployed in a natural environment and collect atmospheric data like humidity and temperature values as well as record audio and pictures of possible bird visitors. At the same time the collected data will be available to the end user through a dashboard platform for atmospheric parameters and the digital electronic twin which will play the collected sound or pictures. With the above system we aim to achieve a greater and meaningful engagement of children with their natural habitat even when their urban environment makes it impractical or unfeasible. © 2022 ACM.

关键词： Industry 4.0

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Real-time Monitoring Using AWS Cloud Platform For Agriculture Soil 1

Real-time Monitoring Using AWS Cloud Platform For Agricultur...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Tirtakusuma, Danar Ikram Sukaridhoto, Sritrusta Nourma Budiarti, Rizqi Putri Mulyo, Dwi Alfarezi, Fitratama Aminputra, Bima Briliandhika Hakim, Oktafian Sultan Politeknik Elektronika Negeri Surabaya Dept. of Creative Multimedia Technology Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Indonesia Universitas Nahdlatul Ulama Surabaya Indonesia

ISBN: (纸本)9798350365191

In the era of modern agriculture, real-time monitoring of soil conditions is crucial for increasing productivity and efficiency. This research proposes an agricultural soil monitoring system that utilizes the AWS Cloud platform to capture, analyze, and present real-time soil data. This system is designed to integrate various sensors that can measure humidity, pH, temperature and soil nutrients, all connected via the Internet of Things (IoT). The data collected by these sensors is sent to the AWS Cloud, where it is processed using cloud computing services such as AWS Lambda and Amazon EC2. The use of the AWS Cloud enables high scalability, reliability, and data security, and facilitates big data analysis using Amazon S3 and Amazon RDS. By using an intuitive dashboard, farmers can see soil analysis results in real-time and make informed decisions based on the data. Additionally, this system is equipped with machine learning algorithms that run on the AWS Cloud to predict soil needs and provide optimal fertilizer recommendations. This research shows how implementing AWS Cloud in agriculture can bring digital transformation to agriculture, enabling farmers to increase crop yields while reducing waste and operational costs. © 2024 IEEE.

关键词： Cloud platforms

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Ethereum Fraud Prevention: A Supervised Learning Approach for Fraudulent Account Recognition 1

Ethereum Fraud Prevention: A Supervised Learning Approach fo...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Dutta, Sourav Sharma, Anjali Rajgor, Jaymin Lovely Professional University Punjab Dept. of Computer Science and Engg. Kolkata India Jss Academy of Technical Education Dept. of Computer Science and Engg. Bangalore India Shan and Anchor Kutchhi Engineering College Dept. of Computer Science and Engg. Mumbai India

ISBN: (纸本)9798350307795

In recent times, blockchain technology, especially Ethereum, has gained tremendous popularity for supporting various financial transactions. Ethereum, the second most prominent cryptocurrency platform after Bitcoin, processes over one million transactions daily, known for its high security and ease of use. However, despite its robust security features, Ethereum faces challenges with illegal activities, which hinder its widespread adoption. To address this issue, this study focuses on detecting illicit activities on the Ethereum blockchain, particularly identifying illegal accounts using machine learning techniques. It proposes a novel method combining a convolutional neural network with an XGBoost classifier, aimed at differentiating between normal and illicit accounts based on transaction history. The XGBoost model, a tree-based ensemble classifier, not only improves accuracy but also prevents overfitting and enhances the model's generalizability. Its parallel tree-building feature also speeds up training, making the model more scalable. The model is trained on a balanced dataset of over 4,000 Ethereum accounts, encompassing both normal and fraudulent accounts. The results are promising, with the model achieving an accuracy of 99.7% and an average AUC of 0.9998 (std: 0.0008), outperforming standard machine learning models. This approach shows significant potential in combating illegal activities within the Ethereum blockchain. © 2024 IEEE.

关键词： Blockchain

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Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications 24

Resource Efficient Federated Learning for Deep Anomaly Detec...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Gkillas, Alexandros Lalos, Aris Industrial Systems Institute Athena Research Center Greece University of Patras Dept. of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350339598

Anomaly data constitute a thorny problem in numerous industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy and reconstruction is achieved with the utilization of large neural networks with more layers and nodes, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the very recent on-device privacy-preserving distributed machine learning paradigm, known as federated learning (FL). This paradigm allows edge devices to locally train and exchange models increasing also the communication cost. Thus, in order to deal with the increased communication, processing and storage challenges introduced by FL based deep anomaly detection NN pruning is expected to have significant benefits towards both reducing the processing, storage and communication complexity but also towards avoiding the over fitting problem. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that simultaneously fusses the local models broadcast by the edge devices and performs pruning generating a much more compressed model to be deployed at the edge devices with no accuracy loss even if the training is performed using a small subset of data instances, achieving compression rates greater than 99%. © 2023 IEEE.

关键词： Anomaly detection

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Comparison of Fast Frequency Response Methods in the Low-Inertia Grid of Cyprus 3

Comparison of Fast Frequency Response Methods in the Low-Ine...

引用

3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Argyrou, Andreas Panagi, Savvas Aristidou, Petros Electricity Authority of Cyprus Distribution System Operator Nicosia Cyprus Cyprus University of Technology Dept. of Electrical & Computer Engineering & Informatics Limassol Cyprus

ISBN: (纸本)9798350375923

Modern electric power systems with high levels of penetration of renewable energy sources (RES) often present frequency security problems. The lack of inertia due to the reduced number of synchronous generators in these systems, in combination with the usual inability of RES to provide frequency support, leads to system operators having to curtail RES or rely heavily on Under Frequency Load Shedding schemes to ensure frequency security. Fast Frequency Reserves (FFR) have been proposed as a solution to strengthen the frequency support of the system and alleviate security problems. FFR allows mitigating Rate of Change of Frequency, Nadir, and post-fault frequency steady-state problems after an event. In this paper, we present, analyze, implement and compare five FFR controllers to alleviate frequency security problems in low-inertia grids. The low-inertia, islanded, Cyprus dynamic model is used to quantify the results and exhibit the impact on a real system. © 2024 IEEE.

关键词： Synchronous generators

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guan, Peiyuan Chen, Chen Chen, Ziru Cai, Lin X. Hao, Xing Taherkordi, Amir University of Oslo Dept. of Informatics Oslo Norway University of Cambridge Dept. of Computer Science and Technology Cambridge United Kingdom Illinois Institute of Technology Dept. of Electrical and Computer Engineering Chicago United States Northwest Univesity School of Information Science and Technology Xi'an China

ISBN: (纸本)9798350351255

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art. © 2024 IEEE.

关键词： Quality of service

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Deep Learning Electric Load Forecasting for the Greek Power System 27

Deep Learning Electric Load Forecasting for the Greek Power ...

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27th International Conference on Circuits, Systems, Communications and computers, CSCC 2023

作者： Zelios, Vasileios Mastorocostas, Paris Kandilogiannakis, George Kesidis, Anastasios Tselenti, Panagiota Voulodimos, Athanasios University of West Attica Dept. of Informatics and Computer Engineering Athens Greece University of West Attica Dept. of Surveying and Geoinformatics Engineering Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9798350337594

Long Short-Term Memory and Gated Recurrent Unit-based networks are examined for short-term load forecasting for the Greek power system, where predictions of the next hour's load are made for the whole year. The recurrent property of the forecasters has the advantage to allow the use of a reduced input set since no prior knowledge of the most suitable previous loads is required for the model to identify the temporal relations that exist in the load time-series. An extensive simulation analysis is conducted, to evaluate the forecasters' performance and to highlight their particular learning characteristics. Both deep learning-based forecasters perform very efficiently, with LSTM achieving an average percentage error of 1.18% and a root mean squared error of 83.8 MW, while GRU performs similarly with respective values of 1.17% and 83.4 MW. © 2023 IEEE.

关键词： Long short-term memory

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