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检索条件"机构=Computer Science Graduate Program Federal University of Pará"

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SIS-ASTROS: An Integrated Simulation Environment for the Artillery Saturation Rocket System (ASTROS) 1

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12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2022

作者： Pozzer, Cesar Tadeu Martins, Joao Baptista Fontoura, Lisandra Manzoni Silva, Luis Alvaro Lima Rutzig, Mateus Beck Nunes, Raul Ceretta de Freitas, Edison Pignaton Graduate Program in Computer Science Federal University of Santa Maria Av. Roraima n. 1000 Santa Maria97105-900 Brazil Graduate Program in Computer Science Federal University of Rio Grande do Sul CP 15064 Porto Alegre91501-970 Brazil

ISBN: (数字)9783031438240

ISBN: (纸本)9783031438233

Simulation is an important technique to support military training. There are different training goals depending on the different ranks of the military personnel being trained, which are addressed by different types of simulation (live, virtual, or constructive). However, a trend in current military training is the combination of different types of simulation in an integrated setup. Observing such kind of blended simulation, the Brazilian Army is making efforts to develop integrated simulation solutions. This work describes the conception of an integrated simulation environment for the Brazilian Army Artillery called SIS-ASTROS. Besides integrating different types of simulators, SIS-ASTROS presents a virtual tactical simulator to train ASTROS artillery batteries deployment activities of mid-rank officers. This simulator addresses aspects that can be classified as constructive, but also virtual ones. Due to this particularity in its design, the conception of this simulator represents an innovative contribution. Moreover, its development demanded solutions in the area of artificial intelligence, computer graphics, and distributed systems applied to simulation problems. This paper presents the key components of the integrated simulation system, highlighting the main contributions in the research and development of the virtual tactical simulator. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Personnel training

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Boosting Engagement and Academic Performance Through Gamification: Leveraging Student Profiles and Game Personas for Enhanced Learning 17

Boosting Engagement and Academic Performance Through Gamific...

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17th International Conference on computer Supported Education, CSEDU 2025

作者： Moura, Waldir Siqueira Delbem, Edgar França, Juliana Baptista dos Santos Dias, Angélica Fonseca da Silva Computer Science Graduate Program PPGI Federal University of Rio de Janeiro Térreo Bloco E CCMN/NCE University City Rio de Janeiro68.530 Brazil

ISBN: (纸本)9789897587467

This study examines the integration of personalized gamification as a strategy to increase student engagement and academic performance, based on the analysis of behavioral profiles and game user personas. Using Detroit: Become Human as a tool to identify collaborative and competitive tendencies, the research aims to address the limitations of traditional methods, which often fail to engage students. The application of Game Theory, combined with the personalization of pedagogical interventions according to each student's profile, enabled the creation of more adapted and motivating approaches. The final results indicate a significant improvement in grades and student engagement levels, with 38.6% of students who were initially below average reaching or exceeding expected performance, along with a marked increase in interest in classroom participation. These findings reinforce the potential of gamified and personalized methodologies to transform the educational experience, adapting it to the individual traits and needs of students. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Students

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Fuzzy Logic for Assessing the Health of a Corn Crop within Family Farming

Fuzzy Logic for Assessing the Health of a Corn Crop within F...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Mendonca, Vanessa Santos, Mikael Munoz, Daniel M. Humberto Llanos, Carlos Federal Institute of Brasilia Department of Computer Science Brazil Mechatronics Graduate Program Department of Mechanical Engineering Brazil Federal Institute of Brasilia Department of Agronomy Brazil University of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program Brasilia Brazil

ISBN: (纸本)9798350374575

Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agriculture gaining substantial traction in the market. However, a gap remains between cutting-edge technology and family farming, presenting a challenge from both social and applied research perspectives. However, there is still a gap between cutting-edge technology and family farming, which creates a challenge from a social and applied research point of view. In this context, this paper proposes a monitoring model based on Fuzzy Logic and sensor automation applied to estimate the health of a corn crop. The proposed Fuzzy inference system involves calculating an indicator of nutrients as well as the average color and area of corn plants. The nutrient indicator is automatically computed by an ESP32 microcontroller using sensor readings, while the average color and area inputs are manually entered via a mobile application. Additionally, the Fuzzy inference is integrated into the ESP32. The model underwent experimental validation on the health of the plantation, and the results were evaluated in four areas: one was designated for testing, and three were for validation. The model achieved an accuracy of 97.5% in Scenario 3, categorized as 'Very Favorable', and an accuracy of 65% in Scenarios 2 and 4, categorized as 'Unfavorable'. The implications of this research contribute to the advancement of AgroTICs among small producers, with the potential to enhance and automate the monitoring of their harvest production. © 2024 IEEE.

关键词： Agribusiness

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Encoding Data Through Tactile Vibrations

Encoding Data Through Tactile Vibrations

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International Conference on Information Visualisation (IV)

作者： Walbert Cunha Monteiro Thiago Augusto Soares de Sousa Anderson Gregório Marques Soares Tiago Davi Oliveira de Araújo Carlos Gustavo Resque dos Santos Bianchi Serique Meiguins Computer Science Graduate Program Federal University of Pará Belém Pará Brazil Campus Capanema Federal Rural University of Amazônia Capanema Pará Brazil IEETA DETI Aveiro North School Aveiro Portugal

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

Data coding and decoding are essential steps in communicating and interpreting information. These encodings are basic units of data representation, which can be combined and are generally organized by a layout of a data visualization technique, a common scenario for digital data visualizations. Technological advances in manipulating materials allowed us to represent data physically, creating an opportunity to use other senses, in addition to vision and hearing, to encode and decode data, such as touch, smell, and taste. In everyday life, touch is already widely used to decode information with few values using vibrations, such as turning devices on/off, communicating alerts on devices, and providing feedback in interaction in video games, among others. Still, it is generally not used to decode a larger set of values. Therefore, this article aims to investigate the encoding and decoding of data through tactile vibrations, identifying the minimum and maximum limits of tactile perception without causing discomfort and minimum intervals that differentiate two consecutive vibration values. This preliminary information allowed us to understand how to map data using vibrotactile scales. The data are mapped according to their value, changing the vibration frequency of the motors. Two stages of testing were carried out. The first aims to find the limits of tactile perception and create the vibrotactile scale, and the second aims to perform data analysis tasks, such as comparison, ordering, and identification. The results demonstrate that users considered find minimum and maximum vibration values tasks easy, while clustering and sorting tasks were classified as the most complex.

关键词： Vibrations Video games Layout Data visualization Turning Motors Encoding Decoding Testing Sorting

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Adding Visual Data and Interactions for Dynamic Data Physicalization with Augmented Reality

Adding Visual Data and Interactions for Dynamic Data Physica...

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International Conference on Information Visualisation (IV)

作者： Vinicius Favacho Queiroz Diego Hortêncio Dos Santos Thiago Augusto Soares De Sousa Walbert Cunha Monteiro Tiago Davi Oliveira De Araújo Bianchi Serique Meiguins Computer Science Graduate Program Federal University of Pará Belém Pará Brazil IEETA DETI University of Aveiro Aveiro Portugal

In recent years, the physicalization area has expanded rapidly and has been utilized in numerous contexts, mostly in big open public areas or data-related places. Most data physicalizations have visual restrictions and no interaction. Hence, multiple technologies, including augmented reality (AR), have been applied to physical visualizations to address the above-mentioned issues. This research proposes dynamic data physicalization using AR virtual content (visual data items). The dynamic bar chart allows the configuration of numerical data to height, categorical data to color, and categorical data to the x-axis. The mobile augmented reality (MAR) application performs some Infovis tasks, such as settings, filters, details on demand, etc. A cloud server selects data, calculates visual elements or additional visualizations, calculates scales for physicalizing data, and enables the communication between the MAR application and dynamic physicalization. Lastly, dynamic and augmented data physicalization characteristics and scenarios are shown.

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Workload Evaluation to Create Data Visualization Using ChatGPT

Workload Evaluation to Create Data Visualization Using ChatG...

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International Conference on Information Visualisation (IV)

作者： Walbert Cunha Monteiro Diego Hortêncio Dos Santos Thiago Augusto Soares De Sousa Vinicius Favacho Queiroz Tiago Davi Oliveira De Araújo Bianchi Serique Meiguins Computer Science Graduate Program Federal University of Pará Belém Pará Brazil IEETA DETI University of Aveiro Aveiro Portugal

The value of good data visualization has already been shown in several scenarios. Still, it is not always easy to obtain it, as it depends on factors such as the dataset, the amount of data, task types, the user profile, the type of interaction, etc. To mitigate the challenges addressed, automated or semi-automated systems have been proposed, emphasizing rule-based/heuristic approaches and machine-learning models. However, many of these applications require specialized knowledge and present results (data visualizations) that are not flexible for customization. Papers have highlighted the ease of tools like ChatGPT in creating various tasks, including creating data charts. This facility, in addition to the intelligent computational model involved, is also due to the expressiveness used in the requests to execute the tasks by the users since these tools use Natural Language Interfaces. Despite adopting these tools overgrowing in different scenarios of society, studies on the best way to use them, integrate them into existing processes, or evaluative studies on their effectiveness or efficiency are still incipient. Thus, this paper will evaluate the workload for creating data visualization using ChatGPT 3.5. For assessment, the NASA Task Load Index (Nasa TLX) methodology was applied, and users with experience creating data visualization created two proposed scenarios. The preliminary results showed high temporal and mental demand, mainly due to the vocabulary used and the completeness of the user instructions. The average time to create and perform InfoVis tasks in two proposed evaluation scenarios was 33 and 44 minutes, and 14 queries were applied on average for both scenarios. The direct consequence was that the users have redone the requests and improved the instructions at each new iteration, and all users completed the proposed tasks.

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Bridging Accuracy and Efficiency: The Role of PINNs in Immune Response Simulations

Bridging Accuracy and Efficiency: The Role of PINNs in Immun...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Fernandes, Thiago E. Dos Santos, Rodrigo W. Lobosco, Marcelo Federal University of Juiz de Fora Graduate Program on Computational Modelling Juiz de Fora Brazil Federal University of Juiz de Fora Institute of Exact Sciences Department of Computer Science Juiz de Fora Brazil

ISBN: (纸本)9798350386226

Myocarditis, characterized by inflammation of the heart muscle, has seen a notable 62.2% increase in incidence over the past three decades, leading to 324,490 deaths in 2019. Despite advancements in understanding its etiology, several critical questions remain unresolved, highlighting the need for improved patient treatment strategies. Computational methods play a crucial role in unraveling the complex interactions between pathogens and the immune system. This study explores the use of Physics-Informed Neural Networks (PINNs) to accelerate a newly developed model for myocardial edema formation in acute infectious myocarditis. The model describes the concentrations of pathogens and leukocytes using a nonlinear system of ordinary differential equations (ODEs). Our findings demonstrate a mean computational speedup of 5.93, while maintaining a Root Mean Squared Error of 0.0015. These results indicate that PINNs can accurately replicate the ODE system's solutions, offering substantial computational efficiency for this application. © 2024 IEEE.

关键词： Pathogens

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Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting 13

Optimization of Node Reduction Using BRKGA for GNN-Based Tra...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Inokuchi, Yuto González, Pedro Henrique Mendonça, Israel Aritsugi, Masayoshi Graduate School of Science and Technology Kumamoto University Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350308365

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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Efficient Hyperparameter Optimization Using Deep Q-Network and BRKGA 13

Efficient Hyperparameter Optimization Using Deep Q-Network a...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kobayashi, Kosei Aritsugi, Masayoshi González, Pedro Henrique Mendonça, Israel Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results). © 2024 IEEE.

关键词： Genetic algorithms

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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting 11

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Inokuchi, Yuto Gonzalez, Pedro Henrique Aritsugi, Masayoshi Mendonca, Israel Kumamoto University Graduate School of Science and Technology Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350367300

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy. © 2024 IEEE.

关键词： Graph neural networks

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