arXiv subjects

Francisco Erivaldo Fernandes Junior

Publications and source records attributed to Francisco Erivaldo Fernandes Junior.

4 recordsLinked to original sources

arXiv2026

Integrating Multi-Source Feedback in Computational Design

In real-world design practice, evaluations rarely rely on a single source of judgment. Designers routinely combine expert opinions, empirical studies, and computational models, each with distinct strengths and limitations. While machine learning offers methods to integrate multiple feedback sources, these approaches remain largely inaccessible to designers without technical expertise. In this paper, we explore how to integrate multiple feedback sources, primarily through: (1) a practical approach for multi-source integration, and (2) its implementation in MUSE, a no-code tool that allows designers to combine and balance diverse sources. Our technical findings show that independent modeling of multiple evaluation sources enables exploration across heterogeneous feedback, accommodates different evaluation speeds, surfaces disagreements between sources, and supports an adaptable evaluation setup that designers can reconfigure during their process. In a visualization design study, participants navigated their own judgments alongside simulator feedback, reporting a perception of enhanced confidence and flexibility. Our results highlight the viability of multi-source integration to support computational design, offering a step toward bridging the gap between advanced optimization methods and design practice.

cs.HC↗

arXiv2025

AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent Design

Developing a reinforcement learning (RL) agent often involves identifying values for numerous parameters, covering the policy, reward function, environment, and agent-internal architecture. Since these parameters are interrelated in complex ways, optimizing them is a black-box problem that proves especially challenging for nonexperts. Although existing optimization-as-a-service platforms (e.g., Vizier and Optuna) can handle such problems, they are impractical for RL systems, since the need for manual user mapping of each parameter to distinct components makes the effort cumbersome. It also requires understanding of the optimization process, limiting the systems' application beyond the machine learning field and restricting access in areas such as cognitive science, which models human decision-making. To tackle these challenges, the paper presents AgentForge, a flexible low-code platform to optimize any parameter set across an RL system. Available at https://github.com/feferna/AgentForge, it allows an optimization problem to be defined in a few lines of code and handed to any of the interfaced optimizers. With AgentForge, the user can optimize the parameters either individually or jointly. The paper presents an evaluation of its performance for a challenging vision-based RL problem.

cs.LG↗

arXiv2025

Simulating Errors in Touchscreen Typing

Empirical evidence shows that typing on touchscreen devices is prone to errors and that correcting them poses a major detriment to users' performance. Design of text entry systems that better serve users, across their broad capability range, necessitates understanding the cognitive mechanisms that underpin these errors. However, prior models of typing cover only motor slips. The paper reports on extending the scope of computational modeling of typing to cover the cognitive mechanisms behind the three main types of error: slips (inaccurate execution), lapses (forgetting), and mistakes (incorrect knowledge). Given a phrase, a keyboard, and user parameters, Typoist simulates eye and finger movements while making human-like insertion, omission, substitution, and transposition errors. Its main technical contribution is the formulation of a supervisory control problem wherein the controller allocates cognitive resources to detect and fix errors generated by the various mechanisms. The model generates predictions of typing performance that can inform design, for better text entry systems.

cs.HC↗

arXiv2020

Pruning Deep Convolutional Neural Networks Architectures with Evolution Strategy

Currently, Deep Convolutional Neural Networks (DCNNs) are used to solve all kinds of problems in the field of machine learning and artificial intelligence due to their learning and adaptation capabilities. However, most successful DCNN models have a high computational complexity making them difficult to deploy on mobile or embedded platforms. This problem has prompted many researchers to develop algorithms and approaches to help reduce the computational complexity of such models. One of them is called filter pruning, where convolution filters are eliminated to reduce the number of parameters and, consequently, the computational complexity of the given model. In the present work, we propose a novel algorithm to perform filter pruning by using Multi-Objective Evolution Strategy (ES) algorithm, called DeepPruningES. Our approach avoids the need for using any knowledge during the pruning procedure and helps decision-makers by returning three pruned CNN models with different trade-offs between performance and computational complexity. We show that DeepPruningES can significantly reduce a model's computational complexity by testing it on three DCNN architectures: Convolutional Neural Networks (CNNs), Residual Neural Networks (ResNets), and Densely Connected Neural Networks (DenseNets).

cs.NE↗