Heitor Silvério Lopes

Federal University of Technology - Paraná/Brazil (UTFPR) | UTFPR · Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial (CPGEI)

The potential implications of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in generating highly realistic deepfake videos have raised concerns regarding the reliability of our human senses. In response to this challenge, we propose a deepfake detection system based on phonemes, the transcribed text, associated mouth movements, and...

This paper aims to address the divergences and contradictions in the definition of intelligence across different areas of knowledge, particularly in computational intelligence and psychology, where the concept is of significant interest. Despite the differences in motivation and approach, both fields have contributed to the rise of cognitive scienc...

The COVID-19 coronavirus pandemic still causes a global health crisis. An effective protection method is using a face mask in public areas, according to the World Health Organization (WHO). Computer vision systems can be allies in monitoring public areas where the face mask is mandatory. However, face mask detection is challenging due to many facto...

The growing number of vehicles in cities has a great impact on our quality of life, such as air and noise pollution, traffic jams and traffic accidents. Cooperative Intelligent Transportation System (C-ITS) relies on communication technologies to provide innovative services and applications for transportation and traffic management. In the C-ITS co...

Incremental learning is a topic of great interest in the current state of machine learning research. Real-world problems often require a classifier to incorporate new knowledge while preserving what was learned before. One of the most challenging problems in computer vision is Human Action Recognition (HAR) in videos. However, most of the existing...

Automatically understanding and describing the visual content of videos in natural language is a challenging task in computer vision. Existing approaches are often designed to describe single events in a closed-set setting. However, in real-world scenarios, concurrent activities and previously unseen actions may appear in a video. This work present...

Transfer learning is a paradigm that consists in training and testing classifiers with datasets drawn from distinct distributions. This technique allows to solve a particular problem using a model that was trained for another purpose. In the recent years, this practice has become very popular due to the increase of public available pre-trained mode...

Human action recognition (HAR) is a topic widely studied in computer vision and pattern recognition. Despite the success of recent models for this issue, most of them approach HAR from the closed-set perspective. The closed-set recognition works under the assumption that all classes are known a priori and they appear during the training and test ph...

Currently, many companies or even cities use surveillance cameras all the time, and due to the COVID-19 pandemic, many places have to limit the number of people in attendance. This paper proposes a method for people counting by gender and age in videos using deep learning techniques. The proposed method is based on a face detection and tracking app...

Tattoos are still poorly explored as a biometrics factor for human identification, especially in public security, where tattoos can play an important role for identifying criminals and victims. Tattoos are considered a soft biometrics, since they are not permanent and can change along time, differently from hard biometrics traits (fingerprint, iris...

Anomaly detection in surveillance videos is an exhaustive and tedious task to be performed manually by humans. Many methods have been proposed to detect anomalous events by learning normal patterns and differentiate them from abnormal ones. However, these methods often suffer from false alarms, as human behaviors and environments can change over ti...

Typical semantic segmentation methods do not recognize unknown pixels during the test or deployment stage. This capability is critical for open-world environment applications where unseen objects appear all the time. Recently, to solve those limitations, Open Set Semantic Segmentation (OSSS) was introduced. This task aims to produce known and unkno...

Soft biometrics traits extracted from a human body, including the type of clothes, hair color, and accessories, are useful information used for people tracking and identification. Semantic segmentation of these traits from images is still a challenge for researchers because of the huge variety of clothing styles, layering, shapes, and colors. To ta...

Recent research has shown that features obtained from pretrained Convolutional Neural Network (CNN) models can be promptly applied to a variety of problems they were not originally designed to solve. This concept, often referred to as Transfer Learning (TL), is a common practice when labeled data is limited. In some fields, such as video anomaly de...

Soft biometrics attributes can be useful to perform identi�cation of individuals, since they provide information that can be used to di�erentiate one individual from another without intrusiveness. Moreover, the large number of surveillance cameras installed in public places allows to acquire videos in real time without much effort. However, this de...

Solving the protein folding problem (PFP) is one of the grand challenges still open in computational Biophysics. Globular proteins are believed to evolve from initial configurations through folding pathways connecting several thermodynamically accessible states in a free energy landscape until reaching its minimum, inhabited by the stable native st...

In One-Class Classification (OCC) problems, the classifier is trained with samples of a class considered normal, such that exceptional patterns can be identified as anomalies. Indeed, for real-world problems, the representation of the normal class in the feature space is an important issue, considering that one or more clusters can describe differe...

Vygotsky's cognitive-developmental theory aroused at a time when the scientific paradigm was fundamentally supported by formal logic, and language was explored only in a semiotic approach. In this way, it stands out for having defined and included the role of culture, always mediated by relationship, in the formation of concepts and personality its...

Este trabalho apresenta uma análise, utilizando técnicas de data mining, da fragmentação partidária existente na Assembléia Legislativa do Estado do Rio Grande do Sul. Para isso, foram coletados dados de votação registrados pelos deputados nas diferentes proposituras no perı́odo entre 2000 e 2017. Resultados obtidos sugerem uma alta similaridade en...

In the contemporary world, a significant number of people use social networking services for a variety of purposes, including, but not limited to, communicating, exchanging messages and searching for information. A popular social network in the political arena is Twitter, a microblogging service for posting messages of up to 280 characters, called...

The Protein Folding Problem (PFP) is considered one of the most important open challenges in Biology and Bioinformatics. Long Short-Term Memory (LSTM) methods have risen recently, achieving the state-of-art performance for several Bioinformatics problems such as, protein secondary and tertiary protein structure prediction. This paper describes the...

The increase in the availability of computational resources gave rise to new technologies to estimate the amount of people in a given area. In this context, algorithm-based solutions for crowd counting can be grouped into image-based and non-image based approaches, the latter considering any other feature that is not visual. Currently, due to the p...

In this paper we propose the association of the skip-gram algorithm, which provides a vector representation of a word set, with the non-supervised clustering algorithm k-means. This methodology has presented an effectiveness of 75.3% on classifying context in natural language written documents. Besides detailing its architecture, in this paper we a...

Papers on Agile Software Development methods are often focused on their applicability in commercial projects or organizations. There are no current studies that we know about addressing the application of these methods in research projects. The objective of this work is to describe the perception of researchers on the application of agile software...

Papers on Agile Software Development methods are often focused on their applicability in commercial projects or organizations. There are no current studies that we know about addressing the application of these methods in research projects. The objective of this work is to describe the perception of researchers on the application of agile software...

This paper presents a methodology to perform multi-class image classification using Gene Expression Program- ming(GEP) in both balanced and unbalanced datasets. Descrip- tors are extracted from images and then their dimensionality are reduced by applying Principal Component Analysis. The aspects extracted from images are texture, color and shape th...

Soft biometrics classification has been gaining acceptance during the recent years for critical applications, mainly in the security field. Recognizing individuals by using only behavioral, physical or psychological characteristics is a task that can be helpful for several purposes. Thus, different Deep Learning approaches have been proposed to per...

This work presents a methodology to perform the classification of soft biometrics in images of pedestrians using a Denoising Con-volutional Autoencoder as feature extractor and a Support Vector Machine as classifier. The Denoising Convolutional Autoencoder was trained with a custom dataset containing a combination of five available datasets (3DPES,...

The Writer Identification Problem has been largely studied in the field of image processing. Music score writer identification is a particular type of the problem that requires identifying the writer of a music score, which is a complex task even for musicologists. Addressing this issue, this paper presents a novel Deep Learning approach based on a...

Papers on Agile Software Development methods are often focused on their applicability in commercial projects or organizations. There are no current studies that we know about addressing the applica- tion of these methods in research projects. The objective of this work is to describe the perception of researchers on the application of agile soft- w...

The detection of anomalous behaviors in automated video surveillance is a recurrent topic in recent computer vision research. Depending on the application field, anomalies can present different characteristics and challenges. Convolutional Neural Networks have achieved the state-of-the-art performance for object recognition in recent years, since t...

This book discusses a number of real-world applications of computational intelligence approaches. Using various examples, it demonstrates that computational intelligence has become a consolidated methodology for automatically creating new competitive solutions to complex real-world problems. It also presents a concise and efficient synthesis of dif...

The Protein Folding Problem (PFP) is considered one of the most important open challenges in Biology and Bioinformatics. This paper describes the application of a parallel ecology-inspired algorithm (pECO) to a hard problem related to the PFP: the protein structure reconstruction from Contact Maps. The fitness function proposed includes information...

In this work it is presented a neurofuzzy network that is applied to the detection of a specific wave of the electrocardiographic signal. The network was trained using genetic algorithms, using a software package publicly available in the Internet. The training procedure, its parameters and details of the application are presented. Results suggest...

In kwonledge extraction from databases, a frequently found problem is noisy data. Neural networks are usually tolerant a noise in the training set, but they are have a poor performance in explaining how a solution is found. In this work it is presented a method for obtainiy correct and comprehensible kwonledge by using rule extraction from trained...

Abstract: The inference of gene regulatory networks (GRNs) from expression profiles is still an important challenge in bioinformatics research. The main difficulty of this problem is associated to the huge number of genes and the small number of samples available, as well as the intrinsic noise in the data acquisition process. In this context, this...

Two evolutionary computation methods are presented in this paper, both variants of the differential evolution (DE) algorithm. Their main difference is the encoding process (binary and continuous) and both methods were successfully applied to the pipeline network schedule problem. A binary mathematical model is proposed to represent the flow of oil...

This work proposes an efficient approach to recover the mechanical strain profile applied on fibre Bragg grating sensors. The proposed method is based on differential evolution and uses only the sensor reflectivity, without requiring phase information. The method has been shown to be highly parallelisable, with the fitness evaluation procedure impl...

Feature selection is a very important procedure in the pattern recognition research field. Feature selection algorithms are particularly important in the inference of Gene Regulatory Networks (GRNs) from its gene expression profiles, which usually involves data with a large number of variables and small number of observations. This work presents th...

In spite of the fact that many simplified model variants of protein structure prediction have been widely studied in the past years, few attention has been given to discrete models with side chains, for which there is no specific benchmark. In this paper, we propose an integer programming model for the 3D-HP side chain protein structure prediction...