With the arrival of COVID-19, the scientific community was mobilized, not only in researches in the biomedical field but also in searching for ways to support professionals working on the front lines of fighting the virus. In the context of information systems (IS), several systems were developed to contribute to this collective effort. In this study, we provide an overview of that profusion of systems designed to support the fight against COVID-19. To achieve this goal, a systematic mapping of the technical state of the art was carried out in patent documents in 2020. The study compared the national technological production to the international scenario and listed the main challenges regarding the technological production during COVID-19.

The COVID-19 pandemic has been pressuring the whole society and overloading hospital systems. Machine learning models designed to predict hospitalizations, for example, can contribute to better targeting hospital resources. However, as the excess of information, often irrelevant or redundant, can impair the performance of predictive models, we propose in this work a hybrid approach to attribute selection. This method aims to find an optimal attribute subset through a genetic algorithm, which considers the results of a classification model in its evaluation function to improve the hospitalization need prediction of COVID-19 patients. We evaluated this approach in a database of more than 200 thousand COVID-19 patients from the State Health Secretariat of Rio Grande do Sul. We provided an increase of 18% in the classification precision for patients with hospitalization necessities. In a real-time application, this would also mean greater precision in targeting resources, as well as, consequently and mainly, improved service to the infected population.

The COVID-19 pandemic forced governments to impose harsh social restrictions as part of the strategy to deal with the situation, forcing society to adapt quickly. In response to this emergency, we offered training in technological skills (i.e., development of information systems) and behavioral skills (i.e., entrepreneurship mindset) in an effort to empower people to better tackle the situation. This paper explains the foundations considered in the proposal of the intervention, as well as the results obtained. Through development in line with university extension principles, the intervention promotes the empowerment and participation of citizens in creating digital solutions that enable society to overcome its problems, especially those caused by the imposition of social distance. Taking advantage of web-centric software platforms, participants of the offered training were able to build support systems for online businesses. The results achieved by the 48 graduates reinforce the viability and importance of citizen participation in the creation of innovations based on digital entrepreneurship.

The COVID-19 epidemic requires clear and reliable information to guide the population. Visualization is a powerful tool to contribute to the understanding of this data. However, just divulging this resource is not enough to guarantee this understanding. It is important to support users in analyzing this data, making this process easier and more transparent, especially for users with little (or no) literacy. In this work, we define an inefficient graphics indicator, that is, with the potential to be misinterpreted or difficult to understand, according to the basic guidelines of the data visualization area. These guidelines were selected through a literature review, forming a repository of practices that guide good visualization design and provide the indicator assessment items. The proposal is apply in the Brazilian scenario of COVID-19, analyzing the official visualizations and highlighting the failures of several epidemiological perspectives on the epidemic. Through a case study where volunteers analyzed the official data and our recommendations, we proved that the indicator is effective in detecting and helping to understand the visualization. We highlight the alert for the need for greater care in the creation of graphics by the government so as not to compromise the understanding of the citizens who use them.

This paper presents the identification of peoples behavioral changes during the Covid-19 pandemic period by analyzing the terms searched on Google and news. We developed an artifact, called CovidTrends, used the DSR (Design Science Research) epistemological approach, the Design Science Research Methodology (DSRM), and the document analysis method to list infodemic or peoples behavioral trending of interest on Google and correlating them to news within the timeframe in which the terms' queries peaked. CovidTrends enabled the identification of three main behaviors, which we verified on news reporting in the media. Then, it proves to be appropriate to support data analysis and identify peoples pandemic behavior.

A materialização da universalização da proteção social, prevista na Constituição do Brasil no capítulo da Seguridade Social, com o tripé de Saúde, Previdência e Assistência Social, especificamente no âmbito da Previdência, se dá através da concessão e manutenção de benefícios a todos os brasileiros que necessitem dessa proteção, o que gera uma demanda enorme de milhões de requisições de benefícios anuais ao INSS, que é o operador desses serviços. Receber e analisar os pedidos de benefícios, entre outros processos, em tempo hábil e com assertividade, é algo complexo e desafiador, seja pelo volume de milhões de pedidos de benefícios anuais, seja pela diversidade de benefícios disponíveis, pelos diferentes critérios de concessão e pela urgência que a natureza desses pedidos exige para a manutenção da vida dos requerentes. Dentro deste contexto, o presente estudo vem no sentido de desenvolver alguns modelos, utilizando técnicas de aprendizado de máquina, e selecionar o melhor deles, que possa predizer se determinada requisição de benefício será concedida ou indeferida, oferecendo uma oportunidade de que possa ser utilizado como mais uma ferramenta para ajudar na análise de novos pedidos de benefício, abrindo espaço para que a dinâmica do processo de análise possa ser direcionada de forma mais ágil e assertiva. A fonte de dados para a construção dos modelos, neste trabalho, foi obtida no Portal de Dados Abertos do INSS, que constam do Plano de Dados Abertos do INSS, com os arquivos mensais de Benefícios Decididos (Concedidos ou Indeferidos) no período de dezembro de 2018 a junho de 2020. Como escopo de análise, foram abordados algoritmos como KNN, SVC, Árvores de Decisão, Regressão Logística etc. Também foram construídos modelos que utilizam as técnicas de Ensemble Bagging e Boosting, chegando a um conjunto de dezessete algoritmos analisados. O algoritmo que obteve o melhor desempenho, utilizando a métrica F1 como determinante, foi o Classificador XGradient Boosting (XGBoost) com 80%. Com este, o modelo realiza a predição com aproximadamente 84% de Precisão, 76% de Sensibilidade e 81% para ROC_AUC. Como resultado do estudo, obteve-se um modelo capaz de efetuar a predição se determinado requerimento de benefício seria concedido ou indeferido, com base nos dados de requerimento, com uma performance dentro das expectativas estabelecidas nos objetivos.

The observation, in the context of the organization and the projects, has been defined, in the recent literature, by the transparency construct, and the development of tools to systematize the transparency has presented as a challenge to the organizations. In this sense, the observatories are presented as information systems that support the collection, organization, storage, analysis, and observation publications, providing transparency. Nowadays, it is possible to identify the existence of observatories related to the most variable themes, such as, health, environment, social media, cities, web, among others. In addition to that, projects and its management may also benefit with the development of these observatories. The use of observatories in the most variable areas of knowledge added to them a striking feature, typological diversity. That is, there is not only one model of observatories; however, the diversity does not stop from establishing guidelines that can guide the conduct of these observatories. Before this scenario, this research has the goal to propose a model that contributes to the comprehension and the development of projects observatories. To achieve the objective of this research, a set of initial exploratory studies was developed with the objective of developing pilot projects for project observatories. In addition, a systematic mapping of the literature on observatories has also been developed. The results of the initial exploratory studies and of the systematic mapping of the literature gave rise to the preliminary version of the model for project observatories.

The management of municipal finances is crucial in providing quality services and infrastructure to citizens and the availability of data and indicators that provide an individualized view of fiscal management is relatively recent. Therefore, we seek to identify which socioeconomic characteristics of brazilian municipalities appear to have the greatest influence on the FIRJAN Fiscal Management Index (IFGF) for the more than five thousand Brazilian municipalities, as well as to identify homogeneous groups of cities based on such characteristics, using the K-Means method for clustering. Among the main conclusions, we highlight that Brazilian cities are very homogeneous and face the same social vulnerabilities and that the average level of municipal investment does not significantly differ between groups, even when we compare groups with greater socioeconomic disparities.

In the last decade, the topic of smart city has been researched, culminating in proposals that aim to improve the quality of life of the population. One of the possibilities found is the design and construction of solutions that use geotechnologies that can be strong allies in the development of cities. The purpose of this article is to present a systematic mapping on geotechnologies used in the context of smart cities, with a focus on identifying which problems they solve, the specificities of their use and which proposals were actually applied by the government. 37 studies were analyzed that address the defined research questions. Among these studies it was possible to see that solutions involving software are the most recurring. Through this mapping it was observed that Geographic Information Systems are the most cited geotechnology in the analyzed publications, and that there is still a lack of applications for proposals by the government.

Brazilian governments have made progress on transparency - including publishing Open Government Data (OGD). Although there are still gaps to be filled on how to address properly OGD supply and demand. The consumption has been limited by data and metadata quality, heterogeneity, format and data structure. In this paper, we explore the Re-public design - a generic process that aims to re-publish OGD in RDF format - based on OWL (Ontology Web Language) ontologies. Re-public purpose is to improve data connectivity and availability to consumption - both can be achieved by adding a semantic layer to data. We applied Re-public on the government budget field: using Constitucional Transfers to Cities dataset (Transferências Constitucionais para Municípios) and DBpedia open data. The results show that, using Re-Public, its possible to publish original CSV files in a new format that enables semantic web tools to be used.

The aging of the population generates the incidence of diseases characteristic of advancing age, among them Alzheimer's Disease (AD). Patients with this illness, which affects neurological functions, need support to maintain maximum independence and security during this stage of life, as the cure and reversal of symptoms have not yet been discovered. This work aims to propose a model that, based on physiological data received from external applications, makes it possible to identify possible dangerous behaviors of patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer's disease using the analysis of Context Histories and Context Prediction, which considering the state of the art, is the only one that uses analysis of Context Histories to perform predictions. The computational model used in its structure an ontology developed by this project for the treatment of contexts within Alzheimer's. In addition, a simulator called DCARE Dataset Simulator, of Activity Daily Living (ADLs) which generates datasets were developed to perform tests of the model and an ontology has been proposed for the treatment of contexts in the context of Alzheimer's. DCARE is based on the experimental research method, to understand the disease and find solutions to minimize its impact on the daily monitoring of patients. Scenarios used in the construction of the model were created over interviews with five specialists in the care for patients with AD. Tests were performed with the mass of data with 1026 scenarios generated by the proposed simulator by this work. The results revealed that the predictions of the model scenarios reached the objective of the work, achieving 97.44% of the average accuracy rate.

Internet of Things (IoT) provides smart objects with the ability to connect to the Internet, allowing the exchange of information among them to provide a certain service and the development of innovative applications in several domains, including e-Health, in which it is called Internet of Health Things (IoHT). This domain can be critical specially when the application deals with the monitoring of the user health in real-time, what demands software quality assurance, even more than in other applications. Measures can be used to support that, for example, measures can suggest which components need refactoring to improve the software code, thus improving the application. In this work, we report how to do that with two existing measures that guide the refactoring process of an IoHT application for fall detection, called WatchAlert. These measures indicate that changes in both the architecture and the algorithms for fall detection should occur. After the refactoring, the app accuracy was improved from 73.3% to 92.7%. We believe that this work can contribute to other studies focusing on developing applications on the IoHT domain using a methodology, a set of refactoring techniques, and lessons learned that could be replicated to improve the quality of this type of application.

Laboratórios de análises de solos demandam volumes grandes de dados empregados na agricultura de precisão. Dentre eles, parâmetros que representam fertilidade de solos como textura e matéria orgânica orientam o processo de adubação. No entanto, este processo pode se tornar demorado, limitando assim sua utilidade. Sendo assim, este artigo propõe uma ontologia denominada Tellus-Onto que estende o estado da arte na classificação de solos brasileiros de acordo com a composição orgânica e textural. Uma série de axiomas e regras semânticas foram empregadas para proporcionar a realização de consultas e inferências sobre sua base instanciada. Para testar a ontologia foram instanciados 98 resultados de amostras de solos e inferidos suas classificações de modo preciso e automático.

Civil construction has increasingly invested in techniques for quality assurance and control. Service inspection is a key activity in the quality control process is the inspection of services. It is executed by specialized professionals in order to register the quality of services. Through these inspections, it is possible to detect faults during construction. To get an overview of the approaches/techniques of information systems that have been used to support the inspection activity, a systematic mapping of the literature has been done. The literature mapping indicates the use of information systems to collect inspection data, mainly using technologies such as laser scanner, augmented reality, internal positioning and forms. Some works also use previous construction models, generated using BIM and 3D CAD models,to aid the inspection activity. Result analysis indicates that there are signs of immaturity within there search area, and it is recommended that existing systems are complemented with inspection issues tracking and quality control history features.

In university restaurants, food waste is frequently, which can generate a significant loss of food produced. Inaccuracies often cause food waste in predicting the number of meals consumed by students. Three different Machine Learning (ML) algorithms were tested and evaluated in this context: K-Nearest Neighbors, Random Forest, and Artificial Neural Networks using data from the Anônimo campus restaurant at the Universidade Anônima. This work performed an analysis and pre-processing of the data, applying them to the ML algorithms, which were evaluated and compared with the human prediction and an algorithm that uses the mean as an estimator. We also developed the software to manage the university restaurant, using ML as an estimator. The results showed that in three of the four scenarios, the ML algorithms did better than the human prediction or an algorithm that does not use ML. In additional comparison, the human prediction did better than one of the four scenarios. We concluded that ML might be a reasonable solution in the future to reduce waste in college restaurants.

This article introduces Mon4Aware, a multi-objective and context-aware approach to decompose monolithic applications. Mon4Aware stands out for: (1) using optimization based on multiple criteria to allow monolithic applications to be modularized in different ways, making its decomposition process flexible; and (2) to propose a context meta-model to allow the decomposed modules of the monolithic application to be able to adapt under certain contextual situations. Software developers can use Mon4Aware as a guide to modernization activities for monolithic applications, making them less error-prone. The approach was evaluated through a case study, in which the conceptual viability of Mon4Aware was demonstrated, as well as promising initial results in generating recommendations for the decomposition of monolithic applications.

Despite all efforts, the requirements elicitation task is still considered non-trivial, especially for complex (non-linear) systems. In these systems, technological support must perform more resiliently, that is, be more adaptable to deal with uncertain situations. The Resilience Engineering provides the Functional Resonance Analysis Method (FRAM) to model these systems based on a description of the actual work (Work-As-Done - WAD). Therefore, unexpected events commonly associated with variability and improvisations become more explicit with that method. Thus, a multidisciplinary approach can contribute to requirements elicitation, since FRAM models deal with variability, unpredictability, and adaptation in complex socio-technical systems. This study applies Design Science Research to project a heuristic model to gather information from FRAM models to elicit functional and non-functional requirements, showing the contributions of Resilience Engineering to Requirements Engineering to identify software requirements for complex systems.

The different ways of representing a source code in different programming languages create a heterogeneous context. In addition, the use of multiple programming languages in a single source code (polyglot programming) brings a wide choice of terms from different languages, libraries and structures. These facts prevent the direct exchange of information between source codes of different programming languages, requiring specialized knowledge of the programming languages involved. In this article, we present an ontology-based method for source code interoperability that provides an alternative to mitigate heterogeneity problems. In this sense, we apply semantic interoperability to ensure that shared information has its meanings understood and operationalized by source code written in different programming languages.

Interoperability is the ability of heterogeneous systems to interact and exchange information efficiently and effectively through a planned process. Interoperability has several faces and usually, the syntactic interoperability type is pointed out as the most basic one for keeping information systems interoperable. Several other types of interoperability, such as semantics, pragmatics, syntactic, organizational have been addressed as important non-functional requirements for information systems. Still lacking consensus, there are several terms used to characterize interoperability. Thus, can bring confusion and ambiguity in the use of these terms, making it difficult to have a comprehensive view of related or similar interoperability proposals. While some primary studies address specific solutions for interoperability aspects, secondary studies seek to summarize some research directions and practical knowledge. This paper presents a tertiary study performed to investigate the current research state on interoperability, organizing knowledge that has already been defined, about types of interoperability known and the new ones. We conducted a tertiary study to achieve the stated goal based on a set of three research questions. The study identified selected and analyzed 15 secondary studies to answer the formulated research questions. Although twenty-seven different interoperability types were found, a specific subset is the most cited among these secondary studies. Its results can help to identify points that still require further investigation in the interoperability research field.

As different cloud computing services have emerged over the years, the diversity of technologies and the lack of standardization has given rise to an interoperability problem in cloud computing. Cloud computing services include those such as Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Data as a Service (DaaS). In this context, interoperability enables a service to communicate with another service transparently. Among the solutions proposed in the literature, a middleware can be used to intermediate such communication and to mitigate the lack of interoperability in cloud computing. For instance, the middleware MIDAS (Middleware for DaaS and SaaS) provides transparent interoperability between SaaS and DaaS. Although MIDAS current version promotes syntactic interoperability, semantic interoperability is only superficially addressed. In collaboration with this project, we develop an OWL-based ontology to formally represent the communication between SaaS and DaaS, and discuss its strengths in providing semantic interoperability on MIDAS. We conduct a set of experiments to validate our ontology. We evaluate intrinsic (consistency, correctness, acceptance) and extrinsic (integration between ontology and MIDAS) issues. Results provide evidence that a semantic MIDAS interoperability can be enhanced by our ontology.

A System-of-systems (SoS) is defined as an arrangement of independent systems that work together to accomplish missions that could not be achieved by a single system in isolation. SoS can be observed in several domains, including mobility, healthcare, and safety. A significant concern of SoS engineers refers to the constituent systems? independence, given that they may decide to change the level of contribution or abandon the SoS, decreasing the overall reliability. In this paper, we performed an analysis of an SoS running in a Brazilian public organization focusing on identifying reliability issues. To do so, we first characterized the SoS by identifying the missions, constituent systems, relationships, and capabilities. We further conducted a focus group and a survey with the SoS engineering team to identify situations that decrease SoS reliability and how they can be treated. Both the focus group and survey allowed us to propose an initial mKAOS extension that supports reliability analysis. We expected that our findings and the proposed extension can assist SoS engineers to realize some initial reliability issues.

Although the approaches conceptualizing software ecosystems (SECO) have gained more relevance since 2010, there are not many studies addressing proprietary SECO. In a proprietary SECO, organizations are concerned with keeping their assets protected by intellectual property so that they are not exposed publicly, preventing this information from being used by competitors. Understanding how the governance mechanisms works and how organizations handle incidents on their platforms are real challenges in this scenario. In this work, we report three main goals: i) providing an update on SECO governance mechanisms and SECO health metrics; ii) analyzing SECO classifications and evaluating the evolution of proprietary SECO; and iii) exploring the organization's strategies and investigating SECO incident management process. To do so, a longitudinal literature study was performed on SECO covering 2006 to 2015. As such, we analyzed 422 studies from 2016 to 2020 and provided an updated perspective based on seven research questions, as well as a refined perspective on proprietary SECO and a deeper understanding about incident management process. Our findings reveal that governance strategies aimed to share knowledge provides continuous innovation and increases competitive advantage in the software market.

Context: The Software Ecosystem (SECO) strategy dominates the digital game industry. The leading game companies adopted SECO dynamics, forcing external actors (e.g., game studios) to adapt to a highly competitive scenario. Objective: Considering the significant growth of the Brazilian digital games industry in the last years, this study aims to present and evaluate a business model proposed for the independent Brazilian game studios immersed in Game SECO (GSECO) context. Method: We explained the proposed model, and conducted 15 online interviews to capture comments and suggestions. Three researchers applied the Grounded Theory (GT) procedures to extract actions to improve the business model. Results: Twenty-seven suggestions to improve the proposed business model emerged. Finally, we use the business model concepts and subdomains to define the suggestions to be adopted, and identify opportunities for future work. Conclusion: The first version of the business model for independent studios in GSECO reached a high level of relevance in its first evaluation. Some interviewees highlighted the differential of the model in directing the construction of a digital game business contextualized in the Brazilian scenario and how the tool can directly contribute to the maturity of the industry. Finally, as future work, we identified the need to expand the model for all subdomains and the evaluation's continuity with actors from Brazil's various regions.

The management of several information systems (IS) faces challenges with the number of existing, autonomous, distributed, and heterogeneous databases. By the need for an integrated access, these arrangements have been explored as federated IS (FIS). FIS are affected by several situations that generate tensions in their management. In order to discover these organizational tensions, a qualitative case study in a real context - bank credit - based on interviews was conducted with stakeholders of a real FIS. Next, we performed the scenario analysis with the data from the interviews and used the GUT Matrix tool to analyze and identify the tensions. Finally, a decision map to analyze and identify the tensions and an ontology, based on the Knowledge Intensive Process Ontology (KIPO), were used to map the tensions in the management of bank credit SIF. The results show that both the decision map and the developed ontology can be used as a practical tool to support practitioners in industry in the FIS management. As a contribution to the FIS area, this work presents another approach to support IS management focused on conceptual modeling and knowledge intensive process decisions. In addition, the developed ontology also allows the description of tacit knowledge to support FIS managers in the investigated context.

A proliferação da tecnologia da informação e comunicação e o advento da Internet resultaram em uma mudança no nível de complexidade inerente à sociedade e, consequentemente, às organizações, acarretando em sistemas de informação cada vez mais complexos e integrados. SI complexos são compreendidos como uma classe especial de sistemas formados por um grande número de subsistemas heterogêneos, caracterizados por propriedades coletivas e emergentes, que interagem e influenciam uns aos outros através de uma diversidade de conexões e loops de feedback. Nesta perspectiva, os sistemas de informação em saúde pública, compostos por diversos subsistemas que necessitam interagir, são caracterizados como complexos. A pesquisa objetivou analisar, na ótica da teoria da complexidade, os efeitos da integração dos sistemas de informação nos fluxos de assistência previstos para o SUS. Foram mapeados 72 sistemas de informação em saúde diferentes, os quais formaram uma rede densa e inter-relacionada. Os resultados da pesquisa revelaram os seguintes efeitos: que a auto-organização tende a diminuir o nível de completude e detalhamento da informação que circula no SUS; a coadaptação maximiza o acoplamento dos sistemas; a fractalidade sobrecarrega as informações nos níveis superiores (federal e estadual) do sistema; a emergência, por outro lado, ajuda a criar estruturas e sistemas inovadores.

Nowadays, there are several technologies (methods, techniques, among others) that help agile development teams to improve the Usability and User eXperience (UX) in their projects. Usability and UX are relevant aspects of software quality. In the literature, we find few technologies that help to improve the Usability and UX jointly at the beginning of agile process, mainly during the design phase, where to perform changes is easier and cheaper. Therefore, in our paper, we presented the technique User Experience and Usability Guidelines for Agile Project (UXUG-AP), that aims to support the Usability and UX design. UXUG-AP is composed by categories, subcategories, and specific guidelines to help the prototyping process. We also presented an observational study with agile professionals. The study was done to evaluate the ease of use, perceived usefulness and the intention to future use from UXUG-AP. The results showed that is necessary to have requirements well defined in order to have a better use of the technique. In additional, we presented an interview with experts in agile design which provided their opinions about UXUG-AP. The interview results showed that some designers are accustomed to evaluate their projects after development, instead of avoiding usability and UX issues at the beginning of process.

Micro and small companies face different challenges related to entering international markets. Knowledge about these aspects is essential for understanding how to start internationalization activities. This information is present in the literature and in agencies/programs that promote international activities. This article presents a descriptive documentary analysis of the internationalization processes of software companies. To carry out the analysis, scientific articles and internationalization guides were found in the main internationalization incentive programs. The results presented the 5 basic questions about the internationalization process, namely: motivations, products offered, how companies enter the international market, when international expansion takes place, and the main countries of entry according to the literature and guides of internationalization. The organizational characteristics of the companies were identified, the paths followed by the software companies, services and how information is provided by the incentive agencies.

In this work, we formally build the construct of games as information systems. Starting from models and definitions that describe them, we present models that show game elements: components, players, environment, and their relations; as information systems elements. This paper also presents the analysis of five games and their elements based on that premise. As a ratification of our rationale, we present tools and visions shared by both fields. The presented premise and rationale could be easily linked to video games but, moreover, they are valid to all forms of games like tabletop games or sports.

This paper aims to identify and analyze which individual perceptions (behavior, feelings, difficulties, among others) are more frequent in the transition process between traditional and agile methods in the context of software development in a large company. The motivation for this work is due to the observation of the growing adoption of agile work models by large companies and the human aspects involved in this scenario. Thus, the particularity of this research is to offer a perspective focused on the individual aspect of each person involved in the process, allowing the ideas and feelings formed through individual perceptions to be identified and analyzed. This objective is achieved through the qualitative analysis of the speech of four professionals in the I.T. area of a large company in relation to their perceptions about a transition from a traditional development model to an agile development model. To survey the interview questions, the GQM method was used and an analysis of the collected data was carried out according to procedures of the Grounded Theory method, aiming to identify emerging topics among the views exposed by the interviewees. Thus, the following topics were highlighted in the context of the transition: perceived benefits for the team, difficulties faced and solutions to these difficulties.

Traditional software engineering approaches were developed based on the old manufacturing process, in term of roles, responsibilities, tasks and hierarchy. Modern approaches, such as, Agile approaches, are based on a fusion of these traditional approaches and the Agile Manifesto, which changes the perspective of main software process objectives. These approaches are designed to encourage self-management, which may be challenging even in big or very small software companies. This paper proposes a software engineering complementary framework called Atta Plug-in 2.0 (Atta 2), that is an approach inspired by a nature organization and aims to deal with the difficulties of self-organization issues. We performed a case study on a start-up company, that follows Scrum guidelines, has a very small team and maintain several projects at the same time. As the outcome, we have a successfully running the Atta 2 and obtained gains in term of self-organization, structure and visibility. Based on this we advocate that Atta 2 improved Sprint planning and execution, tasks creation and distribution and slightly the quality of delivered artefacts. We also focus that Atta 2 helped to integrate new team members and to extract the best from the new ones and old team members. We advocate that Atta 2, may have value to the community, once it is a complementary option to aggregates value to well known agile or traditional approaches. Thus, we concluded that Atta 2 can be used jointly with other management approaches, in order to promote the best-aggregated value.

We present in this paper a novel measurement approach to the Bourdieusian Social Capital (BSC) within Facebook Institutional Pages (fanpages). Supported by Pierre Bourdieuś Theory,we researched for guidelines to identify and capture data related to sociability practices, for instance actions such as Post, Like, Comment and Share. We use such data to extract generalized sequential patterns by modelling social interactions as sequences of actions. Next, we also use such data to measure the frequency of occurrence and the number of actions performed which it is part of each sequence. These values are used to measure the effective mobilization capacity of each page. Finally, the volume of BSC accumulated within a page is measured by using the effective mobilization capacity, the number of social interactions and the number of followers. The results obtained are aligned to the Bourdieuś Theory and our measurements allow to compute the continuous sociability effort of each fanpage to maintain and expand its BSC.

Social networks such as Twitter have become the main platforms for political debate in the field of Information Systems, making it an effective tool for candidates and parties to promote campaigns and government projects before, during and after the election period. In this context, this study aims to understand the dynamics of campaigns in Twitter during the first round of the government election in the State of Rio de Janeiro, Brazil, in 2018, and whether the electoral dispute was reflected in any way by activities on such platform. The approach involved observing candidates' and users' behaviors based on posts and interactions. We also observed the presence of automated accounts during the campaign, using the Botometer API and cross-validating the collected data set for the selected election period. Finally, for this state government election, it was observed that the most active and most cited candidate was not the first one in the dispute. However, it was possible to visualize the sudden change that occurred in the last days of the electoral race characterized in the collected data.

The interactions in Communities Question Answer (CQA) havehigh dimensionality, generating dispersed and vast informationabout the users behavior. Understanding this behavior and whatcharacteristics qualify users as the best contributors is still a chal-lenge. In this paper, we rely on Persuasion Theory to identify userswho stand out at CQA. We use graph incorporation for reducingdata dimensionality to analyze six communities. As a result of theexperiments, we found that there is a strong correlation betweenthe intensity of user activity and reputation, but it is not a linearrelationship. Also, contrary to the literature, the best contributorsare not the top 10-20%, but it varies from community to commu-nity.With the results of this work, users can design better strategiesfor collaboration, headhunters can identify best talents, marketingcompanies can identify influencers and developers can adapt theirsocial reputation algorithms.

The identification of crime suspects on social networks (e.g. pedophilia, terrorism, etc.) has been one of the most relevant topics in social network analysis. The vast majority of methods are based on the extraction of textual content, senders and receivers from the network, but do not take into account the interactions that occur inside the textual content. The present work raises the hypothesis that these interactions, if taken into consideration, can lead to better results when it comes to identifying crime suspects. To validate this hypothesis, an algorithm named TROY was developed to define and represent the interactions that occur in Youtube. Then, it is proposed as the first stage of the INSPECTION method, which analyzes the textual content exchanged on a social network, in order to identify crime suspects. This method is based on the use of a controlled vocabulary with terms categorized according to a certain domain (for example, pedophilia, cyberbullying, terrorism, etc.). Experiments on the pedophilia domain were carried out applying the INSPECTION method to Youtube. The first stage of the method may use any algorithm that is able to extract social interactions. In this case, we used TROY and another algorithm called CRAWLER, which does not take into account the previously mentioned interactions. The results showed that the TROY algorithm obtained better results than the CRAWLER algorithm, validating the hypothesis raised.

Every 2 seconds, a woman is a victim of physical or verbal violence in Brazil, and every 1.4 seconds a woman is a victim of harassment. Due to the growth of anthropology and notions of feminism, violence against women have been seen as a public problem. This work proposes a method to analyse reports of violence against women using text mining techniques in order to characterize the aggression or the aggressor. We collected data from electronic newspapers, sites, and social networks, pre-processed them, extracted topics from the data using the Latent Dirichlet Allocation (LDA), and classified the text according the frequency of occurrence (constant or sporadic) using the Naive Bayes algorithm. Among the analyzed cases, most of them indicate that the case of violence was sporadic, that is, it happened once. The results indicate that the majority of reports of constant violence - meaning that they happen frequently - are related to someone close to the victim, such as a family member, spouse or friend. Words such as "speak", "say" and "report" are frequent, indicating the victim's willingness to express the aggression. When categorizing documents into topics, it is possible to find scenarios of family abuse executed by the father or a brother, and also the presence of sexual violence as one of the aggressions suffered.

O interesse em sistemas baseados em Inteligência Artificial (IA) vem ganhando força em um ritmo acelerado, tanto para times de desenvolvimento de software quanto para a sociedade como um todo. Este trabalho tem como objetivo identificar as diretrizes e princípios éticos para sistemas baseados em Inteligência Artificial. Foi adotado a metodologia Design Science Research com o intuito de compreender as diversas diretrizes e princípios existentes na literatura. A partir do cenário atual é apresentado o corpo de conhecimento no campo da ética em Inteligência Artificial, com o propósito de auxiliar os desenvolvedores e \textit{Product Owners} a identificar as diretrizes e os princípios éticos existentes na literatura para que possam ser utilizados durante o processo de desenvolvimento de software. Assim, este trabalho irá contribuir com as diversas partes interessadas no desenvolvimento de sistemas éticos no contexto de IA, tais como: legisladores, eticistas, usuários, organizações, cientistas de dados, times de desenvolvimento, entre outros.

In the last decade, there has been a great insertion of bots in several social media. Among the potentially harmful effects of these software agents, there are: the spread of computer viruses and different internet scams, and the spread of fake news, with emphasis on political-electoral and public health-related news. This work presents a new approach for bots' detection on Twitter, combining the use of feature selection, clustering, and classification algorithms. The proposed approach was compared with more conventional ones (for example, without the use of clustering) and the premise used in this work proved to be true: the use of clustering, together with the features selection, allowed the production of better classification models in order to identify not only the bots who have an activity profile considered non-human (extremely active on Twitter) but also other bots whose profiles are more similar to humans' ones. The best results of automatic detection of bots reached an overall accuracy of 96.8% and F1 score equal to 0.622. As an additional advantage, these values were achieved by decision-tree models, which can be considered explainable artificial intelligence models.

An autonomous car is a driverless vehicle for the purpose of navigating autonomously and safely over the earth's surface. Interest in this type of transport has increased significantly in recent years and it is possible that the number of these vehicles on the streets will increase, changing the daily lives of cities. In this context, this work aims to understand the technological limitations and impacts of using autonomous cars, observing the feasibility, adaptation, traffic legislation, and perceived benefits. Following the method of systematic literature review proposed by Kitchenham, 69 primary studies from relevant bases in the area of Technology were analyzed. These studies showed some evidence by indicating, for example, that there will be a change in the way people relate to cars, particularly in terms of sharing them, in addition to impacts on urban mobility.

In recent years, we have seen an expressive increase in the number of users participating in social networks. Social networks, in general, have proven to be quite effective in spreading opinions and influencing people as messages can be shared with thousands of people in a few minutes. However, this ability has been exploited in a negative way, to manipulate opinions and spread misinformation and/or fake news. A common way of doing this is through the use of bots, computer algorithms that mimic human behavior, disseminating topics and news, demonstrating support or rejection to personalities, and interacting with other users, which can impact even democratic discussions. For this reason, the present work aims to show and compare approaches for detecting social bots using Twitter user's posts data extracted during the Brazilian presidential election period of 2018. Using a dataset of Twitter users labeled as bots or humans, this research applies five natural language processing (NLP) techniques to extract characteristics from the content of the user's messages on the social network. In order to analyze the impact of features extracted through NLP in the task of detecting bots, five different classifiers were tested including pre-processing techniques and feature selection. The best results were achieved through a union of all the extracted features using the Random Forest classifier, achieving an accuracy of 0.91 for the bot class and AUC of 0.83.

Social bots are automated users who make use of social networks to produce content and interact with network users, in order to mimic or attempt to alter user behaviors, with the purposes, among others, of spreading spam and malicious content, violate users privacy or mislead information in order to influence financial markets or electoral disputes, causing numerous losses. Detecting these bots is a major challenge since, as detection mechanisms evolve, its hiding properties are also enhanced to avoid such mechanisms, either by more sophisticated strategies for emulating real users or by organizing groups of bots in sophisticated networks with the same purpose (botnets). This paper presents a survey about social bot detection approaches, considering the techniques used, the set of characteristics considered for the classification as well as the target of identification (individual or botnets). The main open points identified as well as possible advances in research in the area are also discussed.

Facial detection is an extremely important topic in biometrics. In particular, its application in uncontrolled environments presents a great challenge for the various existing techniques. This work aims to present and evaluate recent studies on facial detection applied in uncontrolled environments through a systematic review of the literature that addresses what are the main techniques and bases of faces used, as well as what are the main uncontrolled scenarios explored in them. Were formulated three research questions and 85 papers were selected for analysis. Based on the analysis of these works, it can be concluded that the techniques based on convolutional neural networks are the most explored techniques in the literature and that the FDDB face database is the most used in the experiments and specific scenarios present in uncontrolled environments, as occlusion and variation of illumination, are still little investigated.

The objective of this research is to study, from the theoretical perspective of information infrastructures (II), the evolution and socio-technical tensions in the formation of the Integrated Management System of the Federal University of Rio Grande do Norte, with significant use in the area of education Brazilian higher education. The research is based on an exploratory qualitative strategy, using the case study as a method. The methods for data collection were three: collection of documents, interviews and questionnaires. For the stage of selection, analysis and interpretation of the data, a framework was developed that was based on the theoretical foundations of the II and its objective is to systematize the analysis of the results. As a result of this research, it is possible to identify the process of how the evolution and tensions suffered by the GIS occurred and occurs, as well as an analysis framework that can be used in the continuation of this work and for the scientific community.

Digital transformation is changing the attitudes of companies to creating value for customers and, this requires making alterations in their working models, organizational structure and technological systems. However, companies have either legacy systems or monolithic systems, which raises a complex problem since there is a need for technological adaptability to meet the commercial demand for the creation of new digital products or services. In this context, this article conducts a systematic mapping study to assess how far the current literature investigates modernized systems that enable digital transformation to take place in companies. The results suggest there is a conspicuous gap with regard to studies that explore modernization models with integrated systems, or can ensure these systems can evolve without compromising the business. The study also finds gaps in the examination of methods or architectural models, which allow modernized systems to evolve gradually without interrupting the business activities.

Context: Organizations have to align a wide variety of organiza-tional goals and strategies to achieve business objectives, grow, and survive. In addition to ensuring the alignment between strategies in place and indicators with business objectives, IT service organizations must keep the quality of service by fulfilling Service Level Agreements (SLA) sealed with their clients. Objec-tive: We present SINIS-LA, which supports elicitation, alignment, and monitoring of business objectives, indicators, and strategies focused on controlling SLA of the IT services provided. Method: We created SINIS-LA by applying Design Science Research (DSR). Results: SINIS-LA was used by the IT service management team at an IT service organization. By using SINIS-LA the team achieved IT alignment and monitored it. SINIS-LA was considered applica-ble for identifying, reviewing, and monitoring service objectives, strategies, indicators, and related SLA. Conclusion: The results showed evidence that SINIS-LA supports the definition and monitoring of the alignment of objectives, strategies, indicators, and SLA in an IT service organization. (Artigo em Português)

Organizational process improvement initiatives typically apply some kind of process assessment. Even partial automation of these process assessments can be beneficial. In this sense, the aim of this study is to investigate the state of the art in automated process assessment. A systematic literature mapping is performed in order to answer the research question: What are the approaches to automated process assessment? Thus, the state of the art is analyzed following the steps of protocol definition, search execution, study selection and data extraction and synthesis. We found 16 studies that directly or indirectly use some kind of automated process assessment support. The approaches reported in these studies cover most of the steps expected in typical process assessments and used nine different software tools to support these steps. The main observed results of process assessment were reduction of effort, time and complexity and automated assessment feedback. There are two major contributions of this research: a comprehensive and structured overview of the state of the art in automated process assessment, contributing to the knowledge organization of this field; and an initial guide to organizations that want to apply efforts to automate their process assessments in choosing the tools, techniques and approaches, and knowing the expected outcomes of this kind of initiative.

Identifying anomalies in business processes is a challenge organizations face daily and are critical for their operations data flow, whether public or private. Most current techniques face this challenge by requiring prior knowledge about business process models or specialists intervention to support the usage of state of the art methods, such as supervised machine learning. Also, the techniques tend to perform offline towards achieving consistent predictive results. In this work, we propose identifying the effectiveness of an online clustering method, particularly Autocloud. This algorithm is able to perform anomaly detection in trace streams meeting real-life requirements. Autocloud is an autonomous, evolutionary, recursive online clustering algorithm that requires little memory to provide insights from anomalous patterns in real-time. Moreover, this clustering algorithm does not require previous training or even prior knowledge from the application domain. Experiments were carried out with six processes schemes, six different anomalies over 1,000, 5,000 and 10,000 event traces, generating a total of 630 datasets. The experiments confirmed the algorithms ability to detect anomalies in those event traces, paving the way for more reliable information systems grounded on an automatic conformance checking of desirable business process execution.

In order to adapt to a competitive business scenario, the decision-making needs to be fast and reliable. In this panorama, agile business intelligence emerges as a resource to provide agile solutions. To achieve agile business intelligence solutions, organizations consider real-time data warehousing a powerful technique. Thus, we propose in this paper a framework based on data warehouse virtualization and real-time data warehousing concepts, called Agile ROLAP. The framework is comprised of an approach designed to be compatible with the main consolidated DW concepts. Furthermore, a set of components that enable the deployment of each step of the Agile ROLAP process was implemented. We evaluated our proposed approach in an experimental study where we deployed dimensional models from three distinct databases. It was analyzed the approach viability and the performance through query performance. The results indicate that the approach is viable, and the performance is satisfactory for no very large databases.

Context: A strong information security culture in organizations contributes to reduce incidents related to leaks of sensitive and private information. Considering that one of the main factors that cause such leaks is human action, it is necessary to evaluate the current state of organizations culture.Objective: This work aims to identify methods for assessing the culture of information security in organizations and to characterize the current state of this topic. Method: We conducted a survey using an evaluation instrument that includes dimensions of the information security culture pro- posed in the literature. Results: The survey received 75 responses, mostly from employees of private institutions. We observed that there is a need for training of employees on information security, and there is incongruity between knowing, understanding and ap- plying the procedures described in the information security policy. Conclusions: This work provided an understanding of the current status of the information security culture in organizations whose re- sults can be expanded and used in future studies to improve security practices in organizations.

Context: Data privacy and data security became a priority among the problems faced by many Brazilian organizations that should be compliant with the Lei Geral de Proteção de Dados Pessoais (LGPD). This law defines the privacy rights on user data and penalties to the ones that break it. Problem: In a compliance program, business processes are of fundamental importance since they are the most important pillar of information security. However, an approach to guide companies to assess and achieve compliance with LGPD on their business processes is missing. Objective: This work propo- ses the LGPD4BP (LGPD for Business Process) method, which is composed by an evaluation questionnaire and a modelling method with a modelling patterns catalog. Method: To develop LGPD4BP, we carried out a literature review, an analysis of privacy laws, in particular the LGPD, and relevant works on the area. Results: The method was applied on a case study of Colégio de Aplicação from Federal University of Pernambuco and validated by a postgradu- ate class which applied the method and answered a questionnaire about easiness and completeness of the method. Conclusions: The results from students evaluations showed that the most hard step is the business process modeling and not the components from the proposed method.

The rapid growth of Internet of Things (IoT) solutions development and the rise of agile development utilization, combined with the so-called ?low touch economy? and the recent discussions on privacy and data protection brought several demands related to Information Security. Despite the existence of several efforts ? either academic or not ? focused on the definition and implementation strategies for certification of Information Security models designed for Information Technology and Communications (ICT) solutions, these aren't widely adopted. In addition, there are significant differences between typical IoT solutions and ICT solutions as traditionally presented, which ends up demanding different certification strategies. Continuous and more dynamic certification models (using cutting edge technologies such as blockchain, self-regulation, analytics, and artificial intelligence) are demanded in this context. This work discusses more effective forms of certification, using innovative edge concepts and technologies, at first aiming to identify a set of inhibiting factors, offenders, challenges or issues that need to be addressed correctly when developing an effective large-scale security certification model.

As the academic information on the internet became broadly available in the shape of academic social networks and academic profiles, its usage to help to resolve tasks like the discovery of specialists in a given area, identification of potential scholarship holders, or suggestion of collaborators, for example, had a growth in importance and relevance. In the case of academic social networks, the Brazilian government created the Lattes Platform in order to manage academic data from Brazilian researchers as well as use it to help in the evaluation of researchers and groups of researchers. However, in order to use the Lattes Platform information to help in the aforementioned tasks, it is important to check the quality of the data, because most of it is declared by the users and does not have any verification of its veracity, specially regarding the declared main expertise area. Thus, this article explores the usage of machine learning techniques to recognize the main areas of expertise of researchers using several numerical representations to represent its scientific production titles as data source for the algorithms. We have been able to surpass the current state-of-art results to resolve this problem by using a TF-IDF character n-gram representation for the text in the titles, achieving an accuracy of 95.91%.

Currently, the amount of information available to Web users is very large, and this situation is similar for scientific communities when searching for articles for their academic research. Recommender Systems (RSs) can be a solution to this problem, suggesting items according to the user's profile and interests. The increase in the impact and scope of recommendations in the users' lives, leads to the result on the ethical issues involved in the generation of recommendations and indicators for visualizing the results of the algorithms found. This paper presents a Recommendation System for the Human-Computer Interaction (HCI) community, indicating articles from the Brazilian Symposium on Human Factors in Computing Systems related to the user's profile applied to a post-processing strategy. After the development of the SR and implemented in a Web environment, results were obtained on the impact that the tool had on the community, demonstrated through the evaluation of the system.

Social Commerce has risen and evolved in the last years due to changes either in e-commerce or social networks applications. On top of that, the number of online ads and transactions in Social Commerce has grown. This environment is attractive to either good users and bad users. The bad users cause harm to their victims by making them lose money or suffer psychological damage. Since the volume of transactions is high and the fraud occurrence is low, the manual detection is highly inefficient (too much resource required for low detection) and unscalable. The existing solutions for automatic fraud detection in Social Commerce are based on structured information available in ads such as price, product type, brand, new/used, among others. However, such solutions ignore possible fraud signs from the ads' images that exhibit the products sold. Therefore, this work aims to evaluate if combining structured information and images available in the ads provides more effective models than the ones considering only structured information. To this end, it proposes FDSC, a method that combines information obtained from ads' images through deep learning with structured information available in the corresponding ads, in order to detect fraud in Social Commerce. Experimental evidence shows an incremental opportunity of 7% in F-score by the adoption of FDSC.

The advancement of information technology makes social media networks increasingly gain popularity and insertion in daily life aspects. Thus, the analysis of people's opinions and habits is essential for many companies' modernization and survival. On social networks, people generally share their views and visit other people's opinions about products, news, and trends, and the concept of "influential person" emerges. An influential person (or social media influencer) today is considered a marketing strategy. The Brazilian dairy industry has been standing out every year, and one of the promising areas is cheese production. The 2019 annual report by ABLV (Associação Brasileira da Indústria de Lácteos Longa Vida) indicates that there was an increase of 32% in liters of milk destined for cheese production in Brazil compared to 2009, which is greater than the percentage growth of milk UHT (26%). Intending to collect information from social networks to find influential people, who appreciate artisanal cheeses, and who can influence potential new consumers, this work presents REDIC, a proposal for analysis, recommendation, and content propagation network, considering the Brazilian artisanal cheese market. REDIC classifies the user's content and interactions using ontologies and complex networks, deriving new relationships and allowing interconnecting information on different social networks. REDIC was developed to support the market research of artisanal cheeses in a renowned Brazilian agribusiness institution. The results obtained through feasibility studies showed that the solution allows the search for communities of digital influencers who talk about artisanal cheeses and the dissemination of information on the network.

As Tábuas de Mortalidade são tabelas estruturadas contendo dados epidemiológicos traduzidos em probabilidades de morte associada a cada idade de vida, utilizadas no mercado de previdência e seguros. Este artigo discorre sobre a aplicação do modelo de redes neurais para a construção de tábuas de mortalidade futuras, tendo como comparação o modelo Lee-Carter de construção de tábuas de mortalidade. O modelo de Rede Neural proposto foi a Rede LSTM. Esta rede tem como característica o processamento sequencial de dados ao longo do tempo. Os dados para a predição são oriundos de dados históricos de tábuas de mortalidade elaboradas pelo IBGE. Os resultados apontam para uma utilização razoável de Rede Neurais LSTM como ferramenta auxiliar de predição das probabilidades de morte.

In the context of information systems in data management, several proposals for entity resolution usually perform on structured data or on long texts that contains contextual information. In short texts, such as microblogs, the lack of context may complicate the disambiguation of named entities mentioned in these texts. On the other hand, word embeddings have been demonstrated as promising techniques for enriching contextual information or being used on similarity estimations. Thus, in this work, we propose an approach for disambiguating named entities gathered from short texts, linking them to documents in a knowledge base using word embeddings and three strategies to find the correct document. Strategy 1 is based on other entity names in the short text. Strategy 2 exploits categories in candidate documents to be linked to the names. And Strategy 3 is based on similarity between documents associated to other named entities from the text and the candidate documents to be linked to the target named entity. In our experimental evaluation, our proposed approach outperforms other approaches usually used in the entity resolution task.

Startup ecosystems are business communities continually unfolding where different actors interact in symbiotic activities to create mutual benefits. A smart startup ecosystem demands an understanding of the interests, capabilities, and affinities among members to take harmony and ensure the group's prosperity. The absence of such mechanisms would compromise the innovation process efficiency, and the environmental imbalance might lead to behaviors harmful to each participant's community to identify convergences and possible partnerships that can help in the development of new business. From this problem, our main contribution is the Coral framework - a social network analysis approach to assist in evaluating relationships in communities. Based on a set of two observational studies in industrial cases, we verified that it is possible to describe the social and material aspects needed to enhance integration and provide greater network efficiency.

Modern information systems are becoming increasingly complex due to the need to combine heterogeneous software. A common understanding of interoperability issues is not a trivial task since complex systems may contain many independent software components. This work presents a Conceptual frAmework for Pragmatic InTeroperAbiLity (CAPITAL) to enhance a pragmatic interoperability unified definition. We evaluate our framework through a modeling and coding guide, a controlled experiment, and applying CAPITAL in the Cloud Computing domain. Results suggest that CAPITAL positively influences the understanding, modeling, and codification of pragmatic interoperability solutions, facilitating pragmatic interoperability standardization opportunities.

Digital platforms have dramatically changed the way news is produced, disseminated, and consumed in our society. A key problem today is that digital platforms have become a place for campaigns of misinformation that affect the credibility of the entire news ecosystem. The emergence of fake news in these environments has quickly evolved into a worldwide phenomenon, where the lack of scalable fact-checking strategies is especially worrisome. Thus, automatic solutions for fake news detection could be used as an auxiliary tool for fact-checkers to identify content that is more likely to be fake, or content that is worth checking. In this context, we investigate practical approaches for the automatic detection of fake news in digital platforms. First, we survey a large number of recent and related works as an effort to implement all potential features to detect fake news. We propose novel features and explore labeled datasets proposing new ones to assess the prediction performance of current supervised machine learning approaches. Our results reveal that these proposed computational models have a useful discriminative capacity for detecting fake news disseminated in digital platforms. We then propose an unbiased framework for quantifying the informativeness of features for fake news detection. As part of our proposed framework, we present an explanation of factors contributing to model decisions, thus promoting civic reasoning by complementing our ability to evaluate digital content and reach warranted conclusions. We also analyze features and models that can be useful for detecting fake news from different scenarios: the US and Brazilian elections. Finally, we propose and implement into a real system a new mechanism that accounts for the potential occurrence of fake news within data, significantly reducing the number of content pieces journalists and fact-checkers have to go through before finding a fake story.

Companies need to manage expectations and relationships with their customers. Customer relationship management (CRM) systems provide tools to support these tasks. Evaluating and selecting a CRM system is not a trivial task. This work identified the important characteristics and sub-characteristics of quality for CRM systems in a Literature Systematic Mapping, comparing results obtained with the software quality models from the 25010 ISO/IEC standard and conducting a survey in the Brazilian CRM systems industry. Finally, an available CRM on the market was evaluated to verify the feasibility of evaluating CRM products using the set of identified characteristics.

News consumption is increasingly done on social media websites. In this environment, all types of entities and people present themselves as news sources. These new outlets might focus on specific audiences, and some exhibit the news less objectively. Facebook is one of these platforms, which categorizes an extensive group of pages as a kind of news media. To analyze this phenomenon, it is crucial to characterize all pages that disseminate information in this ecosystem. Our main objective is to create an in-depth diagnostic of news stories and opinions, focusing on Brazilian Facebook. Our contributions are: (i) a new method to measure the political bias of Facebook pages on a given country, and (ii) a detailed characterization of a comprehensive sample of these pages.

The choice of testing technique to be adopted in a software testing project persists based on the tester's knowledge and often does not consider all of the testing techniques available in the industry or academia. Therefore, a characterization scheme was proposed and implemented in the SeleCTT tool, which is composed of a set of attributes that considers characteristics of concurrent programs, and they are used to calculate which of these attributes are suitable to guide the selection of testing techniques for a particular software project. [Objective:] The selection of the testing technique at each stage of a software's life cycle depends on many factors. Our work aims to help testers to select a better testing technique according to the characteristics of a software project, contributing to the selection of the most suitable testing technique to increase the efficiency of the software test execution process, which in turn influences the development and delivery of a more robust and quality product. Considering that the testing techniques are complementary, another goal is to allow a set of testing techniques to be selected and not just one. [Methodology:] To achieve this goal, a systematic mapping study was conducted to identify and analyze papers that represent the current state of the literature about testing techniques selection. We surveyed software testing practices carried in Brazil software companies and identified the testing practices to know and have an overview on the latest testing techniques, tools, and metrics used, the challenges faced by testers, and the selection testing technique process. With this study, it is expected to specify project attributes that can be used to improve the existing recommendation system in the SeleCTT tool and propose ways of combining testing techniques, contributing to industry and academia, and bring insights into the context of testing techniques selection.

In the Covid-19 pandemic, it was already possible to obtain recommendations to assist in the control of contamination. In this study, we considered the implementation science concept in a simulation effort based on changes in prevention behaviors. We also considered the use of an information system and wearable IoT devices for monitoring people in environments where social isolation is complex. We conceived four scenarios with different approaches, where health data of the simulated agents were collected for monitoring and providing predictions. Agents with more preventive habits got contamination rates of 12.11% against the worst scenario, with 77.00%.

A malária é uma doença com risco de vida evitável e curável, mas houve mais de 228 milhões de casos de malária e 405.000 mortes por malária em 2018. Enquanto mais de 42 milhões de brasileiros estão sob risco de malária, 99% de todos os casos de malária no Brasil estão localizados dentro ou ao redor da floresta amazônica. Apesar do declínio de casos e mortes, a malária continua sendo um grande problema de saúde pública no Brasil. Em resposta a pedidos de novas pesquisas sobre estratégias de eliminação da malária para atender às condições locais, este artigo propõe modelos de machine learning e deep learning para prever a probabilidade de casos de malária no Estado do Amazonas. Usando um conjunto de dados de aproximadamente 6 milhões de registros, avaliamos os modelos {Random Forest, LSTM e GRU e comparamos desempenho por área geográfica usando a classificação de regionais de saúde do Estado do Amazonas e clusters através do algoritmo k-means. Os resultados sugerem que todos os modelos têm uma precisão satisfatória e forte potencial para prever novos casos de malária na região.

Funcionalidades que permitem a interação entre usuários dentro das Redes Sociais Online configuram práticas de sociabilidade em rede, ou seja, ações como Publicar, Comentar, Curtir e Compartilhar têm um significado especial quando estão sob o foco da Sociologia Digital. Com o apoio da teoria de Pierre Bourdieu, neste trabalho são identificados e capturados dados relacionados às práticas de sociabilidade em rede dentro de Páginas Institucionais no Facebook. Esses dados são utilizados no aprendizado da sequência de ações. A partir da frequência de ocorrências das seqüências aprendidas, um indicador do Capital Social acumulado é apresentado. A partir desse indicador e do número de interações sociais, o volume de Capital Social é computado.

Este artigo discute o desempenho, consumo de recursos e a utilização de alguns Web Application Firewall (WAF) open source. Será analisado o desempenho para cada uma das três ferramentas propostas. O objetivo deste artigo é que o leitor seja capaz de compreender o que é um WAF, para que serve o mesmo, e que seja capaz de escolher com base em nossos resultados qual WAF se adequa melhor ao seu servidor web. No final tiramos algumas conclusões sobre a eficácia geral dos Wafs propostos.

Este trabalho apresenta a construção de um conjunto de dados para análise estática de ransomwares. A base de dados resultante consiste de 338 arquivos PE, separados em 21 diferentes famílias e contendo informações gerais dos arquivos e características dos ransomwares analisados. O conjunto produzido, aqui disponibilizado publicamente, pode ser utilizado para identificação e classificação de famílias de ransomware.

A polarização política popular caracteriza-se quando há uma clara divisão de linhas partidárias em relação a questões políticas, políticas governamentais e figuras públicas, onde um dos lados observa o outro como ameaça. Este tipo de polarização pode ser estudado por meio de textos compartilhados em redes sociais, como no Facebook e no Twitter. Muitas vezes, pesquisadores e interessados coletam textos destas redes sociais, mas não mostram como desenvolver o processo de coleta e tratamento destes textos. Mostramos, neste artigo, o desenvolvimento de um sistema para coleta e tratamento de textos envolvendo polarização política "popular" por meio do Twitter, capaz de incluir as localizações e analisar os sentimentos existentes nestes textos.

A editoração de trabalhos de eventos científicos em formato de anais é um processo árduo, mas essencial para que sua indexação seja feita corretamente e com qualidade. É um processo que envolve diferentes Sistemas de Informação cuja troca de informação é feita, geralmente, de forma manual. Dessa forma, este trabalho visa propor um Sistema de Sistemas de Informação para suporte ao processo de editoração de anais de eventos gerenciados pelo Journal and Event Management System (JEMS) e publicados no Open Journal System (OJS), versão 3. Resultados preliminares mostram uma boa aceitação dos usuários e o potencial de reduzir o tempo gasto nesse processo.

A pandemia causada pelo COVID-19 traz desafios para a indústria de software. Para o estudo desse contexto, a utilização da Literatura Cinza é de grande relevância, porém, há uma lacuna no que diz respeito a ferramentas de busca para esse conteúdo. Esse trabalho traz informações acerca de uma ferramenta de Web Scraping, desenvolvida para busca de literatura cinza no contexto de desenvolvimento ágil de software. Utilizamos o método de Design Science e apresentamos resultados preliminares compostos por mais de 7900 documentos capturados pela ferramenta, que deve ser aprimorada futuramente e já se mostra útil no meio acadêmico.

Com o avanço de novas tecnologias, veículos elétricos passam a fazer parte da paisagem urbana. Entretanto, encontram-se problemas relacionados à autonomia destes veículos e à escassez de postos de abastecimentos. Nosso trabalho de pesquisa propõe uma aplicação para apoiar a compreensão do motorista quanto ao consumo de bateria de um veículo elétrico, como também, aos modos de condução do mesmo. Simulou-se a captação de dados através de um simulador de contexto capaz de reproduzir o consumo e autonomia de um veículo elétrico. Para avaliar as informações obtidas através das simulações foi utilizada a metodologia de Design Science Research (DSR).

Disaster Information Management Systems (DIMS) are systems of most importance for public management and can be used before, during and after emergency situations. Associating this type of system with Voluntary Geographic Information Systems (VGI) represents a major advance in the relationship of society with its managers. Thus, this work presents a framework for the development of DIMS using VGI, seeking to follow patterns of data sharing and usability.

This work shows a mobile robot platform and application of robot low cost in a task of visual detection and chasing an object, using a smartphone as a sensor and performing the object detection from Computer Vision. Also, a comparison will be made with another simpler technique, showing that the proposed method presents comparable results while offering much greater flexibility.

Com o crescente desenvolvimento de novas tecnologias no campo da domótica e Internet das Coisas surgem novas oportunidades para a criação de ambientes inteligentes para usuários que carecem de algum tipo de tecnologia assistiva. Considerando os custos associados à construção desse tipo de ambientes, simuladores se tornam um componente importante para o seu desenvolvimento, de modo a minimizar o tempo e o custo de implementação do projeto. Assim, este trabalho apresenta uma proposta de framework para simular casas inteligentes que atendam as necessidades e especificidades do usuário.

Nesse artigo é feito um estudo de modelos de aprendizado profundo para auxiliar os agricultores na melhor tomada de decisão da colheita dos cafeeiros. Este é um problema pouco explorado na literatura e se torna muito difícil por conta dos frutos amadurecerem de maneira desuniforme na planta. Nesse sentido, coletamos milhares de imagens de cafeeiros as quais foram posteriormente classificadas por especialistas da área, Além disso, triplicamos a base de dados utilizando o processo de aumento de dados e três arquiteturas de redes neurais convolucionais foram treinadas e tiveram seu desempenho preditivo comparado. Os resultados mostram que nosso melhor obteve uma taxa de acerto de 93\% e F1-score de 92\% em imagens separadas para os testes.

Interorganizational relationships are initiatives between organizations that aim to facilitate resource sharing and information exchange. In this context, information systems are developed to support the involved organizations, often encompassing several smaller systems. However, little is known about interorganizational information systems (IOIS), especially regarding their state of practice. This paper investigates the use of IOISs in real scenarios. For this, we performed a non-systematic search of IOISs examples and identified repositories on GitHub about these systems. Hence, it was possible to map a set of sources and requirements that help understand how IOISs work and why they are adopted.

This work presents the development of the Mediador Game, a digital game based on business processes to support the training of organizational processes, applied in the mediation of conflicts in the judiciary. The game was designed using the method, developed in previous research, which describes the steps to transform elements of business process models into game design elements.

In this work, a tool for the automatic inference of calorie content in cooking recipes is proposed, through a Text Classification approach. This tool will be part of a Recommendation System under development, to assist health professionals and users in general to elaborate healthy diets.

Problemas na elicitação e documentação de requisitos podem afetar todo o desenvolvimento do software. Dentre os possíveis problemas estão ambiguidade, inconsistência, conteúdo fora do padrão e documentação não clara. Um dos meios de evitar tais problemas é através de técnicas que buscam reduzir a informalidade deste processo. Neste trabalho se utilizou conceitos de Engenharia Dirigida por Modelos (MDE) para representar requisitos funcionais seguindo a representação de caso de uso, propondo uma sintaxe concreta para uma sintaxe abstrata e semântica já existentes. O uso de MDE permite uma identificação mais clara dos elementos do caso de uso e suas relações, criando um formato estruturado de modo a aumentar a qualidade do modelo.

As disciplinas iniciais de programação e algoritmos dos cursos da área de computação exigem um certo grau de abstração que, normalmente, não é cobrado no ensino médio. A partir de estudos prévios das maiores dificuldades de alunos dos cursos de programação e, tentando colaborar para diminuir as barreiras iniciais, este trabalho apresenta um simulador desenvolvido em plataforma Web, no qual é possível realizar manipulações de estruturas de dados com resultados visuais.

Na última década, as Plataformas digitais ganharam importância na orquestração das relações interorganizacionais, tornando-se pontos de controle em diversos Ecossistemas de negócio. Isso pode gerar conflitos entre os Líderes das Plataformas e outros participantes na busca de oportunidades para criação e apropriação de valor. Nesse trabalho, a Governança das Plataformas digitais é investigada pela perspectiva dos Complementors e busca-se criar mecanismos que permitam prognosticar como o controle do Líder sobre a Plataforma pode afetar a criação e apropriação de valor no Ecossistema. Ao final, será proposto um artefato que apoie tanto pesquisadores quanto praticantes na realização desses prognósticos.

Sistemas de Informação Federados (SIF) se interconectam para obter funcionalidades que exigem muitas integrações que, sozinhos, não conseguiriam realizar. SIF juntos cumprem uma missão maior, tal como a disponibilização de crédito ao cliente bancário. Por sua vez, os Sistemas de Informação (SI) são aqueles que fazem parte de uma mesma plataforma tecnológica comum com seus atores internos e externos. Ao conjunto destes SI, denomina-se Ecossistema de Software (ECOS). Partindo destes conceitos, o arranjo de SIF pode ser denominado Ecossistema de Software Federado (ECOSF). Nesse contexto, com o forte avanço da evolução tecnológica, cada vez mais se torna necessário que os SIF migrem para novas tecnologias e esta evolução pode causar novas tensões organizacionais (TO) entre os stakeholders durante essas migrações e na convivência dos SIF novos com legados. Isto ocorre porque existem diversos desafios envolvidos nesta migração: processos tradicionais; resistência a mudanças; questões orçamentárias, dentre outros. Este trabalho propõe investigar a evolução da plataforma tecnológica comum de um ECOSF e analisar como emergem as TO. Como contribuição para a indústria, pretende-se encontrar os elementos que influenciam na evolução da plataforma tecnológica comum de um ECOSF, auxiliando gestores na tomada de decisão na evolução dos SIF envolvidos. Como contribuição para a academia, será realizada uma teorização da evolução dos SIF, fornecendo mais uma forma de pensar, teorizar e entender sistemas desta natureza.

To adapt to constant changes, organizations that apply business process management need to continuously train their professionals to institutionalize new processes, spending time and effort. This resaerch work presents the proposal of designing, application and effectiveness evaluation of using business-process based digital games in process training.

Digital Games Based on Business Processes are a genre of games that represent organizations' business processes. They allow players to understand aspects of the processes. The narratives are a crucial point for the players to understand the game context and, consequently, the process represented by them. This research proposes a method for generating scripts for games based on business processes models. The research uses Design Science Research Methodology (DRSM) to carry it out. The evaluation will be carried out by qualitative and quantitative approaches and, the analysis of data obtained from the answers of questionnaires and interviews performed with game designers specialists.

Promover ações sustentáveis e menos desiguais visando o crescimento urbano ordenado é um desafio que as cidades inteligentes se propõem a resolver. Os sistemas de informação têm papel fundamental para enfrentamento dos desafios da gestão do espaço urbano, pois suas abordagens compreendem o envolvimento de pessoas, organizações e tecnologias. Contudo, estas abordagens podem gerar conflitos de interesses quando desconsideram os cidadãos em seus objetivos. Este projeto pesquisa de dissertação tem por objetivo investigar possibilidades e limites para as cidades inteligentes e propor uma arquitetura de referência para guiar pesquisadores e governos na abordagem social das cidades inteligentes.

With the growth of cities, greater investments are required in urban planning, the objective of which is to guide actions of municipal public management in order to minimize urban problems and improve the quality of life of citizens. The organization and analysis of data about a municipality can help this process insofar as it allows for more assertive decisions. The data in question can be from different sources, structured or not, sensitive and also have inconsistencies. In order to solve this problem, the proposed solution for this work is the elaboration and development of a model capable of handling heterogeneous data, capturing, integrating and making them available for decision making by stakeholders in order to assist municipal urban planning.

This research is in the context of Systems of Systems (SoS), a class of complex systems composed from other independent systems, which have their own characteristics and functionalities. The interaction among the constituents of a SoS might produce new behaviours, known as emergent behaviours, about which still there is not full dominance regarding modeling and simulation. Thus, this paper intends to collaborate to advance at this understanding by means of the elaboration of a simulator which permits comprehend how the inclusion and/or exclusion of one or more constituents of a SoS affects its global behaviour.

Sistemas de Apoio à Decisão fazem parte do dia a dia de todos os ambientes, atuando no suporte ao processo de tomada de decisão, auxilia na utilização de dados como embasamento, possibilitando decisões mais eficientes e vantajosas. O aumento do volume de dados produzidos digitalmente traz inúmeras oportunidades e as organizações tem aperfeiçoado seus SAD como meio de descobrir novas informações ocultas nestes dados. Como meio de resolver o problema de sobrecarga de dados com uma das dificuldades, o Visual Analytics (VA) é aplicado para um melhor uso e exploração desses dados. No entanto, a implementação de VA em um SAD não é uma tarefa trivial, exigindo atualmente técnicas de desenvolvimento e avaliação. O presente trabalho tem como objetivo apresentar uma taxonomia de VA em SAD. Para tanto, o projeto adotou uma metodologia sistemática para o estabelecimento da taxonomia. A principal contribuição deste projeto é apresentar uma taxonomia que possa classificar e descrever as características de VA nas arquiteturas dos SAD.

Produtores rurais, engenheiros agrônomos, profissionais especializados e vários outros atuantes da área agronômica carecem de uma fonte agregada, confiável e gratuita a respeito da compatibilidade de produtos fitossanitários. Em vista desse problema, este trabalho apresenta a criação de uma plataforma que unifica resultados e informações científicas a respeito da compatibilidade de produtos em misturas de tanque com calda herbicida em um software. A plataforma foi desenvolvida utilizando tecnologias atuais e a sua primeira versão já conta em sua base de dados com 224 combinações entre os 990 produtos comerciais disponíveis no Brasil, todos estes, herbicidas.

Por meio de uma pesquisa, foi identificado que grande parte dos estudantes do IFB já perdeu alguma oportunidade em não ter acesso à notícias em tempo. Este trabalho tem como público-alvo instituições de ensino, estudantes e desenvolvedores de sistemas. E nele foi desenvolvido um chatbot baseado em regras a fim de otimizar a forma como os estudantes do IFB acessam às informações e notícias do portal oficial da instituição.

Este artigo descreve a experiência de um projeto de ensino realizado em uma instituição de ensino profissional e tecnológica com a criação de um podcast sobre assunstos relacionados ao curso de bacharelado em sistemas de informação. O objetivo do projeto era criar 25 episódios com assuntos que abordassem desde redes de computadores, sistemas distribuídos, algoritmos até desenvolvimento de sistemas para web. Quatro alunos e alunas bolsistas criaram 25 roteiros para gravação dos episódios, destes foram gravados e publicados 7 (sete) até o momento.

O Plano Nacional de Educação 2014-2024 estabeleceu a adequação dos projetos pedagógicos dos cursos de graduação para inserção de atividades de extensão nos seus currículos, assegurando no mínimo 10% do total de créditos, o que foi denominado de curricularização da extensão. Este artigo tem como objetivo apresentar um relato sobre a concepção da primeira proposta de curricularização da extensão em um curso do IFAL, contemplada no Bacharelado em Sistemas de Informação desde o semestre 2018-1. A proposta consistiu na oferta de três disciplinas obrigatórias (duas de projetos integradores e uma de tecnologias sociais e assistivas) e participações em outras atividades extensionistas institucionais (programas, projetos, cursos, serviços de extensão tecnológica e voluntariado).

Gamification is often used as a tool for the teacher to transmit all the necessary pedagogical content in a motivating, engaging and creative way. This work aims to analyze the influence of the student's player profile on the performance of gamified tools in the teaching-learning process.

A regulamentação da prática extensionista como componente curricular obrigatório nos projetos pedagógicos dos cursos de graduação tem se apresentado como um desafio. Este artigo tem como objetivo apresentar um panorama sobre as estratégias de curricularização da extensão adotadas nos cursos de Bacharelado em Sistemas de Informação (BSI) até o presente momento. A metodologia adotada envolveu uma pesquisa documental nos sites institucionais dos próprios cursos e uma pesquisa bibliográfica por relatos de experiências. Os resultados sinalizaram que, timidamente, as instituições de ensino estão reformulando seus projetos pedagógicos e que as estratégias comumente adotadas são aquelas que envolveram disciplinas específicas de extensão ou de projetos e programas no atendimento à comunidade.

Em face ao Plano Nacional de Educação (PNE 2014-2024), o Conselho Nacional de Educação regulamentou as diretrizes para a extensão na Educação Superior Brasileira em 2018. Uma das metas prioritárias definidas é garantir uma quantidade mínima de créditos curriculares para extensão em todos os cursos de graduação do país. Com isso, além de reformular seus Projetos Político Pedagógico atender à legislação vigente, as Universidades precisam assegurar que todos os estudantes cumpram a carga horária mínima de atividades de extensão. Assim, este artigo apresenta um relato de experiência sobre a estratégia de reformulação dos cursos de Sistemas de Informação da Universidade Federal de Uberlândia nesse contexto.

Entender o gasto público no município é necessário para a cidadania incidir nas políticas públicas no território onde vive. O objetivo deste estudo é descrever a experiência de envolvimento de diferentes atores no design da plataforma em cada fase do seu desenvolvimento. É empregado um arcabouço teórico para analisar sua atual etapa de avaliação. Para isso, descrevemos: o envolvimento de diferentes atores no processo de design, respectiva participação nas decisões de design e, principalmente, o aprendizado mútuo. Como trata-se de uma iniciativa pioneira e, ao mesmo tempo, um projeto de longo prazo, referência nacional e internacional, os achados podem contribuir na replicação em outras cidades.

O Estatuto da Cidade (EC) é uma lei federal brasileira que regulamenta a política urbana em todo o país através de instrumentos legais elaborados para organizar o crescimento das cidades que devem ser desenvolvidos de forma participativa. Visando atender este objetivo, este artigo propõe a utilização do Design Participativo na elaboração destes instrumentos usando a tecnologia de informação e comunicação como meio para ampliar as oportunidades de colaboração entre os membros envolvidos neste processo.

The industry underwent several transformations initiated by the first Industrial Revolution at the end of the 18th century. Today we are experiencing the Fourth Industrial Revolution, where equipment is capable of processing data and connect to communication networks. Maintenance planning can use large volume of data generated by IoT devices to act preventively . This work aims to propose an architecture that uses an outlier detection algorithm, Local Outlier Factor, to detect anomalies in machine failure records, producing information to support equipment maintenance decisions.