Brazil

BRACIS 2020

C3 Live - FURG

Welcome to the Capstone Conference of the [CES Transformation Fund](https://ces-transformationfund.org/) which was a hybrid conference held at Durham University (UK). The conference celebrated the achievements of 22 awardees of the grant scheme. Together they are helping to build a richer and more diverse cultural evolution field, whilst catalysing the application of cultural evolution research to global human futures.

Over the 2 days, each funded [Research Project, Applied Working Group, and Seedcorn Project](https://ces-transformationfund.org/our-projects/) presented their research. A professional live scribe from [Nifty Fox Creative](https://www.niftyfoxcreative.com/) created and showcased public communication illustrations for each presentation. On Thursday afternoon, there was a workshop on generating societal impact from research, and in the evening there was a ‘show and tell’ session with drinks reception (plus a dinner for awardees and invited others), while Friday wrapped up with a discussion of future directions for the cultural evolution field.
 

Organised by the CES Transformation Fund [Team](https://ces-transformationfund.org/people/):

* Rachel Kendal (Project Leader and PI)
* Lorna Winship (Grant Manager)
* Sarah Wright (Assistant Manager)
* Dr. Eva Reindl (Postdoctoral Researcher)

The grant scheme and capstone conference is generously funded by the [John Templeton Foundation](https://www.templeton.org/).


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CES Transformation Fund Capstone Conference 2024

The Capstone Conference of the CES Transformation Fund celebrates the achievements of 22 awardees of the grant scheme. Together they are helping to build a richer and more diverse cultural evolution field, whilst catalysing the application of cultural evolution research to global human futures.

technical paper

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.

Authorship attribution is the process of identifying the author of a particular document. This task has been performed by experts in the field. However, with the advancement of natural language processing tools and machine learning techniques, this activity has also been performed by computer systems. Authorship attribution has applicability from the detection of plagiarism and copyright to the resolution of forensic problems. There are several works on this subject in the English idiom, however those that consider texts in Portuguese are few. Therefore, this paper aims to study authorship attribution of texts of Brazilian literature. We carried out our experiments using Naïve Bayes and Random Forests methods, and for the feature extraction we considered Term Frequency - Inverse Document Frequency and Part of Speech techniques. The results showed that the Random Forests using as input the textual features extracted by Part of Speech presented the best cross-validation accuracy, although not the best runtime.

Authorship attribution of Brazilian literary texts through machine learning techniques

People’s opinions can impact the real-world in many different ways. The election of politics, the sales of products, stock market prices, and consumer habits are just a few examples. However, exploring this relationship between people’s opinions and real-world events requires data from both sides, which is usually expensive and hard to obtain. In this study, on one side, we address this problem by extracting data from Twitter, and on the other side, the real-world outcomes of a reality show. We carefully select a reality show that uses the audience’s opinion to define the elimination of participants. This relationship brings an interesting case of a causal relationship between audience opinion and real-world events. From Twitter, we obtained simple features, such as the counts of likes, retweets, followers, specific hashtags along with sentiment analysis counts obtained from a fine-tuned BERT. From the TV show, we obtained the eliminated candidate and the percentage of audience rejection of the eliminated candidate. To answer the question posed in the title, we empirically evaluate eleven standard machine learning algorithms using the collected features. The models were able to achieve 88.23% of accuracy to predict the eliminated candidate in the reality show.

Can Twitter data estimate Reality Show outcomes?

Filter selection in convolutional neural networks aims at finding the most important filters in a convolutional layer, with the goal of reducing computational costs and needed storage, as well as understanding the networks’ inner workings. In this paper we propose an entropy-based filter selection method that ranks filters based on the mutual information between their activations and the output classes using validation data. Our proposed method outperforms using filters’ absolute weights sum by a large margin, allowing to regain better performance with fewer filters.

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Authorship attribution of Brazilian literary texts through machine learning techniques

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