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4th International Conference on Smart Trends for Computing and Communications, SmartCom 2020

作者： Richard Heryadi, Yaya Lukas Trisetyarso, Agung Information Systems Department School of Information Systems Bina Nusantara University Jakarta11840 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara Univesity Jakarta11840 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (纸本)9789811552236

Ethereum is one of the most popular blockchain platforms with a high number of adoption in the blockchain world today. Ethereum token (ERC-20) can tokenize any real-world object while it is also possible to exchange the token across the network participants. This utility from the token drives many blockchain enthusiasts to create decentralized applications. ERC-20 token could be used to collect project funds, decision voting, or even can be used to provide investment platforms for people that want to hedge the volatility of the currency with buying an asset that is back to back with the crypto-token. This research shows the main characteristics of asset-backed token and its implementation on a complete end-to-end "digitalization assets system" (assets buy and assets redemption). The result shows that asset-backed token needs support from government and other legalizing parties to verify the legitimacy of the asset that backing the token itself. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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A practical experience of a software process line creation 18

A practical experience of a software process line creation

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18th Ibero-American Conference on Software engineering, CIBSE 2015

作者： Magdaleno, Andreá M. Araujo, Renata M. Werner, Cláudia M L. Batista, Carlos Freud Alves COPPE - Systems Engineering and Computer Science Department UFRJ - Federal University of Rio de Janeiro P.O. Box 68511 Rio de Janeiro - RJ Brazil UNIRIO Brazil Petrobras - TIC/CPSW/PGOD/PMSW Brazil

ISBN: (纸本)9789972825804

Software process definition is a complex, time consuming and error prone activity. Such activity can be facilitated by a process reuse strategy. This strategy can be implemented through process line and components structures. This work presents a case study of a real process line creation in the context of an oil and gas company in Brazil. The results indicate both practical use and its potential to assess completeness and to identify inconsistencies in the organizational process. Copyright © 2015 by the authors.

关键词： computer software reusability

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Crowd and Urban Storytelling: Evaluating a Collective Intelligence Model to Support Discussions about the City 25

Crowd and Urban Storytelling: Evaluating a Collective Intell...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Chaves, Ramon Motta, Claudia Correia, Antonio Paredes, Hugo Caetano, Barbara De Souza, Jano Moreira Schneider, Daniel Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Federal University of Rio de Janeiro Brazil Inesc Tec University of Tr s-os-Montes e Alto Douro Utad Quinta de Prados Apartado 1013 Vila Real Portugal Systems Engineering and Computer Science Program PESC/COPPE/UFRJ Rio de Janeiro Brazil

ISBN: (纸本)9781665405270

In recent years, digital technologies have been used to support discussions about the city and also to involve citizens in participatory public processes. However, despite the widespread use of social media platforms, old issues related to engagement and participation still persist in digital initiatives. The main goal of this study is to carry out an empirical evaluation of a collective intelligence model that combines crowdsourcing and social storytelling to support discussions about the city from a bottom-up perspective. Within a design science research approach we designed a participatory action study that was carried out through a workshop with students and professionals from different areas, such as architecture, urban design and information technology. As a result, we were able to assess whether the collective intelligence model was acceptable to the participants by investigating whether the behavioral assumptions were valid and thus outlining some contributions to the field of urban informatics. © 2022 IEEE.

关键词： Crowdsourcing

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Fuzzy Markov predictor in multi-step electric load forecasting

Fuzzy Markov predictor in multi-step electric load forecasti...

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha Systems Engineering and Computer Science-COPPE Federal University of Rio de Janeiro Rio de Janeiro Brazil

We present three different approaches for multi-step prediction using the fuzzy Markov predictor (FMP). The FMP is a modification of the hidden Markov model in order to enable it to predict numerical values. In the first approach, the one normally used in neural networks, past predictions are used as input for the next predictions. The second and third approaches follow the standard way of making multi-step prediction in a dynamic Bayesian network. FMP using these three approaches is applied to the task of monthly electric load multi-step forecasting and successfully compared with two Kalman filter models, BATS and STAMP, and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.

关键词： Load forecasting Hidden Markov models Predictive models Multidimensional systems Machine learning Power engineering and energy systems engineering and theory computer science Neural networks Bayesian methods

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Physics-informed neural networks for modeling water flows in a river channel

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1001-1015页

作者： Nazari, Luis Fernando Camponogara, Eduardo Seman, Laio Oriel Department of Mathematics Federal Institute of Education Science and Technology Catarinense Rio do Sul89160-202 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianopolis88040-900 Brazil Program in Applied Computer Science Universidade Do Vale Do Itajai Itajai88302-901 Brazil

The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for

关键词： Floods

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Fuzzy hidden Markov predictor in electric load forecasting

Fuzzy hidden Markov predictor in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha Systems Engineering and Computer Science-COPPE Federal University of Rio de Janeiro Rio de Janeiro Brazil

We present a new hybrid system that merges fuzzy logic with dynamic Bayesian networks (DBN's): the fuzzy hidden Markov predictor. It is a modification of the hidden Markov model, a particular case of DBN's, in order to enable it to predict continuous values of a time series. A DBN is a Bayesian network that represents a temporal probability model. This hybrid system is applied to the task of monthly electric load single-step forecasting and successfully compared with three regression-by-discretization systems, two fuzzy hybrid systems, two Kalman filter models, and Box-Jenkins and Winters exponential smoothing. The employed time series present a sudden significant changing behavior at their last years, as it occurs in an energy rationing.

关键词： Hidden Markov models Load forecasting Bayesian methods Fuzzy systems Predictive models Random variables Fuzzy logic Smoothing methods Machine learning Pattern recognition

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Using co-evolved RTS opponents to teach spatial tactics

Using co-evolved RTS opponents to teach spatial tactics

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2010 IEEE Conference on Computational Intelligence and Games, CIG2010

作者： Smith, Greg Avery, Phillipa Houmanfar, Ramona Louis, Sushil Behavior Analysis Program Dept. of Psychology United States Evolutionary Computing Systems Laboratory Dept. of Computer Science and Engineering University of Nevada Reno NE United States

ISBN: (纸本)9781424462971

This paper describes a co-evolutionary algorithm for generating simple spatially oriented tactics and considers whether students can learn better by playing against co-evolved opponents or by playing against an expert system or other similar hard-coded opponent. Although a number of artificially intelligent tutoring and e-learning systems exist, our work looks at using co-evolution to generate competent opponents for human students to learn from. This paper describes and discusses early results on using computationally intelligent opponents for tactical training of human students. Initial results indicate that the learning environment for human players, measured by game difficulty and transfer of training, is comparable across co-evolved and hard-coded computer opponents. © 2010 IEEE.

关键词： Students

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A Machine Learning-Based Digital Twin Model for Pressure Prediction in the Fuel Injection System 48

A Machine Learning-Based Digital Twin Model for Pressure Pre...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Duarte, Edwin P. Viegas, Eduardo K. Santin, Altair O. Graduate Program in Computer Science PPGIa Pontificia Universidade Catolica Do Parana-Pucpr Brazil Robert Bosch GmbH Engineering Systems Curitiba Brazil Secure Systems Research Center Abu Dhabi United Arab Emirates

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Over the last years, the engine calibration task has mostly been conducted based on the engineers' knowledge. As a result, considering the complexity of modern engines, finding the most suitable configuration for each situation has become an impractical and expensive task. Apart from causing engines to be produced with inadequate calibration configuration, it can also decrease the lifespan of their components, degrading their efficiency. This paper proposes a machine learning-based digital twin model for pressure prediction in a fuel injection system, split into two steps. First, we extract statistical engine features based on a predefined time window to represent the engine behavior over time. Second, a digital twin implemented through a machine learning model is used to predict pressure levels in the fuel injection system. As a result, the predicted values can be used to assist the engine common rail system module in avoiding undesired engine states. Experiments performed on a new dataset, built over a real diesel-based engine, consisting of 208 features and over 1.3 million instances, have shown the feasibility of our proposal. The proposed scheme can predict in an advance time of 0.1 seconds the pressure levels for a fuel injection system with only 0.057 RMSE. Moreover, it increases its error rate by only 10.6% if a 0.5-second time advance is required. © 2022 IEEE.

关键词： Machine learning

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Finding optimal random boolean networks for reservoir computing

Finding optimal random boolean networks for reservoir comput...

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13th International Conference on the Simulation and Synthesis of Living systems: Artificial Life 13, ALIFE 2012

作者： Snyder, David Goudarzi, Alireza Teuscher, Christof Department of Computer Science Portland State University Portland OR 97201 United States Systems Science Graduate Program Portland State University Portland OR 97201 United States Department of Electrical and Computer Engineering Portland State University Portland OR 97201 United States

ISBN: (纸本)9780262310505

Reservoir Computing (RC) is a computational model in which a trained readout layer interprets the dynamics of a component called a reservoir that is excited by external input stimuli. The reservoir is often constructed using homogeneous neural networks in which a neuron's in-degree distributions as well as its functions are uniform. RC lends itself to computing with physical and biological systems. However, most such systems are not homogeneous. In this paper, we use Random Boolean Networks (RBN) to build the reservoir. We explore the computational capabilities of such a RC device using the temporal parity task and the temporal density classification. We study the sufficient dynamics of RBNs using kernel quality and generalization rank measures. We verify findings by Lizier et al. (2008) that the critical connectivity of RBNs optimizes the balance between the high memory capacity of RBNs with (K) 2. We show that in a RBN-based RC system, the optimal connectivity for the parity task, a processing intensive task, and the density classification task, a memory intensive task, agree with Lizier et al.'s theoretical results. Our findings may contribute to the development of optimal selfassembled nanoelectronic computer architectures and biologically-inspired computing paradigms. © 2012 Massachusetts Institute of Technology.

关键词： computer architecture

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