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Numerous problems in machine learning require some type of dimensionality reduction. Unsupervised metric learning deals with the definition of intrinsic and adaptive distance functions of a dataset. Locally linear embedding (LLE) consists of a widely used manifold learning algorithm that applies dimensionality reduction to find a more compact and meaningful representation of the observed data through the capturing of the local geometry of the patches. In order to overcome relevant limitations of the LLE approach, we introduce the LLE Kullback–Leibler (LLE-KL) method. The proposed method employs the KL divergence between patches of the $k$-nearest neighbors (KNN) graph instead of the pointwise Euclidean metric. Our objective with such a methodological modification is to increase the robustness of the LLE to the presence of noise or outliers in the data. Our empirical results using several real-world datasets indicate that the proposed method delivers a superior clustering allocation compared to state-of-the-art methods of dimensionality reduction-based metric learning.

BRACIS 2021

A Kullback-Leibler divergence-based locally linear embedding method: a novel parametric approach for cluster analysis

locally linear embedding

unsupervised metric learning

dimensionality reduction

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 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

[Register now](http://c4ai.inova.usp.br/bracis2021/)

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Organized by C4AI, the 10th Brazilian Conference on Intelligent Systems (BRACIS) will take place from November 29th to December 3rd, 2021.

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.

Improving a genetic clustering approach with a CVI-based objective function.

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.

Evaluating Clustering Meta-Features for Classifier Recommendation

Co-clustering is a specific type of clustering that addresses the problem of simultaneously clustering objects and attributes of a data matrix. Although general clustering techniques find non-overlapping co-clusters, finding possible overlaps between co-clusters can reveal embedded patterns in the data that the disjoint clusters cannot discover. The overlapping co-clustering approaches proposed in the literature focus on finding global overlapped co-clusters and they might overlook interesting local patterns that are not necessarily identified as global co-clusters. Discovering such local co-clusters increases the granularity of the analysis, and therefore more specific patterns can be captured. This is the objective of the present paper, which proposes the new Overlapped Co-Clustering (OCoClus) method for finding overlapped co-clusters on binary data, including both global and local patterns. This is a non-exhaustive method based on the co-occurrence of attributes and objects in the data. Another novelty of this method is that it is driven by an objective cost function that can automatically determine the number of co-clusters. We evaluate the proposed approach on publicly available datasets, both real and synthetic data, and compare the results with a number of baselines. Our approach shows better results than the baseline methods on synthetic data and demonstrates its efficacy in real data.

A Kullback-Leibler divergence-based locally linear embedding method: a novel parametric approach for cluster analysis

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Aggressive Language Detection using VGCN-BERT for Spanish Texts

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