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15th Iberian Conference on Information Systems and Technologies, CISTI 2020

作者： Eliseo, Maria Amelia Oyelere, Solomon Sunday Silva, Camilla Almeida Da Silveira, Ismar Frango Tomczyk, Lukasz Hercovici, Mariana Amato, Cibelle A. De La Higuera Akyar, Ozgur Yasar Martins, Valeria Farinazzo Mackenzie Presbyterian University Computing and Informatics Department São Paulo Brazil University of Eastern Finland School of Computing Joensuu Finland Institute of Educational Studies Pedagogical University of Cracow Cracow Poland Mackenzie Presbyterian University Developmental Disorders Graduate Program São Paulo Brazil Hacettepe University Faculty of Sport Sciences Ankara Turkey

ISBN: (纸本)9789895465903

The elderly population has been increased due better health conditions and quality of life. With a healthier aging, the elderly start to have a more active and participative life in society. They pursue, among other things, to acquire new knowledge and despite being a generation which access to existing technology was restricted, there is an increase in the use of digital technologies, especially in the educational field. However, with advancing age, cognitive and motor skills decrease. Given this scenario, the creation of accessible didactic materials that include the elderly population is necessary. This paper shows a framework to create inclusive digital material for elderly learning. The framework will help teachers to create digital contents that support elderly learners' personalized educational goals. © 2020 AISTI.

关键词： E-learning

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Adaptive optimal multi key based encryption for digital image security

Adaptive optimal multi key based encryption for digital imag...

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作者： Shankar, K. Lakshmanaprabu, S.K. Gupta, Deepak Khanna, Ashish de Albuquerque, Victor Hugo C. School of Computing Kalasalingam Academy of Research and Education Krishnankoil India B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Maharaja Agrasen Institute of Technology Delhi India Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

The security of digital images is an essential and challenging task on shared communication Model. Generally, high secure working environment and data are also secured with an encryption and decryption method by using secret and public keys. In this paper, the innovative encryption technique for image security, ie, Multiple key-based Homomorphic Encryption (MHE) technique is proposed. For increasing the security level of encryption and decryption processes, the optimal key is selected using Adaptive Whale Optimization (AWO) algorithm. Fitness function was considered for optimization as PSNR of plain and cipher images. The original image was transformed into blocks and then rearranged utilizing encryption process, this work achieved maximum security, much better than other encryption techniques. From the outcomes, one can achieve incredible quality of the proposed model, the maximum PSNR, and the minimum MSE contrasted with other encryption schemes. © 2018 John Wiley & Sons, Ltd.

关键词： Cryptography

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29. The competitiveness of bioenergy by-products in optimized livestock diet formulation

Animal - Science proceedings

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Animal - Science proceedings 2022年第4期13卷 543-544页

作者： J.G.O. Marques N.R.D. de Souza F. Senna V.H.M. Mourão P.C. Machado S.R. Medeiros L.G. Barioni D. Moran R. de Oliveira Silva Global Academy of Agriculture and Food Security University of Edinburgh Edinburgh UK Interinstitutional Graduate Program in Bioenergy (USP/UNICAMP/UNESP) University of Campinas Campinas Brazil Institute of Mathematics Statistics and Scientific Computing University of Campinas Campinas Brazil Embrapa Agricultural Informatics Campinas Brazil Xtra Factors Inc. Ephrata PA USA Embrapa Gado de Corte Campo Grande Mato Grosso do Sul Brazil

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RPOIA: A Method of Selecting Learning Objects Using Petri Nets

RPOIA: A Method of Selecting Learning Objects Using Petri Ne...

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Frontiers in Education (FIE) Conference

作者： Joethe Moraes de Carvalho Josias Gomes Lima José Francisco de Magalhães Netto Ruiter Braga Caldas Post-Graduate Program in Informatics (PPGI) Institute of Computing (ICOMP) Federal University of Amazonas (UFAM) Manaus AM Brazil Institute of Computing (ICOMP) Federal University of Amazonas (UFAM) Manaus AM Brazil

ISBN: (数字)9781728189611

ISBN: (纸本)9781728189628

This Research Full Paper presents a method to select Learning Objects based on students' cognitive profiles, aiming to facilitate the explained content understanding and programming skills improve. STEM approach has favored the programming skills development in Computer Science courses. Due to initial concepts complexity, many students find it difficult to understand this discipline, causing demotivation or course abandonment. We propose a method using Petri Nets formalism to select a Learning Object that addresses the subject being studied. Petri Nets are formal description techniques used to specify competing systems through graphical and mathematical modeling. Intelligent Agents are computer programs created to automate and perform a certain task or direct interaction with student and propose solutions considered appropriate, based on based on knowledge obtained during the interactions. This research uses the Petri Net to create an Intelligent Agent model, which chooses the Learning Object from answers obtained by applying VAK, an educational questionnaire and results obtained shown to be promising and method was well evaluated by students.

关键词： Petri nets programming programming profession Education Games Intelligent agents Visualization

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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A Unified Framework for Human Centered Design of a Substance Use, Abuse, and Recovery Support System

A Unified Framework for Human Centered Design of a Substance...

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AHFE Virtual Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, and the International Conference on Human Factors in Aging and Special Needs, 2020

作者： Schooley, Benjamin Feldman, Sue Tipper, Bradley Health Information Technology Program College of Engineering and Computing University of South Carolina 550 Assembly Street ColumbiaSC29208 United States Graduate Programs in Health Informatics Department of Health Services Administration School of Health Professions University of Alabama - Birmingham 1716 9th Ave South SHPB #590K BirminghamAL35294 United States

ISBN: (纸本)9783030508371

This research explores the analysis, design, and testing of a software application to support opioid peer specialists working to assist substance users, abusers, those in recovery, and their families. The paper presents a framework integrating best practice approaches from Design Science Research, Community Based Participatory Research, and human centered and user experience design principles for the purpose of: 1) guiding community based, goal-directed software design, and 2) building and evaluating a substance use, abuse, and recovery software application. Focus group findings from 33 participants suggest that using the framework enables alignment between human centered system requirements;personal, family, and community-based program goal achievement;and local/state population health directives. End user feedback indicates that the software application design addresses specific human interaction needs for the complex high-stakes context of opioid use, abuse, and recovery. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Application programs

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Perspectives on risk prioritization of data center vulnerabilities using rank aggregation and multi-objective optimization

arXiv

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arXiv 2022年

作者： Grisci, Bruno Iochins Kuhn, Gabriela Colombelli, Felipe Matter, Vítor Kehl Lima, Leomar Heinen, Karine Pegoraro, Mauricio Borges, Marcio Rigo, Sandro José Barbosa, Jorge Luis Victória da Rosa Righi, Rodrigo da Costa, Cristiano André de Oliveira Ramos, Gabriel Software Innovation Laboratory - Softwarelab Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos Av. Unisinos 950 RS São Leopoldo93022-750 Brazil Institute of Informatics Federal University of Rio Grande do Sul Av. Bento Gonçalves 9500 RS Porto Alegre91501-970 Brazil Dell Technologies Brazil Av. Industrial Belgraf 400 Eldorado do Sul RS 92990-000 Brazil

Nowadays, data has become an invaluable asset to entities and companies, and keeping it secure represents a major challenge. Data centers are responsible for storing data provided by software applications. Nevertheless, the number of vulnerabilities has been increasing every day. Managing such vulnerabilities is essential for building a reliable and secure network environment. Releasing patches to fix security flaws in software is a common practice to handle these vulnerabilities. However, prioritization becomes crucial for organizations with an increasing number of vulnerabilities since time and resources to fix them are usually limited. This review intends to present a survey of vulnerability ranking techniques and promote a discussion on how multi-objective optimization could benefit the management of vulnerabilities risk prioritization. The state-of-the-art approaches for risk prioritization were reviewed, intending to develop an effective model for ranking vulnerabilities in data centers. The main contribution of this work is to point out multi-objective optimization as a not commonly explored but promising strategy to prioritize vulnerabilities, enabling better time management and increasing security. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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VLSI Design of Tree-Based Inference for Low-Power Learning Applications

VLSI Design of Tree-Based Inference for Low-Power Learning A...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Brunno A. Abreu Mateus Grellert Sergio Bampi Graduate Program in Microelectronics (PGMICRO) INF-UFRGS Federal University of Rio Grande do Sul Porto Alegre Brazil Department of Informatics and Statistics − INE Federal University of Santa Catarina (UFSC) Florianópolis Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

The use of Machine Learning techniques in battery-powered devices has increased in recent years. Therefore, evaluating power-accuracy trade-offs of inference models has become important. This work explores decision trees architectures, analyzing the effects of model complexity and approximation in power and accuracy. By quantizing the inputs to limited widths, we increased the accuracy of the models in up to 8.7% compared to precise cases. In terms of power, the variation in model complexity was more significant than in input width, as we obtained reductions of up to 97% by decreasing the tree depth, compared to 88% when decreasing the width.

关键词： Complexity theory Hardware Integrated circuit modeling Training Analytical models Decision trees Quantization (signal)

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Low-Power and Memory-Aware Approximate Hardware Architecture for Fractional Motion Estimation Interpolation on HEVC

Low-Power and Memory-Aware Approximate Hardware Architecture...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Wagner Penny Guilherme Correa Luciano Agostini Daniel Palomino Marcelo Porto Gabriel Nazar Bruno Zatt Video Technology Research Group − Graduate Program in Computing − Federal University of Pelotas − Pelotas Brazil Sul-rio-grandense Federal Institute − Pelotas Brazil Institute of Informatics − Federal University of Rio Grande do Sul − Porto Alegre Brazil

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Nowadays, current video coding standards like the High Efficiency Video Coding (HEVC) implement several complex coding tools, like the Fractional Motion Estimation (FME). An alternative to improve performance and save power is allying the hardware acceleration with approximate computing solutions, focusing on such complex tools. In this work, we present a low-power and memory-aware hardware architecture for the HEVC FME interpolator, proposing the development of two novel hardware designs for the interpolation filters, called Approximate Unified FME Filters (AUFF). These solutions exploit the usage of approximate computing at both algorithmic and data levels, leading to a reduction in dissipated power and memory bandwidth. The proposed design is capable of real-time interpolation of UHD (Ultra High Definition) 4K and 8K videos when synthesized using a 40 nm standard-cell library, with a power dissipation ranging from 22.04 to 62.06 mW.

关键词： Finite impulse response filters Hardware Interpolation Encoding Computer architecture Approximate computing Motion estimation

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SIMROAA Multi-Agent Recommendation System for Recommending Accessible Learning Objects

SIMROAA Multi-Agent Recommendation System for Recommending A...

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Frontiers in Education (FIE) Conference

作者： Andreza Bastos Mourão José Francisco Magalhães Netto Post-Graduate Program in Informatics (PPGI) Institute of Computing of the Federal University of Amazonas (ICOMP-UFAM) State University of Amazonas (UEA) Manaus Brasil Post-Graduate Program in Informatics (PPGI) Institute of Computing of the Federal University of Amazonas (ICOMP-UFAM) Manaus Brasil

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

This full paper presents the Multi-Agent Recommendation System for Recommending Accessible Learning Objects developed to attend students with disabilities and professors' computation area. The system was proposed based on the qualitative analysis of the questionnaires applied and answered by the professors of the area, to classify, organize and provide the most appropriate Learning Objects according to professors' preferences. In this way, the professor informs the type of impairment (visual, physical, hearing and/or cognitive), discipline and content, based on these requirements, receives the best recommendations of the intelligent agent. The methodology that we use is Multiagent Systems Engineering. The web page was developed using the Laravel Framework PHP, the data repository was created using the MySQL database, and to perform the implementation we used the Java Agent Development Framework. As a result of the research, we present a recommendation system of accessible learning objects using intelligent agents and communities of practice through the recommendation mechanism based on trust. The results obtained through the implementation of the system provide a new motivational practice trough accessible educational content, which contributes significantly to the teaching-learning process of the computation course and to inclusive education.

关键词： Metadata Education Intelligent agents Multi-agent systems Web pages Tools Standards

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