Brazil

Social media have become trendy environments for communication. Because of that, analyze the sentiment that the user expresses in their social media posts is an important research field. However, detecting polarity in such contents is a challenge, partially because the amount of labeled data to train classifiers is scarce in many situations. This paper explores strategies for reusing a model learned from a source dataset to classify instances in a target dataset. The experiments are conducted with 22 tweets sentiment analysis datasets and approaches based on similarity metrics. The results point out that the size of the source training set plays an essential role in the classifiers’ performance when they were applied to the target data.

BRACIS 2021

Exploring model transfer strategies for sentiment analysis in Twitter

data mining

sentiment analysis

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

Register now

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

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware.
In particular, we study the impact over a dataset with few examples and one that is being constructed.
We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM).
Our experiments show that deep learning algorithms have a performance advantage over classical techniques.
However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources.
Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost.
The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

Iterative machine learning applied to annotation of text datasets

Subjects change frequently in moderated debates with several participants,
such as in parliamentary sessions, electoral debates, and trials. Partitioning a debate into blocks with the same subject is essential for understanding. Often a moderator is responsible for defining when a new block begins so that the task of automatically partitioning a moderated debate can focus solely on the moderator's behavior. 
In this paper, we (i) propose a new algorithm, DEBACER, which partitions moderated debates; (ii) carry out a comparative study between conventional and BERTimbau pipelines; and (iii) validate DEBACER applying it to the minutes of the Assembly of the Republic of Portugal. Our results show the effectiveness of DEBACER.

DEBACER: a method for slicing moderated debates

Predicting review helpfulness is an important task in Natural Language Processing. It is useful for dealing with the huge amount of online reviews on varied domains and languages, helping and guiding users on what to read and consider in their daily decisions. However, there are limited initiatives to investigate the nature of this task and how hard it is. This paper aims to fulfill this gap, providing a better understanding of it. Two complementary experiments are performed in order to uncover patterns of usefulness evaluation as performed by humans and relevant features for machine prediction. To assure our results, we run the experiments for two different domains: movies and apps. We show that humans agree on the process of assigning helpfulness to reviews, despite the difficulty of the task. More than this, people perform this process systematically and consistently. Finally, we empirically identify the most relevant content features for machine learning prediction of review helpfulness.

The Challenges of Modeling and Predicting Online Review Helpfulness

Abstract. The Brazilian Exclusive Economic Zone, or the “Blue Amazon”, with its extensive maritime area, is the primary means of transport for the country’s foreign trade and is important due to its oil reserves, gas and other mineral resources, in addition to the significant influence on the Brazilian climate. We have manually built a question answering (QA) dataset based on crawled arti- cles and have applied an off-the-shelf QA system based on a fine-tuned BERTim- bau Model, achieving an F1-score of 47.0. More importantly, we explored how the proper visualization of attention weights can support helpful interpretations of the system’s answers, which is critical in real environments.


Interpretability of Attention in Question and Answering System about the Blue Amazon in Portuguese.

RBF-based neural networks models (RBFNN) are classic machine
learning methods that contain a layer of Radial Basis Functions (RBF) which
act as the feature extractor for the final layer, which performs the pattern recognition. The estimation of RBF radius during the model training is one of the most
crucial tasks for this method and directly affects its generalization power and
accuracy. This works presents a new heuristic for radius estimation and employs
computational experiments to assess its performance against other approaches
using 14 classification problems. The proposed method is shown competitive
performance, winning the other approaches in 9 out of 14 problems.

A New Heuristic for Radius Estimation in RBF Neural Networks

This talk will present the result of a time-series classification task on the stock market, compare the results with well-known methods. We propose the
Trend Classification Trading Algorithm (TCTA) which is based on time series
classification and trend forecasting to perform trade. TCTA first employs the
K-means to cluster 5-days closing price segments and label them according to
its trend. A deep learning classification model is then trained with these label
sequences to estimate the next trend. Trading points are given by the alternation
on trend estimates. Results considering 20 shares from Ibovespa show TCTA
present higher profit than buy-and-hold and trading schemes based on Moving
Average Convergence Divergence (MACD) or Bollinger bands.

A stock trading algorithm based on trend forecasting and time series classification

O objetivo do presente trabalho é investigar o desempenho de agentes jogadores baseados em aprendizado por reforço - mais especificamente, nos algoritmos Q-Learning e Deep Q-Networks (DQN) - por meio da plataforma Unity. Para tanto, os autores implementam nela agentes jogadores de Basic e GridWorld que são treinados segundo tais algoritmos. A fim de avaliar o desempenho desses agentes, foi efetuada uma análise comparativa entre eles e os melhores agentes jogadores desses jogos disponibilizados na plataforma Unity, os quais são baseados nas técnicas de aprendizagem Proximal Policy Optimization (PPO) e Soft Actor-Critic (SAC).

Técnicas de Aprendizado por Reforço Aplicadas em Jogos Eletrônicos na Plataforma Geral do Unity

Creating computer generated human animations without the use of motion capture technology is a tedious and time consuming activity. Although there are several publications regarding animation synthesis using data driven methods, not many are dedicated towards the task of inbetweening, which consists of generating transition movements between \textit{frames}.
In this work, we are interested on short-term transitions and different kinds of movement, such as martial arts and Indian dance using the least amount of input \textit{frames} as possible.

Short-term Inbetweening of 3D Human Motions

Due to the increasing use of artificial intelligence for decision making and the observation of biased decisions in many applications, researchers are investigating solutions that attempt to build fairer models that do not reproduce discrimination. Some of the explored strategies are based on adversarial learn- ing to achieve fairness in machine learning by encoding fairness constraints through an adversarial model. Moreover, it is usual for each proposal to assess its model with a specific metric, making comparing current approaches a com- plex task. In that sense, we defined a utility and fairness trade-off metric. We assessed 15 fair model implementations and a baseline model using this metric, providing a systemically comparative ruler for other approaches.

Assessing Fair Machine Learning Strategies Through a Fairness-Utility Trade-off Metric

The complexity of distributed algorithm’s distribution policies often impedes the use of established mathematical approaches for performance analysis, such as asymptotic analysis, recurrence techniques and probabilistic analysis. The aforementioned techniques have no proper means to assess the impact on the algorithm’s run time caused by the gradual increment of the number of processors. Therefore, this article proposes both a visual and formal approach for valuating the processor saturation point and speedup of distributed algorithms utilized in Artificial Intelligence, based on automatic simulations of Hierarchical Colored Petri Nets in the Colored Petri Nets Tools software. The aforementioned approach was demonstrated in a case study on the training of Multilayer Perceptrons based on error backpropagation

Premium content

Downloads

Next from BRACIS 2021

Iterative machine learning applied to annotation of text datasets

Similar lecture

Transfer learning for Twitter sentiment analysis: Choosing an effective source dataset