Comparing Different Methods for Disfluency Structure Detection

This paper presents a number of experiments focusing on assessing the performance of different machine learning methods on the identification of disfluencies and their distinct structural regions over speech data. Several machine learning methods have been applied, namely Naive Bayes, Logistic Regression, Classification and Regression Trees (CARTs), J48 and Multilayer Perceptron. Our experiments show that CARTs outperform the other methods on the identification of the distinct structural disfluent regions. Reported experiments are based on audio segmentation and prosodic features, calculated from a corpus of university lectures in European Portuguese, containing about 32h of speech and about 7.7% of disfluencies. The set of features automatically extracted from the forced alignment corpus proved to be discriminant of the regions contained in the production of a disfluency. This work shows that using fully automatic prosodic features, disfluency structural regions can be reliably identified using CARTs, where the best results achieved correspond to 81.5% precision, 27.6% recall, and 41.2% F-measure. The best results concern the detection of the interregnum, followed by the detection of the interruption point

Reflexões sobre branded content: uma análise netnográfica do público de Tour das Tours

O presente artigo busca compreender a reação de um público à ação de branded content, uma estratégia de comunicação de marca que pressupõe a associação do entretenimento à publicidade. Para tanto, foi realizada uma netnografia no maior grupo LGBT do Facebook na América Latina, o LDRV, alvo da ação de branded content Tour das Tours, criada pela marca Skol. Encontraram-se três percepções frequentes em relação à produção, o que permitiu dividir os membros do grupo em: “desconfiados”, “promotores” e “detratores”, sendo que, de modo geral, pouco se aludiu ao aspecto mercadológico da campanha, o que será analisado à luz do conceito de literacia publicitária.Reflections on branded content: a netnography of Tour das Tours’ targetAbstractThis article intends to comprehend the reaction of the target of a branded content campaign, a brand communication strategy that presupposes the merge of entertainment and advertising. Therefore, a netnography was carried out on the largest LGBT group on the Facebook in the Latin American, LDRV, the target of the branded content campaign Tour das Tours, conceived by the beer brand Skol. Three recurring perceptions regarding the webseries were found, which allowed share the group’s members between: “wary”, “promoters” and “detractors”, being that, in general, they barely noticed the campaign’s marketing aspect, which will be analyzed in the light of the advertisement literacy concept.Keywords: branded content; netnography; advertising literacy

Avaliação preliminar do reaproveitamento da biomassa de fruta para produção de bioetanol

The search for cleaner and more sustainable alternative energy sources, such as the use of agricultural waste to produce fuel, has been the focus of different research with promising results. Brazil stands out as one of the largest tropical fruit producers in the world, generating large amounts of agro-industrial waste, which are, in most cases, disposed of as waste without reuse. In the present study the use of the waste generated by the fruit consumption in the production of  bioethanol was evaluated. The fruit biomass was subjected to alcoholic fermentation, followed by fractional distillation for alcohol separation. Results using this methodology indicate that the alcohol percentage obtained from the samples was satisfactory, ranging from 23 to 34% when compared to data obtained from the literature

Multi-parameter characterization of surface sediments from the Minho estuary

Project NATURA MIÑO-MINHO "Valorización de los recursos de la cuenca hidrográfica del MIÑO-MINHO

Rice crop detection using LSTM, Bi-LSTM, and machine learning models from sentinel-1 time series

The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul

Evidências científicas sobre o tratamento cirúrgico da queratose actínica / Scientific evidence on the surgical treatment of actinic keratosis

As queratoses actínicas são máculas, pápulas ou placas queratósicas ou escamosas resultantes da proliferação intraepidérmica de queratinócitos atípicos em resposta à exposição prolongada à radiação ultravioleta. As queratoses actínicas são uma preocupação porque a maioria dos CECs cutâneos que surgem de queratoses actínicas pré-existentes, e as queratoses actínicas que irão progredir para o CEC não podem ser distinguidas de queratoses actínicas que se resolverão espontaneamente ou persistirão, devido a esses fatores, a maioria dos estudos recomedam tratar rotineiramente as queratoses actínicas.  As opções de tratamento para queratose actínica  incluem terapias destrutivas direcionadas à lesão (por exemplo, cirurgia, crioterapia, dermoabrasão) e terapias direcionadas ao campo com medicamentos tópicos como fluorouracil, imiquimod e  mebutato de ingenol, ou terapia fotodinâmica. As terapias de campo são indicadas para o tratamento de áreas com múltiplas queratoses actínicas, lesões subclínicas que não são detectadas por inspeção visual ou palpação e cancerização de campo

Produção de bioetanol a partir de coproduto gerado no descaroçamento de azeitona

Na busca por fontes alternativas de energia menos poluentes e mais sustentáveis, o uso de resíduos agrícolas para a produção de biocombustíveis tem se mostrado uma alternativa promissora. Neste contexto, o presente trabalho teve como objetivo avaliar a viabilidade da produção do bioetanol a partir da polpa de azeitona, um coproduto gerado no processo de descaroçamento das azeitonas de mesa. Avaliaram-se dois tipos de hidrólise como prétratamentos: hidrólise via ácido clorídrico 1% e hidrólise por explosão a vapor com ácido sulfúrico 14%. O líquido hidrolisado foi fermentado por 4 dias à temperatura ambiente, utilizando-se a levedura Saccharomyces cerevisiae. O melhor rendimento em etanol (4,6% v/v) foi obtido com a polpa pré tratada com ácido sulfúrico

Deep semantic segmentation of center pivot irrigation systems from remotely sensed data

The center pivot irrigation system (CPIS) is a modern irrigation technique widely used in precision agriculture due to its high efficiency in water consumption and low labor compared to traditional irrigation methods. The CPIS is a leader in mechanized irrigation in Brazil, with growth forecast for the coming years. Therefore, the mapping of center pivot areas is a strategic factor for the estimation of agricultural production, ensuring food security, water resources management, and environmental conservation. In this regard, digital processing of satellite images is the primary tool allowing regional and continuous monitoring with low costs and agility. However, the automatic detection of CPIS using remote sensing images remains a challenge, and much research has adopted visual interpretation. Although CPIS presents a consistent circular shape in the landscape, these areas can have a high internal variation with different plantations that vary over time, which is difficult with just the spectral behavior. Deep learning using convolutional neural networks (CNNs) is an emerging approach that provokes a revolution in image segmentation, surpassing traditional methods, and achieving higher accuracy and efficiency. This research aimed to evaluate the use of deep semantic segmentation of CPIS from CNN-based algorithms using Landsat-8 surface reflectance images (seven bands). The developed methodology can be subdivided into the following steps: (a) Definition of three study areas with a high concentration of CPIS in Central Brazil; (b) acquisition of Landsat-8 images considering the seasonal variations of the rain and drought periods; (c) definition of CPIS datasets containing Landsat images and ground truth mask of 256×256 pixels; (d) training using three CNN architectures (U-net, Deep ResUnet, and SharpMask); (e) accuracy analysis; and (f) large image reconstruction using six stride values (8, 16, 32, 64, 128, and 256). The three methods achieved state-of-the-art results with a slight prevalence of U-net over Deep ResUnet and SharpMask (0.96, 0.95, and 0.92 Kappa coefficients, respectively). A novelty in this research was the overlapping pixel analysis in the large image reconstruction. Lower stride values had improvements quantified by the Receiver Operating Characteristic curve (ROC curve) and Kappa, and fewer errors in the frame edges were also perceptible. The overlapping images significantly improved the accuracy and reduced the error present in the edges of the classified frames. Additionally, we obtained greater accuracy results during the beginning of the dry season. The present study enabled the establishment of a database of center pivot images and an adequate methodology for mapping the center pivot in central Brazil

Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach

Instance segmentation is the state-of-the-art in object detection, and there are numerous applications in remote sensing data where these algorithms can produce significant results. Nevertheless, one of the main problems is that most algorithms use Red, Green, and Blue (RGB) images, whereas Satellite images often present more channels that can be crucial to improve performance. Therefore, the present work brings three contributions: (a) conversion system from ground truth polygon data into the Creating Common Object in Context (COCO) annotation format; (b) Detectron2 software source code adaptation and application on multi-channel imagery; and (c) large scene image mosaicking. We applied the procedure in a Center Pivot Irrigation System (CPIS) dataset with ground truth produced by the Brazilian National Water Agency (ANA) and Landsat-8 Operational Land Imager (OLI) imagery (7 channels with 30-m resolution). Center pivots are a modern irrigation system technique with massive growth potential in Brazil and other world areas. The round shapes with different textures, colors, and spectral behaviors make it appropriate to use Deep Learning instance segmentation. We trained the model using 512 × 512-pixel sized patches using seven different backbone structures (ResNet50- Feature Pyramid Network (FPN), Resnet50-DC5, ResNet50-C4, Resnet101-FPN, Resnet101-DC5, ResNet101-FPN, and ResNeXt101-FPN). The model evaluation used standard COCO metrics (Average Precision (AP), AP50, AP75, APsmall, APmedium, and AR100). ResNeXt101-FPN had the best results, with a 3% advantage over the second-best model (ResNet101-FPN). We also compared the ResNeXt101-FPN model in the seven-channel and RGB imagery, where the multi-channel model had a 3% advantage, demonstrating great improvement using a larger number of channels. This research is also the first with a mosaicking algorithm using instance segmentation models, where we tested in a 1536 × 1536-pixel image using a non-max suppression sorted by area method. The proposed methodology is innovative and suitable for many other remote sensing problems and medical imagery that often present more channels