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index.html
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<!DOCTYPE html>
<html lang="en"><head>
<script src="index_files/libs/clipboard/clipboard.min.js"></script>
<script src="index_files/libs/quarto-html/tabby.min.js"></script>
<script src="index_files/libs/quarto-html/popper.min.js"></script>
<script src="index_files/libs/quarto-html/tippy.umd.min.js"></script>
<link href="index_files/libs/quarto-html/tippy.css" rel="stylesheet">
<link href="index_files/libs/quarto-html/light-border.css" rel="stylesheet">
<link href="index_files/libs/quarto-html/quarto-html.min.css" rel="stylesheet" data-mode="light">
<link href="index_files/libs/quarto-html/quarto-syntax-highlighting.css" rel="stylesheet" id="quarto-text-highlighting-styles"><meta charset="utf-8">
<meta name="generator" content="quarto-1.4.555">
<title>index</title>
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no, minimal-ui">
<link rel="stylesheet" href="index_files/libs/revealjs/dist/reset.css">
<link rel="stylesheet" href="index_files/libs/revealjs/dist/reveal.css">
<style>
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
div.columns{display: flex; gap: min(4vw, 1.5em);}
div.column{flex: auto; overflow-x: auto;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
ul.task-list li input[type="checkbox"] {
width: 0.8em;
margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */
vertical-align: middle;
}
</style>
<link rel="stylesheet" href="index_files/libs/revealjs/dist/theme/quarto.css">
<link href="index_files/libs/revealjs/plugin/quarto-line-highlight/line-highlight.css" rel="stylesheet">
<link href="index_files/libs/revealjs/plugin/reveal-menu/menu.css" rel="stylesheet">
<link href="index_files/libs/revealjs/plugin/reveal-menu/quarto-menu.css" rel="stylesheet">
<link href="index_files/libs/revealjs/plugin/quarto-support/footer.css" rel="stylesheet">
<style type="text/css">
.callout {
margin-top: 1em;
margin-bottom: 1em;
border-radius: .25rem;
}
.callout.callout-style-simple {
padding: 0em 0.5em;
border-left: solid #acacac .3rem;
border-right: solid 1px silver;
border-top: solid 1px silver;
border-bottom: solid 1px silver;
display: flex;
}
.callout.callout-style-default {
border-left: solid #acacac .3rem;
border-right: solid 1px silver;
border-top: solid 1px silver;
border-bottom: solid 1px silver;
}
.callout .callout-body-container {
flex-grow: 1;
}
.callout.callout-style-simple .callout-body {
font-size: 1rem;
font-weight: 400;
}
.callout.callout-style-default .callout-body {
font-size: 0.9rem;
font-weight: 400;
}
.callout.callout-titled.callout-style-simple .callout-body {
margin-top: 0.2em;
}
.callout:not(.callout-titled) .callout-body {
display: flex;
}
.callout:not(.no-icon).callout-titled.callout-style-simple .callout-content {
padding-left: 1.6em;
}
.callout.callout-titled .callout-header {
padding-top: 0.2em;
margin-bottom: -0.2em;
}
.callout.callout-titled .callout-title p {
margin-top: 0.5em;
margin-bottom: 0.5em;
}
.callout.callout-titled.callout-style-simple .callout-content p {
margin-top: 0;
}
.callout.callout-titled.callout-style-default .callout-content p {
margin-top: 0.7em;
}
.callout.callout-style-simple div.callout-title {
border-bottom: none;
font-size: .9rem;
font-weight: 600;
opacity: 75%;
}
.callout.callout-style-default div.callout-title {
border-bottom: none;
font-weight: 600;
opacity: 85%;
font-size: 0.9rem;
padding-left: 0.5em;
padding-right: 0.5em;
}
.callout.callout-style-default div.callout-content {
padding-left: 0.5em;
padding-right: 0.5em;
}
.callout.callout-style-simple .callout-icon::before {
height: 1rem;
width: 1rem;
display: inline-block;
content: "";
background-repeat: no-repeat;
background-size: 1rem 1rem;
}
.callout.callout-style-default .callout-icon::before {
height: 0.9rem;
width: 0.9rem;
display: inline-block;
content: "";
background-repeat: no-repeat;
background-size: 0.9rem 0.9rem;
}
.callout-title {
display: flex
}
.callout-icon::before {
margin-top: 1rem;
padding-right: .5rem;
}
.callout.no-icon::before {
display: none !important;
}
.callout.callout-titled .callout-body > .callout-content > :last-child {
padding-bottom: 0.5rem;
margin-bottom: 0;
}
.callout.callout-titled .callout-icon::before {
margin-top: .5rem;
padding-right: .5rem;
}
.callout:not(.callout-titled) .callout-icon::before {
margin-top: 1rem;
padding-right: .5rem;
}
/* Callout Types */
div.callout-note {
border-left-color: #4582ec !important;
}
div.callout-note .callout-icon::before {
background-image: url('data:image/png;base64,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');
}
div.callout-note.callout-style-default .callout-title {
background-color: #dae6fb
}
div.callout-important {
border-left-color: #d9534f !important;
}
div.callout-important .callout-icon::before {
background-image: url('data:image/png;base64,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');
}
div.callout-important.callout-style-default .callout-title {
background-color: #f7dddc
}
div.callout-warning {
border-left-color: #f0ad4e !important;
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<div class="slides">
<section>
<section class="title-slide slide level1 center" data-background-iframe="colored-particles/index.html">
<h1></h1>
<br><br>
<div style="text-align: center; font-size: 100px;">
<strong>Inteligência artificial na pesquisa sobre desinformação em saúde, extremismos políticos e análise de dados sociais</strong>
</div>
<div style="text-align: center; font-size: 60px;">
<strong>LABHDUFBA</strong>
</div>
<div style="text-align: center; font-size: 50px;">
14/03/2025
</div>
<p><img data-src="images/qrcode.png" class="absolute" style="bottom: 0px; right: 5px; width: 275px; height: 300px; "></p>
</section>
<section class="slide level2">
<h2></h2>
<div style="text-align: center; font-size: 55px;">
<strong>O LABORATÓRIO DE HUMANIDADES DIGITAIS DA UFBA</strong>
</div>
<p><br></p>
<div class="columns">
<div class="column" style="width:60%;">
<ul>
<li><p>Surge em 2018 em associação com o Programa de Pós-Graduação em Ciências Sociais (PPGCS) da UFBA</p></li>
<li><p>Tem como missão promover pesquisa e ensino entre as humanidades e o campo ciência de dados</p></li>
<li><p>Contatos:</p>
<ul>
<li><a href="https://labhd.ufba.br">Website</a></li>
<li><a href="https://twitter.com/labhdufba">Twitter</a></li>
<li><a href="https://t.me/labhdufba_labhdufrj">Canal do Telegram</a></li>
<li><a href="https://bsky.app/profile/labhdufba.bsky.social">Bluesky</a></li>
</ul></li>
</ul>
</div><div class="column" style="width:40%;">
<p><img data-src="images/logo.png"></p>
</div>
</div>
</section>
<section class="slide level2">
<h2></h2>
<div style="text-align: center; font-size: 55px;">
<strong>O LABHDUFBA investiga, desde 2020, grupos e canais extremista do Telegram…</strong>
</div>
<p><br></p>
<div class="columns">
<div class="column" style="width:65%;">
<div>
<ul>
<li class="fragment"><p>Em parceria com PPGAS/UFSC (Prof. Letícia Cesarino) e apoio do InternetLab <br><br></p></li>
<li class="fragment"><p>+ de 70 milhões de mensagens <br><br></p></li>
<li class="fragment"><p>+ 3.5 milhões de imagens e + de 900 mil vídeos <br><br></p></li>
<li class="fragment"><p>+ de 3 milhões de links do Youtube <br><br></p></li>
<li class="fragment"><p>+ 30 Terabytes de dados</p></li>
</ul>
</div>
</div><div class="column" style="width:35%;">
<p><img data-src="images/telegram.webp"></p>
</div>
</div>
</section>
<section class="slide level2">
<h2></h2>
<div style="text-align: center; font-size: 55px;">
<strong>Aliança entre raciocínio computacional com a compreensão interpretativa das ciências sociais</strong>
</div>
<p><br><br></p>
<div class="columns">
<div class="column" style="width:60%;">
<div>
<ul>
<li class="fragment"><p><strong>Ciência de dados com ciências sociais</strong> <br><br></p></li>
<li class="fragment"><p><strong>Rastreabilidade</strong> total dos conteúdos <br><br></p></li>
<li class="fragment"><p>Detecção de <strong>“talkatives”</strong> e porta-vozes <br><br></p></li>
<li class="fragment"><p><strong>Fine-tunning de modelos de NLP</strong>: NER, Topic Modeling, Clustering e detecção automatizada de violência online, como <a href="https://huggingface.co/leofn3/modelo_racismo_setfit_5jan24">racismo</a>, misoginia e anti-semitismo. <br><br></p></li>
<li class="fragment"><p>Telegram enquanto <strong>espaço de experimentações</strong></p></li>
</ul>
</div>
</div><div class="column" style="width:40%;">
<div style="font-size: 80%;">
<div>
<ul>
<li class="fragment"><a href="https://revista.internetlab.org.br/wp-content/uploads/2023/01/publicos.pdf">Públicos refratados: grupos de extrema-direita brasileiros na plataforma Telegram</a> ____</li>
<li class="fragment"><a href="https://www.tandfonline.com/doi/abs/10.1080/09505431.2022.2105691">Demarcating patriotic science on digital platforms: Covid-19, chloroquine and the institutionalisation of ignorance in Brazil</a> ____</li>
<li class="fragment"><a href="https://periodicosnic.emnuvens.com.br/revista/index">“Intankaveis contra o Bostil”: racismo, misoginia e anti-semitismo em chats do Telegram (2020-2023) - em Breve!</a></li>
</ul>
</div>
</div>
</div>
</div>
</section>
<section class="slide level2">
<h2>Ecossistema multiplataforma de desinformação e radicalização</h2>
<p><br><br></p>
<div class="columns">
<div class="column" style="width:40%;">
<br>
<div style="text-align: justify">
A utilização <strong>sistemática e estratégica</strong> de ferramentas, tecnologias e serviços que permitem a criação e disseminação de desinformação e incitação à radicalização política em <strong>diferentes plataformas</strong>, como redes sociais, sites de notícias e aplicativos de mensagens.
</div>
</div><div class="column" style="width:60%;">
<div class="quarto-figure quarto-figure-center">
<figure>
<p><img data-src="images/DALL.webp" class="quarto-figure quarto-figure-center"></p>
</figure>
</div>
</div>
</div>
</section></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 55px;">
<strong>QUAIS SÃO OS DESAFIOS?</strong>
</div>
<p><br><br></p>
<div>
<ul>
<li class="fragment"><p>Acesso aos dados <br><br></p></li>
<li class="fragment"><p>Complexidade metodológica <br><br></p></li>
<li class="fragment"><p>Recursos computacionais</p></li>
</ul>
</div>
</section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 55px;">
<strong>ACESSO AOS DADOS</strong>
</div>
<p><br></p>
<div>
<ul>
<li class="fragment"><p><strong>Política de acesso aos dados digitais</strong> de todas as plataformas <br><br></p></li>
<li class="fragment"><p><strong>“Sem API não há solução!”</strong> <br><br></p></li>
<li class="fragment"><p>Plataforma pública e auditável de <strong>armazenamento</strong> de conteúdos <br><br></p></li>
<li class="fragment"><p>Pensar os <strong>dados digitais enquanto memória social</strong> <br><br></p></li>
</ul>
</div>
</section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 55px;">
<strong>COMPLEXIDADE METODOLÓGICA</strong>
</div>
<p><br></p>
<div>
<ul>
<li class="fragment"><p>Dados digitais contituem um <strong>“objeto epistêmico complexo”</strong> <a href="http://dx.doi.org/10.1177/2053951715609066">Aradau & Blanke, 2015</a> <br><br></p></li>
<li class="fragment"><p><strong>Estratégia penelopeana de desinformação e radicalização</strong> <br><br></p></li>
<li class="fragment"><p>Coleta de dados em tempo real: a <strong>vigilância-como-método</strong> <br><br></p></li>
<li class="fragment"><p>Necessidade de <strong>atualização do Sistema CEP/Conep</strong> diante das pesquisas com dados digitais <br><br></p></li>
<li class="fragment"><p>Esboçar, de modo inequívoco, a <strong>relação dos dados coletados de plataformas digitais com a LGPD</strong></p></li>
</ul>
</div>
</section>
<section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 55px;">
<strong>Acesso a recursos computacionais</strong>
</div>
<p><br></p>
<div>
<ul>
<li class="fragment"><p><strong>Necessidade de uma divisão igualitária de recursos com a região norte-nordeste</strong>; <br><br></p></li>
<li class="fragment"><p><strong>Estrutura pública de servidores (VMs) e armazenamento (S3)</strong> ou “cotas” em nuvens federadas; <br><br></p></li>
<li class="fragment"><p>Acesso a <strong>créditos para uso de APIs de IA generativa e a modelos avançados de processamento de linguagem natural</strong>.<br>
<br><br></p></li>
</ul>
</div>
</section>
<section class="slide level2">
<h2></h2>
<div style="text-align: center; font-size: 55px;">
<strong>A partir de 2025 vamos investigar a desinformação em saúde</strong>
</div>
<p><br></p>
<div class="columns">
<div class="column" style="width:65%;">
<div>
<ul>
<li class="fragment"><p>Em parceria com o Programa Integrado em Economia, Tecnologia e Inovação em Saúde (PECS/ISC/UFBA) e apoio do CNPq. <br><br></p></li>
<li class="fragment"><p>Coletar e analisar dados de redes sociais, sites de notícias e aplicativos de mensagens; <br><br></p></li>
<li class="fragment"><p>Implantação de aplicações de IA para classificação automática de conteúdos multimodais.; <br><br></p></li>
<li class="fragment"><p>Painel de detecção de conteúdos em tempo real.</p></li>
</ul>
</div>
</div><div class="column" style="width:35%;">
<p><img data-src="images/pecs.png"></p>
</div>
</div>
</section></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 55px;">
<strong>Resultados preliminares</strong>
</div>
<p><br></p>
</section>
<section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 50px;">
<strong>Uso de Knowledge graphs (KGs)/redes semânticas para análise de polarização política em momentos extremos</strong>
</div>
<p><br></p>
<div class="columns">
<div class="column" style="width:50%;">
<div>
<ul>
<li class="fragment"><p><strong>Servem para a compreensão contextualizada de dados, especialmente em contextos de grandes volumes de informações heterogêneas</strong>; <br><br></p></li>
<li class="fragment"><p><strong>Em contexto políticos os KG ajudam a conectar conceitos possibilitando análises sobre as dinâmicas de narrativas (OSMONOVA; TIKHONOV; YAMSHCHIKOV, 2024)</strong>; <br><br></p></li>
</ul>
</div>
</div><div class="column" style="width:50%;">
<p><img data-src="images/kg1.png"></p>
</div>
</div>
</section>
<section class="slide level2">
<h2></h2>
<div style="text-align: center; font-size: 55px;">
<strong>Uso de Knowledge graphs (KGs)/redes semânticas para análise de polarização política em momentos extremos</strong>
</div>
<p><br></p>
<div>
<ul>
<li class="fragment"><p><strong>945k de mensagens de 01/01/2023 a 10/01/2023 em 207 grupos e 268 canais do Telegram</strong>.<br>
<br><br></p></li>
<li class="fragment"><p><strong>Amostra de 7610 mensagens (5825 de grupos e 1785 de canais) que continham os termos “forças armadas” ou “militares”</strong>; <br><br></p></li>
<li class="fragment"><p><strong>Utilizamos o LLMGraphTransformer + gpt-4o-mini para: identificar entidades, relações, tipos de nós e conexões e extração de propriedades</strong>. <br><br></p></li>
</ul>
</div>
</section>
<section class="slide level2">
<h2></h2>
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<iframe src="./html/grafo_interativo.html" style="width: 300vw; height: 500vh; border: none;">
</iframe>
</section>
<section class="slide level2">
<h2></h2>
<div style="text-align: center; font-size: 55px;">
<strong>Uso de Knowledge graphs (KGs)/redes semânticas para análise de polarização política em momentos extremos</strong>
</div>
<p><br></p>
<div>
<ul>
<li class="fragment"><p><strong>Em seguida, usamos os mesmos dados na plataforma <a href="https://infranodus.com/">InfraNodus</a>, apenas para acelerar o processo de visualização</strong>;<br>
<br><br></p></li>
<li class="fragment"><p><strong>KG resultante apresenta quatro grandes narrativas ou (clusters) principais: a) intervenção militar; b) força nacional; c) Brasil popular e d) governo Bolsonaro</strong>; <br><br></p></li>
<li class="fragment"><p><strong>Resultados são valiosos em cenários mutáveis em que precisamos entender os impacto de narrativas políticas no comportamento social (JUNGHERR, 2015).</strong>. <br><br></p></li>
</ul>
</div>
</section>
<section class="slide level2">
<h2></h2>
<div style="text-align: center; font-size: 30px;">
<strong>Knowledge Graph de mensagens com os termos “forças armadas” ou “militares” (01/01/2023 - 00:00h a 10/01/2023 - 23:59h)</strong>
</div>
<p><br></p>
<img data-src="images/infranodus1.svg" class="quarto-figure quarto-figure-center r-stretch"></section></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Intervenção Militar: expressa o papel dos militares e as demandas por intervenção</strong>
</div>
<img data-src="images/infranodus2.svg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Governo Bolsonaro: inclui debates sobre dinâmicas políticas, críticas ao ex-presidente Lula e discursos sobre a legitimidade do novo governo</strong>
</div>
<img data-src="images/infranodus5.svg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Força Nacional: refere-se à atuação da Força Nacional, explorando questões relacionadas à segurança pública para garantir a ordem no dia 08 de Janeiro</strong>
</div>
<img data-src="images/infranodus4.svg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong> Brasil Popular: narrativas voltadas para a população brasileira, com um discurso mais emocional para a mobilização popular unida em torno de valores nacionais e religiosos</strong>
</div>
<img data-src="images/infranodus3.svg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 55px;">
<strong>Clusterizar imagens de grupos masculinistas do Telegram</strong>
</div>
<p><br></p>
<div>
<ul>
<li class="fragment"><p><strong>22k de imagens de 10 grupos masculinistas postadas do surgimento até dezembro de 2024 </strong>; <br><br></p></li>
<li class="fragment"><p><strong>Aplicação do modelo CLIP (Contrastive Language-Image Pretraining), desenvolvido pela OpenAI</strong>; <br><br></p></li>
<li class="fragment"><p><strong>Entender formas imagéticas de violência, mais difíceis de detecção automatizada</strong>.<br>
<br><br></p></li>
</ul>
</div>
</section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Cluster 01</strong>
</div>
<img data-src="images/mosaico_cluster_7.jpg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Cluster 02</strong>
</div>
<img data-src="images/mosaico_cluster_10.jpg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Cluster 03</strong>
</div>
<img data-src="images/mosaico_cluster_11.jpg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Cluster 04</strong>
</div>
<img data-src="images/mosaico_cluster_15.jpg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 55px;">
<strong>Clusterização de imagens de grupos anti-vacina em grupos do Telegram</strong>
</div>
<p><br></p>
<div>
<ul>
<li class="fragment"><p><strong>Amostra de 10k de imagens de 7 grupos anti-vacina de jan/dez de 2024</strong>; <br><br></p></li>
<li class="fragment"><p><strong>Aplicação do modelo CLIP (Contrastive Language-Image Pretraining), desenvolvido pela OpenAI</strong>; <br><br></p></li>
<li class="fragment"><p><strong>Semântica Rica, Zero-Shot Learning e Escalabilidade</strong>.<br>
<br><br></p></li>
</ul>
</div>
</section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Frequência mensal de postagens das imagens nos grupos analisados </strong>
</div>
<img data-src="images/antivax-tsvb.png" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Cluster 0</strong>
</div>
<img data-src="images/mosaico_cluster_0.jpg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Cluster 11</strong>
</div>
<img data-src="images/mosaico_cluster_111.jpg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Cluster 6</strong>
</div>
<img data-src="images/mosaico_cluster_66.jpg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Cluster 7</strong>
</div>
<img data-src="images/mosaico_cluster_77.jpg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center">
<h1></h1>
<div style="text-align: center; font-size: 30px;">
<strong>Cluster 2</strong>
</div>
<img data-src="images/mosaico_cluster_2.jpg" class="quarto-figure quarto-figure-center r-stretch"></section>
<section class="title-slide slide level1 center" data-background-iframe="colored-particles/index.html">
<h1></h1>
<br>
<div style="text-align: center; font-size: 200px;">
<strong>Obrigado!</strong>
</div>
<div class="quarto-auto-generated-content">
<p><img src="images/logo.png" class="slide-logo"></p>
<div class="footer footer-default">
<p>Slides by <a href="https://leofn.com">Leonardo F. Nascimento</a> (<span class="citation" data-cites="leofn3">@leofn3</span>). Code available <a href="https://github.com/LABHDUFBA/openai_ufba_2025">on GitHub</a></p>
</div>
</div>
</section>
</div>
</div>
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