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The DEVStone Metric: Performance Analysis of DEVS Simulation Engines

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arXiv 2023年

作者： Cárdenas, Román Henares, Kevin Arroba, Patricia Risco-Martín, José L. Wainer, Gabriel A. Integrated Systems Laboratory Universidad Politécnica de Madrid Madrid Spain Dept. of Systems and Computer Engineering Carleton University OttawaON Canada Dpt. of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain

The DEVStone benchmark allows us to evaluate the performance of discrete-event simulators based on the DEVS formalism. It provides model sets with different characteristics, enabling the analysis of specific issues of simulation engines. However, this heterogeneity hinders the comparison of the results among studies, as the results obtained on each research work depend on the chosen subset of DEVStone models. We define the DEVStone metric based on the DEVStone synthetic benchmark and provide a mechanism for specifying objective ratings for DEVS-based simulators. This metric corresponds to the average number of times that a simulator can execute a selection of 12 DEVStone models in one minute. The variety of the chosen models ensures we measure different particularities provided by DEVStone. The proposed metric allows us to compare various simulators and to assess the impact of new features on their performance. We use the DEVStone metric to compare some popular DEVS-based simulators. © 2023, CC BY-SA.

关键词： Discrete event simulation

Physical and Digital Twin with Computational Thinking to Foster STEM Vocations in Primary Education

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Physical and Digital Twin with Computational Thinking to Fos...

IEEE Education engineering (EDUCON)

作者： Xavier Pi I Palomés Joan Verdaguer-Codina Pau Fonseca I Casas José Luis Rubiés-Viera Computer Science Multimedia and Telecommunications Dpt Universitat Oberta de Catalunya (UOC) Barcelona Spain Engineering Associations of Catalonia Barcelona Spain Statistics and Operation Research Dpt Universitat Politècnica de Catalunya - BarcelonaTech (UPC) Barcelona Spain Digital Society Commission Political Science and Sociology Association of Catalonia Barcelona Spain

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

This work aims to explore the application of the physical and digital twin pair as a computational thinking instrument to support learning and exercise proportional reasoning at the ages at which pursuing STEM studies is decided. Diverse studies reveal that the initial decision about the vocation in studies is made towards the end of the primary education stage, also from 8–9 years old. The Digital Transformation Commission of the engineering Associations of Catalonia is concerned about the low number of young people who decide to pursue STEM studies. STEM explorations in early childhood lay the foundation for lifelong learning. In addition, from age 5, children informally begin to practice proportional reasoning, and in the third year of primary school (8–9 years old), the subject is formally introduced into the Primary Education curriculum. The notion of proportionality is very transversal, and it is one of the fundamental concepts of the STEM field. The consequences of an incomplete understanding of it directly or indirectly affect the attitude of subjects toward mathematics and the STEM world. With the emergence of Industry 4.0, the digital twin concept is becoming one of the central instruments of Digital Transformation. In this paper, we propose using computational thinking to help the understanding of proportional thinking using a physical and digital twin pair. In 2022, computational thinking was introduced into the Spanish and Catalan educational system's early childhood, primary, and secondary education curricula. It includes programming with block languages such as Scratch or Snap!. A digital twin of the system has also been implemented in Snap!. Through the collaboration of a primary school, a workshop has been prepared in which they exercise the concepts of proportional reasoning, such as ratio, proportion, scale, or percentage. The approach followed has consisted of developing a physical system of a floating ball, controlling the electric motor's power

关键词： computers Instruments Conferences Toy manufacturing industry Documentation Cognition Digital twins

A Machine Learning approach for routing in satellite Mega-Constellations

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A Machine Learning approach for routing in satellite Mega-Co...

2020 International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2020

作者： Cigliano, Andrea Zampognaro, Francesco Guglielmo Marconi University Dpt. of Computer Engineering Via Plinio 44 Rome00193 Italy University of Rome 'Tor Vergata' Dpt. of Electronic Engineering Via del Politecnico 1 Rome00133 Italy

ISBN: (纸本)9781665422222

Current packet-switched networks, and Internet in particular, are experiencing explosive growth in traffic, mainly due to the rapid development of new communication technologies and the associated applications. Existing network routing policies might not be sophisticated enough to cope with the ever-changing network conditions resulting from huge traffic growth, opening to possible inefficiencies. Deep learning, as a recent turning point in the area of Machine Learning, has received significant research attention in numerous areas of Artificial Intelligence, such as computer Vision, Natural Language Processing, Autonomous Driving, Robotics Process Automation and so forth. Machine learning applications in network-related areas is relatively recent but it appears to represent an innovative approach to conFigure and manage networks in a more intelligent, efficient and autonomous way. On the other hand, we are witnessing as well the huge investments on satellite-based networks for global broadband Internet access, called mega-constellations, leveraging thousands of satellites in LEO orbit and expected to process Tbit/s. The aim of this paper is to describe the adoption of an innovative Machine Learning-driven and Data-driven approach applied to the routing of packets in such future generation of satellite mega constellations, enhancing services and applications QoS. In this work, we demonstrated the effectiveness of a Deep Learning based routing approach in comparison with the commonly used network routing approach of the Shortest Path, in a preliminary performance evaluation with few satellite nodes. The proposed approach can be applied to mega constellation satellite networks such as Starlink by SpaceX and conceptually applicable also to other (also terrestrial-only) networks. © 2020 IEEE.

关键词： Reinforcement learning

EVision: Influenza Forecasting Using CDC, WHO, and Google Trends Data

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EVision: Influenza Forecasting Using CDC, WHO, and Google Tr...

2020 IEEE / ITU International Conference on Artificial Intelligence for Good, AI4G 2020

作者： Shaghaghi, Navid Calle, Andres Qian, Yuhang Biolnnovation and Design Lab Santa Clara University Dpt. of Math and Computer Science Santa ClaraCA United States

ISBN: (纸本)9781728170312

Influenza, more commonly known as the flu, is a contagious respiratory illness caused by viruses which in the 2018-19 flu season, infected 37.4 to 42.9 million people in the United States alone. Of those, 431 to 647 thousand were hospitalized and 36,400 to 61,200 (most of them elderly and children) succumbed to the disease. At the time of this writing, the best known defense against influenza is vaccination. However, due to the annual mutation of the very many strands of the flu virus, new vaccines must be administered every flu season. Hence, the prediction of the rate of growth in reported infection cases of each strand of the flu is paramount to ensuring the correct supply of vaccines per strand. Machine learning-specifically Neural Networks-are a great tool for making future predictions using existing data. Long Short-Term Memory (LSTM) neural networks are utilized by Santa Clara University's EPIC (Ethical, Pragmatic, and Intelligent Computing) and BioInnovation Design laboratories for continued research and development of a tool named eVision (Epidemic Vision) to predict the trend of influenza cases throughout the flu season. eVision has been trained on data gathered across 4 flu seasons from the 2014-15 season to the 2017-18 season of the Center for Disease Control and Prevention (CDC) records as well as the World Health Organization (WHO) and Google Trends search result data gathered across the same period of time. eVision has been able to make 7 weeks in advance predictions about the flu trend in the 2018-19 United States flu season with 90.15% accuracy. This paper is to report the achievements of eVision thus far and to delineate next phases for the project which aims to provide a tool for the pharmaceutical and healthcare industries to more accurately predict the trend of flu (and other) epidemics in order to meet the demands for vaccines and test kits ahead of time. © 2020 IEEE.

关键词： Vaccines

Towards a Blockchain and Opportunistic Edge Driven Metaverse of Everything

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arXiv 2024年

作者： Fraga-Lamas, Paula Lopes, Sérgio Ivan Fernández-Caramés, Tiago M. Dpt. of Computer Engineering Centro de investigación CITIC Faculty of Computer Science Universidade da Coruña A Coruña15071 Spain ADiT-Lab Instituto Politécnico de Viana do Castelo Viana do Castelo4900-348 Portugal CiTin Centro de Interface Tecnológico Industrial Arcos de Valdevez4970-786 Portugal IT—Instituto de Telecomunicações Campus Universitário de Santiago Aveiro3810-193 Portugal

Decentralized Metaverses, built on Web 3.0 and Web 4.0 technologies, have attracted significant attention across various fields. This innovation leverages blockchain, Decentralized Autonomous Organizations (DAOs), Extended Reality (XR) and advanced technologies to create immersive and interconnected digital environments that mirror the real world. This article delves into the Metaverse of Everything (MoE), a platform that fuses the Metaverse concept with the Internet of Everything (IoE), an advanced version of the Internet of Things (IoT) that connects not only physical devices but also people, data and processes within a networked environment. Thus, the MoE integrates generated data and virtual entities, creating an extensive network of interconnected components. This article seeks to advance current MoE, examining decentralization and the application of Opportunistic Edge Computing (OEC) for interactions with surrounding IoT devices and IoE entities. Moreover, it outlines the main challenges to guide researchers and businesses towards building a future cyber-resilient opportunistic MoE. © 2024, CC BY.

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Symbolic Meta-Modeling of RIS-Assisted FSO Communication Channels

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TechRxiv

TechRxiv 2023年

作者： Ndjiongue, Alain Richard Ngatched, Telex M.N. Dobre, Octavia A. Haas, Harald Shin, Hyundong The Faculty of Engineering and Applied Science Memorial University of Newfoundland Canada The Department of Electrical and Computer Engineering McMaster University Canada The LiFi Research and Development Center Dpt. Electronic and Electrical Engineering The University of Strathclyde Glasgow United Kingdom The School of Electronics and Information Kyung Hee University 1 Seocheon Kihung Kyungki Yongin446-701 Korea Republic of

Due to their advantages and role in the beyond fifth-generation mobile network, free-space optical (FSO) communication systems continue to gain attention. The line-of-sight (LoS) link in FSO communications is fundamental as it delivers all the power detected by the user. Therefore, its obstruction is detrimental to the transmission system. In the event of an obstruction, the LoS requirements can be met by introducing the reconfigurable intelligent surface (RIS) technology. However, with the implementation of an RIS infrastructure, FSO channels are divided into two sub-channels, creating an inherent diversity end-to-end (e2e) channel model. In this paper, leveraging on the Meijer G-functions, which are symbolic representations of mathematical functions, we derive symbolic models for e2e RIS-assisted FSO systems that are built on the probability density functions (PDFs) of the fading model characterizing the sub-channels. We study some of the most popular models, derive closed-form expressions of PDFs, cumulative distributions, and moment-generating functions that are further exploited to analyze systems’ performance. By comparing scenarios in which the sub-channels are similar and different, we provide a clear understanding of how the overall RIS-assisted FSO channel behaves, along with comparative results based on commonly used models. © 2023, CC BY.

关键词： Probability density function

Multi-Objective Genetic Algorithm for the Joint Optimization of Energy Efficiency and Rule Reduction in Software-Defined Networks 11

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Multi-Objective Genetic Algorithm for the Joint Optimization...

11th International Conference on Network of the Future, NoF 2020

作者： Galan-Jimenez, Jaime Berrocal, Javier Herrera, Juan Luis Polverini, Marco University of Extremadura Dpt. of Computer Systems and Telematics Engineering Spain University of Rome 'Sapienza' Diet Department Rome Italy

ISBN: (纸本)9781728180557

Although the use of Ternary Content-Addressable Memories (TCAMs) in the flow tables of the Software-Defined Network (SDN) switches increases the efficiency of packets matching procedure, drawbacks such as their large power consumption and the limitation on the number of flow rules that can be installed must be taken into account. This paper tackles the joint problem of power consumption and TCAM size limitation in SDN. By exploiting the Rate Adaptation technique and compression methods, a Multi-Objective Genetic Algorithm is proposed to practically solve it. Simulations on a real network topology show that our proposed solution outperforms other state-of-The-Art approaches, both in terms of power saving gains (20% at non-peak TM) and maximum TCAM utilization (5%). © 2020 IEEE.

关键词： Energy efficiency

Mean first exit times of Ornstein-Uhlenbeck processes in high-dimensional spaces

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arXiv 2022年

作者： Kersting, Hans Orvieto, Antonio Proske, Frank Lucchi, Aurelien Inria Ecole Normale Supérieure PSL Research University Paris France Department of Computer Science ETH Zürich Zürich Switzerland Department of Mathematics University of Oslo Oslo Norway Dpt. of Mathematics & Computer Science University of Basel Basel Switzerland

The d-dimensional Ornstein–Uhlenbeck process (OUP) describes the trajectory of a particle in a d-dimensional, spherically symmetric, quadratic potential. The OUP is composed of a drift term weighted by a constant θ ≥ 0 and a diffusion coefficient weighted by σ > 0. In the absence of drift (i.e. θ = 0), the OUP simply becomes a standard Brownian motion (BM). This paper is concerned with estimating the mean first-exit time (MFET) of the OUP from a ball of finite radius L for large d >> 0. We prove that, asymptotically for d → ∞, the OUP takes (on average) no longer to exit than BM. In other words, the mean-reverting drift of the OUP (scaled by θ ≥ 0) has asymptotically no effect on its MFET. This finding might be surprising because, for small d ∈ N, the OUP exit time is significantly larger than BM by a margin that depends on θ. As it allows for the drift to be ignored, it might simplify the analysis of high-dimensional exit-time problems in numerous areas. Finally, our short proof for the non-asymptotic MFET of OUP, using the Andronov–Vitt–Pontryagin formula, might be of independent interest. © 2022, CC BY.

关键词： Brownian movement

MULTICAST: MULTI CONFIRMATION-LEVEL ALARM SYSTEM BASED on CNN and LSTM to MITIGATE FALSE ALARMS for HANDGUN DETECTION in VIDEO-SURVEILLANCE

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arXiv 2021年

作者： Olmos, R. Pérez-Hernández, Francisco Tabik, S. Lamas, Alberto Herrera, F. Dpt. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada 18071 Spain Dpt. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada 18071 Spain

Despite the constant advances in computer vision, integrating modern single-image detectors in real-time handgun alarm systems in video-surveillance is still debatable. Using such detectors still implies a high number of false alarms and false negatives. In this context, most existent studies select one of the latest single-image detectors and train it on a better dataset or use some pre-processing, post-processing or data-fusion approach to further reduce false alarms. However, none of these works tried to exploit the temporal information present in the videos to mitigate false detections. This paper presents a new system, called MULTI Confirmation-level Alarm SysTem based on Convolutional Neural Networks (CNN) and Long Short Term Memory networks (LSTM) (MULTICAST), that leverages not only the spacial information but also the temporal information existent in the videos for a more reliable handgun detection. MULTICAST consists of three stages, i) a handgun detection stage, ii) a CNN-based spacial confirmation stage and iii) LSTM-based temporal confirmation stage. The temporal confirmation stage uses the positions of the detected handgun in previous instants to predict its trajectory in the next frame. Our experiments show that MULTICAST reduces by 80% the number of false alarms with respect to Faster R-CNN based-single-image detector, which makes it more useful in providing more effective and rapid security responses. Copyright © 2021, The Authors. All rights reserved.

关键词： Long short-term memory