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2,880 results on '"BERNARDO A"'

1. A Modular and Optimized Toolbox for Side-Channel Analysis

Author

Alberto Fuentes Rodriguez, Luis Hernandez Encinas, Agustin Martin Munoz, and Bernardo Alarcos Alcazar

Subjects
Cryptography, information security, open source software, side-channel attacks, software tools, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The aim of this paper is to describe the characteristics of a toolbox developed to carry out a side-channel analysis of cryptosystems implemented in physical devices. The toolbox, called RamDPA and developed in C++, is flexible and modular and has been initially designed to carry out differential power analysis attacks. Different modules are used to enter text, keys, and hypothetical values related to the cryptographic algorithm under analysis, to control the digital oscilloscope in charge of the acquisition of power consumption traces, and to carry out the statistical operations to get the unknown key. An analysis of the different options to program the modules has been performed in order to optimize the toolbox in terms of memory consumption and execution time. Usually, the optimization of side-channel attacks comes from the selection of points-of-interest and re-sampling. Instead of that approach, the proposed toolbox optimizes DPA attacks from caching strategy and multithreading. Results are presented by applying RamDPA to extract an AES-128 key with the traces provided by the CHES2016 capture The Flag challenge.

Published
2019
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2. Difference Based Metrics for Deep Reinforcement Learning Algorithms

Author

Bernardo A. G. Oliveira, Carlos A. P. Da S. Martins, Flavia Magalhaes, and Luis Fabricio W. Goes

Subjects
Machine learning, artificial intelligence (AI), differential equations, metrics, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Although state-of-the-art deep reinforcement learning often achieves superhuman levels on some tasks, the authors still struggle to analyze, compare or report the obtained results due to the unstable nature of the algorithms and the diversity of metrics used in the literature. Furthermore, these metrics fail to show some characteristics of the learning process, leading to misinterpretations by the analyst. The objective of this paper is to propose, implement and analyze a difference based evaluation metric that highlight different aspects of the learning process, allowing for more detailed results and analysis that can be used by automated software or analysts, experienced or not. A possible applicability for the proposed metric is to create automated evaluation systems, that can detect anomalies during the training process automatically.

Published
2019
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3. Fast and Lightweight Object Detection Network: Detection and Recognition on Resource Constrained Devices

Author

Bernardo Augusto Godinho de Oliveira, Flavia Magalhaes Freitas Ferreira, and Carlos Augusto Paiva da Silva Martins

Subjects
Computer vision, deep learning, machine learning, object detection, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The intrinsic ability of humans to rapidly detect, differentiate, and classify objects allows us to make quick decisions in regards to what we see. Several appliances can make use of fast and lightweight automated object detection for images or videos. Throughout the last five years, the technology industry has constantly introduced computational and hardware solutions, such as devices with impressive processing and storage capabilities. However, object detection methods usually require either high processing power or large storage availability, making it hard for resource constrained devices to perform the detection in real-time without a connection to a powerful server. The model presented in this paper requires only 95 megabytes of storage and took 113 ms in average per image running on a laptop CPU, making it suitable for standalone devices that can be used on the go.

Published
2018
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4. A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing Activities

Author

Diego Rivera, Luis Cruz-Piris, Susel Fernandez, Bernardo Alarcos, Antonio Garcia, and Juan R. Velasco

Subjects
Activity recognition, classification algorithms, Internet of Things, pattern recognition, sensor systems and applications, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Autonomous devices able to evaluate diverse situations without external help have become especially relevant in recent years because they can be used as an important source of relevant information about the activities performed by people (daily habits, sports performance, and health-related activities). Specifically, the use of this kind of device in childhood games might help in the early detection of developmental problems in children. In this paper, we propose a method for the detection and classification of movements performed with an object, based on an acceleration signal. This method can automatically generate patterns associated with a given movement using a set of reference signals, analyze sequences of acceleration trends, and classify the sequences according to the previously established patterns. This method has been implemented, and a series of experiments has been carried out using the data from a sensor-embedded toy. For the validation of the obtained results, we have, in parallel, developed two other classification systems based on popular techniques, i.e., a similarity search based on Euclidean distances and machine-learning techniques, specifically a support vector machine model. When comparing the results of each method, we show that our proposed method achieves a higher number of successes and higher accuracy in the detection and classification of isolated movement signals as well as in sequences of movements.

Published
2018
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7. Designing a Mood-Mediated Multi-Level Reasoner.

Author

Gomes, Samuel, Rocha, Jose Bernardo, Dias, Joao, and Martinho, Carlos

Abstract

Psychology-oriented research extensively studied how affect influences human decision making. Particularly, the cognitive tuning assumption suggests that mood can serve to regulate between shallow and deliberative decisions – a more negative mood means higher deliberation. This work proposes a model that mimics the cognitive tuning assumption. To that end, the Collective Risk Dilemma (CRD) game For the Planet was designed and created, a process allowing for distinct levels of reasoning was defined and tested in that game, and mood was integrated to control the level of reasoning of such a process. Several distinct Artificial Intelligence (AI) profiles were created to verify that the developed model was able to dynamically change the level of reasoning and adjust the resulting AI behavior in our CRD game. Results revealed that the distinct AI profiles were influenced by their affective states and experienced circumstances, and that the emergent behaviors were consistent with the cognitive tuning assumption, thus demonstrating that we managed to construct an innovative and flexible model that can use mood to dynamically adjust the level of reasoning of an AI agent. [ABSTRACT FROM AUTHOR]

Published
2024
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8. FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data

Author

Melzi, Pietro, primary, Tolosana, Ruben, additional, Vera-Rodriguez, Ruben, additional, Kim, Minchul, additional, Rathgeb, Christian, additional, Liu, Xiaoming, additional, DeAndres-Tame, Ivan, additional, Morales, Aythami, additional, Fierrez, Julian, additional, Ortega-Garcia, Javier, additional, Zhao, Weisong, additional, Zhu, Xiangyu, additional, Yan, Zheyu, additional, Zhang, Xiao-Yu, additional, Wu, Jinlin, additional, Lei, Zhen, additional, Tripathi, Suvidha, additional, Kothari, Mahak, additional, Zama, Md Haider, additional, Deb, Debayan, additional, Biesseck, Bernardo, additional, Vidal, Pedro, additional, Granada, Roger, additional, Fickel, Guilherme, additional, Führ, Gustavo, additional, Menotti, David, additional, Unnervik, Alexander, additional, George, Anjith, additional, Ecabert, Christophe, additional, Shahreza, Hatef Otroshi, additional, Rahimi, Parsa, additional, Marcel, Sébastien, additional, Sarridis, Ioannis, additional, Koutlis, Christos, additional, Baltsou, Georgia, additional, Papadopoulos, Symeon, additional, Diou, Christos, additional, Di Domenico, Nicolò, additional, Borghi, Guido, additional, Pellegrini, Lorenzo, additional, Mas-Candela, Enrique, additional, Sánchez-Pérez, Ángela, additional, Atzori, Andrea, additional, Fenu, Gianni, additional, Boutros, Fadi, additional, Marras, Mirko, additional, and Damer, Naser, additional

Published
2024
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19. Hardware-in-the-Loop Simulation of an On-Board Energy-Driven Scheduling Algorithm for CubeSats.

Author

Bernardo, Vinicius Pimenta, Seman, Laio Oriel, Bezerra, Eduardo Augusto, and Ribeiro, Brenda Fernandes

Abstract

Optimizing energy consumption and management in small satellites includes evaluating the most efficient electrical power system architecture. The primary goal is to improve energy harvesting using solar panels and techniques, such as maximum power point tracking (MPPT). In this sense, in this letter, an integrated thermal-electrical nanosatellite framework is employed to test different strategies of heater activation profiles in order to improve a state-of-the-art scheduling algorithm. Results show that a direct allocation of resources to heaters after the task selection phase leads to an increase in correlation between ideal and achieved power generation from solar panels and a more consistent battery temperature. The task scheduling strategy is then implemented in a hardware-in-the-loop simulation to verify its ability to meet real-time constraints and evaluate the impact on computation time. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Comparative Analysis of Discrete-time Dual-Loop and Servo System Control Systems for CubeSats: An Optimized Design Approach

Author

Filho, Antonio Cristóvão Kipper, primary, Recktenvald, Bernardo Eckert, additional, dos Santos, Davi Lima Mendes, additional, da Silva, Enzo Filippo Centenaro, additional, França, Louise Bomfim Magalhães, additional, Bellinaso, Lucas Vizzotto, additional, Cardoso, Rafael, additional, and Burger, Eduardo Escobar, additional

Published
2023
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46. A Neurally Guided Patch-Based Style Transfer for Mobile Devices

Author

Silva, José Ivson S., primary, Vargas, Kevin Ian R., additional, Carlos, Antônio A., additional, de Albuquerque, Lucas Pontes, additional, de Almeida, Mateus Baltazar, additional, Vasconcelos, Allan Soares, additional, Oliveira, Victor Ximenes C., additional, Tavares, José Gabriel P., additional, Alves, Danilo Vaz Marcolino, additional, Santiago, Diêgo J. C., additional, de Oliveira, Bernardo Augusto, additional, Padilha, Carlos, additional, and Ren, Tsang Ing, additional

Published
2023
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