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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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Replacing the FitzHugh-Nagumo Electrophysiology Model by Physics-Informed Neural Networks 23rd

Replacing the FitzHugh-Nagumo Electrophysiology Model by P...

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23rd International Conference on Computational science, ICCS 2023

作者： Werneck, Yan Barbosa dos Santos, Rodrigo Weber Rocha, Bernardo Martins Oliveira, Rafael Sachetto Graduate Program in Computational Modeling Federal University of Juiz de Fora Juiz de Fora Brazil Graduate Program in Computer Science Federal University of São Jõao del Rei São Jão del Rei Brazil

ISBN: (纸本)9783031360206

This paper presents a novel approach to replace the FitzHugh-Nagumo (FHN) model with physics-informed neural networks (PINNs). The FHN model, a system of two ordinary differential equations, is widely used in electrophysiology and neurophysiology to simulate cell action potentials. However, in tasks such as whole-organ electrophysiology modeling and drug testing, the numerical solution of millions of cell models is computationally expensive. To address this, we propose using PINNs to accurately approximate the two variables of the FHN model at any time instant, any initial condition, and a wide range of parameters. In particular, this eliminates the need for causality after training. We employed time window marching and increased point cloud density on transition regions to improve the training of the neural network due to nonlinearity, sharp transitions, unstable equilibrium, and bifurcations of parameters. The PINNs were generated using NVIDIA’s Modulus framework, allowing efficient deployment on modern GPUs. Our results show that the generated PINNs could reproduce FHN solutions with average numerical errors below 0.5%, making them a promising lightweight computational model for electrophysiology and neurophysiology research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electrophysiology

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ACO with Reinforcement Learning Applied to Rescues Operations on Urban Forests 13

ACO with Reinforcement Learning Applied to Rescues Operation...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Alves, Cláudio Mendonça, Israel De Almeida Guimarães, Vanessa González, Pedro Henrique System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Coppead Graduate Business School Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

In the context of smart cities where green infras-tructure is incentived, besides important benefits like regulating temperatures and absorbing pollutants among others, tour by urban forests is a way to experience closer contact with nature near of big urban centers. Eventually, visitors get lost, and helping these people with velocity is important to avoid severe incidents. Normally, rescue operations mobilize firefighters, ex-pensive equipment like helicopters and public resources. Following that idea of reducing search time in rescue operations, this paper considers the Data Mule Routing Problem with Limited Autonomy (DMRP-wLA). To find high-quality solutions, this paper proposes an Ant Colony Optimization algorithm enhanced with Reinforcement Learning to create an adaptive decision-making algorithm. © 2024 IEEE.

关键词： Ant colony optimization

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Exploring Cooperation and Competition in computer science Education: An Investigation Based on Game Theory 17

Exploring Cooperation and Competition in Computer Science Ed...

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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

The study investigates the use of the game Overcooked as a pedagogical tool in primary education, focusing on cooperation and competition. The research, conducted throughout 2022, analyzed one semester as a baseline and another with pedagogical interventions. Four groups were studied: control (no interventions), game without instructions, game with teamwork guidance, and game without verbal communication. Questionnaires and qualitative/quantitative data assessed engagement, motivation, and cooperation. The interventions significantly improved performance, motivation, and skills such as teamwork and problem-solving, validating gamification as an educational resource. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Teaching

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An Approach Based on Feature Engineering and Machine Learning for the Prediction of Child Violence

An Approach Based on Feature Engineering and Machine Learnin...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Osses, Anibal T. S. Ricardo Fernandes, A.S. Federal University of São Carlos Graduate Program in Computer Science São Carlos Brazil

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

According to technical reports prepared by United Nations International Children's Emergency Fund and World Health Organization, there is a high percentage of child violence occurring around the world. This fact is even more evident in developing countries. However, it is difficult to determine the type of violence the child is suffering, since the problem is dependent on reports of the victims and these ones could be imprecise. In this sense, decision support tools are of utmost importance for agents of social assistance programs. Thus, this paper proposes an approach based on feature engineering and machine learning to classify the violence suffered by a child. For this purpose, it was used a dataset obtained from a Chilean organization of social assistance for children. After the training and validation processes, the best machine learning models reached areas under the curve between 0.84 and 0.97, surpassing the state-of-the-art results. © 2022 IEEE.

关键词： Developing countries

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Robot Finder v2: Search and Ingestion of Educational Robotics Data from Youtube 16

Robot Finder v2: Search and Ingestion of Educational Robotic...

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16th Brazilian Symposium on Robotics and 15th Workshop on Robotics in Education, SBR-WRE 2024

作者： Urbano Lopes, Gabriel Batistuta de Santana Costa, Ryllari Raianne Marques Alves Filho, Sebastião Emidio B.Sc. in Computer Science Rio Grande do Norte State University RN Mossoró Brazil Graduate Program in Computer Science - PPgCC UERN/UFERSA Rio Grande do Norte State University Federal University of the Semi-Arid Region RN Mossoró Brazil

ISBN: (纸本)9798350391084

Educational Robotics offers an interactive learning approach where students can turn abstract theories into concrete projects. With the exponential growth in content creation on internet platforms, finding specific, high-quality learning objects about robotics in education can be challenging. Platforms like YouTube, for example, have a vast collection of educational videos that cover various topics related to educational robotics. However, they are scattered among a massive amount of available data, making it difficult to have more assertive searches and identify the best resources. This work aims to present Robot Finder v2, a software that uses an automated approach to feed RepositORE, a repository of Learning Objects in Educational Robotics. The Robot Finder v2 searches and stores data from YouTube videos to make the search for this content simpler and more effective. The collected data feeds the RepostiORE, where these objects can be adapted and researched by users looking to acquire skills in educational robotics. For the objects to be found accurately, the software uses the YouTube Data API v3, which has a set of metadata in addition to the descriptions of the videos provided by their creators. The processing done in the Robot Finder optimizes the search and registration of information by mapping API endpoints and seeking correlations of metadata between RepositORE objects and YouTube videos. ©2024 IEEE.

关键词： Educational robots

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A Review of Techniques for Reducing Shortcomings in Classical Information Visualization Charts

A Review of Techniques for Reducing Shortcomings in Classica...

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International Conference on Information Visualisation (IV)

作者： Natã Ferreira Lobato Tiago Davi O. de Araujo Bianchi Serique Meiguins Carlos Gustavo Resque dos Santos Graduate Program in Computer Science Federal University of Pará - UFPA Belém PA Brazil IEETA - ESAN University of Aveiro Oliveira de Azeméis Portugal

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

This study aims to review academic research publications on techniques that deal with the shortcomings of classic information visualization charts. This review used snowballing to capture the academic papers and applied a filter to focus on articles that developed a new technique, resulting in 28 articles. We proposed some research questions and a taxonomy to classify the papers and conduct a detailed analysis of each study. In this way, the study identified one solution proposal per article that makes use of one or more of the ten (10) visual shortcomings mitigation techniques found for each of the five (5) problems related to Visual shortcomings that affect one of the five (5) information visualization techniques. As a result, we found solutions and mapped them to each of the identified visual shortcomings, including overlap, outliers, categorical occlusion, misuse of space, and non-perceptible elements. These solutions help to minimize the problem as a whole or in part, making it easier for the user to understand the information in the graphs. This article characterizes an initial effort to study academic papers that solved or mitigated shortcomings in classical information visualization charts.

关键词： Visualization Reviews Prevention and mitigation Taxonomy Information filters Proposals

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Investigating Cases and Clusters-Based Reuse Policies for Card-Playing Agents 5

Investigating Cases and Clusters-Based Reuse Policies for Ca...

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5th Annual IEEE Conference on Games, CoG 2023

作者： Paulus, Gustavo B. Vargas, Daniel P. Assuncao, Joaquim V. C. Silva, Luis A. L. Federal Institute of Education Science and Technology of Rio Grande do Sul Ibirubá Brazil Federal University of Santa Maria Graduate Program in Computer Science Santa Maria Brazil Federal University of Santa Maria Applied Computing Department Santa Maria Brazil

ISBN: (纸本)9798350322774

computer games continue to present challenging experimental fields for developing Artificial Intelligence (AI) models. With Case-Based Reasoning and Clustering, this work proposes novel cases and clusters-based reuse criteria for implementing card-playing agents with diversified playing skills. Using the game of Truco, a common game in South America, we detail how game actions are reused from past cases selected as query answers for given game problems. In doing so, the majority rule, the probability-based lottery, the probability of victory, and the number of points won reuse policies are used to select a cluster of cases. Then these policies are also used to select game actions from the cases within the selected cluster. Investigating the combined exploration of reuse policies, experiments of different natures evaluate the performance of implemented Truco bots disputing matches against each other. Players in these tournaments are equipped with varied policies and use case bases constructed differently. © 2023 IEEE.

关键词： Case based reasoning

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Unified Approach to Video-Based AI Inference Tasks in Augmented Reality Systems Assisted by Mobile Edge Computing 31

Unified Approach to Video-Based AI Inference Tasks in Augmen...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： Sousa, Alexandre Ladeira De Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Graduate Program in Information Engineering Sao Paulo Santo Andre Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

In the rapidly evolving field of Augmented Reality (AR), delivering real-time, immersive experiences places a significant demand on computational resources, particularly in the context of video-based Artificial Intelligence (AI) inference tasks. Some solutions explorate optimize the tasks using the potential of Mobile Edge Computing (MEC), in which MEC brings the advantages of reduced latency and network congestion, which are critical for AR systems. In this work, we designed a novel algorithm named AR-MEC Optimization for the distribution of AI inference tasks among edge servers while considering several factors including task complexity, server capacity, and network conditions. Our approach not only ensures low-latency responses but also improves overall system efficiency and the user's Quality of Experience (QoE). Experimental results demonstrate that our unified approach significantly outperforms traditional cloud-based methods in terms of response time and computational efficiency, thereby highlighting the potential of MEC-assisted AR systems for real-world applications. © 2023 university of Split, FESB.

关键词： Augmented reality

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A Novel Resource Allocation in Software-Defined Networks for IoT Application 31

A Novel Resource Allocation in Software-Defined Networks for...

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31st International Conference on Software, Telecommunications and computer Networks, SoftCOM 2023

作者： De Sousa, Alexandre Ladeira Okey, Ogobuchi Daniel Rosa, Renata Lopes Saadi, Muhammad Rodriguez, Demostenes Zegarra Federal University of Lavras Department of Computer Science Minas Gerais Lavras Brazil Federal University of ABC Santo Andre Graduate Program in Information Engineering Sao Paulo Brazil University of Central Punjab Department of Electrical Enginnering Punjab Pakistan Federal University of Lavras Computer Science Department Minas Gerais Lavras Brazil

ISBN: (纸本)9798350301076

The rapid proliferation of Internet of Things (IoT) devices has posed significant challenges for network resource allocation and management. In this paper, we propose a novel methodology for efficient resource allocation in Software-Defined Networks (SDNs) to meet the unique requirements of IoT applications. Our approach leverages the flexibility and programmability of SDN to dynamically allocate network resources based on the specific needs of IoT devices. Our proposal combines reinforcement learning with dynamic resource allocation, demonstrating its effectiveness in optimizing resource allocation and enhancing network performance. The results highlight the potential of our approach to addressing the resource challenges in SDN-based IoT environments. The proposed method obtains results superior to 30% of throughput in comparison to a traditional resource allocation in SDN to IoT applications. © 2023 university of Split, FESB.

关键词： Internet of things

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