About: iSys is an academic journal published by Brazilian Computer Society. The journal publishes majorly in the area(s): Computer science & Medicine. It has an ISSN identifier of 1984-2902. It is also open access. Over the lifetime, 24 publications have been published receiving 5 citations. The journal is also known as: iSys, Revista Brasileira de Sistemas de Informação.
TL;DR: In this paper , a qualitative feature validation with two stages is proposed to identify which characteristics are relevant to health professionals, aiming at machine learning and deep learning solutions to depression detection, and validate this set of features using a semi-structured interview with three psychologists.
Abstract: Understanding individuals, social dynamics, and data consumption within social media platforms arouse curiosity and attention in the scientific community and society. The scientific community has shown how a user's mental health can be affected by technology and its digital environment. For example, a user exposed to constant explicit hate speech may suffer an impact on its well-being. There are already efforts in this research area that propose automated solutions to identify users who require professional health attention. However, these solutions do not frequently use the experience and background from the health acknowledgment area in their contribution construction. To fill this gap, we propose a qualitative feature validation with two stages to identify which characteristics are relevant to health professionals, aiming at machine learning and deep learning solutions to depression detection. First, we validate this set of features using a semi-structured interview with three psychologists. Afterward, we apply a survey with domain experts to validate the information extracted from the first stage. This feature validation will allow us to have a detailed view of how functional and practical are the features commonly used in machine-learning-based solutions and how they are close to clinical analysis.
TL;DR: O G-Priv é um guia para auxiliar na especificação de requisitos de privacidade em conformidade com a LGPD.
Abstract: A Lei Geral de Proteção de Dados (LGPD) visa proteger os dados pessoais, inclusive nos meios digitais, processado por pessoa natural ou por pessoa jurídica de direito público ou privado. Atualmente, as organizações precisam implementar várias medidas para garantir que seus sistemas de software estejam em conformidade com a lei. No entanto, a LGPD, assim como outras legislações é de difícil entendimento por parte de analistas de requisitos. Em particular, existem dificuldades para extrair e operacionalizar requisitos de privacidade. Este artigo propõe um catálogo de padrões de privacidade e um guia G-Priv, para auxiliar a especificação de requisitos de privacidade em conformidade com a LGPD. Finalmente, conduzimos um survey com 18 profissionais para avaliar o G-Priv.
TL;DR: In this paper , the authors investigate the application of text analysis methods to help health-related scientific communicators produce educational material to combat misinformation in a weekly manner due to the ephemeral nature of COVID-19 misinformation in social media.
Abstract: Misinformation has plagued citizens’ lives, especially on social networks. During the COVID-19 pandemic, the proliferation of competing narratives and dissemination of false or inaccurate news about the pandemic has reached such a state that led the World Health Organization to classify it as an infodemic. However, few resources are available to combat misinformation in this new and evolving domain, especially considering how social networks allow the rapid spreading of false narratives. In this case, the lack of resources, such as methods, tools, and reliable information on the virus, hinders our ability to combat this misinformation. In this work, we investigate the application of Text Analysis methods to help health-related scientific communicators produce educational material to combat misinformation. This study was conducted in association with the Scientific Communication sector of FIOCRUZ, a health research institution in Brazil, aiming to monitor COVID-19-related fake news and produce educational material to combat misinformation in a weekly manner due to the ephemeral nature of COVID-19 misinformation in social media. As the main findings of this work, we provide (1) a pipeline for automatically collecting and analyzing news and social media posts regarding COVID-19 in orderto provide science communicators with a weekly contextualized view of topics related to COVID-19 in social media; (2) we analyzed the effect of different resources and methods in the analytical tools employed in this work for detecting health-related misinformation in the Portuguese language, and finally, (3) we provided to journalists and science communicators in FIOCRUZ computational tools to automatically monitor COVID-related misinformation in social media, focusing on Twitter, aiming to contribute to definition of the weekly science communication agenda of the institution. Indeed, we indicate the type of resources to combat misinformation in the pandemic, and our approach can handle the detection of misinformation on Twitter social networks within the COVID-19 domain.
TL;DR: In this article , the authors model a multipartite network to perform cross-state comparison analyses based on the cosine distance for Brazilian reading preferences and explore the impact of the relationships between geographic, socioeconomic and demographic factors and both shared books and literary genres across Brazilian states.
Abstract: As a multicultural and ethnically diverse nation, Brazil has singular cultural identities in accents, gastronomy and traditions, also reflected in its literature. Here, we model a multipartite network to perform cross-state comparison analyses based on the cosine distance for Brazilian reading preferences. We also explore the impact of the relationships between geographic, socioeconomic, and demographic factors and both shared books and literary genres across Brazilian states. Finally, we extract the backbone of networks to identify cultural clusters in Brazil and each of its macro-regions. Such cross-state analyses highlight the country’s rich cultural diversity, where each region shows its own identity. Our findings open opportunities to the book industry by enhancing current knowledge on social indicators related to reading preferences.
TL;DR: The Scripting Your Process (SYP) as discussed by the authors is an artefato científico concebido por meio da Design Science Research Methodology (DSRM).
Abstract: As narrativas são essenciais para jogos e para modelos de processos de negócio por serem uma forma de comunicação mais próxima da linguagem das pessoas, facilitando a transmissão e entendimento de contextos. Em Jogos Digitais Baseados em Processos de Negócio (JDBPN) as narrativas auxiliam no entendimento e ambientação dos jogadores, apresentando o processo de negócio de maneira lúdica. No entanto, projetar narrativas para JDBPN não é simples. É necessário que os responsáveis por elas saibam traduzir os elementos do modelo do processo para elementos narrativos para que ambos sejam coerentes. Desta maneira, o objetivo desse trabalho é apresentar o método Scripting Your Process (SYP), criado para fornecer suporte ao projeto de narrativas a partir modelos de processo de negócio de forma fácil, viável e replicável. O método SYP é um artefato científico concebido por meio da Design Science Research Methodology (DSRM). Como previsto nessa metodologia, o SYP foi demonstrado e avaliado e os dados coletados na avaliação foram analisados por abordagens quantitativas e qualitativas. Assim, os resultados dão indícios que o método SYP cumpre os objetivos da pesquisa, contribuindo com lacunas identificadas para o design de JDBPN, além de ser uma possibilidade alternativa de apresentar e representar modelos de processo de negócio em formato narrativo.