Padrões léxico-gramaticais na especificação de propósito e resultado em abstracts de artigos científicos: aplicações no ensino de EAP e na construção de ferramentas de suporte à escrita científica

FAPESP (Processos 2007/52405-3 e 2008/08963-4

Predictive analysis of COVID-19 symptoms in social networks through machine learning

Social media is a great source of data for analyses, since they provide ways for people to share emotions, feelings, ideas, and even symptoms of diseases. By the end of 2019, a global pandemic alert was raised, relative to a virus that had a high contamination rate and could cause respiratory complications. To help identify those who may have the symptoms of this disease or to detect who is already infected, this paper analyzed the performance of eight machine learning algorithms (KNN, Naive Bayes, Decision Tree, Random Forest, SVM, simple Multilayer Perceptron, Convolutional Neural Networks and BERT) in the search and classification of tweets that mention self-report of COVID-19 symptoms. The dataset was labeled using a set of disease symptom keywords provided by the World Health Organization. The tests showed that Random Forest algorithm had the best results, closely followed by BERT and Convolution Neural Network, although traditional machine learning algorithms also have can also provide good results. This work could also aid in the selection of algorithms in the identification of diseases symptoms in social media content.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the Project Scope: DSAIPA/AI/0088/2020info:eu-repo/semantics/publishedVersio

Classification of control/pathologic subjects with support vector machines

The diagnosis of pathologies using vocal acoustic analysis has the advantage of been noninvasive and inexpensive technique compared to traditional technique in use. In this work the SVM were experimentally tested to diagnose dysphonia, chronic laryngitis or vocal cords paralysis. Three groups of parameters were experimented. Jitter, shimmer and HNR, MFCCs extracted from a sustained vowels and MFCC extracted from a short sentence. The first group showed their importance in this type of diagnose and the second group showed low discriminative power. The SVM functions and methods were also experimented using the dataset with and without gender separation. The best accuracy was 71% using the jitter, shimmer and HNR parameters without gender separation.info:eu-repo/semantics/publishedVersio

Long short term memory on chronic laryngitis classification

The classification study with the use of machine learning concepts has been applied for years, and one of the aspects in which this can be applied is for the analysis of speech acoustics applied to the analysis of pathologies. Among the pathologies present, one of them is chronic laryngitis. Thus, this article aims to present the results for a classification of chronic laryngitis with the use of Long Short Term Memory as a classifier. The parameters of relative jitter, relative shimmer and autocorrelation was used as input of the LSTM. A dataset of about 1500 instances were used to train, validate and test along 4 experiments with LSTM and one feedforward Artificial Neural Network (ANN). The results of the LSTM overcome the ones of the feedforward ANN, and was about 100% accuracy, sensitivity and specificity in test set, denoting a promising future for this classification tool in the voice pathologies diagnose.info:eu-repo/semantics/publishedVersio

Harmonic to noise ratio measurement - selection of window and length

Harmonic to Noise Ratio (HNR) measures the ratio between periodic and non-periodic components of a speech sound. It has become more and more important in the vocal acoustic analysis to diagnose pathologic voices. The measure of this parameter can be done with Praat software that is commonly accept by the scientific community has an accurate measure. Anyhow, this measure is dependent with the type of window used and its length. In this paper an analysis of the influence of the window and its length was made. The Hanning, Hamming and Blackman windows and the lengths between 6 and 24 glottal periods were experimented. Speech files of control subjects and pathologic subjects were used. The results showed that the Hanning window with the length of 12 glottal periods gives measures of HNR more close to the Praat measures.info:eu-repo/semantics/publishedVersio

Yin Yang Convolutional Nets: Image Manifold Extraction by the Analysis of Opposites

Computer vision in general presented several advances such as training optimizations, new architectures (pure attention, efficient block, vision language models, generative models, among others). This have improved performance in several tasks such as classification, and others. However, the majority of these models focus on modifications that are taking distance from realistic neuroscientific approaches related to the brain. In this work, we adopt a more bio-inspired approach and present the Yin Yang Convolutional Network, an architecture that extracts visual manifold, its blocks are intended to separate analysis of colors and forms at its initial layers, simulating occipital lobe's operations. Our results shows that our architecture provides State-of-the-Art efficiency among low parameter architectures in the dataset CIFAR-10. Our first model reached 93.32\% test accuracy, 0.8\% more than the older SOTA in this category, while having 150k less parameters (726k in total). Our second model uses 52k parameters, losing only 3.86\% test accuracy. We also performed an analysis on ImageNet, where we reached 66.49\% validation accuracy with 1.6M parameters. We make the code publicly available at: https://github.com/NoSavedDATA/YinYang_CNN.Comment: 12 pages, 5 tables and 6 figure

Estimação de idade em imagens digitais a partir de deep learning para apoiar análise pericial

Com o decorrer da evolução tecnológica de redes sociais e comunidades online, a privacidade e segurança na Internet se tornaram questionáveis. O elevado número de informações compartilhadas pela rede sustenta a propagação de conteúdos ilícitos envolvendo pornografia infantil. A visão computacional, fazendo uso de redes neurais como técnica de aprendizado profundo, tem a capacidade de reconhecer características associadas à classificação de conteúdo pornográfico infantil. Aplicadas em imagens digitais e utilizando estruturas neurais pré-treinadas para identificação de rosto infantil e exposição de pele em bases de imagens pré-selecionadas, este trabalho teve o objetivo de fornecer subsídios capazes de agregar conhecimento aos métodos de análise pericial

A single speaker is almost all you need for automatic speech recognition

We explore the use of speech synthesis and voice conversion applied to augment datasets for automatic speech recognition (ASR) systems, in scenarios with only one speaker available for the target language. Through extensive experiments, we show that our approach achieves results compared to the state-of-the-art (SOTA) and requires only one speaker in the target language during speech synthesis/voice conversion model training. Finally, we show that it is possible to obtain promising results in the training of an ASR model with our data augmentation method and only a single real speaker in different target languages.Comment: Submitted to INTERSPEECH 202

Portal Min@s: uma ferramenta de apoio ao processamento de córpus de propósito geral

This paper presents Portal Min@s, a general web-based corpus processing tool. Many corpus processing tools available focus on specific tasks, such as lexicography or translation. Portal, on the other hand, took the challenge of being a general purpose corpus processing tool which deals with different types of corpus, languages and linguistic annotations. We present the features provided by this tool and compare it with two other alternatives.Este artigo apresenta a ferramenta Portal Min@s, criada para apoiar a tarefa de processamento de córpus. Enquanto muitas ferramentas disponíveis focam em pesquisas específicas como lexicografia ou tradução, o Portal fornecendo recursos para tarefas mais gerais, processando córpus com diferentes propósitos, anotação e estruturação. Os recursos disponibilizados são detalhados e comparados com duas ferramentas similares.CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Transfer learning with audioSet to voice pathologies identification in continuous speech

The classification of pathological diseases with the implementation of concepts of Deep Learning has been increasing considerably in recent times. Among the works developed there are good results for the classification in sustained speech with vowels, but few related works for the classification in continuous speech. This work uses the German Saarbrücken Voice Database with the phrase “Guten Morgen, wie geht es Ihnen?” to classify four classes: dysphonia, laryngitis, paralysis of vocal cords and healthy voices. Transfer learning concepts were used with the AudioSet database. Two models were developed based on Long-Short-Term-Memory and Convolutional Network for classification of extracted embeddings and comparison of the best results, using cross-validation. The final results allowed to obtaining 40% of f1-score for the four classes, 66% f1-score for Dysphonia x Healthy, 67% for Laryngitis x healthy and 80% for Paralysis x Healthy.info:eu-repo/semantics/publishedVersio