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IEEE/ACM Symposium on Edge Computing (SEC)

作者： Kostas Blekos Dimitrios Kosmopoulos Computer Engineering & Informatics Dept. University of Patras Rio Greece

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

We investigate a quantum data reduction technique with application to video classification. A hybrid quantum-classical step performs data reduction on the video dataset generating “representative” distributions for each video class. These distributions are used by a quantum classification algorithm to firstly reduce the size of the videos and then classify the reduced videos to one of $k$ classes. We verify the method using sign videos and demonstrate that the reduced videos contain enough information to successfully classify them using a quantum classification process. The proposed data reduction method showcases a way to alleviate the “data loading” problem of quantum computers for the problem of video classification. Data loading is a huge bottleneck, as there are no known efficient techniques to perform that task without sacrificing many of the benefits of quantum computing.

关键词： computers Quantum computing Loading Hybrid power systems Classification algorithms Task analysis Edge computing

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Some insights about the Uncapacitated Examination Timetabling Problem 6

Some insights about the Uncapacitated Examination Timetablin...

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6th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2021

作者： Gogos, Christos Dimitsas, Angelos Nastos, Vasileios Valouxis, Christos Dept. of Informatics and Telecommunications University of Ioannina Arta47100 Greece Dept. of Electrical and Computer Engineering University of Patras 26500 Greece

ISBN: (纸本)9781665427425

Timetabling problems easily become hard due to their inherent combinatorial structure. In this work we investigate the examination timetabling problem for Universities. We step back and analyze the best known variant of the problem, the Uncapacitated Examination Timetabling Problem (UETP). In the process we unmask some properties of the UETP. Firstly, we detect components that can be used for decomposition of problem instances by running a connected components identification algorithm. This process reveals the presence of more of the so called noise students and noise examinations that can safely be removed from the problem without affecting the essence of the problem. Secondly, we revisit the idea of identifying lower bounds based on penalties that are impossible to avoid. We strengthen those bounds by exploiting the theoretically maximum number of students that could possibly be scheduled in a single period. Thirdly, we propose a novel way of breaking symmetry, based on ordering examinations that share common characteristics. Since examination timetabling is an active research problem, counting several contributions each year, the present work aims to serve as a useful resource, by revealing properties of the UETP that were not identified before. © 2021 IEEE

关键词： Scheduling

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A bi-directional shortest path calculation speed up technique for RDBMS 7

A bi-directional shortest path calculation speed up techniqu...

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Koritsoglou, Kyriakos Laskas, Petros Patras, Vaios Boursianis, Achilles D. Grigoriadis, Konstantinos Fudos, Ioannis University of Ioannina Dept. of Informatics and Telecommunications Arta Greece University of Ioannina Dept of Computer Science and Engineering Ioannina Greece Aristotle University of Thessaloniki ELEDIA@AUTH School of Physics Thessaloniki Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In Relational Database Management Systems (RDBMS) the representation and storage of graph datasets is achieved by using two data tables. One representing the nodes' data and another for the edges' data of the graph. Using this model every search process in a big data volume requires multiple joins of large data tables, which significantly reduces their performance. The increasing use of graph datasets in various computer science fields such as (big data, social and computer networks, networks, Semantic Web, knowledge graphs, biological networks, and road networks) creates the need to research methods that will improve the performance of RDBMS when managing this type of data. In this paper, unlike most of the solutions proposed in the literature to date, a purely relational approach is presented using only the native RDBMS query analyzer and not either aggregate functions not implemented in all RDBMSs or an additional plugin layer that analyzes and processes the graph queries in order to translate them into the corresponding SQL versions. With the proposed solution we proved that by designing a bi-directional BFS algorithm that passes through properly selected intermediate pivot points, it is possible to query graph datasets significantly larger than those a complex SQL query can handle, with no major overhead. © 2022 IEEE.

关键词： Digital storage

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A Preprocessor for Creation of Large-Scale Computational Applications

A Preprocessor for Creation of Large-Scale Computational App...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Victor S. Simonov Marat S. Khairetdinov dept. Computer Engineering Novosibirsk State Technical University Novosibirsk Russian Federation dept. Geophysical Informatics Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russian Federation

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The emergence of large-scale computing paradigms required the evolution of software development methods, moreover, the need to effectively transform traditionally sequential applications into parallelizable and scalable architectures. This transformation is key in harnessing the computing power of modern multicore and distributed systems. Python, with its vast ecosystem and simplicity, is becoming a powerful language that facilitates this transition. This article discusses the methodologies and development of a Python preprocessor tool aimed at converting sequential applications into forms suitable for large-scale computing environments. Through detailed analysis and implementation, we demonstrate the use of Spark functions and third-party libraries to automate this transition, making large-scale computing more accessible to a wider range of applications and developers. The tool includes a set of rules that identify different patterns of code input (for example, list-sequential looping) and transform consistent code. This article introduces a Python preprocessor designed to automate the transformation of sequential Python applications into ones that harness the power of large-scale computing environments.

关键词： Performance evaluation Codes Multicore processing Scalability Transforms computer architecture Libraries

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On the Way to Realize the 5thIndustrial Revolution: Achievements, Challenges and Research Areas 36

On the Way to Realize the 5thIndustrial Revolution: Achievem...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Dimirakopoulos, George Gutt, Thomas Ehm, Hans Hoess, Alfred Karathanasopoulou, Konstantina Lackner, Anna Schneider, Germar Harokopio Univesity of Athens Dept. of Informatics and Telematics Athens Greece Infineon Technologies A.G. Munich Germany University of Applied Sciences Amberg-Weiden Dept. of Electrical Engineering Media and Computer Science Amberg Germany Eesy-innovation GmbH Unterhaching Germany Infineon Technologies Dresden GmbH&Co.KG Dresden Germany

ISBN: (纸本)9781665477161

The industrial world is rapidly moving towards its 5th industrial revolution (a.k.a. 'Industry5.0'). Industry5.0 is reflected on the digital sovereignty in comprehensively sustainable production, through adopting, extending and implementing A I-enabled hardware, as well as A I tools & methods and semiconductors technology across the whole industrial value chain. It is expected that in doing so, manufacturing costs will be decreased, while at the same time, product quality will be increased through AI-enabled innovation, time-to-market will be shortened and user acceptance of versatile technology offerings will be achieved and global supply chains stabilized. The above will, in turn, foster a sustainable development, in an economical, ecological and societal sense and act as enablers for the Green Deal. This paper accordingly discusses on the fundamental research areas relevant to further advancing the digitalized industry, its research and development achievements so far, as well as the challenges confronted, in order to boost industrial competitiveness through interdisciplinary innovations, establishing sustainable value chains and therefore contribute to the Digital Sovereignty. © 2023 IEEE.

关键词： Sustainable development

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Quantum Data Reduction with Application to Video Classification

arXiv

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arXiv 2022年

作者： Blekos, Kostas Kosmopoulos, Dimitrios Computer Engineering & Informatics Dept. University of Patras Rio Greece

We investigate a quantum video classification method using a hybrid algorithm. A quantum-classical step performs a data reduction on the video dataset and a quantum step—which only has access to the reduced dataset—classifies the video to one of k classes. We verify the method using sign videos and demonstrate that the reduced dataset contains enough information to successfully classify the data, using a quantum classification process. The proposed data reduction method showcases a way to alleviate the "data loading" problem of quantum computers for the problem of video classification. Data loading is a huge bottleneck, as there are no known efficient techniques to perform that task without sacrificing many of the benefits of quantum computing. © 2022, CC BY.

关键词： Data reduction

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Real-time Monitoring Using AWS Cloud Platform For Agriculture Soil

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

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IEEE International Symposium on Consumer Electronics (ISCE)

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

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

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.

关键词： Temperature sensors Temperature measurement Productivity Cloud computing Soil Real-time systems Agriculture Sensors Internet of Things Monitoring

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Implementation of Cloud Based Action Recognition Backend Platform

Implementation of Cloud Based Action Recognition Backend Pla...

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2021 International Conference on Artificial Intelligence and Mechatronics Systems, AIMS 2021

作者： Iksan, Luqmanul Hakim Awal, Mahaputra Ilham Fhamy, Rizky Zull Pratama, Afis Asryullah Basuki, Dwi Kurnia Sukaridhoto, Sritrusta Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Electronic Engineering Surabaya Indonesia

ISBN: (纸本)9781665424820

The Internet of Things (IoT) growth are rapidly in various fields such as industry 4.0, smart cities, and smart homes. Implementation of IoT for electronic assistance had been researched to increase the longevity of human life. However, not all IoT implementation as human life assistance provides action recognition monitoring on multiple elderly people, provide information such as real-time action monitoring, and real-time streaming in a mobile application. Therefore, this research intends to create a system that can receive and provide information on each elderly people who registered. The Action Recognition Backend Platform will be working as cloud computing to receive and manage input data from Edge Computing Action Recognition. This platform integrated Deep Learning, Data Analytics, Big Data Warehouse that implemented Extract, Transform, and Load (ETL) methods, communication services with MQTT, and Kafka Streaming Processor. The test result showed that the edge computing action recognition got better model accuracy performance from our last model [1], which can predict with 50,7% accuracy in 0.5 confidence threshold. Moreover, the backend platform had been successfully implemented a simple IoT paradigm and got an average delivery time of MQTT communication at 204ms, for streaming data process took an average delay of 680ms. © 2021 IEEE.

关键词： Internet of things

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Real-Time Mobile Crowd Sensing Model for Remote Detection of Flying Uavs

SSRN

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SSRN 2024年

作者： Ahmed, Ashfaq Alkadi, Ruba Belwafi, Kais Shoufan, Abdulhadi Dept. of Computer & Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates Dept. of Computer Engineering College of Computing & Informatics University of Sharjah Sharjah United Arab Emirates Khalifa University Abu Dhabi127788 United Arab Emirates

Conformance monitoring is crucial for ensuring the safe operation of unmanned aerial vehicles (UAVs). It aims to alert relevant parties to any deviations from authorized flight plans. The European Union Aviation Safety Agency mandates conformance monitoring and suggests integrating it into UAV traffic management (UTM) systems. Although traditional monitoring systems, such as wireless sensor networks, can serve this purpose, the associated expenses for installation and maintenance make them impractical for large-scale implementations. To tackle this challenge, we propose a monitoring system that capitalizes on UAV remote identification technology and mobile crowd sensing. Remote identification (RID) is becoming a global regulatory requirement. It enables ground observers to identify drones using standard mobile devices. Our solution collects RID data by gathering reports from these observers to assess UAV operations in real-time. A significant component of this approach is determining the optimal number of reports that should be considered to allow reliable and quick evaluation. While processing more reports can enhance the evaluation accuracy, it increases the computational demands and may compromise the system’s real-time performance. Moreover, in a reward-based system, processing more reports incurs higher costs. Therefore, our system uses a mechanism that adjusts the number of received and processed reports based on airspace conditions and the crowd density in the area of interest. We validated the proposed model through Monte Carlo simulations. Results indicate that, compared to a naive model, our approach outperforms in terms of received RIDs, paid rewards, and real-time performance. © 2024, The Authors. All rights reserved.

关键词： Wireless sensor networks

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