Abstract
Wikipedia is an online, free, multi-language, and collaborative encyclopedia, currently one of the most significant information sources on the web. The open nature of Wikipedia contributions raises concerns about the quality of its information. Previous studies have addressed this issue using manual evaluations and proposing generic measures for quality assessment. In this work, we focus on the quality of health-related content. For this purpose, we use general and health-specific features from Wikipedia articles to propose health-specific metrics. We evaluate these metrics using a set of Wikipedia articles previously assessed by WikiProject Medicine. We conclude that it is possible to combine generic and specific metrics to determine health-related content's information quality. These metrics are computed automatically and can be used by curators to identify quality issues. Along with the explored features, these metrics can also be used in approaches that automatically classify the quality of Wikipedia health-related articles.

Abstract
Archives are evolving. Analog archives are becoming increasingly digitized and linked with other cultural heritage institutions and information sources. Diverse forms of born-digital archives are appearing. This diversity asks for systematic ways to characterize existing archives managing physical or digital records. We conducted a systematic review to identify and understand how archives are characterized. From the 885 identified articles, only 15 were focused on archives’ characterization and, therefore, included in the study. We found several characterization features organized in three main groups: archival materials, provided services, and internal processes. © 2021, Springer Nature Switzerland AG.

Abstract
Wikipedia is a free, multilingual, and collaborative online encyclopedia. Nowadays, it is one of the largest sources of online knowledge, often appearing at the top of the results of the major search engines, being one of the most sought-after resources by the public searching for health information. The collaborative nature of Wikipedia raises security concerns since this information is used for decision-making, especially in the health area. Despite being available in hundreds of idioms, there are asymmetries between idioms, namely regarding their quality. In this work, we compare the quality of health information on Wikipedia between idioms with 100 million native speakers or more, and also in Greek, Italian, Korean, Turkish, Persian, Catalan and Hebrew, for historical tradition. Quality metrics are applied to health and medical articles in English, maintained by WikiProject Medicine, and their versions in the above idioms. With this, we contribute to a clarification of the role of Wikipedia in the access to health information. We demonstrate differences in both the quantity and quality of information available between idioms. English is the idiom with the highest quality in general. Urdu, Greek, Indonesian, and Hindi achieved lower values of quality.

Abstract
O cenário atual apresenta, cotidianamente, novas ferramentas tecnológicas, que auxiliam e que permitem dar maior significado ao processo educacional. Nesse contexto, destacamos, principalmente, as tecnologias baseadas em Inteligência Artificial (IA), que são capazes de interpretar, estruturar e cruzar dados, gerando predições com as informações analisadas, podendo, ainda, realizar inferências automáticas e interação com os usuários. Diante da existência de poderosos recursos tecnológicos e a massificação de cursos on-line, podemos afirmar que as ferramentas de acompanhamento evolutivo dos participantes, a interação e as metodologias educacionais tornam-se fundamentais no processo de ensino-aprendizagem. Esse artigo tem o objetivo de nortear a especificação de requisitos do modelo tecnológico, que visa ao auxílio no processo de ensino-aprendizado em curso on-line. Essa investigação utilizou-se de entrevistas e inquéritos com gestores e alunos de universidades brasileiras, para identificar como a IA, aliada a outras tecnologias, poderia auxiliar o processo de ensino-aprendizado, além de proporcionar, aos gestores e professores (mediadores pedagógicos), mais informações operacionais, táticas e estratégicas, nas tomadas de decisão acadêmica. Por último, ratificou-se que, nas instituições analisadas, ainda, não existe o uso da IA e, também, há uma carência de tecnologias emergentes, que possibilitariam automatizar atividades operativas realizadas pelos professores.

Abstract
Despite the significant increase in the use of digital devices, and the access to e-books by younger ages, the printed book still remains very important. Nowadays, although many communication processes and information exchanges have a digital support, the importance of using printed paper is acknowledged in many contexts. Both the paper and the digital media have unique advantages: digital media integrate with audiovisual and interactive resources, and the paper book supports interactions such as tactile and kinesthetic feedback given to both hands. In recent years there have been several commercial products designated as "augmented books", using augmented reality technologies to provide the reader with more layers of information, thereby fostering the use of the book in new ways. So, in this concept paper we describe part of the research and outcomes of project CHIC – C3, aimed at designing and developing a platform for managing the production of digital content connected with printed books. Furthermore, we propose a model for the gamification of digital content based on the printed book, mainly aimed at educational purposes. A proof of concept for the model was built in the form of a companion platform, supported by the Moodle LMS, fully integrated with the main CHIC website. Readers are able to access the platform, engage in several content related games, and interact with other readers.

Abstract
Task scheduling in fog paradigm is highly complex and in the literature, there are still few studies. In the cloud architecture, it is widely studied and in many researches, it is approached from the perspective of service providers. Trying to bring innovative contributions in these areas, in this paper, we propose a model to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization; requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique.