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2019 International Joint Conference on Neural Networks, IJCNN 2019

作者： Nogueira Lorena, Luiz Henrique Goncalves Quiles, Marcos Nogueira Lorena, Luiz Antonio De Carvalho, Andre C. P. L. F. Cespedes, Juliana Garcia Science and Technology Institute Federal University of São Paulo São José dos Campos Brazil Computing and Applied Mathematics National Institute for Space Research São José dos Campos Brazil Computer Science Institute University of São Paulo São Carlos Brazil

ISBN: (纸本)9781728119854

Qualitative data clustering is a fundamental data analysis task, with applications in many areas, like medicine, sociology, and economics. An appealing way to deal with this task is via Integer Linear Programming, as it avoids inappropriate inferences by the final user. This approach has two main advantages: the data are directly used, without the need of being converted to quantitative values, and the optimal number of clusters is automatically obtained by solving the optimization problem. However, it might create large and redundant models, which can limit the size of the problems it can be applied. Recently, models that are more compact and able to avoid some redundancy have been proposed in the literature. These models consume less memory and are faster to obtain the optimal solution set. In this study, a new model is introduced and compared with the state-of-the-art alternatives using datasets from different application domains. Empirical results show that the new model outperforms its predecessors, achieving the optimal solution set with lower computational time and memory consumption. © 2019 IEEE.

关键词： Integer programming

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Comparison of Sampling-based Path Planners for Robocup Small Size League

Comparison of Sampling-based Path Planners for Robocup Small...

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IEEE Latin American Robotics Symposium, LARS

作者： Felipe C.R. Pinheiro Marcos R. O. A. Máximo Takashi Yoneyama Autonomous Computational Systems Lab (LAB-SCA) Computer Science Division Aeronautics Institute of Technology Săo José dos Campos SP Brazil Electronics Division Aeronautics

ISBN: (数字)9780738111537

ISBN: (纸本)9781665419857

The path planning problem is an important issue in robotics. In the context of the Small Size Soccer league, a widely used algorithm for path planning is the Rapidly-Exploring Random Tree (RRT), which is a single-query planner. However, properties such as asymptotic optimality and anytime resolution may not be achieved by some of the algorithms used in the competition. Some of the possible path planning solvers with some of these properties are the Batch Informed Trees (BIT*), Fast Marching Trees (FMT*), and other RRT implementations (such as Extended Execution RRT - ERRT and RRT*). The contribution of this paper is to carry out a benchmark comparative investigation with a selected set of path planners found in the literature, evaluating their performance in the context of Robocup Small Size League Competition. The results show good convergence properties of BIT* when the planning time requirement is larger. ERRT performs well to find a solution, but it falls short concerning the solution quality. RRT* did not have a good convergence to optimal paths, but succeeded to find paths with short planning time. Since FMT* does not present anytime resolution, the algorithm did not present a satisfactory convergence with larger planning time. To improve the performance of the planners, it may be interesting to conceive heuristic strategies to allow trivial problems to be solved faster.

关键词： Path planning Robot kinematics Planning Sports Search problems Convergence Computational modeling

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ROBOSTEAMSEN Project - Training SEN Teachers to Use Robotics for Fostering STEAM and Develop Computational Thinking*

ROBOSTEAMSEN Project - Training SEN Teachers to Use Robotics...

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International Symposium on computers in Education (SIIE)

作者： Miguel Á. Conde Francisco J. Rodríguez-Sedano Francisco J. García-Peñalvo Jose Gonçalves Ilkka Jormanainen Andrea Anzanello Jonny Filipe Ribeiro Alves Raquel Fernández Hernáandez Alina- Andreia Ailincai Dept. of Mechanics Computer Science and Aerospace Engineering Universidad de Leon Leon Spain Dept. Electrical Systems and Automation Engineering Universidad de Leon Leon Spain Dpto. de Informatica y Autormatica - IUCE Universidad de Salamanca Salamanca Espana Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politecnico de Braganca Braganca Portugal School of Computing University of Eastern Finland Joensuu Finland Dept of Project Management Pixel Florence Italy Colegio Internato Dos Carvalhos Carvalhos Portugal Colegio de Educacion Especial Ntra. Sra. del Sagrado Corazon de Leon Leon Spain EuroEd Primary School Lasi Romania

ISBN: (数字)9798350376616

ISBN: (纸本)9798350376623

Our contemporary society necessitates professionals equipped with 21st-century skills. Disciplines within science, Technology, Engineering, Arts, and Mathematics (known as STEAM) have been particularly effective in fostering these skills. However, when considering students with disabilities, especially those with intellectual or developmental disabilities (IDD), this assertion often falls short. In this context, the RoboSTEAMSEN project emerges as an initiative designed to enhance educational processes by providing teachers of IDD students with the necessary resources to promote STEAM engagement. The project proposes the use of active learning methodologies and robotics to achieve this goal. The primary objective of the project is realized through several strategies: understanding the needs of students with disabilities and adapting the use of robotics and active learning methodologies accordingly; training teachers in the use of these resources; and creating a platform to exchange experiences, resources, lessons learned, tools, case scenarios, etc., while reaching other potential stakeholders such as caregivers and policymakers. The main outcomes of the project are teacher training programs and the development of associated competencies, tools to identify and classify resources for the students, and technological platforms to ensure the sustain ability of the project once it concludes.

关键词： Training computers Ecosystems Collaboration Fasteners People with disabilities Mathematics

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Generation of consistent sets of multi-label classification rules with a multi-objective evolutionary algorithm 20

Generation of consistent sets of multi-label classification ...

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2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Miranda, Thiago Zafalon Sardinha, DIorge Brognara Basgalupp, Márcio Porto Jin, Yaochu Cerri, Ricardo Departamento de Computação Universidade Federal de São Carlos São Carlos São Paulo Brazil Instituto de Ciência e Tecnologia Universidade Federal de São Paulo São José dos Campos São Paulo Brazil Department of Computer Science University of Surrey Guildford Surrey United Kingdom

ISBN: (纸本)9781450371278

Multi-label classification consists in classifying an instance into two or more classes simultaneously. Recently, the interest in interpretable classification models have grown, partially as a consequence of regulations such as the General Data Protection Regulation. In this context, we propose a multi-objective evolutionary algorithm that generates multiple rule-based multi-label classification models, allowing users to choose among models that offer different compromises between predictive power and interpretability. The most important contributions of this work are: the generated models are based on sets (unordered collection) of rules and the rule creation mechanism employs a conflict avoidance algorithm which guarantees that all rules within a model are consistent with each other. We conducted experiments on synthetic and real-world datasets and compared our results with state-of-the-art algorithms in terms of predictive performance (F-Score) and interpretability (model size), and demonstrate that our best models had comparable F-Score and smaller model sizes. © 2020 Owner/Author.

关键词： Multiobjective optimization

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Semantic Hierarchy-based Convolutional Neural Networks for Image Classification

Semantic Hierarchy-based Convolutional Neural Networks for I...

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International Joint Conference on Neural Networks (IJCNN)

作者： Matheus Inoue Carlos Henrique Forster Antonio Carlos dos Santos Polytechnic School University of São Paulo São Paulo Brazil Computer Science Division Aeronautics Institute of Technology São José dos Campos Brazil Data Science Team Itaú-Unibanco São Paulo Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this work, three variations of hierarchical topologies of Convolutional Neural Networks (CNNs), two of which being original proposals introduced by this work, were tested to assess their impact on image classification problems. The hierarchical structure groups the images based on the semantic meaning of the classes, from the coarsest classes to the finest classes, forming hierarchical levels. The hierarchical models made were compared to a baseline regular CNN on benchmark image classification datasets, the Fashion-MNIST and CIFAR-100 datasets. Another contribution of this work is a new training strategy for hierarchical CNNs, that aims to be simple to implement and to produce a smooth loss during training, increasing stability, while maintaining characteristics like the transitioning from coarse-to-fine level emphasis during training, learning first high-level details and then specific details that differentiate the fine level classes. The hierarchical models produce outputs for each hierarchical level, which can lead to more interpretable results. Results suggest that providing semantic hierarchies can improve fine level accuracy on CNNs, while bringing relevant hierarchical information from their other coarser level outputs.

关键词： Training Semantics Cats Feature extraction Topology Vegetation

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A multipurpose system for gamified experiences 20

A multipurpose system for gamified experiences

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20th International Conference on Enterprise Information Systems, ICEIS 2018

作者： Timbó, Samuel Moreira De Melo Bezerra, Juliana Hirata, Celso Massaki Computer Science Department ITA Sao Jose dos Campos Brazil

ISBN: (纸本)9789897582981

Gamification is the application of game elements and game design techniques to non-gaming contexts, aiming to provide incentives to people overcome obstacles towards a desired engagement and behavior. Nowadays gamification is applied in different areas such as Education, Business, Human Resources, Health, and Entertainment. Generally, existent applications are tied to a specific context, making it hard to replicate ideas and to adapt to new scenarios. Here, we present a multipurpose system where users are responsible for creating their own gamified experiences. The system is based in a generalized gamification process and it allows customizations due to its platform with predefined game elements. We conducted an experiment where we confirmed the applicability of the proposed system by investigating aspects as potential to motivate users, flexibility to be applied in distinct contexts, and overall usability. Copyright © 2018 by SCITEPRESS - science and Technology Publications, Lda. All rights reserved.

关键词： Game design

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On Applying Performance Portability Metrics

On Applying Performance Portability Metrics

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Performance, Portability and Productivity in HPC (P3HPC)

作者： Daniela F. Daniel Jairo Panetta Computer Science Division (IEC) Aeronautics Institute of Technology (ITA) Sao Jose dos Campos Brazil Computer Science Division (IEC) Aeronautics Institute of Technology (ITA) São José dos Campos Brazil

ISBN: (纸本)9781728160047

As we prepare for further technological advance- ment in supercomputing, the diversity of hardware architec- tures and parallel programming languages has increased to new levels. At the same time, extracting performance from so many architectures is even more difficult. In this context, the appearance of portable languages capable of generating executable code for multiple architectures has become a recurrent research target. We port a set of seven parallel benchmarks from SPEC ACCEL suite and a wave propagation code to one such portable language: the Kokkos C++ programming library. Using the original OpenACC versions of the eight codes, we apply a known performance portability metric on the OpenACC and Kokkos versions of those codes across a variety of hardware platforms and problem sizes. We observe that the portability metric is sensitive to the problem size. To remedy this deficiency, we propose a novel metric for performance portability, apply the proposed metric to the eight codes and discuss the results.

关键词： Kokkos OpenACC Performance Portability Benchmark Metrics Portability BENCHMARKS Metronidazole Hardware Platform Metric Codes Technological Advancement Language ARCHITECTURE

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Deep Reinforcement Learning for Humanoid Robot Dribbling

Deep Reinforcement Learning for Humanoid Robot Dribbling

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IEEE Latin American Robotics Symposium, LARS

作者： Alexandre F. V. Muzio Marcos R.O. A. Maximo Takashi Yoneyama Autonomous Computational Systems Lab (LAB-SCA) Computer Science Division Aeronautics Institute of Technology Praça Marechal Eduardo Gomes Sào José dos Campos SP Brazil Electronic Engineering Division Aeronautics Institute of Technology Praça Marechal Eduardo Gomes Sào José dos Campos SP Brazil

ISBN: (数字)9780738111537

ISBN: (纸本)9781665419857

Humanoid robot soccer is a very traditional competitive task that aims to push the boundaries of state-of-the-art robotics. One of the many challenges of playing soccer is walking and running while not losing balance. Deep Reinforcement Learning (DRL) has been used to solve complex continuous control problems such as those in robotics. In this work, we focused on learning humanoid robot behavior to dribble a ball against a single opponent. Instead of learning how to control joint commands directly, we adopt an approach where the learning agent interacts with a predefined walking engine. Using DRL model-free algorithms (namely, Deep Deterministic Policy Gradients, Trust Region Policy Optimization, and Proximal Policy Optimization), we effectively learn a high level policy that allows a humanoid robot to fulfill this task. Finally, the learned dribble policy was evaluated on a simulated Nao robot from the RoboCup 3D Soccer Simulation League. According to our results, the learned agent was able to surpass the hand-coded behavior effectively used by the ITAndroids robotics team in the RoboCup competition.

关键词： Sports Robots Task analysis Optimization Humanoid robots Legged locomotion Engines

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Performance and energy trade-offs for parallel applications on heterogeneous multi-processing systems

arXiv

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arXiv 2020年

作者： Coutinho, Demetrios A.M. de Sensi, Daniele Lorenzon, Arthur Francisco Georgiou, Kyriakos Nunez-Yanez, Jose Eder, Kerstin Xavier-de-Souza, Samuel Instituto Federal do Rio Grande do Norte Pau dos Ferros59900-000 Brazil Computer Science Department Università di Pisa Pisa56127 Italy Computer Science Department Universidade Federal de Pampa Alegrete97546-550 Brazil Department of Computer Science University of Bristol BristolBS8 1UB United Kingdom Department of Computer Engineering and Automation Universidade Federal do Rio Grande do Norte Natal59078-970 Brazil

This work proposes a methodology to find performance and energy trade-offs for parallel applications running on Heterogeneous Multi-Processing systems with a single instruction-set architecture. These offer flexibility in the form of different core types and voltage and frequency pairings, defining a vast design space to explore. Therefore, for a given application, choosing a configuration that optimizes the performance and energy consumption is not straightforward. Our method proposes novel analytical models for performance and power consumption whose parameters can be fitted using only a few strategically sampled offline measurements. These models are then used to estimate an application's performance and energy consumption for the whole configuration space. In turn, these offline predictions define the choice of estimated Pareto-optimal configurations of the model, which are used to inform the selection of the configuration that the application should be executed on. The methodology was validated on an ODROID-XU3 board for eight programs from the PARSEC Benchmark, Phoronix Test Suite and Rodinia applications. The generated Pareto-optimal configuration space represented a 99% reduction of the universe of all available configurations. Energy savings of up to 59.77%, 61.38% and 17.7% were observed when compared to the performance, ondemand and powersave Linux governors, respectively, with higher or similar performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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Learning humanoid robot motions through deep neural network

arXiv

引用

arXiv 2019年

作者： Melo, Luckeciano C. Maximo, Marcos R.O.A. da Cunha, Adilson Marques Autonomous Computational Systems Lab Computer Science Division Aeronautics Institute of Technology São José dos Campos Brazil Computer Science Division Aeronautics Institute of Technology São José dos Campos Brazil

Controlling a high degrees of freedom humanoid robot is acknowledged as one of the hardest problems in Robotics. Due to the lack of mathematical models, an approach frequently employed is to rely on human intuition to design keyframe movements by hand, usually aided by graphical tools. In this paper, we propose a learning framework based on neural networks in order to mimic humanoid robot movements. The developed technique does not make any assumption about the underlying implementation of the movement, therefore both keyframe and model-based motions may be learned. The framework was applied in the RoboCup 3D Soccer Simulation domain and promising results were obtained using the same network architecture for several motions, even when copying motions from another teams. Copyright © 2019, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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