Brazil

Semantic textual similarity (STS) measures how semantically similar two sentences are. In the context of the Portuguese language, STS literature is still incipient but includes important initiatives like the ASSIN and ASSIN 2 shared tasks. The state-of-the-art
for those datasets is a contextual embedding produced by a Portuguese
pre-trained and fine-tuned BERT model. In this work, we investigate
the application of Sentence-BERT (SBERT) contextual embeddings to
these datasets. Compared to BERT, SBERT is a more computationally efficient approach, enabling its application to scalable unsupervised
learning problems. Given the absence of SBERT models pre-trained in
Portuguese and the computational cost for such training, we adopt multilingual models and also fine-tune them for Portuguese. Results showed
that SBERT embeddings were competitive especially after fine-tuning,
numerically surpassing the results of BERT on ASSIN 2 and the results
observed during the shared tasks for all datasets considered.

BRACIS 2021

Comparing contextual embeddings for semantic textual similarity in Portuguese

semantic textual similarity

word embeddings

natural language processing

deep learning

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

technical paper

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

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BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

Machine learning models are widespread in many different fields due to their remarkable performances in many tasks. Some require greater interpretability, which often signifies that it is necessary to understand the mechanism underlying the algorithms. Feature importance is the most common explanation and is essential in data mining, especially in applied research. There is a frequent need to compare the effect of features over time, across models, or even across studies. For this, a single metric for each feature shared by all may be more suitable. Thus, analysts may gain better first-order insights regarding feature behavior across these different scenarios. The β-coefficients of additive models, such as logistic regressions, have been widely used for this purpose. They describe the relationships among predictors and outcomes in a single number, indicating both their direction and size. However, for black-box models, there is no metric with these same characteristics. Furthermore, even the β-coefficients in logistic regression models have limitations. Hence, this paper discusses these limitations together with the existing alternatives for overcoming them, and proposes new metrics of feature importance. As with the coefficients, these metrics indicate the feature effect’s size and direction, but in the probability scale within a model-agnostic framework. An experiment conducted on openly available breast cancer data from the UCI Archive verified the suitability of these metrics, and another on real-world data demonstrated how they may be helpful in practice.

Interpreting classification models using feature importance based on marginal local effects

Mobility data analysis has received significant attention in the last few years. Enriching spatial-temporal trajectory data with semantic information, which is the definition of Multiple Aspect Trajectories, presents lots of opportunities, but also many challenges. Regarding trajectory classification, the state-of-the-art method called MASTERMovelets has shown to have the best classification accuracy over several datasets. Indeed, this method generates interpretable patterns called movelets which are the most discriminant sequences of points. Despite its increased performance, the method is computationally expensive and does not scale well, which makes its application unfeasible for large datasets. In this paper we propose a pivot based approach to reduce the search space, selecting only most promising trajectory points to extract movelets. We additionally provide a method to define a limited number of semantic dimensions for movelets. Experiments show that the proposed method is at least 50% faster for extracting the movelets, and shows a average drop of 82% of input to the classification models while keeping a similar classification accuracy level. Additionally, our scalability analysis with respect to computation time shows that the proposed method scales better than the other methods as the dataset grows in number of points, trajectories and dimensions.

Fast Movelet Extraction and Dimensionality Reduction for Robust Multiple Aspect Trajectory Classification

Important information for the design and operation of oceanic systems can be obtained by assessing local sea state parameters such as significant height, peak period and incidence direction. Techniques for motion-based inference and their possible drawbacks have been extensively discussed in the literature (their motivation coming from the simplicity of the required instrumentation when compared to traditional measuring systems), and machine learning approaches are now appearing in a few investigations. This paper addresses the estimation problem through supervised learning, using time series with the movement of a moored vessel to train neural networks models so as to estimate the sea state. Such time series are obtained through simulations, that consider a model of a spread-moored FPSO (Floating Production Storage and Offloading) platform with constant draft, out of a set of metocean conditions observed at Brazil’s Campos Basin. A sensitivity analysis for different classes of neural networks was run, based on the significant height estimation, to choose the network architecture with the best results with respect to the mean absolute error metric. That topology was trained and employed in the estimation of the remaining sea state parameter, separately. The outcomes of the proposed models were confronted with other neural networks-based methods and showed up a comparable or slightly better performance in the error metrics. A preliminary discussion of the ability of the approach to deal with some classical issues on motion-based estimation is presented.

Sea State Estimation with Neural Networks based on the Motion of a Moored FPSO subjected to Campos Basin Metocean Conditions

One important task in the COVID-19 clinical protocol involves the constant monitoring of patients to detect possible signs of insufficiency, which may eventually rapidly progress to hepatic, renal or respiratory failures. Hence, a prompt and correct clinical decision not only is critical for patients prognosis, but also can help when making collective decisions regarding hospital resource management. In this work, we present a network-based high-level classification technique to help healthcare professionals on this activity, by detecting early signs of insufficiency based on Complete Blood Count (CBC) test results. We start by building a training dataset, comprising both CBC and specific tests from a total of 2,982 COVID-19 patients, provided by a Brazilian hospital, to identify which CBC results are more effective to be used as biomarkers for detecting early signs of insufficiency. Basically, the trained classifier measures the compliance of the test instance to the pattern formation of the network constructed from the training data. To facilitate the application of the technique on larger datasets, a network reduction option is also introduced and tested. Numerical results show encouraging performance of our approach when compared to traditional techniques, both on benchmark datasets and on the built COVID-19 dataset, thus indicating that the proposed technique has potential to help medical workers in the severity assessment of patients. Especially those who work in regions with scarce material resources.

Detecting Early Signs of Insufficiency in COVID-19 Patients from CBC Tests Through a Supervised Learning Approach

Early identification of patients with COVID-19 is essential to enable adequate treatment and to reduce the burden on the health system. The gold standard for COVID-19 detection is the use of RT-PCR tests. However, due to the high demand for tests, these can take days or even weeks in some regions of Brazil. Thus, an alternative for detecting COVID-19 is the analysis of Digital Chest X-rays (XR). Changes due to COVID-19 can be detected in XR, even in asymptomatic patients. In this context, models based on deep learning have great potential to be used as support systems for diagnosis or as screening tools. In this paper, we propose the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in XR. The proposed methodology consists of a preprocessing step of the XR, data augmentation, and classification by the convolutional architectures DenseNet121, InceptionResNetV2, InceptionV3, MovileNetV2, ResNet50, and VGG16 pre-trained with the ImageNet dataset. The obtained results for our methodology demonstrate that the VGG16 architecture presented a superior performance in the classification of XR, with an Accuracy of 85.11%, Sensitivity of 85.25%, Specificity of 85.16%, F1-score of 85.03%, and an AUC of 0.9758.

Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays

Computational determination of protein stability upon point mutations is very useful in a wide field of applications. Reliability of such computational predictions is essential. Unfortunately, existing computational tools frequently disagree in their results. In the present study, the usage of Ensemble Learning Algorithms to aggregate the results from different stability prediction tools is investigated. Techniques of Stacking, Bagging, and Boosting as well as different Machine Learning algorithms as combiner function are explored. All the investigation is carried out in real dataset ProTherm for which experimental results are known. The proposed methodology was validated considering two different experiments according to the training set. Results show that our proposed ensemble approach is appropriate to predict the effect of point mutations on protein stability showing more reliable results than the individual tools improving overall accuracy, precision and/or recall.

Ensemble of Protein Stability Upon Point Mutation Predictors

Family farming represents a critical segment of Brazilian agriculture, involving more than 5 million properties and generating 74% of rural jobs in the country. Yield losses caused by crop diseases and pests can be devastating for small-scale producers. However, successful disease control requires correct identication, which challenges smallholders, who often lack technical assistance. The present work proposes a system that detects disease symptoms in images of plant leaves to assist phytopathology experts. The objective is to decrease the experts' workload and enable consulting services for free or at nominal cost. In addition, the required digital communication channel will promote the formation of a caring community ready to offer unpaid advice for family farmers. The machine learning and renement of the assistance system are described in detail. The developed classication system achieves a recall value of 95%.

Supervised Training of a Simple DIgital Assistant for a Free Crop Clinic

Collaborative filtering recommender systems are essential tools in many modern applications. Their main advantage compared with the alternatives is that they require only a matrix of user-item interactions to recommend a subset of relevant items for a given user. However, the increasing volume of the data consumed by these systems may lead to a representation model with very high sparsity and dimensionality. Several approaches to overcome this problem have been proposed, neural embeddings being one of the most recent. Since then, many recommender systems were made using this representation model, but few consumed temporal information during the learning phase. This study shows how to adapt a pioneering method of item embeddings by adding a sliding window over time, in conjunction with a split in the user's interaction history. Results indicate that considering temporal information when learning neural embeddings for items can significantly improve the quality of the recommendations.

Time-Dependent Item Embeddings for Collaborative Filtering

In software development, code autocomplete can be an essential tool in order to accelerate coding. However, many of these tools built into the IDEs are limited to suggesting only methods or arguments, often presenting to the user long lists of irrelevant items. Since innovations introduced by transformer-based models that have reached the state of the art performance in tasks involving natural language processing (NLP), the application of these models also in tasks involving code intelligence, such as code completion, has become a frequent object of study in recent years. In this paper, we present a transformer-based model trained on 1.2 million Java files gathered from top-starred Github repositories. Our evaluation approach was based on measuring the model's ability to predict the completion of a line, proposing a new metric to measure the applicability of the suggestions that we consider better adapted to the practical reality of the code completion task. With a recently developed Java web project as a test set, our experiments showed that in 55.9% of the test cases the model brought at least one suggestion applicable, while the best baseline model presented this in 26.5%.

Code autocomplete using Transformers

Time series classification has a wide variety of possible applications, like outlines of figure types, signs of movement and sensor signals. This diversity may present different results with the application of machine learning techniques. Among the different ways to classify time series, two, in particular, are explored in this paper: the shapelet primitive and the neural network classification. A shapelet is a sub-sequence of the time series symbolic, like a high-level descriptor, that are representative for the class to which they belong. These features act as common knowledge used by domain experts. This paper proposes a CNN training protocol to achieve better results in the classification of time series.
The idea consists of decomposing the original time series into shapelets and noise. Then, the shapelets are used to train a classifier while the no-shapelets (“signal noise”) is used to train another classifier.
The original time series is then used to train two final classifiers starting with the weights of shapelet and no-shapelet classifiers. This previous extraction of this representation can improve classification ability in a convolutional neural network using transfer learning. The experimental evaluation shows that the pre-selection of shapelets before the network training changes the classifier results for several databases and, consequently, improves classification accuracy.

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Interpreting classification models using feature importance based on marginal local effects

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