Brazil

Data availability in a wide variety of domains has boosted the use of Machine Learning techniques for knowledge discovery and classification. 
The performance of a technique in a given classification task is significantly impacted by specific characteristics of the dataset, which makes the problem of choosing the most adequate approach a challenging one. Meta-Learning approaches, which learn from meta-features calculated from the dataset, have been successfully used to suggest the most suitable classification algorithms for specific datasets. This work proposes the adaptation of clustering measures based on internal indices for supervised problems as additional meta-features in the process of learning a recommendation system for classification tasks. The gains in performance due to Meta-Learning and the additional meta-features are investigated with experiments based on 400 datasets, representing diverse application contexts and domains. Results suggest that (i) meta-learning is a viable solution for recommending a classifier, (ii) the use of clustering features can contribute to the performance of the recommendation system, and (iii) the computational cost of Meta-Learning is substantially smaller than that of running all candidate classifiers in order to select the best.

BRACIS 2021

Evaluating Clustering Meta-Features for Classifier Recommendation

clustering problems

characterization measures

meta-features

meta-learning

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

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. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

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**.

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

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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

Competitive intelligence (CI) is a relevant area of a corporation and can support the strategic business area by showing those responsible, helping decision-making on how to position an organization in the market. This work uses the Bidirectional Transformer Encoding Representations (BERT) to process a sentence and its named entities and extract the parts of the sentences that represent or describe the semantic relationship between these named entities. The approach was developed for the Portuguese language, considering the financial domain and exploring deep linguistic representations without using other lexical-semantic resources. The results of the experiments show a precision of 73.5\% using the Jaccard metric that measures the similarity between sentences. The second contribution of this work is the manually constructed dataset with more than 4.500 tuples (phrase, entity, entity) annotated.

Entity Relation Extraction from News Articles in Portuguese for Competitive Intelligence based on BERT

Deep learning models have been the state-of-the-art for a variety of challenging tasks in natural language processing, but to achieve good results they often require big labeled datasets. Deep active learning algorithms were designed to reduce the annotation cost for training such models. Current deep active learning algorithms, however, aim at training a good deep learning model with as little labeled data as possible, and as such are not useful in scenarios where the full dataset must be labeled. As a solution to this problem, this work investigates deep active-self learning algorithms that employ self-labeling using the trained model to help alleviate the cost of annotating full datasets for named entity recognition tasks. The experiments performed indicate that the proposed deep active-self learning algorithm is capable of reducing manual annotation costs for labeling the complete dataset for named entity recognition with less than 2\% of the self labeled tokens being mislabeled. We also investigate an early stopping technique that doesn't rely on a validation set, which effectively reduces even further the annotation costs of the proposed active-self learning algorithm in real world scenarios.

Deep Active-Self Learning Applied to Named Entity Recognition

The increasing influence from users in social media has made that aggressive content disseminates over the internet. To tackle this problem, recent advances in Aggressive Language Detection have demonstrated a good performance of Deep Learning techniques. Recently Transformer based architectures such as Bidirectional Encoder Representations from Transformer (BERT) outperformed previous aggressive text detection baselines. However, most of the Transformers-based approaches are unable to properly capture global information such as language vocabulary. Thus, in this work, we focus on aggressive content detection using the combination of Vocabulary Graph Convolutional Network (VGCN) to capture global information and BERT to model local information. This combined approach called VGCN-BERT allows us to improve the feature level representation in Spanish aggressive language detection. Our experiments were performed on a benchmark called MEX-A3T aggressiveness dataset which is composed of aggressive and non-aggressive Tweets written in the Mexican Spanish variant. We report 86.46\% in terms of F1-score using this VGCN-BERT approach which allows us to obtain comparable results with the current state-of-the-art, ensemble BERT, so as to detect aggressive content regarding the track MEX-A3T 2020.

Aggressive Language Detection using VGCN-BERT for Spanish Texts

Studies in sentiment analysis have examined how different features are effective in identifying subjective sentences. Several studies on sentiment analysis exist in the literature that have already performed some evaluation of NLP classifications. However, the vast majority of them did not handle texts in the Brazilian Portuguese language, and there is no one to consider the combination of sets of text features of NLP tasks with classifiers. Therefore, in our investigation, we combined empirical features to identify subjective sentences in Portuguese and provide a comprehensive analysis of each set of features’ relative importance using a representative set of user reviews.

An Empirical Study of Text Features for Identifying Subjective Sentences in Portuguese

Indexing artwork is not only a tedious job; it is an impossible task to complete manually given the amount of online art. In any case, the automatic classification of art styles is also a challenge due to the relative lack of labeled data and the complexity of the subject matter. This complexity means that common data augmentation techniques may not generate useful data; in fact, they may degrade performance in practice. In this paper, we use Generative Adversarial Networks for data augmentation so as to improve the accuracy of an art style classifier, showing that we can improve performance of EfficientNet B0, a state of art classifier. To achieve this result, we introduce Class-by-Class Performance Analysis; we also present a modified version of the SAGAN training configuration that allows better control against mode collapse and vanishing gradient in the context of artwork.

How to Generate Synthetic Paintings to Improve Art Style Classification

The iris flower dataset is a ubiquitous benchmark task in machine learning literature. With its 150 instances, four continuous features, and three balanced classes, of which one is linearly separable from the others, the iris is generally considered an easy problem. Hence researchers usually rely on other datasets when they need more challenging benchmarks. A similar situation happens with computer vision datasets such as MNIST and ImageNet, which have been widely explored. The state of the art models essentially solves these problems, motivating the search for more challenging tasks. Therefore, this paper introduces a new computer vision toy dataset featuring iris flowers. Users of a nature photography application took the pictures, thus they include noisy background information. Additionally, certain desirable features are not guaranteed, such as single, similarly-sized objects at the center of each picture, which makes the task more challenging. Our benchmark results show that the dataset can be challenging for traditional machine learning algorithms without any pre-processing steps, while the state of the art deep learning architectures achieve around 82% accuracy, which means some effort will be necessary to drive this accuracy closer to what has been accomplished for MNIST and ImageNet.

Iris-CV: Classifying Iris Flowers Is Not as Easy as You Thought

In this work we evaluate the You Only Look Once (YOLOv3) architecture for real-time detection of insect-pests in soybean. Soybean crop images were collected on different days, locations and weather conditions, between the phenological stages R1 to R6, considered of high occurrence of soybean pests. For training and testing the neural network, we used a 5 fold cross-validation considering four metrics to evaluate the classification results: precision, recall, F-score and accuracy; and three metrics to evaluate the detection results: mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination R². The experimental results showed that the YOLOv3 architecture trained with batch size 32 leads to higher classification and detection rates compared to batch sizes 4 and 16. The results indicate that the evaluated architecture can support experts and farmers in monitoring pest control action levels in soybean fields.

Performance analysis of YOLOv3 for real-time detection of pests in soybeans

The field of neural networks has seen significant advances in recent years with the development of deep and convolutional neural networks. Although many of the current works address real-valued models, recent studies reveal that neural networks with hypercomplex-valued parameters can better capture, generalize, and represent the complexity of multidimensional data. This paper explores the quaternion-valued convolutional neural network application for a pattern recognition task from medicine, namely, the diagnosis of acute lymphoblastic leukemia. Precisely, we compare the performance of real-valued and quaternion-valued convolutional neural networks to classify lymphoblasts from the peripheral blood smear microscopic images. The quaternion-valued convolutional neural network achieved better or similar performance than its corresponding real-valued network but using only 34% of its parameters. This result confirms that quaternion algebra allows capturing and extracting information from a color image with fewer parameters.

Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis

Aspect Sentiment Triplet Extraction (ASTE) is an Aspect-Based Sentiment Analysis subtask (ABSA). It aims to extract aspect-opinion pairs from a sentence and identify the sentiment polarity associated with them. For instance, given the sentence ``Large rooms and great breakfast", ASTE outputs the triplet T = {(rooms, large, positive), (breakfast, great, positive)}. Although several approaches to ASBA have recently been proposed, those for Portuguese have been mostly limited to extracting only aspects without addressing ASTE tasks. This work aims to develop a framework based on Deep Learning to perform the Aspect Sentiment Triplet Extraction task in Portuguese. The framework uses BERT as a context-awareness sentence encoder, multiple parallel non-linear layers to get aspect and opinion representations, and a Graph Attention layer along with a Biaffine scorer to determine the sentiment dependency between each aspect-opinion pair. The comparison results show that our proposed framework significantly outperforms the baselines in Portuguese and is competitive with its counterparts in English.

A Deep Learning approach for Aspect Sentiment Triplet Extraction in Portuguese

Genetic-based clustering meta-heuristics are important bioinspired algorithms. One such technique, termed Genetic Algorithm for Decision Boundary Analysis (GADBA), was proposed to support Structural Health Monitoring (SHM) processes in bridges. GADBA is an unsupervised, non-parametric approach that groups data into natural clusters by means of a specialized objective function. Albeit it allows a competent identification of damage indicators of SHM-related data, it achieves lackluster results on more general clustering scenarios. This study improves the objective function of GADBA based on a Cluster Validity Index (CVI) named Mutual Equidistant-scattering Criterion (MEC) to expand its applicability to any real-world problem.

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Entity Relation Extraction from News Articles in Portuguese for Competitive Intelligence based on BERT

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