Bezerra, Eduardo
A Machine Learning Approach to Automatic Fall Detection of Soldiers
Soares, Leandro, Venturini, Gustavo, Gomes, José, Efigenio, Jonathan, Rangel, Pablo, Gonzalez, Pedro, Santos, Joel dos, Brandão, Diego, Bezerra, Eduardo
Military personnel and security agents often face significant physical risks during conflict and engagement situations, particularly in urban operations. Ensuring the rapid and accurate communication of incidents involving injuries is crucial for the timely execution of rescue operations. This article presents research conducted under the scope of the Brazilian Navy's ``Soldier of the Future'' project, focusing on the development of a Casualty Detection System to identify injuries that could incapacitate a soldier and lead to severe blood loss. The study specifically addresses the detection of soldier falls, which may indicate critical injuries such as hypovolemic hemorrhagic shock. To generate the publicly available dataset, we used smartwatches and smartphones as wearable devices to collect inertial data from soldiers during various activities, including simulated falls. The data were used to train 1D Convolutional Neural Networks (CNN1D) with the objective of accurately classifying falls that could result from life-threatening injuries. We explored different sensor placements (on the wrists and near the center of mass) and various approaches to using inertial variables, including linear and angular accelerations. The neural network models were optimized using Bayesian techniques to enhance their performance. The best-performing model and its results, discussed in this article, contribute to the advancement of automated systems for monitoring soldier safety and improving response times in engagement scenarios.
GLARE: Guided LexRank for Advanced Retrieval in Legal Analysis
Gregório, Fabio, Castro, Rafaela, Belloze, Kele, Lopes, Rui Pedro, Bezerra, Eduardo
The Brazilian Constitution, known as the Citizen's Charter, provides mechanisms for citizens to petition the Judiciary, including the so-called special appeal. This specific type of appeal aims to standardize the legal interpretation of Brazilian legislation in cases where the decision contradicts federal laws. The handling of special appeals is a daily task in the Judiciary, regularly presenting significant demands in its courts. We propose a new method called GLARE, based on unsupervised machine learning, to help the legal analyst classify a special appeal on a topic from a list made available by the National Court of Brazil (STJ). As part of this method, we propose a modification of the graph-based LexRank algorithm, which we call Guided LexRank. This algorithm generates the summary of a special appeal. The degree of similarity between the generated summary and different topics is evaluated using the BM25 algorithm. As a result, the method presents a ranking of themes most appropriate to the analyzed special appeal. The proposed method does not require prior labeling of the text to be evaluated and eliminates the need for large volumes of data to train a model. We evaluate the effectiveness of the method by applying it to a special appeal corpus previously classified by human experts.
A Population-based Hybrid Approach to Hyperparameter Optimization for Neural Networks
Serqueira, Marcello, González, Pedro, Bezerra, Eduardo
In recent years, large amounts of data have been generated, and computer power has kept growing. This scenario has led to a resurgence in the interest in artificial neural networks. One of the main challenges in training effective neural network models is finding the right combination of hyperparameters to be used. Indeed, the choice of an adequate approach to search the hyperparameter space directly influences the accuracy of the resulting neural network model. Common approaches for hyperparameter optimization are Grid Search, Random Search, and Bayesian Optimization. There are also population-based methods such as CMA-ES. In this paper, we present HBRKGA, a new population-based approach for hyperparameter optimization. HBRKGA is a hybrid approach that combines the Biased Random Key Genetic Algorithm with a Random Walk technique to search the hyperparameter space efficiently. Several computational experiments on eight different datasets were performed to assess the effectiveness of the proposed approach. Results showed that HBRKGA could find hyperparameter configurations that outperformed (in terms of predictive quality) the baseline methods in six out of eight datasets while showing a reasonable execution time.
A Bimodal Learning Approach to Assist Multi-sensory Effects Synchronization
Abreu, Raphael, Santos, Joel dos, Bezerra, Eduardo
In mulsemedia applications, traditional media content (text, image, audio, video, etc.) can be related to media objects that target other human senses (e.g., smell, haptics, taste). Such applications aim at bridging the virtual and real worlds through sensors and actuators. Actuators are responsible for the execution of sensory effects (e.g., wind, heat, light), which produce sensory stimulations on the users. In these applications sensory stimulation must happen in a timely manner regarding the other traditional media content being presented. For example, at the moment in which an explosion is presented in the audiovisual content, it may be adequate to activate actuators that produce heat and light. It is common to use some declarative multimedia authoring language to relate the timestamp in which each media object is to be presented to the execution of some sensory effect. One problem in this setting is that the synchronization of media objects and sensory effects is done manually by the author(s) of the application, a process which is time-consuming and error prone. In this paper, we present a bimodal neural network architecture to assist the synchronization task in mulsemedia applications. Our approach is based on the idea that audio and video signals can be used simultaneously to identify the timestamps in which some sensory effect should be executed. Our learning architecture combines audio and video signals for the prediction of scene components. For evaluation purposes, we construct a dataset based on Google's AudioSet. We provide experiments to validate our bimodal architecture. Our results show that the bimodal approach produces better results when compared to several variants of unimodal architectures.