Abstract
Medico-scientific concepts are not easily understood by laypeople that frequently use lay synonyms. For this reason, strategies that help users formulate health queries are essential. Health Suggestions is an existing extension for Google Chrome that provides suggestions in lay and medico-scientific terminologies, both in English and Portuguese. This work proposes, evaluates, and compares further strategies for generating suggestions based on the initial consumer query, using multi-concept recognition and the Unified Medical Language System (UMLS). The evaluation was done with an English and a Portuguese test collection, considering as baseline the suggestions initially provided by Health Suggestions. Given the importance of understandability, we used measures that combine relevance and understandability, namely, uRBP and uRBPgr. Our best method merges the Consumer Health Vocabulary (CHV)-preferred expression for each concept identified in the initial query for lay suggestions and the UMLS-preferred expressions for medico-scientific suggestions. Multi-concept recognition was critical for this improvement.

Abstract
Archives are faced with great challenges due to the vast amounts of data they have to curate. New data models are required, and work is underway. The International Council on Archives is creating the RiC-CM (Records in Context), and there is a long line of work in museums with the CIDOC-CRM (CIDOC Conceptual Reference Model). Both models are based on ontologies to represent cultural heritage data and link them to other information. The Portuguese National Archives hold a collection with over 3.5 million metadata records, described with the ISAD(G) standard. The archives are designing a new linked data model and a technological platform with applications for archive contributors, archivists, and the public. The current work extends CIDOC-CRM into ArchOnto, an ontology-based model for archives. The model defines the relevant archival entities and properties and will be used to migrate existing records. ArchOnto accommodates the existing ISAD(G) information and takes into account its implementation with current technologies. The model is evaluated with records from representative fonds. After the test on these samples, the model is ready to be populated with the semi-automatic transformation of the ISAD records. The evaluation of the model and the population strategies will proceed with experiments involving professional and lay users.

Abstract
Looking for health information is one of the most popular activities online. However, the specificity of language on this domain is frequently an obstacle to comprehension, especially for the ones with lower levels of health literacy. For this reason, search engines should consider the readability of health content and, if possible, adapt it to the user behind the search. In this work, we explore methods to assess the readability of health content automatically. We propose features capable of measuring the specificity of a medical text and estimate the knowledge necessary to comprehend it. The features are based on information retrieval metrics and the log-likelihood of a text with lay and medico-scientific language models. To evaluate our methods, we built and used a dataset composed of health articles of Simple English Wikipedia and the respective documents in ordinary Wikipedia. We achieved a maximum accuracy of 88% in binary classifications (easy versus hard-to-read). We found out that the machine learning algorithm does not significantly interfere with performance. We also experimented and compared different features combinations. The features using the values of the log-likelihood of a text with lay and medico-scientific language models perform better than all the others.

Abstract
Research data management is the basis for making data more Findable, Accessible, Interoperable and Reusable. In this context, little attention is given to research data in image format. This article presents the preliminary results of a study on the habits related to the management of images in research. We collected 107 answers from researchers using a questionnaire. These researchers were PhD students, fellows and university professors from Life and Health Sciences, Exact Sciences and Engineering, Natural and Environmental Sciences and Social Sciences and Humanities. This study shows that 83.2% of researcher use images as research data, however, its use is generally not accompanied by a guidance document such as a research data management plan. These results provide valuable insights into the processes and habits regarding the production and use of images in the research context.

Abstract
Application execution required in cloud and fog architectures are generally heterogeneous in terms of device and application contexts. Scaling these requirements on these architectures is an optimization problem with multiple restrictions. Despite countless efforts, task scheduling in these architectures continue to present some enticing challenges that can lead us to the question how tasks are routed between different physical devices, fog nodes and cloud. In fog, due to its density and heterogeneity of devices, the scheduling is very complex and in the literature, there are still few studies that have been conducted. However, scheduling in the cloud has been widely studied. Nonetheless, many surveys address this issue from the perspective of service providers or optimize application quality of service (QoS) levels. Also, they ignore contextual information at the level of the device and end users and their user experiences. In this paper, we conducted a systematic review of the literature on the main task by: scheduling algorithms in the existing cloud and fog architecture; studying and discussing their limitations, and we explored and suggested some perspectives for improvement.

Abstract
Escalonamento na arquitetura cloud e no paradigma fog continuam a apresentar alguns desafios aliciantes. Na cloud, segundo o conhecimento dos autores, ela é amplamente estudada e em muitas pesquisas é abordada na perspetiva de provedores de serviço. Na fog, é muito complexo e, existem poucos estudos. Procurando trazer contributos inovadores nas áreas de escalonamento de tarefas, neste artigo, propomos uma solução para o problema de escalonamento de aplicações móveis sensíveis ao contexto para o paradigma fog computing onde diferentes parâmetros de contexto são normalizados através da normalização Min-Max, as prioridades são definidas através da aplicação da técnica da Regressão Linear Múltipla (RLM) e o escalonamento é feito recorrendo a técnica de Otimização de Programação Não Linear Multi-objetivo (MONLP).;Scheduling in cloud architecture and in the fog paradigm continue to present some exciting challenges. In the cloud, according to the authors' knowledge, it is widely studied and in many researches, it is addressed from the perspective of service providers. In fog, it is very complex and there are few studies. Trying to bring innovative contributions in the areas of task scheduling, in this paper we propose a solution to the problem of context-aware scheduling of mobile applications for the fog computing paradigm, where different context parameters are normalized through Min-Max normalization, priorities are defined by applying the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Nonlinear Programming Optimization (MONLP) technique.