bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Large Language Model Performance on ENEM Math Questions Using Retrieval-Augmented Generation
Authors: Superbi João , Pinto Heitor , Santos Emanoel , Lattari Lucas , Castro Bianca
Published: October 2024

In this study, we explore the use of Retrieval-Augmented Generation (RAG) to improve the performance of large language models (LLMs), such as GPT-3.5 Turbo and GPT-4o, in solving ENEM mathematics questions. Our experiments demonstrate that RAG potentially provides significant improvements in accuracy by introducing relevant contextual information. With RAG, GPT-4o consistently outperforms GPT-3.5 Turbo, underscoring the potential of this technique to enhance educational AI tools. This research illustrates the potential of RAG-enhanced LLMs to advance educational applications and encourages further exploration in this field.
Explorando Dados de Notificações em Farmacovigilância
Authors: Sousa Ana Beatriz Bento , Perciliano Luiz , Belloze Kele
Published: October 2024

O desenvolvimento de novos medicamentos é um processo complexo e caro, envolvendo descobertas iniciais, estudos pré-clínicos e testes clínicos. Na sequência, a farmacovigilância monitora a segurança dos medicamentos no mercado, identificando as Reações Adversas a Medicamentos (RAM). No Brasil, a ANVISA gerencia as notificações de RAMs por meio dos sistemas VigiMed e Notivisa. O objetivo deste trabalho é apresentar uma análise exploratória de dados de notificações do VigiMed disponibilizados no Portal Brasileiro de Dados Abertos. Além da visão geral sobre as notificações no país, espera-se contribuir com percepções sobre as RAMs para fomentar pesquisas futuras.
On the Role of Semantic Word Clusters — CluWords — in Natural Language Processing (NLP)
Authors: Viegas Felipe , Rocha Leonardo , Gonçalves Marcos André
Published: October 2024

Esta tese de doutorado tem como foco a proposta, concepção e avaliação de uma nova representação textual de documentos que combina o “melhor de dois mundos”: a informação frequentista, eficiente e eficaz (representações TFIDF), com informações semânticas derivadas de representações de word embeddings. Especificamente, nossa proposta — denominada CluWords — agrupa palavras relacionadas sintática e semanticamente em clusters e aplica esquemas de filtragem e ponderação específicos para o domínio e orientados à aplicação, visando construir representações documentais poderosas e ajustadas às necessidades específicas de cada tarefa. O conceito inovador de CluWords foi aplicado em quatro aplicações de Processamento de Linguagem Natural (PLN): modelagem de tópicos, modelagem hierárquica de tópicos, construção de léxicos de sentimentos e análise de sentimentos. As contribuições principais desta dissertação incluem: (i) a introdução de uma nova representação de dados; (ii) o desenvolvimento de componentes do CluWords capazes de aprimorar a eficácia em aplicações de Modelagem de Tópicos, Modelagem Hierárquica de Tópicos e Análise de Sentimentos; (iii) a proposta de duas novas métricas para avaliar a qualidade tópica das estruturas hierárquicas. Nossos extensos experimentos demonstram que CluWords alcança o estado da arte atual em modelagem de tópicos e modelagem hierárquica de tópicos. No contexto da análise de sentimentos, os resultados mostram que a filtragem e ponderação proporcionadas pelo CluWords podem mitigar o ruído semântico, superando até mesmo arquiteturas poderosas baseadas em Transformadores. Os resultados foram publicados em algumas das principais conferências e revistas científicas da área, conforme detalhado neste documento. Este trabalho foi apoiado por dois Google Research Awards.
I-DataMig: Uma Ferramenta Inteligente para Migração Eficiente de Bancos de Dados
Authors: Carneiro Victor Misael B. F. , Moraes Gustavo , Brayner Angelo
Published: October 2024

A migração eficiente de bancos de dados entre diferentes sistemas é fundamental no cenário atual, onde a adoção de novas tecnologias é um imperativo. Assim, este trabalho apresenta a ferramenta I-DataMig, uma solução desenvolvida para automatizar o processo de migração de bancos de dados, proporcionando uma interface intuitiva, além de um processo tolerante a falhas e auditável. I-DataMig permite um controle detalhado dos passos do processo de migração, desde a transferência de esquemas e instâncias de tabelas até a migração de restrições de integridade e índices entre diferentes sistemas de bancos de dados. Este trabalho apresenta uma visão geral da ferramenta, bem como detalha sua arquitetura e avaliação de desempenho.
Creating Resources and Evaluating the Impact of OCR Quality on Information Retrieval: A Case Study in the Geoscientific Domain
Authors: Lima de Oliveira Lucas , P. Moreira Viviane
Published: October 2024

The evaluation paradigm in Information Retrieval (IR) requires a test collection with documents, queries, and relevance judgments. Creating such collections demands significant human effort, mainly to provide relevance judgments. As a result, there are still many domains and languages that, to this day, lack a proper evaluation testbed. To bridge this gap, we developed REGIS (Retrieval Evaluation for Geoscientific Information Systems), a test collection for the geoscientific domain in Portuguese. The documents in REGIS are in PDF. Optical Character Recognition (OCR) is typically used to extract the textual contents of scanned texts. The output of OCR can be noisy, especially when the quality of the scanned image is poor, which in turn can impact downstream tasks such as Information Retrieval. This work evaluates the impact of OCR extraction and correction on IR. Our results have shown significant differences in IR metrics for the different digitization methods.
Differentially Private Release of Count-Weighted Graphs
Authors: Brito Felipe T. , Machado Javam C.
Published: October 2024

This work proposes many contributions to privacy in complex systems, mainly ones modeled as count-weighted graphs. As graph data usually contain users’ sensitive information, preserving privacy when releasing this type of data becomes a crucial issue. In this context, differential privacy (DP) has become the de facto standard for data release under strong mathematical guarantees. However, various challenges persist in effectively implementing DP to graph data, including balancing privacy protection with data utility and scalability concerns. To bridge these gaps, we propose several efficient techniques and approaches to release graph data while maintaining a robust level of privacy protection. Our results were published in the top-tier venues in the field of data management. Additionally, we disseminated our knowledge and expertise obtained during this Ph.D. research through tutorials and short courses presented at both national and international conferences.
brModeloPD: Uma Extensão da Ferramenta BrModelo para Incorporar os Requisitos e as Restrições da LGPD ao Projeto de Banco de Dados
Authors: Barros Patrícia Vieira da S. , França José Caique Militão , M. Filho José Maria da S. , Machado Javam C.
Published: October 2024

A Lei Geral de Proteção de Dados Pessoais (LGPD) determina como deve ser realizado o tratamento, o armazenamento e o descarte de dados pessoais, inclusive nos meios digitais, sempre com autorização prévia do concedente. Por outro lado, os sistemas de informação atuais estão fortemente baseados na utilização de dados pessoais e, por conseguinte, precisam estar em conformidade com a LGPD. Neste contexto, o sistema de bancos de dados passa a ser um componente ainda mais importante no desenvolvimento de software, uma vez que este é responsável pelo armazenamento, atualização e recuperação dos dados. Contudo, as ferramentas utilizadas para o projeto de bancos de dados não incorporam os requisitos e as restrições da LGPD, dificultando assim a conformidade entre os bancos de dados e a legislação vigente. Este artigo apresenta uma extensão da ferramenta brModelo, denominada brModeloPD, que possibilita incorporar as imposições e preceitos da LGPD ao projeto de bancos de dados. A ferramenta bRModeloPD fornece suporte aos projetos conceitual, lógico e físico.
MedTalkAI: Assisted Anamnesis Creation With Automatic Speech Recognition
Authors: Gonçalves Yanna Torres , Alves João Victor B. , Sá Breno Alef Dourado , Silva Lázaro Natanael da , Macedo José A. Fernandes de , Coelho da Silva Ticiana L.
Published: October 2024

Conventional approaches to documenting patient medical histories are often time-consuming and require significant healthcare professional involvement. This paper introduces MedTalkAI, which integrates ASR models, including Whisper and Wav2Vec 2.0, to transcribe audio recordings of patient histories in Brazilian Portuguese efficiently. MedTalkAI validates, corrects, and evaluates transcriptions, facilitating the creation of a unique medical audio-text database. Additionally, MedTalkAI enhances ASR models for medical applications using language models. This approach aims to improve medical history transcription and analysis, contributing to the development of more reliable ASR models and automating the documentation process.
A Epidemia Silenciosa: Explorando os Determinantes Comportamentais e Socioeconômicos da Deficiência Renal Crônica no Brasil
Authors: Sousa Marco T. , Zarate Luis E.
Published: October 2024

De acordo com a Pesquisa Nacional da Saúde ( PNS ), a Deficiência Renal Crônica ( DRC ) afeta cerca de dois milhões de pessoas. A DRC é uma condição médica que resulta de uma interação complexa de fatores biológicos e sociais, e é conhecida por ter dificuldades tanto no diagnóstico quanto no prognóstico. Diante disso, o objetivo é aplicar técnicas de aprendizado de máquina para caracterizar o perfil de indivíduos portadores da doença considerando diversos fatores socioeconômicos, de saúde, etc. O trabalho propõe um método para descoberta de conhecimento com a aplicação dos algoritmos Árvore de Decisão, Naive Bayes, e Floresta Aleatória, tendo este último alcançado o melhor desempenho. Esse estudo fornece uma discussão sobre os fatores identificados, incluindo a forte relação entre hipertensão, consumo de sal, tipo de cidade onde mora, etc.
Monitoramento do mercado de ativos brasileiro: uma proposta de pipeline de dados para detecção de bolhas financeiras
Authors: Bomfim Uiliam B. , Nascimento Flávia Maristela S.
Published: October 2024

Especulações e crises financeiras podem causar problemas econômicos, especialmente em países emergentes. Por isso, agentes econômicos buscam identificar bolhas financeiras antecipadamente, para que possam tomar medidas que proporcionem estabilidade econômica. Nesse cenário, apresentamos um pipeline de dados que facilita a identificação de bolhas financeiras na B3. A análise revela a eficácia do pipeline de dados que identificou dois períodos de bolhas financeiras no mercado brasileiro.
LLMusic: Modelagem de tópicos em letras de músicas combinando LLM, Engenharia de Prompt e BERTopic
Authors: Rojas Jesus Daniel Yepez , Becker Karin
Published: October 2024

Letras de músicas impõem desafios adicionais à modelagem de tópicos, já que o discurso nem sempre é explícito, devendo ser compreendido dentro de seu contexto, sua linguagem figurativa e poética, gírias, etc. Este artigo propõe o LLMusic, uma nova abordagem de modelagem de tópicos que explora o potencial de Grandes Modelos de Linguagem (LLMs) para analisar letras de música. LLMs e prompting são usados para resumir trechos de músicas em temas centrais, em um processo iterativo e não supervisionado aplicado a um corpus representativo do gênero. Esses temas são agrupados em um conjunto enxuto e coerente de tópicos usando BERTopic. Através de prompts zeroshot, pode-se classificar novos trechos de letras com base nesses tópicos. No estudo de caso desenvolvido, LLMusic capturar os fenômenos sociais à base do funk brasileiro, mostrando seu potencial para análise em larga escala.
Estudo experimental sobre justiça algorítmica aplicada em modelos de análise de crédito
Authors: Oliveira Tiago A. , Oliveira João V. L. , Farias Tarcísio P. , Cruz Erick W. R. , Andrade Leandro J. S. , Pita Robespierre
Published: October 2024

Modelos de Machine Learning (ML) para tomada de decisão algorítmica são amplamente aplicados para suportar a gestão de risco e análise de crédito. Contudo, o sensível aumento de dados disponíveis, a complexidade dos modelos mais modernos e o escrutínio público em torno da inteligência artificial acirraram o debate sobre a necessidade de identificação e mitigação de vieses em predições. Este estudo propõe analisar a relação entre medidas quantitativas de justiça algorítmica e métricas de qualidade obtidas por modelos de ML em tarefas de análise de crédito. Os resultados iniciais indicam que determinados modelos conseguem alcançar níveis promissores de desempenho sem necessariamente afetar ou deteriorar a justiça em suas predições.
Profissionais de Dados centrados em Pessoas: Ética no Uso e Tratamento de Dados
Authors: Santos Kellyne Marques
Published: October 2024

O uso de tecnologias emergentes por profissionais de dados pode trazer riscos éticos no uso das informações, caso estes profissionais não se guiem por princípios éticos para o tratamento dos dados. Aspectos como transparência, confiabilidade na qualidade do dado e responsabilização pelas soluções de dados criadas devem estar presentes nas soluções conjuntamente às mais recentes técnicas de inteligência artificial. A falta de limite no uso dos dados ou os vieses criados devido a questões diversas nas informações manipuladas podem causar danos a vidas humanas, além de potencialmente ferir direitos humanos como a privacidade, o anonimato e gerar preconceito; invalidando parte dos benefícios que estas novas tecnologias podem trazer.
Sobrepreço em compras públicas: Metodologia baseada na identificação de valores discrepantes
Authors: Soares Diogo , Silva João Pedro D. da , Zibetti Andre Wüst , Werner Simone S.
Published: October 2024

Um dos objetivos das licitações públicas é evitar contratações com sobrepreço, no entanto, identificá-las nem sempre é trivial. Este artigo apresenta uma metodologia para identificar sobrepreço, focando na detecção de valores discrepantes, considerando como um passo anterior o ajuste de modelos para descrever o comportamento do preço unitário. Para exemplificar, utilizou-se itens de Notas Fiscais Eletrônicas (NF-e) de gasolina comum, adquirida por Instituições Públicas de Santa Catarina. Os resultados mostraram que a utilização do processo de modelagem do preço anteriormente à aplicação de técnicas de identificação de valores discrepantes é importante para evitar a identificação equivocada ou não identificação notas de itens com sobrepreço.
Fast ISP Mode Decision for the Versatile Video Coding Intra Prediction Using Machine Learning
Authors: Araújo Larissa , Duarte Adson , Zatt Bruno , Correa Guilherme , Palomino Daniel
Published: October 2024

The Versatile Video Coding (VVC) standard achieves high compression rates by introducing new encoding tools, such as the Intra Subpartition Prediction (ISP). However, the ISP increases the computational effort necessary to perform the mode decision of the intra prediction step. This paper proposes a fast intra-mode decision solution for the ISP using machine learning. A Decision Tree is employed to predict the most promising ISP modes to be optimal to avoid the costly RDO test of ISP modes that are less likely to be chosen. By reducing the number of modes fully evaluated by the RDO process, the proposed solution achieves an average time-saving of 3.15% with only 0.11% of coding efficiency loss when tested for the common test conditions of VVC. Unlike the related works, our solution avoids the time overhead of calculating image features by adopting features from the encoding process. Compared with related works, our solution presents competitive time-saving and coding efficiency results.
Empowering Older Adults through Smart Home Automation for Better Quality of Life
Authors: Pereira Antonio Jorge Oliveira , Paula Franciele Alessandra de , Melo Thalia Bianca da Silva , Carvalho Dárlinton B. Feres , Viana Matheus Carvalho
Published: October 2024

Population ageing comes with multifaceted challenges that stretch beyond health-related advancements and demographic shifts. Older adults are confronted by significant healthcare obstacles, requiring special needs, remarkably in their home environment. The present study draws upon Smart Home Automation technology to develop an Ambient Assisted Living (AAL) prototype aimed at enhancing daily living activities, health monitoring and independence for older adults, thereby improving their overall quality of life. Employing the Design Science Research (DSR) method of research combined with the Double Diamond process of design, this study intends to create and evaluate a solution particularly tailored to the needs of older adults, furthering a more safe and dignified ageing experience.
Evidências de disseminação de conteúdo no Telegram durante o ataque aos órgãos públicos brasileiros em 2023
Authors: Venâncio Otávio R. , Gonçalves Gabriel H. S. , Ferreira Carlos H. G. , Silva Ana Paula C. da
Published: October 2024

Given the increasing importance of instant messaging apps in the dissemination of information, this study investigates coordination patterns in user groups on the Telegram in post-2022 election Brazil. Text messages shared in approximately 270 politically oriented groups during the first two weeks of January 2023 were analyzed using a methodology that includes media-centric network modeling, backbone extraction techniques, and community detection algorithms. Our results reveal evidence of user coordination in promoting antidemocratic content, demonstrating how these communities are capable of efficiently disseminating information on a large scale. Specifically, we observed significant activity related to the attack on Brazilian public institutions, including the government, Supreme Federal Court (STF), Senate, and Congress.
A Machine-Learning-Driven Fast Video-based Point Cloud Compression (V-PCC)
Authors: Rehbein Gustavo , Costa Eduardo , Corrêa Guilherme , Santos Cristiano , Porto Marcelo
Published: October 2024

In recent years, 3D point cloud content has gained attention due to its application possibilities, such as multimedia systems, virtual, augmented, and mixed reality, through the mapping and visualization of environments and/or 3D objects, real-time immersive communications, and autonomous driving systems. However, raw point clouds demand a large amount of data for their representation, and compression is mandatory to allow efficient transmission and storage. The MPEG group proposed the Video-based Point Cloud Compression (V-PCC) standard, which is a dynamic point cloud encoder based on the use of video encoders through projections into 2D space. However, V-PCC demands a high computational cost, demanding fast implementations for real-time processing and, especially, for mobile device applications. In this paper, a machine-learning-based fast implementation of V-PCC is proposed, where the main approach is the use of trained decision trees to speed up the block partitioning process during the point cloud compression. The results show that the proposed fast V-PCC solution is able to achieve an encoding time reduction of 42.73% for the geometry video sub-stream and 55.3% for the attribute video sub-stream, with a minimal impact on bitrate and objective quality.
Middleware para Aplicações Distribuídas de Vídeo com Suporte à Computação na Borda na Indústria 4.0
Authors: Ramos Neto Otacílio de A. , Chaves Rafael C. , Nascimento Alysson P. , Gomes Ruan D.
Published: October 2024

Within the scope of Industry 4.0, computer vision is extensively employed for monitoring and control functions with stringent demands on performance and latency. A viable approach to meeting these requirements is distributed processing at the edge and in the cloud. In this context, this paper presents a middleware tailored for Industry 4.0 and distributed video applications using edge computing. A versatile communication protocol was developed, with support to both UDP and TCP, and incorporating two methods for frame delivery prioritization (either Last In, First Out or First In, First Out). Also, the protocol performs fragmentation, enabling the transmission of high-resolution images. Initial experiments have shown that the proposed middleware allows the distribution of high-resolution videos without significant overhead, while at the same time offering a high level of transparency for applications, which can be implemented as if getting the video stream from a locally connected camera.
A Coding-Efficiency-Aware Fast Heuristic for VVC Intra-Frame Prediction Targeting 360° Videos
Authors: Rosado Rogério , Santos Otávio , Oliveira Franklin , Silva Lucas , Sanchez Gustavo , Storch Iago , Palomino Daniel , Agostini Luciano
Published: October 2024

360° videos have gained prominence among immersive media, providing users with an impressive immersive scene experience. But the computational effort necessary to encode these videos is very high since the 360º videos demand more data to be represented. This paper presents a heuristic-based decision solution for the intraframe prediction in Versatile Video Coding (VVC) focused on 360º videos. The main goal is to reduce the computational effort of the intra-frame prediction process while ensuring minimal impact on coding efficiency. This is achieved exploring the distortions introduced by the equirectangular projection used to encode 360º videos. The results of the proposed heuristic present time savings of 8.34 % with a minor coding efficiency loss of only 0.16% BD-BR. This loss is the smallest among related works, allowing the proposed solution to achieve the best balance between time savings and coding efficiency losses among works in the literature.
Detecção de Fake News via Sinais Implícitos de Crowds: Uma Abordagem para Mitigar o Cold-Start no Cálculo da Reputação
Authors: Thomé Viviane Antonia Corrêa , Freire Paulo Márcio Souza , Goldschmidt Ronaldo Ribeiro
Published: October 2024

The evolution of Digital Media for News Distribution has changed how people share content. Anyone can freely share information, including fake news (i.e., false information shared intentionally). In this scenario, fake news detection approaches have been proposed, with notable attention given to the one that uses crowd signals. Such approaches explore the collective sense by combining opinions (i.e., signals) of a high number of users (i.e., crowd), considering the reputations of these users regarding their capacity to identify fake news. Although promising, the crowd signals approaches have a significant limitation when many users in the crowds have not interacted with prior news. This lack of information leads to a cold-start problem when calculating those users’ reputations. The present work raises the following hypothesis: the performance of the crowd signals-based detection models can be improved if they mitigate the cold-start problem by inferring the users’ reputation based on the behavior those users would present in the face of prior news. This hypothesis is grounded on the fact that people tend to share news that seems familiar to their beliefs. In a search to validate the raised hypothesis, SCS, a crowd signals-based fake news detection method, is proposed. SCS considers similarities among texts (e.g., title and content) of past news to infer the reputation of users with the cold-start problem. BERT, a known Large Language Model (LLM) was used to provide embeddings that represent the texts. Preliminary experimental results demonstrate the effectiveness of the proposed method when compared to the crowd signals-based SOTA in detecting fake news.
Ferramenta para análises descritivas e preditivas em dados criminais: Um estudo de caso em Minas Gerais
Authors: Andrade Yan , Amarante Gabrielle , Pimenta Matheus , Meira Jr. Wagner , Teodoro George , Rocha Leonardo , Ferreira Renato
Published: October 2024

This article examines the underutilization of detailed criminal data, collaborating with the Military Police of Minas Gerais, Brazil. We propose a new methodology, materialized in a tool, that is able to transform raw data into strategic information for public security decision-making. The tool evaluation unfolds in three phases: characterizing the data, a descriptive analysis of a real case study, and a predictive analysis. This work highlights the untapped potential in detailed criminal data, emphasizing the pivotal role of precise analysis in deciphering complex dynamics. Collaborating with law enforcement aims to bridge the gap between data abundance and actionable insights for effective public security strategies.
GECET: Garotas nas Engenharias, Ciências Exatas e Tecnologias: Projeto de Extensão voltado para a participação feminina nas áreas STEM
Authors: Benvenuto Giovana A. , Brandi Analice C. , Colnago Marilaine , Ferreira Rafaella S. , Picoli Helen S.
Published: October 2024

The GECET: Garotas nas Engenharias, Ciências Exatas e Tecnologias is an extension project linked to the São Paulo State University “Júlio de Mesquita Filho” (UNESP), with the aim of increasing female participation in STEM fields (Science, Technology, Engineering, and Mathematics). The project operates on several fronts to promote gender equity, ranging from basic education to higher education and postgraduate studies. The GECET has had a significant impact, increasing female interest in STEM fields, raising awareness about gender stereotypes, developing educational materials, and generating academic production with participation in congresses. The strategic use of social media was also essential for scientific dissemination and engagement, resulting in recognition and awards for the impact on promoting gender equity.
Estado da Arte sobre Engenharia de Requisitos e Explicabilidade em Sistemas Baseados em Aprendizado de Máquina
Authors: Mancine Lívia , Soares João Lucas , Kudo Taciana Novo , Bulcão-Neto Renato F.
Published: October 2024

With the recent growth in the use of Machine Learning (ML)-based software, concerns arise regarding explaining the results generated. Explanations help with transparency and increase stakeholder trust. Explainability, a term used to refer to these explanations, is considered a non-functional requirement (NFR) that substantially impacts the quality of ML systems. Explainability has become a mandatory requirement outlined in various laws in several countries. Additionally, Explainable Artificial Intelligence (XAI) is a field that studies methods supporting explainability in ML-based systems, focusing mainly on technical explanations. This study is not limited to technical explanations but provides a comprehensive overview of Requirements Engineering (RE) and the explainability requirement in AM-based systems. To achieve this, we planned and executed a Systematic Mapping Study protocol, adopting automatic searches in six databases. From the 200 returned articles, after applying selection criteria, we analyzed and reported the results of 27 articles. Our findings reveal that explainability is an emerging quality NFR in ML-based systems, challenging classical RE paradigms.
LightningBR: Ferramenta Web para Visualização e Análise de Ocorrência de Raios no Brasil
Authors: Alves Elton R. , V. Júnior Warley M. , Moraes Alife S. de , Alvino Diogo da S. , Balieiro Andson M.
Published: October 2024

Monitoring lightning occurrences is extremely important due to the potential negative impacts this phenomenon can cause to human and animal life, civil aviation, and electrical power and telecommunication systems. In this regard, this work presents the LightningBR a web tool developed for analysis and visualization of cloud-to-ground and intra-cloud lightning occurrences, using raw data from the ENTLN (Earth Networks Total Lightning Network). The development of this work consisted of two main parts: the first part involved the creation of the Back-end using a Node.js server, MongoDB Atlas database, and Leaflet JS API. The second part involved the development of the Front-end responsible for the visual aspects of the system. In this stage, HTML (HyperText Markup Language), CSS (Cascading Style Sheets), JavaScript, and Figma were used. The tool’s functionalities were tested with lightning data from an anonymous city in Brazil, proving to be feasible for continuous monitoring of lightning occurrences.
Arpeggion-H: Uma Interface Interativa para Reprodução de áudio MPEG-H
Authors: Moreira Bruno Augusto R. de M. , Muchaluat-Saade Débora C.
Published: October 2024

This paper provides an overview of the Arpeggion-H audio player, able to reproduce and interpret the metadata extracted from mhm1 encoded MP4 files, based on the MPEG-H part 3 norm. It is also able to display and alter the available configuration inferred from the audio’s metadata at any time. This tool will be useful to academic and common users, as it is able to play and allow user interaction out of a codec that is normally not supported. In the present implementation, this tool uses the decoder implement by the Fraunhofer-IIS group called “mpeghdec”, but it may support any other decoder if properly integrated.
Urban Perception Extraction from Texts Shared on Social Media: Framework and Applications
Authors: Santos Frances Albert , Silva Thiago Henrique , Villas Leandro Aparecido
Published: October 2024

In this Ph.D. thesis, we proposed an automatic and generic framework composed of 5 major layers (Data Collection, Preprocessing and Embeddings, Models Training, Knowledge Extraction, and Applications) to extract the user’s urban perception from Location-Based Social Network (LBSN) data. The framework employs advanced deep learning algorithms, including sentence embeddings, to capture lexical and semantic relationships in textual data and, thus, effectively extract content related to urban perceptions from LBSNs. Moreover, this framework circumvents the need for labor-intensive field surveys or manual extraction processes, enabling scalable and real-time analysis of urban perception. Studying some urban areas from Chicago, New York City, and London, we demonstrate the framework’s effectiveness in extracting valuable insights related to urban perceptions from LBSN data. Furthermore, we conducted a comparative evaluation using a public dataset derived from volunteers’ perceptions in a controlled experiment, where it was possible to observe that both results yielded a very similar level of agreement. Finally, we introduce a novel tool called Real-Estate Urban Perceptions (REAL-UP), which aims to enhance the real-estate marketplace as a proof-of-concept for our work. REAL-UP provides rich knowledge regarding urban areas in the form of interactive 2D maps, more specifically, the emotion and sentiment perceived, and a short review generated by a Large Language Model (LLM) based on LBSN messages, for every city’s neighborhood, in addition to information commonly provided by such applications, as rent price, property type, and so on.
Paralelização de Tarefas de Codificação de Vídeo VVC utilizando GPGPUs
Authors: Storch Iago , Palomino Daniel , Bampi Sergio
Published: October 2024

The compression required in video-based applications imposes a significant computational workload. Moreover, computing systems are becoming increasingly heterogeneous, and GPUs have gained popularity. This Ph.D. Thesis proposes a GPU acceleration methodology that provides guidelines for efficiently leveraging heterogeneous CPU+GPU systems to accelerate video coding applications. Experimental results demonstrate that the solutions developed following the proposed methodology accelerate the encoding and improve its energy efficiency while posing minor coding efficiency losses.
Proposta de um framework de apoio à etapa de avaliação de soluções computacionais
Authors: Santos Suzane Santos dos , Rodrigues Marcus V. Santos , Rodrigues Kamila Rios da Hora
Published: October 2024

Evaluating a product is essential to understand whether or not the solution meets the defined requirements. In software development, a proper evaluation highlights issues that were not identified in earlier stages of development. However, to achieve a successful evaluation, it is necessary to consider different characteristics, from the target user to the environment in which the evaluation will be conducted, so selecting the proper evaluation method is essential. However, choosing the suitable method can be challenging, especially in Human-Computer Interaction, a field that thrives on its interdisciplinary nature. This project proposes an integrated framework combining tools from different domains to assist computing professionals and others in systematically applying these instruments.
Dimensional Speech Emotion Recognition: a Bimodal Approach
Authors: Guder Larissa , Aires João Paulo , Griebler Dalvan
Published: October 2024

Considering the human-machine relationship, affective computing aims to allow computers to recognize or express emotions. Speech Emotion Recognition is a task from affective computing that aims to recognize emotions in an audio utterance. The most common way to predict emotions from the speech is using pre-determined classes in the offline mode. In that way, emotion recognition is restricted to the number of classes. To avoid this restriction, dimensional emotion recognition uses dimensions such as valence, arousal, and dominance, which can represent emotions with higher granularity. Existing approaches propose using textual information to improve results for the valence dimension. Although recent efforts have tried to improve results on speech emotion recognition to predict emotion dimensions, they do not consider real-world scenarios, where processing the input in a short time is necessary. Considering these aspects, this work provides the first step towards creating a bimodal approach for Dimensional Speech Emotion Recognition in streaming. Our approach combines sentence and audio representations as input to a recurrent neural network that performs speech-emotion recognition. We evaluate different methods for creating audio and text representations, as well as automatic speech recognition techniques. Our best results achieve 0.5915 of CCC for arousal, 0.4165 for valence, and 0.5899 for dominance in the IEMOCAP dataset.
Diretrizes de apoio ao processo de construção de sistemas conversacionais de recomendação acessíveis: um estudo com usuários idosos
Authors: Araujo Lucas , Rodrigues Kamila , Manzato Marcelo
Published: October 2024

Elderly individuals often face challenges when using computational tools due to physical, cognitive, and hearing limitations. To improve accessibility, adaptive conversational systems have emerged as a promising option. This work provided guidelines for developing accessible conversational recommendation systems. Using the PLuRaL method, it was possible to conduct tests and evaluations in a participatory manner. In addition to the guidelines, the work presents various discussions that pave the way for new research and explore future possibilities in the development of more inclusive conversational technologies.
Jogos Sérios para Auxiliar o Exercício Cognitivo de Idosos Utilizando o Robô Social EVA
Authors: Silva Pedro Lucas Pereira da , Cecim João Vitor Carvalho , Rocha Marcelo Marques da , Muchaluat-Saade Débora C.
Published: October 2024

Socially Assistive Robots (SARs) are a new class of robots that are designed to engage in social interactions with humans. This paper proposes some Serious Games for the SAR called EVA, detailing how these games can be used in cognitive therapy for older adults. The applications developed for robots not only promote cognitive stimulation and mental health but also aim to improve the overall quality of life of older adults promoting greater independence and reducing feelings of loneliness. This study highlights the potential of SARs in transforming cognitive care and highlights the broader implications for the integration of robotics in older adult care.
Die UNUM24 in München
Authors: Oelze Andreas
Published: October 2024

Die Olympiahalle in München. Ansteckende, von einer beeindruckenden Lichtshow begleitete Musik, zu der gut 6.000 Menschen begeistert tanzen und mitsingen. Es mutet wie ein Popkonzert an, entpuppt sich aber als einer der zahlreichen gottesdienstlichen Lobpreisteile, die während der UNUM24-Konferenz vom 20. bis 23. Juni diesen Jahres in München stattfanden.
The Jadar lithium mining project in Serbia: imperatives mean complex decision-making
Authors: Allen Calvin , Galgóczi Béla
Published: October 2024

This article examines the decision by the Serbian government to approve the highly controversial Jadar project to mine lithium at a site in the western part of Serbia. Lithium is a critical raw material with a variety of applications including, most strategically, in terms of its role in the batteries that power electric mobility. While it is not particularly rare, known global reserves of this critical raw material are heavily concentrated in a small number of places very few of which are in Europe. Consequently, there is sizable political pressure to develop an industry, where reserves can be found, within Europe. The authors – editors of the SEER Journal – consider why Europe needs to develop a lithium industry as well as some of the environmental considerations surrounding lithium mining, these being sizable enough for protesters to have played a significant role in halting the Jadar project in 2022. They conclude by locating the decision to reopen the project in the context of Serbia’s European integration and point to some of the implications for Serbia’s relations both with the EU and with its neighbours.
Post-Yugoslav municipalism: dis/obedient democratic initiatives in Zagreb and Belgrade
Authors: Tiedemann Norma
Published: October 2024

New municipalism as a political-strategic approach is a reaction to the authoritarian management of the deep and multiple crises of capitalism after 2008. From their base in Spain, municipalist platforms have extended elsewhere across the globe, including in the south-east European periphery in Zagreb and Belgrade – via Zagreb Je NAŠ! and Ne Da(vi)mo Beograd – both of which have subsequently transformed themselves into political movements. This article analyses these two initiatives guided by three questions: in which conjuncture and in the context of what kind of state did these municipalist platforms in Zagreb and Belgrade emerge? What is the character of new municipalism in Serbia and Croatia? And what is their emancipatory potential? The article concludes that the platforms, and the new parties into which they transformed, are not experiments in radical democracy but are characterised by an intention to establish the rule of law and democratic freedoms. As such, the turbulence they create is providing a challenge to the prevailing, widespread system of patronage and thus questioning the current regimes.
Reformen ohne Akzeptanz
Authors: Knieps Franz
Published: October 2024

Man kann dem umtriebigen Bundesminister für Gesundheit, Karl Lauterbach, vieles vorwerfen, aber wohl nicht, dass er tradierte Strukturen, Institutionen und Prozesse im Gesundheitswesen nicht in Frage stellen und grundlegende Veränderungen anstreben wolle. Nach zwei Dritteln der Legislaturperiode haben aber nur die wenigsten der Reformversuche den Weg ins Bundesgesetzblatt oder gar in die praktische Umsetzung gefunden. Der folgende Beitrag versucht, die Gründe hierfür zu finden und einen Überblick über das Labyrinth der aktuellen Gesundheitspolitik zu ermöglichen. Der Verfasser ist sich bewusst, dass er keine umfassende rechts- oder sozialwissenschaftliche Analyse auf engem Raum schaffen kann, sondern allenfalls eine kursorische normative Bewertung des Sichtbaren vornehmen will. Gerade die jüngsten publizistischen Enthüllungen um die sog. RKI-Files zeigen ja, dass das Sichtbare manch Unsichtbares verbirgt und die Wissenschaft offenbar durch politische Einflussnahme instrumentalisiert wird. Das gilt selbst dann, wenn ein Wissenschaftler zum Minister ernannt wird.
Value-Transformation
Authors: Hager Lutz
Published: October 2024

Mehr Regulierung oder mehr Wettbewerb? In jedem Fall ist eine grundlegende Veränderung, Umbau oder Transformation unserer Gesundheitsversorgung dringend erforderlich. Value-Based Care (VBC) propagiert kooperative Geschäftsmodelle, die dies organisch leisten sollen. Patient:innenzentrierung geht Hand in Hand mit Gesundheit und Gesunderhaltung, was wiederum Folgekosten vermeidet. Dies verlangt nicht nur funktionierende Daten- und Kommunikationsinfrastrukturen, sondern auch die Überwindung von Risikozonen langfristiger Gesundheitsinvests und neuer Interventionen. Ein gesundheitspolitischer dritter Weg macht sich dies zunutze und setzt Anreize so, dass Gesundheitsversorger aus eigener Initiative Patient:innen, Beschäftigte, und gesellschaftliche Stakeholder für Veränderungen gewinnen.
Gemeindeschwester plus
Authors: Urbanski-Rini Dominika , Küppers Katrin , Heischling Fabia , Haß Wolfgang , Püschner Franziska
Published: October 2024

Im Folgenden werden ausgewählte Ergebnisse der Evaluation des Landesprogramms Gemeindeschwester plus in Rheinland-Pfalz dargestellt. Ziel des Angebots Gemeindeschwester plus ist es, Hochbetagten in Rheinland-Pfalz weiterhin ein selbstständiges Leben in der eigenen Häuslichkeit zu ermöglichen. Die Evaluation untersuchte die Ist-Struktur, die Umsetzung sowie die Wirkung des Angebots aus Sicht der Zielgruppe. Es konnte u. a. gezeigt werden, dass die Bedarfe der Hochbetagten gut identifiziert und dort, wo infrastrukturell möglich, passende Angebote vermittelt werden. Die Hochbetagten bewerteten das Angebot mehrheitlich sehr positiv.
Entwicklung der Pflegekammern in Deutschland
Authors: Koch Peter , Postel Sandra
Published: October 2024

Die Etablierung von Pflegekammern in Deutschland ist ein bedeutender Schritt zur Professionalisierung der Pflegeberufe. Pflegekammern dienen als berufsständische Organisationen, die die Interessen der Pflegenden vertreten und die Qualität der Pflege sichern sollen. Diese Institutionen haben in zwei Bundesländern positive Entwicklungen gezeigt, während sie in anderen vor großen Herausforderungen standen. Ein Beispiel für Letzteres ist der gescheiterte Aufbau der Pflegekammer in Baden-Württemberg.
Emotionen und Humor in der Sozialen Arbeit
Authors: Schulze-Krüdener Jörgen
Published: October 2024

Soziale Arbeit ist Emotionsarbeit. Dabei stellt vor allem der Humor eine Ressource dar, die in der Praxis Sozialer Arbeit gewinnbringend genutzt werden kann. Welchen Stellenwert haben Emotionen im Rahmen des sozialpädagogischen und sozialarbeiterischen Denkens und Handelns? Inwiefern ist der reflektierte Umgang mit Emotionen und Humor ein Kennzeichen von Professionalität? Und wie kann Humor als Fähigkeit im Kontext Sozialer Arbeit geschult und gezielt eingesetzt werden? Das Studienbuch erläutert prägnant die Relevanz von Emotionen sowie die Wirkung von Humor als Medium professioneller Interaktion und Kommunikation. Anschauliche Beispiele zeigen die vielfältigen Einsatzmöglichkeiten von Humor im beruflichen Alltag und verdeutlichen, wie ein humorvoller Umgang mit prekären Situationen geübt und weiterentwickelt werden kann. Dabei werden Studierende und Fachkräfte der Sozialen Arbeit auch für die kultur-, geschlechts- und milieuspezifischen Facetten von Emotionen und Humor sensibilisiert. Ein praxisnaher Wegweiser, der unterstreicht: Humor ist ein ernstzunehmender Bestandteil der Handlungsmethodenausbildung in der Sozialen Arbeit!
Methoden der empirischen Forschung
Authors: Lüdders Lisa , Zeeb Hajo
Published: October 2024

Die Beschäftigung mit empirischen Methoden ist für ein wissenschaftliches Studium als auch für die Berufspraxis von wesentlicher Bedeutung. Dieses Handbuch erleichtert Ihnen den Einstieg in den Kosmos der empirischen Forschung: Dr. Lisa Lüdders und Prof. Dr. Hajo Zeeb bringen Ihnen nicht nur methodische Fachbegriffe und Definitionen auf verständlich und greifbare Art näher. Sie vermitteln Ihnen zudem das nötige Handwerkszeug, das Sie brauchen, um wissenschaftliche Forschungen sinnvoll und nachvollziehbar durchzuführen. Von der präzisen Formulierung der Fragestellung, über die Durchführung sowohl qualitativer als auch quantitativer Erhebungen, bis hin zur Evaluation und Kommunikation der Ergebnisse: Die Autoren Dr. Lisa Lüdders und Prof. Dr. Hajo Zeeb laden Sie mit diesem Methodenbuch dazu ein, selbst zum Forscher zu werden und die vorgestellten Verfahren und Methoden aktiv auszuprobieren. Eine Einführung in die Methoden empirischer Forschung – geeignet für Studium und Praxis!
Schutzimpfung Masern
Authors: Gaudlitz Julia
Published: October 2024

Masern gehören zu den ansteckendsten Infektionskrankheiten des Menschen. Dies zeigen aktuelle Ausbrüche in Deutschland und Europa, die mit zum Teil schweren Komplikationen für die Betroffenen oder sogar Todesfällen einhergehen. Obwohl die Impfung im Kleinkindalter einen effektiven Schutz ermöglicht, wird diese Maßnahme immer wieder kritisch diskutiert. Niedrige Impfquoten sind die Folge. Welche Motive haben Eltern, ihre Kinder nicht impfen zu lassen? In welchem Verhältnis stehen Erkrankungsfolgen und Impfnebenwirkungen? Und vor allem: Wie lässt sich der Impfmüdigkeit effektiv entgegenwirken? Die Autorin vermittelt detaillierte Fakten zur Erkrankung und Impfung und klärt über die weit verbreitete Fehleinschätzung der Gefährlichkeit von Masern auf. Mithilfe der Analyse bisheriger Impfprogramme entwickelt sie eine Handlungsempfehlung für ein Interventionsprogramm, das sich speziell an Eltern von Kleinkindern richtet.
Burnout in der stationären Altenpflege
Authors: Tanski Björn
Published: October 2024

Burnout: Ein Berufsrisiko für Pflegekräfte in der stationären Versorgung? Fakt ist: Immer mehr Menschen in helfenden Berufen und damit auch in der stationären Altenpflege fühlen sich müde, gestresst und ausgebrannt. Betroffen sind nicht nur die Pflegekräfte selbst, sondern auch ihre Angehörigen, ihre Arbeitskollegen sowie das gesamte private und berufliche Umfeld. Die vorliegende Arbeit bietet einen detaillierten und wissenschaftlichen Einblick in die berufsspezifischen Burnout-Risiken, will eine stärkere Sensibilisierung für Burnout schaffen und liefert praktische Handlungsempfehlungen zur Stressbewältigung in der Altenpflege. Eine Pflichtliteratur für Pflegekräfte, betroffene Angehörige und alle Interessierten aus Wissenschaft und Forschung, die sich mit den weit verbreiteten Phänomenen Stress und Burnout in der stationären Versorgung auseinandersetzen möchten – oder müssen.
Tabakprävention in Deutschland: Was hilft wirklich?
Authors: Godehardt Matthias
Published: October 2024

Rauchen ist das größte vermeidbare Gesundheitsrisiko in Deutschland und mit jährlich etwa 110.000 Todesfällen die führende Ursache vorzeitiger Sterblichkeit. Insbesondere in jungen Jahren stellen Zigaretten oft eine Einstiegsdroge dar: Die Gefahr einer Abhängigkeit und die daraus resultierenden gesundheitlichen Folgen steigen. Vor dem Hintergrund, dass 80 % aller Raucher bis zum Alter von 18 Jahren mit dem Rauchen anfangen, ist die nachhaltige Verringerung des Tabakkonsums bzw. die Vermeidung des Raucheinstiegs bei Jugendlichen ein wichtiges Ziel der Gesundheitspolitik. Neben allgemeiner Aufklärung konnten insbesondere tabakpolitische Maßnahmen den Konsum deutlich eindämmen. Doch welche Maßnahmen sind nach aktueller wissenschaftlicher Erkenntnislage dazu geeignet, den Tabakkonsum von Jugendlichen wirksam und effektiv zu senken? Dieser Frage geht der Autor mit einer Bestandsaufnahme nach. Das Buch bietet damit nicht nur eine aufschlussreiche Lektüre, sondern liefert praxisorientierte Handlungsempfehlungen für die Umsetzung von Präventionsmaßnahmen, die sich gleichermaßen an Wissenschaftler und Praktiker richten.