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Disertaciones |
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1
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MATHEUS MAGALHÃES BATISTA DOS SANTOS
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Continuous Authentication of Individuals based on Anomaly Detection Algorithms
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Líder : MAURICIO PAMPLONA SEGUNDO
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MIEMBROS DE LA BANCA :
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MAURICIO PAMPLONA SEGUNDO
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RUBISLEY DE PAULA LEMES
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FILLIPE DIAS MOREIRA DE SOUZA
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Data: 22-ene-2020
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Resumen Espectáculo
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Authentication methods such as passwords and access cards have become commonplace in society's day-to-day life. Because an increasing concern with security, biometrics has become a common form of access control. However, like other access control methods, they only verify the user's identity only once. No further verification is carried out at a later date and therefore there is no guarantee that the permitted user is the same as use a system or resource throughout its use. To solve this problem, continuous authentication performs the checking constantly, thus ensuring that the authorized user is the same during the entire use of the system. Numerous efforts have been made to improve the performance of verification in continuous authentication, such as the use of biometrics increasingly secure, but there are not many jobs that aim to improve the continuous authentication method itself. Because of similarities between the objectives of anomaly detection techniques and continuous authentication, this work proposes to use an anomaly detection technique in continuous authentication to make it secure regardless of the type of biometrics used. After the experiments with 4 different biometric characteristics, the proposed work proved to be equivalent state-of-the-art in continuous authentication with the advantage of not requiring training.
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2
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RICARDO BARROS DUARTE D'OLIVEIRA
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A procedural approach to multi-scale planetary terrain generation based on Brownian fractal noise and tessellation
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Líder : ANTONIO LOPES APOLINARIO JUNIOR
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MIEMBROS DE LA BANCA :
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ANTONIO LOPES APOLINARIO JUNIOR
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RODRIGO LUIS DE SOUZA DA SILVA
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VINICIUS MOREIRA MELLO
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Data: 29-ene-2020
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Resumen Espectáculo
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The generation of planetary terrains, in the context of electronic games, presents several challenges. Among them, the main one is the management of multi-scale data. A player may be orbiting the planet in an instant and then descend to battle on its surface. To deal with this challenge, we present a method of procedural generation of planets with support for multiple scales, making use of Brownian fractal noise functions with analytical partial derivatives. Through several iterations of this fractal noise we can simulate different types of terrain. The multiscale terrain model is managed by an indexed quadtree with hash tables. This same structure supports the view-frustum culling process, which optimizes the rendering process. In addition, our approach is able to manage levels of detail based on the observer's point of view and the introduction of high resolution details through the use of the GPU tessellation process. The results show that our method is capable of generating realistic planetary bodies, with temporal coherence and diversity of geological compositions in real time, and, therefore, being applicable to the domain of electronic games.
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3
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FABRICIO DE FREITAS CARDIM
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Using machine learning and source code metrics to technical debt identification
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Líder : CLAUDIO NOGUEIRA SANT ANNA
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MIEMBROS DE LA BANCA :
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CLAUDIO NOGUEIRA SANT ANNA
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RODRIGO OLIVEIRA SPINOLA
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TATIANE NOGUEIRA RIOS
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Data: 04-feb-2020
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Resumen Espectáculo
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"Using technical debt as an instrument to assess the maintainability of the software is still a challenge. This is because the existing tools for detecting technical debt generate many false positives because they do not take into account information related to the context, domain, size and design of the software. In addition, most debt detection tools only detect code smells previously cataloged in the literature. Although the belief that code smells negatively affect maintainability is widely accepted, there are experimental results that contradict this theory. developers may have differing opinions on the impact of code smells on software maintenance, on the other hand, building a universal solution taking into account the specific characteristics of each project may be humanly unviable. In this context, we evaluate the effectiveness of using algorithms machine learning in co together with code metrics to detect technical debts that may negatively affect the maintainability of the software, taking into account the opinion of the developers. To achieve this goal, we have carried out three experimental studies, in the context of the industry, with the participation of developers. The results showed that machine learning algorithms, used in conjunction with code metrics, can be a viable option to assess code maintainability. As a side contribution, we implemented a tool for automatic detection of technical code debt for the VB.NET language, called CodeAnalyzerVB, and applied this tool at the Agency
Promotion of the State of Bahia (DESENBAHIA). "
Utilizar dívida técnica como instrumento para avaliar a manutenibilidade do software ainda é um desafio. Isso porque as ferramentas existentes para detecção de dívida técnica geram muitos falsos positivos por não levarem em consideração informações relacionadas ao contexto, ao domínio, ao tamanho e ao design do sistema analisado. Além disso, a maioria das ferramentas de detecção de dívidas detectam apenas code smells previamente catalogados na literatura. Embora a crença de que code smells afetam negativamente a manutenibilidade seja amplamente aceita, existem resultados experimentais que contradizem essa teoria. Indo mais além, desenvolvedores podem ter opiniões divergentes sobre o impacto de code smells na manutenção do software. Por outro lado, construir uma solução universal levando em consideração as características específicas de cada projeto pode ser humanamente inviável. Diante desse contexto, nós avaliamos a eficácia da utilização de algoritmos de aprendizado de máquina em conjunto com métricas de código para detectar dívidas técnicas que possam afetar negativamente a manutenibilidade do software, levando em consideração a opinião dos desenvolvedores. Para atingir este objetivo, nós realizamos três estudos experimentais, no contexto da indústria, com a participação de desenvolvedores. Os resultados mostraram que algoritmos de aprendizado de máquina, utilizados em conjunto com métricas de código, podem ser uma opção viável para avaliar a manutenibilidade do código. Como contribuição lateral, nós implementamos uma ferramenta para detecção automática de dívida técnica de código para a linguagem VB.NET, denominada CodeAnalyzerVB, e aplicamos essa ferramenta na Agência de Fomento do Estado da Bahia (DESENBAHIA). |
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4
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ALINE MEIRA ROCHA
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Semantic Annotations in Academic Repositories: a case study with UFBA Institutional Repository
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Líder : LAIS DO NASCIMENTO SALVADOR
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MIEMBROS DE LA BANCA :
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DANIELA BARREIRO CLARO
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FLAVIA GOULART MOTA GARCIA ROSA
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LAIS DO NASCIMENTO SALVADOR
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Data: 03-mar-2020
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Resumen Espectáculo
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Institutional Repositories (IR) are academic repositories that enable the storage and dissemination of scientific productions from universities and research centers. These repositories allow self-archiving, where the researcher himself can deposit his publication. The information about each deposited item is stored in its metadata, but as the researcher himself usually does this manually, the terms chosen do not always help in this description, which leads to the intervention of librarians in the process. Suggesting keywords during metadata validation would certainly help librarians as it would identify representative terms of each publication and semantically enrich these metadata, favoring the retrieval of items in an IR. Binary textual classification machine learning methods may suggest that a publication is also associated with another collection if it is identified that it is multidisciplinary work. You can also help classify publications not yet filed with IR that are not organized by collection but by other criteria, such as collegiate date or date of defense. Based on the representative terms of each community and sub-community, it is possible to train a multi-hierarchical classifier to identify which community and sub-community each job should belong to. On the other hand, semantic annotation of metadata referring to collections and keywords enables the enrichment of RI item descriptions and facilitates the retrieval process. In this context, the objective of this paper is to semantically annotate the items of an academic repository in Dspace semi-automatically using the Dublin Core RDF standard from the results obtained in the textual classification and validation of keyword suggestions. Additionally, a case study was conducted at the UFBA IR, where domain specialists validated the extracted keywords, in this case, the SIBI (UFBA Library System) librarians. Thus, the semantic annotation of the results obtained in the textual classification experiment and the case study were performed. The main contributions of this project relate to the exploratory study of publication deposit validation and classification methods, as well as the semantic description and enrichment method of an IR item.
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5
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TÁSSIO GUERREIRO ANTUNES VIRGÍNIO
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Empirical evaluation of the automated generation of software tests from the perspective of Test Smells
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Líder : IVAN DO CARMO MACHADO
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MIEMBROS DE LA BANCA :
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CLAUDIO NOGUEIRA SANT ANNA
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HEITOR AUGUSTUS XAVIER COSTA
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IVAN DO CARMO MACHADO
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Data: 13-mar-2020
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Resumen Espectáculo
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The constant search for quality is always highlighted in the Software Engineering field. Among the various disciplines dedicated to this theme, software testing has been established as one of the most important, given its effectiveness in identifying defects, prior to the release of software systems to the market. Software testing is a key activity for the development of quality software. However, developing tests is just as or more expensive than developing the production code. An alternative for reducing the costs associated with software testing is the intensive use of test automation tools. The purpose of these tools is to reduce production time without affecting the quality of the code. Despite this premise, it is not common to find approaches that include a quality check layer of the automatically generated tests, which can reduce the reliability of the effectiveness of these tests. In this scenario, the purpose of this dissertation is to empirically analyze masses of test data, from the perspective of test smells, in order to assess the quality of the tests produced by automated software test generation tools. Test smells are poor choices in the design of tests and have symptomatic characteristics and can lead to a decrease in the quality of systems. Considering the test smells in test code, the study analyzes the tests generated by two widely accepted tools by the software testing community: Evosuite and Randoop. A set of twenty-one open source software projects, available on the Github platform, were considered in the study. The analysis considered the dispersion of test smells in the test code of these projects, as well as the existence of potential correlations between test smells and the relationships with structural metrics. As main results, we found strong correlations between the test smells and the code coverage metrics, significant differences between the data found in the test suites generated automatically and with the pre-existing tests in the evaluated projects.
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6
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LEILA KARITA DOS ANJOS DO ESPÍRITO SANTO
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Characterizing Sustainability in Software Engineering through a Multi-Method Approach
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Líder : IVAN DO CARMO MACHADO
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MIEMBROS DE LA BANCA :
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IVAN DO CARMO MACHADO
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RITA SUZANA PITANGUEIRA MACIEL
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MONALESSA PERINI BARCELLOS
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Data: 26-mar-2020
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Resumen Espectáculo
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The interest of the Information and Communication Technology community on sustainability has grown considerably in recent years. Although still at an early stage, the theme has become of great relevance since it forces us to think about what we have done to ensure the planet and future generations' continuity. In the Software Engineering context, when we think about sustainable software development, we face gaps, since this subject is still nebulous for software engineers and developers, as well as for the research community in Software Engineering. For software to be produced in a sustainable way, software engineers need to understand how sustainability concepts are incorporated into software development, so that they can have a clear, common and shared understanding of that knowledge. However, the development of a recent study on the state-of-the-art about software approaches that support sustainable Software Engineering showed that there is still a gap about what is sustainable software development, in fact. The lack of such an understanding can prevent the industry from building software with sustainable awareness. Given the question, this research aims to characterize sustainable software engineering by highlighting the sustainable concerns present in the software development life-cycle. To achieve this goal, this dissertation adopted a multi-method approach and produced a series of qualitative studies. The multi-method approach is a methodological research strategy that combines two or more qualitative research methods or two or more quantitative data collection and analysis methods. We elaborated: (1) a systematic mapping study with the intention of knowing the Sustainable Software Engineering domain; (2) a survey to obtain the software industry perception on the adoption of sustainable practices; and (3) a grounded theory, with the aim of generating a theory to provide an understanding of sustainable software development to readers. The theory was organized around the sustainability dimensions. This dissertation concluded that sustainable software development is explained through the following propositions: (a) technical, environmental and social concerns are present in all phases of sustainable software development. This means that researchers and software engineers are concerned with considering the longevity of the software produced, as well as environmental resources, in addition to social welfare; (b) the sustainable requirements identification must occur in the project initial phase (c) with the support of experts engaged in sustainability, who must be part of the group of stakeholders; and (d) the use of sustainable concerns can generate trade-offs in the project. Therefore, the results contribute to a greater understanding of sustainable software development, from the literature and software practitioners' perspectives; and, consequently, with the evolution of the state-of-the-art in Sustainable Software Engineering.
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7
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PÉTALA GARDÊNIA DA SILVA ESTRELA TUY
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On the use of fuzzy clustering to build fuzzy rule-based systems to address Big Data
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Líder : TATIANE NOGUEIRA RIOS
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MIEMBROS DE LA BANCA :
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TATIANE NOGUEIRA RIOS
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MARCOS ENNES BARRETO
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MATHEUS GIOVANNI PIRES
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Data: 15-abr-2020
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Resumen Espectáculo
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Big Data is a trending topic that has gained attention in the business and academic environments. The term refers to the huge amount of data being generated every day in a variety of sources and formats. An expressive part of Big Data is in the format of text that can be used to solve various real life problems, such as spam detection, author identification, web pages classification and sentiment analysis. Text datasets are specially complicated since its high dimensionality can extend from vertical high dimensionality (high number of instances) to horizontal high dimensionality (high number of attributes). In order to extract useful knowledge from such high dimensional datasets, data analysis techniques must be able to cope with its new challenges: volume, velocity, variety and variability. Fuzzy Rule-Based Classification Systems (FRBCS) have shown to effectively deal with the uncertainty, vagueness, and noise inherent to data. However, the performance of FRBCSs is highly affected by the increasing number of instances and attributes present in Big Data. Previously proposed approaches try to adapt FRBCSs to Big Data by distributing data processing with the MapReduce paradigm, by which the data is processed in two stages: Map and Reduce. In the Map stage, the data is divided into multiple blocks and distributed among processing nodes that process each block of data independently. In the Reduce stage, the results coming from every node in the Map stage are aggregated and a final result is returned. This methodology tackles vertical high dimensionality, but it does not approach datasets with simultaneous vertical and horizontal high dimensionality, as it is the case of text datasets. Horizontal high dimensionality reduction could be done by using common feature selection techniques, such as MI and Chi-squared. However, using such feature selection techniques may not be the best alternative since model accuracy might be affected by the loss of information when keeping only a subset of attributes. In this work, we deal with the aforementioned drawbacks by proposing Summarizer, an approach for building reduced feature spaces for horizontally high dimensional data. To this end, we carry out an empirical study that compares a well-known classifier proposed for vertical high dimensionality datasets with and without the horizontal dimensionality reduction process proposed by Summarizer. Our findings show that existing classifiers that tackles vertical Big Data problems can be improved by adding the Summarizer approach to the learning process, which suggests that an unified learning algorithm for datasets with a high number of instances as well as a high number of attributes might be possible.
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8
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FERNANDA SILVA EUSTÁQUIO
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On fuzzy cluster validity indices for soft subspace clustering of high-dimensional datasets
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Líder : TATIANE NOGUEIRA RIOS
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MIEMBROS DE LA BANCA :
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TATIANE NOGUEIRA RIOS
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HELOISA DE ARRUDA CAMARGO
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RICARDO MARCONDES MARCACINI
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Data: 16-abr-2020
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Resumen Espectáculo
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Most of the well-known and widely used conventional clustering algorithms, as k-Means and Fuzzy c-Means (FCM), were designed by assuming that, in most cases, the number of objects in a dataset will be greater than its number of dimensions (features). However, this assumption fails when a dataset consists of text documents or DNA microarrays, in which the number of dimensions is much bigger than the number of objects. Most studies have revealed that FCM and the fuzzy cluster validity indices (CVIs) perform poorly when they are used with high-dimensional data even when a similarity or dissimilarity measure suitable to this type of data is used. The problems faced by high dimensionality are known as the \textit{curse of dimensionality} and some approaches such as feature transformation, feature selection, feature weighting, and subspace clustering were defined to deal with thousands of dimensions. To be convinced that the number of dimensions should be maintained to learn as much as possible from an object and to know that just one subset of features might not be enough to all clusters, the soft subspace clustering technique was used in the proposed work. Besides FCM, three soft subspace algorithms, Simultaneous Clustering and Attribute Discrimination (SCAD), Maximum-entropy-regularized Weighted Fuzzy c-Means (EWFCM) and Enhanced Soft Subspace Clustering (ESSC) were performed to cluster three types of high-dimensional data (Gaussian mixture, text, microarray) and they were evaluated employing fuzzy CVIs instead of using external measures like Clustering Accuracy, Rand Index, Normalized Mutual Information, that use information from class labels, as usually done in most research studies. From the experimental results, in a general evaluation, all the clustering algorithms had similar performances highlighting that ESSC presented the best result and FCM was better than the remaining soft subspace algorithms. Besides the use of the soft subspace technique, in the search for the cause of the poor performance of the conventional techniques for high-dimensional data, it was investigated which distance measure or value of weighting fuzzy exponent ($m$) produced the best clustering result. Furthermore, the performance of nineteen fuzzy CVIs was evaluated by verifying if some tendencies and problems related to previous research studies are maintained when validating soft subspace clustering results. From the analysis made in this work, it was clear that the type of data was determinant to the performance of the clustering algorithms and fuzzy CVIs.
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9
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AILTON SANTOS RIBEIRO
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VISHNU: An approach to support the customization of avatars in mobile applications
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Líder : VANINHA VIEIRA DOS SANTOS
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MIEMBROS DE LA BANCA :
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CRISTIANO MACIEL
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LYNN ROSALINA GAMA ALVES
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VANINHA VIEIRA DOS SANTOS
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Data: 27-may-2020
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Resumen Espectáculo
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Avatar is a character that represents a particular person in a virtual environment. By allowing interaction in applications, the avatar has become quite popular in games and is also becoming a reality in other domains as a way to favor self-expression. Built from contextual user information, the use of an avatar provides immersion and a sense of presence in applications. Research indicates that people's behavior can be influenced by the characteristics of their avatar. Depending on the context, the avatar can be customized using contextual information from the user (e.g. preferences, anthropomorphic factors; Dynamic factors - location, climatic conditions; aspects related to interaction - facial expressions, gestures, body expressions and others). A gap found in the literature suggests the need to assist developers in creating their avatars in the context of the user. To allow the creation of self-expressive avatars, mechanisms are needed to capture, process and express the information present in the user's context with the application. Many mobile applications that use avatars as a way of interacting with the user still disregard contextual factors. Developers of these applications would benefit from an approach to guide these customizations. The aim of this research is to assess whether context sensitivity can favor self-expression in mobile applications based on avatars. For that, we present the VISHnu approach, a model of customization of avatars to assist the developers of mobile applications. To evaluate the proposed approach, a focus group with seven experts and a case study with two mobile applications based on avatars were carried out. The results indicated its usefulness and applicability to the avatars creation process, allowing an extended discussion about the influence of human, cultural and contextual factors in the customization of avatars. This thought favors the creation and customization of avatars and the perception that human interaction with this avatar goes through aspects, which, depending on the context, converges to a symbiosis between virtual and real beings.
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10
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RAILANA SANTANA LAGO
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RAIDE: a semi-automated approach to Test Smells Identification and Refactoring
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Líder : IVAN DO CARMO MACHADO
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MIEMBROS DE LA BANCA :
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IVAN DO CARMO MACHADO
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PIERRE YVES FRANCOIS MARIE JOSEPH SCHOBBENS
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VANIA DE OLIVEIRA NEVES
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Data: 03-jul-2020
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Resumen Espectáculo
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Unit testing is a specific type of test that deals with the smallest units in the system. It represents the first test after the implementation of a component. When the implementation of unit tests does not follow good practices, anti-standards are likely to be introduced in the code. Anti-patterns in tests, also known as test smells, are poor decisions for designing and implementing test code. Test smells impair the quality of the test code and reduce the ability of developers to interact with the test code, which makes it difficult to understand, read and, consequently, maintainability and evolution of the system. One strategy for removing test smells is to refactor the test code. Few studies in the literature offer automated support for the detection and refactoring of test smells. Thus, the present study provides a semi-automatic approach to identify test smells and suggest refactorings for the test code. Our approach is supported by the RAIDE tool (RefActorIng test Design Errors). We developed the RAIDE integrated with a development IDE to help test engineers refactor test code. RAIDE supports two test smells, Assertion Roulette and Duplicate Assert. Although there are other tools capable of identifying test smells, as far as we know, there is still no evidence of tools that provide automated support for refactoring test smells. In addition, no tool provides a user-friendly interface integrated with an IDE for the identification of test smells. To assess how our approach can help improve the quality of the test code, we also conducted an experimental study. We compared our tool with the state of the art and found that participants who used RAIDE were able to identify test smells more easily and quickly. In addition, RAIDE also proved to be efficient in refactoring test methods. Since RAIDE is a tool integrated with the Eclipse IDE, the identification and refactoring processes are faster and more intuitive compared to the state of the art and manual refactoring.
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11
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PATRICK HERBETH GUIMARÃES AZEVEDO
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A Recommendation System Based on Analysis of Semantic Relationships between Tags
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Líder : FREDERICO ARAUJO DURAO
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MIEMBROS DE LA BANCA :
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FREDERICO ARAUJO DURAO
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DANILO BARBOSA COIMBRA
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ANA LIZ SOUTO OLIVEIRA DE ARAÚJO
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Data: 23-jul-2020
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Resumen Espectáculo
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Recommendation systems have the main characteristic of suggesting items according to the preferences of a specific user. On the Web, Recommendation Systems assist users in discovering content of interest and are present in several Social Web systems such as Youtube.com, Netflix.com, Amazon.com etc. In the scope of this work, the focus will be on tags, keywords associated with resources on the Web. In general, tags are associated by users to describe resources, for example: films, books, music becoming true explicit sources of preference, but without restriction on the syntax of words. Recommendation Systems based on similarity between tags have the challenge of overcoming such problems and aim to evaluate the similarity between tags in order to indicate relevant items for users. However, semantically analyzing tags is a task that has several challenges, such as polysemy and the existence of synonyms. In this work, it is proposed to evaluate semantic links between tags associated with web pages, in order to increase the accuracy of the recommendations. Therefore, the objective of this work is also to propose a system that performs semantic analysis between tags in order to find similarities neglected only by syntactic analysis. It is intended to initially evaluate the system in the context of films given the availability of a set of tags associated with them, using precision metrics in different ranking positions. The idea of the proposal in this research scope differs from existing works, since the system uses an algorithm that mixes Jaccard's similarity coefficients with semantic similarity calculation using different data sources such as WordNet and Linked Open Data in a transparent way to the user .
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12
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MARCOS VINICIUS DOS SANTOS FERREIRA
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Fuzzy Modeling of Deterministic Components for Time Series Prediction
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Líder : RICARDO ARAUJO RIOS
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MIEMBROS DE LA BANCA :
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RICARDO ARAUJO RIOS
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TATIANE NOGUEIRA RIOS
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HELOISA DE ARRUDA CAMARGO
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RODRIGO FERNANDES DE MELLO
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Data: 27-jul-2020
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Resumen Espectáculo
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Data modeling in the temporal domain has been applied to different systems such as meteorology, engineering, medicine and economics. In this sense, the Fuzzy Time Series area has stood out due to its ability to approximate mathematical functions and linguistic variables to create rules, which are easier to be interpreted by specialists. As a consequence of this capacity, more accurate models can be obtained to understand the behavior of systems and, for example, to make predictions of future observations. Since the appearance of fuzzy time series models, researchers have been proposing improvements aiming, for example, to reduce errors in the forecasting task. In general, such tasks are composed of three stages: i) fuzzification; ii) fuzzy logical relationship; and iii) defuzzification. According to the literature, one of the points most studied by researchers is the fuzzification stage, with a focus on partitioning the universe of discourse for modeling fuzzy sets. However, few studies in the literature consider the separation of influences from the stochastic and deterministic components present in order to assist in the modeling process of fuzzy sets. In order to overcome this limitation, the present work presents two approaches that make improvements in the modeling of fuzzy sets in the fuzzification stage, considering influences of stochastic and deterministic components present in time series. In the first approach, the series was initially decomposed, separating its components into different monocomponents. Then, single components with high frequency were removed, resulting in a new smoothed series whose combination with the original series allows to generate a new scatter plot. The second approach was developed to model series with chaotic behavior.
In this sense, instead of modeling the series as a scatter plot, tools from the field of Dynamic Systems and Chaos Theory were used to reconstruct the series in phase space. Although using different methods, the two approaches remove the temporal dependence between the observations to assist in the modeling process of the fuzzy modeling sets, which is conducted considering the Fuzzy C-Means clustering method and cluster validation indexes, to estimate the quantity and function of fuzzy sets. In the experiments, time series with influences of stochastic and deterministic components were used to validate and test the generalization of the approach. The results achieved were promising and superior compared to the state of the art.
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13
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ROSANA GUIMARÃES RIBEIRO
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Novo índice interno de validação de agrupamento de dados temporais
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Líder : RICARDO ARAUJO RIOS
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MIEMBROS DE LA BANCA :
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MARCELO KEESE ALBERTINI
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MARCOS ENNES BARRETO
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RICARDO ARAUJO RIOS
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Data: 29-jul-2020
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Resumen Espectáculo
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Técnicas de Aprendizado de Máquina não-supervisionado foram desenvolvidas visando encontrar estruturas e padrões em conjuntos de dados sem considerar qualquer informação prévia fornecida, por exemplo, por um especialista. Essa ausência de informação impacta diretamente no processo de validação devido à dificuldade em mensurar o conhecimento obtido por meio destas técnicas. Visando solucionar este problema, diversas pesquisas têm sido publicadas na literatura propondo critérios que integram diferentes áreas do conhecimento como Ciência da Computação e Estatística. Esses critérios são comumente divididos em $3$ categorias: relativo, externo e interno. Em geral, tais critérios são desenvolvidos com base em índices com diferentes objetivos e vieses de análise. Entretanto, grande parte desses índices são aplicados sobre dados caracterizados por serem independentes e identicamente distribuídos. A realização de uma Revisão Sistemática da Literatura demonstrou que há um número reduzido de pesquisas que investigam índices de validação de agrupamento para dados com dependência temporal entre suas observações. Este número é ainda mais reduzido quando se trata de índices que utilizam critério interno de validação. Neste sentido, este trabalho de mestrado apresenta um novo índice interno de validação baseado na adaptação da Estatística GAP (Gap Statistic) comumente utilizado na literatura. O índice apresentado foi desenvolvido com o objetivo de mensurar e validar informações extraídas de dados temporais a partir da aplicação de técnicas de Aprendizado de Máquina não-supervisionado. Dessa forma, resultados experimentais demonstram a eficiência do novo índice interno de validação para dados com dependência temporal e confirmam a importância do mesmo para o estado da arte.
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14
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VICTOR MACIEL GUIMARÃES DOS SANTOS
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Temporal Novelty Quantification: a New Method to Quantify Temporal Novelty in Social Networks
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Líder : RICARDO ARAUJO RIOS
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MIEMBROS DE LA BANCA :
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RICARDO ARAUJO RIOS
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DANIELA BARREIRO CLARO
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ANGELO CONRADO LOULA
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Data: 30-jul-2020
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Resumen Espectáculo
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Currently, there is an expressive number of social networks used for different purposes, such as connecting people with a common interest in research, job offers, musical preferences, and general content. These networks have gained significant popularity in recent years. To demonstrate this phenomenon, research shows that 71% of young American adults use a social network at least once a day. With this frequent access and the freedom given by the networks, users started to publish numerous information, from personal photos to texts with opinions on different topics such as politics, entertainment, and health. In this sense, a new volume of information started to be produced, since, before social networks, only specialized professionals with access to conventional media were able to publish their opinions. From a scientific point of view, several techniques have been proposed in the literature aiming at analyzing the content produced in such social networks. Specifically related to the users’ behavior, it is common to observe their modeling through graphs or time series, however, these methods tend to ignore aspects of this behavior, for example, the temporal relationship or the dependence between terms used in publications. Considering these limitations, this research project was developed based on the hypothesis that the adoption of temporal graphs, together with tools from the areas of text Mining and Time Series, allows the detection of changes in the behavior of users of social networks. To validate this hypothesis, a new approach was developed to identify points of change in users’ behavior and to associate them with real events that influenced public opinion. This procedure uses Text Mining techniques to find terms, which will be used later in the creation of temporal graphs, maintaining their relationships in the original texts and their temporal dependencies. A new measure has also been developed, to quantify how users’ opinions evolve with time. Finally, a method for automatic detection of behavior change is presented, which aims to identify points when changes occur. This approach was evaluated considering a historic event in Brazil: the 2018 presidential elections. This period was chosen due to the volume of publications that effectively established social networks as the main mechanism for political activism. The results obtained emphasize the importance of the proposed approach and open new possibilities, for example, for the identification of bots that propagate fake news.
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15
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ILA MASCARENHAS MUNIZ
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The multiple roles of Human-Computer Interaction: understanding messages of metacommunication by Hegelian Dialectic.
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Líder : ECIVALDO DE SOUZA MATOS
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MIEMBROS DE LA BANCA :
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ECIVALDO DE SOUZA MATOS
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INGRID TEIXEIRA MONTEIRO
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LEOBINO NASCIMENTO SAMPAIO
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SUZI MARIA CARVALHO MARINO
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SÍLVIA AMÉLIA BIM
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Data: 07-ago-2020
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Resumen Espectáculo
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Computational artifacts are increasingly integrated into our daily lives, influencing the most varied activities. For this reason, the study of the interaction between humans and computer systems has gained importance in recent years, allowing the construction and improvement of interactive technologies. To solve interaction problems, knowledge of different scientific spaces/fields was articulated. The popularization of collaboration on the web has intensified these problems, mainly opening up the possibility for the user to act in the role of ”designer”. Despite offering greater autonomy to the user, the role of the designer may require technical knowledge in computing that, probably, the average user does not have. Without specialized knowledge, interruptions in the interpretation of interface messages can occur, especially when the user alternately assumes the roles of designer and user during the interaction. With this, this research verified the existence of conflicts of interpretation during the dialectical movement of subjects with multiple roles of interaction in a computer system and analyzed these disruptions in communication using the principles of Hegelian Dialectic. For this, a case study was carried out with the methods of Semiotic Engineering, MIS and MAC, followed by a dialectical analysis of the results. The results obtained open possibilities for reflection on the possibility of conflicts of interpretation in the metacommunication messages of the interface when the user switches between the roles of designer and user at the moment of interaction.
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16
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DANIEL ARAÚJO DE MEDEIROS
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Profile-guided frequency scaling for search workloads
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Líder : VINICIUS TAVARES PETRUCCI
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MIEMBROS DE LA BANCA :
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DANIEL MOSSÉ
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GEORGE MARCONI DE ARAUJO LIMA
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VINICIUS TAVARES PETRUCCI
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Data: 12-ago-2020
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Resumen Espectáculo
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O escalonamento de frequências é uma técnica essencial para maximizar a eficiência dos recursos computacionais existentes, especialmente em sistemas com arquiteturas capazes de executar tais tarefas em núcleos de frequência variável. Um dos fatores mais críticos capazes de afetar a experiência do usuário em serviços como busca ou redes sociais é a latência de cauda, definida como sendo a latência no 95 ou 99-percentil. Esta latência pode ser fortemente influenciada pela resposta mais lenta de um núcleo de baixo desempenho de um sistema, com seu impacto largamente amplificado quão mais núcleos de baixo desempenho estejam hospedados neste mesmo sistema. Do lado corporativo, reduzir a latência de cauda para a meta desejada é tão necessário quanto a diminuição do gasto energético, haja vista que este é diretamente proporcional aos custos financeiros para a operação do serviço e consequentemente ao lucro. Trabalhos anteriores em escalonamento de frequências para núcleos com capacidade de variação de frequência são de granulometria grossa, no sentido que se observa o estado de toda a aplicação para a tomada de decisões (sem distinção entre aplicação, threads ou funções). Tais trabalhos também muitas vezes dependem de um processo externo rodando em paralelo para coletar o comportamento dinâmico de determinada tarefa.
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17
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DANIEL AMADOR DOS SANTOS
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Understanding Replication Challenges: A View on Multiple Replications of a Highly-Configurable Systems Experiment
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Líder : EDUARDO SANTANA DE ALMEIDA
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MIEMBROS DE LA BANCA :
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EDUARDO SANTANA DE ALMEIDA
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MANOEL GOMES DE MENDONCA NETO
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RAFAEL PRIKLADNICKI
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Data: 02-sep-2020
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Resumen Espectáculo
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As Empirical Software Engineering grows in maturity and number of publications, more
replications are needed to provide a solid grounding to the evidence found through prior
research. However, replication studies are scarce in general and some topics suer more
than others. On top, the challenges associated with replicating empirical studies are not
well understood. In this study we aim to ll this gap by investigating diculties emerging
when replicating an experiment. We used an innovative method in which subjects with
distinct background play the role of a research group attempting to replicate an experimental
study. Eight replications in total were performed. We used Grounded Theory's
Constant Comparison method for qualitative analysis. We have seen in our replications
that most results hold comparing with the original experiments. However, the subjects
reported many diculties, mostly related to the clarity of the instructions and the quality
of the replication artifacts. Based on our experience, we also provide recommendations
that can help mitigating issues related to experiment replication.
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18
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LEONARDO THOMAS TORRES SANTOS
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A Real-Time 3D Computer Simulation Proposal for Hydrographic Painting
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Líder : KARL PHILIPS APAZA AGUERO
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MIEMBROS DE LA BANCA :
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ANTONIO LOPES APOLINARIO JUNIOR
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ESTEBAN WALTER GONZALEZ CLUA
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KARL PHILIPS APAZA AGUERO
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Data: 03-sep-2020
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Resumen Espectáculo
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Hydrographic Printing, also called Hydrographics, is a viable method for coloring objects created with 3D printers. However, executing the hydrographic technique leads to a complex interaction between a thin film and a 3D printed object. First, an image is printed on a film, which is put in the water. Then, by chemical reaction with an activator, the film becomes an adhesive sheet that carries the image pigment. When the 3D object dip, the film adheres to the object, projecting the 2D image to the object 3D surface. The hydrographic printing resultant projection is hard to predict precisely, because of the variable stretch in the film and the color whitening in the regions with high stretch. To address the difficulty of predicting the final result of hydrographic printing, we propose a computational solution that enables the generation of a flatted image of an arbitrary texture. Printing this image in a hydrographic film, it is possible to color a 3D object. The proposal allows executing a physical texture mapping in an object, with computational and physical steps. We propose a 3D computational simulation that uses Position-Based Dynamics, a popular technique for simulating deformable bodies and widely used in physics engines. We take advantage of this technique running in parallel a GPU-based simulation with suitable performance. We simulate the film behavior and its interaction with the 3D printed object, as an interaction between a soft-body colliding with a rigid one. To evaluate the achieved performance consistency, we made tests varying the number of vertices and voxels in the bodies involved and observed that the simulation kept running in real-time. We also execute the hydrographic technique in different printed models and compare these results with the simulated models.
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19
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ÍTALO DE CRISTO TEIXEIRA
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Analysis of neighborhood structures: a case study on the problem of scheduling tasks in a job shop environment.
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Líder : TIAGO DE OLIVEIRA JANUARIO
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MIEMBROS DE LA BANCA :
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TIAGO DE OLIVEIRA JANUARIO
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DANILO BARBOSA COIMBRA
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MAYRON CESAR DE OLIVEIRA MOREIRA
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Data: 17-oct-2020
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Resumen Espectáculo
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One of the most crucial characteristics of local search, widely used in optimization problems, is the definition of its neighbourhood. A neighbourhood is a mapping that assigns to each schedule $s \in S$, a set of schedules $ N(s) $ that are neighbours of $s$. Local search procedures use the concept of a neighbourhood to move from one schedule $s$ to a neighbour schedule $s' \in N(s) $. In this project, we performed an experimental performance analysis of six neighbourhood structures for the Job Shop Scheduling Problem. The objective of this problem is to plan the execution of jobs considering a limited set of resources and respecting the established restrictions. For effective analysis of the neighbourhood structures, four evaluation criteria were considered: Efficiency, Convergence, Strength and Improvement. In this work, the neighbourhoods were created from graph theory concepts. The local search procedures were developed based on Hill Climbing and Variable Neighborhood Descent methods, the latter to study the interferences in local search procedures by performing combinations of neighbourhoods. From the analysis of the results obtained, it was possible to demonstrate correlations of performance between the neighbourhoods and obtain useful information to understand why some neighbourhoods perform better than others in the defined evaluation criteria.
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20
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WILLIAN CARLOS SOUZA MARTINHO
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An improved simulation-based iterated local search metaheuristic for gravity fed water distribution network design optimization
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Líder : RAFAEL AUGUSTO DE MELO
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MIEMBROS DE LA BANCA :
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DANIEL ALOISE
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RAFAEL AUGUSTO DE MELO
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TIAGO DE OLIVEIRA JANUARIO
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Data: 19-oct-2020
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Resumen Espectáculo
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The gravity fed water distribution network design (WDND) optimization problem consists in determining the pipe diameters of a water network such that hydraulic constraints are satisfied and the total cost is minimized. Traditionally, such design decisions are made on the basis of expert experience. When networks increase in size, however, rules of thumb will rarely lead to near optimal decisions. Over the past thirty years, a large number of techniques have been developed to tackle the problem of optimally designing a water distribution network. This work tackles the NP-hard water distribution network design (WDND) optimization problem in a multi-period setting where time varying demand patterns occur. A new enhanced simulation-based iterated local search (ILS) metaheuristic is proposed which further explores the structure of the problem in an attempt to obtain high quality solutions. More specifically, four novelties are proposed: (a) a local search strategy to smartly dimension pipes in the shortest paths between the reservoirs and the nodes with highest demands; (b) a technique to speed up convergence based on an aggressive pipe diameter reduction scheme; (c) a novel concentrated perturbation mechanism to allow escaping from very restrained local optima solutions; and (d) a pool of solutions to achieve a good compromise between intensification and diversification. Computational experiments show that the proposed approach is able to improve over a state-of-the-art metaheuristic for most of the performed tests. Furthermore, it converges much faster to low cost solutions and demonstrates a more robust performance in that it obtains smaller deviations from the best known solutions.
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21
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BRENNO DE MELLO ALENCAR
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Machine Learning to Reduce Data Traffic and Latency in the Mist os Things.
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Líder : CASSIO VINICIUS SERAFIM PRAZERES
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MIEMBROS DE LA BANCA :
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CASSIO VINICIUS SERAFIM PRAZERES
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FLÁVIA COIMBRA DELICATO
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MANOEL GOMES DE MENDONCA NETO
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RICARDO ARAUJO RIOS
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Data: 26-oct-2020
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Resumen Espectáculo
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The Internet of Things (IoT) has produced infrastructures/applications that generate large amounts of data. These data are usually data streams, that have the characteristic of being continuous and infinite and also have the peculiarity of modifying their behavior over time. Due to the large capacity of storage, data processing and provisioning of resources, this data is generally processed/analyzed in cloud computing environments. Although Cloud Computing provides the IoT infrastructure with adequate scalability and resource centric features, the distance between devices and the cloud can impose limitations to achieve low latency in data traffic. In order to maintain scalability, achieve low latency and reduce data traffic between the IoT devices and the Cloud, the Fog Computing was proposed. Although the Fog Computing paradigm establishes resource availability at the edge of the network, the technologies and techniques currently used for IoT data processing and analysis may not be sufficient to support the continuous and unlimited data stream that IoT platforms produce. In this way, this work presents an approach for processing and analyzing data stream from the Internet of Things in real time in Fog. The main advantage of using our approach is the possibility of reducing the amount of data transmitted on the network infrastructure, which allows, as a consequence, to perform an online data modeling, by detecting changes in data behavior, and a reduction of the Internet usage. In addition, the proposed platform does not require a constant Internet connection. Finally, we evaluate the proposal from the perspective of performance in a scenario of intelligent objects at the edge of the network.
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22
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JUNOT FREIRE DOS SANTOS NETO
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Heuristics and metaheuristics for a constrained two-dimensional guillotine cutting problem
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Líder : RAFAEL AUGUSTO DE MELO
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MIEMBROS DE LA BANCA :
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BRUNO DE ATHAYDE PRATA
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RAFAEL AUGUSTO DE MELO
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TIAGO DE OLIVEIRA JANUARIO
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Data: 27-nov-2020
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Resumen Espectáculo
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Two-Dimensional bin packing problems (2BP) are classic combinatorial optimization problems belonging to the NP-Hard class and have applications in several sectors such as the textile, metallurgical and glass industries. 2BP consists in allocating a set of rectangular items in larger rectangular plates with a standardized size in order to minimize the waste of raw material. In this dissertation, we approach a restricted version of the 2BP with guillotine cuts presented in the ROADEF/EURO Challenge 2018, in which there is a possibility to rotate items in 90° and soma itens have a order of precedence to be produced, and the rectangular plates may present defects in certain points. We propose two randomized greedy heuristics and a method for improving solutions based on a constraint programming model combined with a random greedy heuristic. The techniques are combined in Multistart and Greedy Randomized Adaptative Search Procedure (GRASP) metaheuristics in order to obtain better solutions. Computational experiments show that the use of the solution improvement method is advantageous, and that some combinations of the proposed techniques are more effective for certain types of instances. A preliminary version of this work was qualified for the final phase of the ROADEF/EURO Challenge 2018.
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23
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GRAZIENO BARBOSA PELLEGRINO RIBEIRO
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OGUM: A Framework to Area Coverage based on a Dynamic Set of UAVs
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Líder : FLAVIO MORAIS DE ASSIS SILVA
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MIEMBROS DE LA BANCA :
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ALLAN EDGARD SILVA FREITAS
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DANIELA BARREIRO CLARO
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FLAVIO MORAIS DE ASSIS SILVA
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Data: 11-dic-2020
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Resumen Espectáculo
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The coordination of cooperative unmanned aerial vehicles (UAVs) has become an active area of research. Approaches to coordinating these swarms often include a solution to a class of problems called Area Coverage. Problems in this class consist of obtaining information about a specific area of interest (usually a polygon or polyhedron) through a set of UAVs, usually with decentralized coordination and minimal human intervention. Area coverage problems model the requirements of different types of applications, such as in the case of emergencies and disasters, gas leak detection, rescue of lost people, among many others. Existing solutions to these problems usually consider a fixed set of UAVs. In this dissertation, a strategy to coordinate dynamic sets of UAVs to solve a specific Area Coverage problem is described. In these dynamic sets, UAVs can enter or leave the set. To implement the strategy, a framework was developed using ROS and Gazebo. The framework ROS is a set of libraries and tools for robots. Area Coverage missions are executed in the Gazebo simulation environment. The framework facilitates the implementation of different strategies for coordinating multiple UAVs. We introduced heat maps to assess the quality of the adopted strategy. A heat map represents the movement of UAVs in the plan.
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24
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Carlos Fernando Silva Fernandes de Abreu Neto
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DYNAMIC GROUPING MODEL OF MULTIPLE DATA FLOWS USING VER IN TRANSLUCENT OPTICAL NETWORKS
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Líder : GUSTAVO BITTENCOURT FIGUEIREDO
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MIEMBROS DE LA BANCA :
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GUSTAVO BITTENCOURT FIGUEIREDO
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HELDER MAY NUNES DA SILVA OLIVEIRA
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JULIANA DE SANTI
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Data: 15-dic-2020
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Resumen Espectáculo
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With the growth in the demand for traffic on computer networks, Elastic Optical Networks emerged as a promising technological evolution of optical networks for the development of solutions focused on the efficient use of network resources. Unlike traditional WDM networks, which work with allocation of fixed grid resources, in EONs the bandwidth of an optical path is scalable and can vary according to the need for each connection, adapting dynamically to each traffic demand, ensuring the most efficient use of spectral resources. In this context, the use of resources is becoming more and more balanced, enabling greater cost reduction in several technological factors. However, with the popularization of the internet and the search to serve a greater range of technological possibilities with efficiency, researchers have been focusing their efforts towards an even more strategic use of these network resources. Translucent Elastic Optical Networks, through efficient strategies, strongly use the concept of elasticity and adaptability with the use of signal regeneration equipment. The Virtualized Elastic Regenerators are devices capable of adapting dynamically according to the traffic demand, where as they are no longer needed, VER is released to use new demands. In this way it is possible to serve a greater number of optical connections and reach greater distances with energy efficiency and cost reduction. These new transmission technologies have complementary characteristics, which using them combined with other concepts, brings great benefits to companies and users. The establishment of term-oriented connections can be postponed until such time as resources are available to meet the required term. The possibility of scheduling a requisition can bring benefits to both users and service providers. If there are no resources available, the establishment of a new connection may be postponed, so it is possible to handle a greater number of requests, avoiding an increase in the blocking rate. In conjunction with the scheduled provisioning of requisitions, the creation of requisition batches contributes to flexibility when establishing new connections. A batch is a set of requests that arrive at a certain time and that are not immediately provisioned, that is, each request will receive a certain deadline where your establishment must be attended to within this period. If there are no resources, at the end of the deadline the connection must be blocked. Scheduling in batches allows for greater combinations, making the most of the available network resources, thus avoiding increased energy consumption and reducing the number of blocking requests. Therefore, this work presents a new methodology to face the problem of exponential increase in traffic on the communication network, considering cost reduction naturally a priority. In order to achieve these expectations, the proposed methodology consists of the development of a dynamic pooling and resource sharing model for regeneration equipment, in addition to the combination of technologies in the creation of requisition batches with scheduled provisioning. The results obtained through simulations by software implemented by the author himself, indicate that the model proved to be effective, and was able to considerably reduce the blocking rate, the amount of resources with VER used and consequently generated greater energy efficiency, when compared to them methods without using this grouping methodology, creating requisition batches and scheduled provisioning.
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25
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VIRGÍNIA DE SOUSA VENEGA
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Softtware Requirements for CMOOC
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Líder : RITA SUZANA PITANGUEIRA MACIEL
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MIEMBROS DE LA BANCA :
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PAULEANY SIMOES DE MORAIS
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IVAN DO CARMO MACHADO
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RITA SUZANA PITANGUEIRA MACIEL
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Data: 16-dic-2020
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Resumen Espectáculo
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ABSTRACT
Massive Open Online Course (MOOC) are open online courses, generally free, with
no prior knowledge required for participation and an inde nite number of participants.
MOOC can be divided into two groups: xMOOC and cMOOC. XMOOC environments
are considered extensions of traditional online course environments as they preserve very
similar pedagogical characteristics, such as minimal asynchronous support, generic assessments,
and a focus on continuous recruitment. This course modality, despite being
the most common type of MOOC and having well-de ned development support, faces
problems, such as high student dropout rates and di culties in the learning assessment
process. For cMOOC environments MOOCs from the perspective of the connectivist approach,
there is still no consensus on the software requirements for this domain. Considering
MOOC as a type of Educational Software (SE), several studies explore the problem
of building SE under the perspective of speci c theories and pedagogical approaches to
direct the development of the software. These theories, methods, and pedagogical approaches
can help to understand the learning & evaluation processes as well as in the design
of courses and MOOCs. It is observed, then, that few software projects consider speci c
theories or learning methods since their conception. In this sense, this work aimed to
identify what are the software requirements needed for the development of cMOOC environments.
To achieve this objective, surveys were applied to computer students from
di erent courses and professionals in the area of Informatics and Education to capture,
the desirable requirements for the development of MOOC according to connectivism.
Two surveys were carried out with students and educators. As a result, 853 potential
requirements were initially identi ed and after successive coding cycles, ltered and
synthesized. The identified and cataloged requirements were based on the dimensions of
Openness, Interactivity, Autonomy, and Diversity, beacons of connectivism. As a result
of this work, a catalog of software requirements for the development of cMOOC was designed.
With this expected to be able to support the requirements engineering process
for speci c educational elds and the development of cMOOC.
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26
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RAPHAEL ALVES DE JESUS LIMA
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Comparing Techniques for Derivation of Source Code Metric Thresholds: A Study with Web Developers
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Líder : CLAUDIO NOGUEIRA SANT ANNA
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MIEMBROS DE LA BANCA :
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CLAUDIO NOGUEIRA SANT ANNA
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EDUARDO MARTINS GUERRA
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RODRIGO ROCHA GOMES E SOUZA
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Data: 17-dic-2020
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Resumen Espectáculo
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Source code metrics quantify dierent software attributes and have the potential to support the identication of design problems that may aect software comprehensibility and maintainability. Identifying design problems can reveal parts of the source code that need to be monitored more closely. However, one of the major challenges in using metrics in source code quality monitoring activities is the denition of threshold values that are capable of identifying design problems that are actually considered problems according to developers' perceptions. Although there are a number of techniques for extracting threshold values, the threshold values obtained by means of them generate many false positives. That is, there are many of code elements (e.g., classes or methods) whose metric values exceed threshold values that, when evaluated by developers, are not considered to be problematic. Therefore, the participation of developers is important to analyze threshold value eectiveness. Few studies evaluate the eectiveness of dierent threshold extraction techniques based on the perception of developers. Therefore, the goal of this work was to conduct an experimental study to evaluate the perception of developers about design problems detected with threshold values obtained by means of ve dierent techniques that extract threshold values from system benchmarks. In this scenario, we conducted two studies, a preliminary one in which we analyzed two systems and two developers' perception, and a second study in which we analyzed four systems and eight developers. The results indicate that developers use design roles to assess whether or not design problems exist, and techniques that take into account some contextual information, such as reference systems or design papers to derive thresholds may reduce the number of false-positives.
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27
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ANDRE LUIZ ROMANO MADUREIRA
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IOTP: On Supporting IoT Data Aggregation Through Programmable Data Planes
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Líder : LEOBINO NASCIMENTO SAMPAIO
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MIEMBROS DE LA BANCA :
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CASSIO VINICIUS SERAFIM PRAZERES
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LEOBINO NASCIMENTO SAMPAIO
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RODOLFO DA SILVA VILLAÇA
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Data: 23-dic-2020
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Resumen Espectáculo
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IoT devices generate large continuous data streams, which causes congestion that compromises the scalability of IoT systems. To face this problem, techniques for data aggregation propose to reduce recurring packet headers, through the assembly of packet data coming from different sources. Due to the energy constraints and limitation of computational resources of devices, most proposals adjust data aggregation according to their features following multilayered-based approaches or coupling the solution to a given network protocol, but overlooking the properties of the communication link. In this dissertation, we introduce the Internet of Things Protocol (IoTP). An L2 communication protocol for IoT programmable data planes that supports the implementation of data aggregation algorithms inside hardware switches, at the network level. Through these features, IoTP provides support for the design of efficient and adaptable aggregation schemes that can function according to network status and based on the different communication technologies used by IoT devices. We implemented IoTP using the P4 language and conducted emulation-based experiments through the Mininet environment. Our findings show that IoTP accomplishes a 78% improvement in network efficiency, as well as allowing control over the average delay generated by data aggregation techniques. Besides that, it was able to reduce the number of packets sent over the network, while also reducing the consumption of network devices computational resources.
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Tesis |
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1
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NICOLLI SOUZA RIOS ALVES
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“Organização De Um Conjunto De Descobertas Experimentais Sobre Causas E Efeitos Da Dívida Técnica Através De Uma Família De Surveys Globalmente Distribuída
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Líder : MANOEL GOMES DE MENDONCA NETO
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MIEMBROS DE LA BANCA :
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CHRISTINA VON FLACH GARCIA CHAVEZ
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CLAUDIO NOGUEIRA SANT ANNA
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EMILIA MENDES
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MANOEL GOMES DE MENDONCA NETO
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TAYANA UCHÔA CONTE
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Data: 26-may-2020
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Resumen Espectáculo
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Contexto: O conceito de dívida técnica (DT) contextualiza o problema das tarefas de desenvolvimento pendentes como um tipo de dívida que traz um benefício a curto prazo para o projeto, mas que poderão ter de ser pagas com juros mais tarde no processo de desenvolvimento. É comum que um projeto de software incorra em dívida durante o processo de desenvolvimento, no entanto, sua presença traz riscos para o projeto e dificulta sua gestão. Diferentes estratégias de gerenciamento da DT têm sido propostas, contudo, considerar ações que possam evitar sua inserção e monitorar seus efeitos ainda não é uma prática comum. Este é um ponto que merece ser melhor investigado por diferentes motivos: (i) conhecer as causas da DT pode auxiliar as equipes de desenvolvimento na definição de ações que possam ser tomadas para evitar sua ocorrência e (ii) conhecer os possíveis os efeitos da DT pode apoiar na realização de análises de impacto mais precisas e também na definição de ações corretivas para minimizar possíveis consequências negativas da dívida inserida. Dessa forma, enquanto o gerenciamento da DT é um tópico de pesquisa importante, também é necessário entender as causas que podem levar equipes de desenvolvimento a incorrer em diferentes tipos de dívida, bem como os efeitos de sua presença em projetos de software. Objetivo: O objetivo desta tese é investigar, através da replicação contínua e independente de surveys distribuídos globalmente, o estado da prática e tendências da indústria sobre DT incluindo causas que levam à sua ocorrência, efeitos de sua existência e como esses problemas se manifestam no processo de desenvolvimento de software. A partir da organização das causas e efeitos identificados, estruturar diagramas de causa e efeito probabilísticos que possam ser utilizados no apoio a atividades de gestão da DT. Método: As atividades realizadas nesta tese são fundamentadas no paradigma da engenharia de software experimental. Inicialmente, foi realizado um estudo terciário com o objetivo de investigar o estado atual da pesquisa sobre DT identificando quais tópicos de pesquisa têm sido considerados, organizando direcionamentos de pesquisa e conhecimentos práticos que já foram definidos, identificando os tipos conhecidos de DT, e mapeando quais atividades, estratégias e ferramentas têm sido propostas para apoiar o gerenciamento da DT. Em seguida, foi planejado InsighTD, uma família de surveys globalmente distribuída. InsighTD foi planejado de forma cooperativa com pesquisadores da área de DT de diferentes instituições ao redor do mundo. Trata-se do primeiro estudo em larga escala realizado na área. Ele busca organizar um conjunto aberto e generalizável de dados experimentais sobre causas e efeitos da DT em projetos de software. Por fim, os diagramas probabilísticos de causa e efeito da DT propostos foram avaliados através de estudo de caso executado na academia. Resultados: Esta tese apresenta os resultados do estudo terciário executado e as análises realizadas sobre a primeira execução de InsighTD no Brasil e sua primeira replicação nos Estados Unidos. Ao total, 107 profissionais da indústria brasileira e 99 da indústria estadunidense de software responderam ao questionário. Os resultados indicam que há uma ampla familiaridade com o conceito de DT. Prazos, planejamento inadequado, falta de conhecimento e falta de um processo bem definido estão entre as 10 causas mais citadas e mais prováveis de levar à ocorrência da DT em projetos de software. Por outro lado, baixa qualidade, atraso na entrega, baixa manutenibilidade, retrabalho e perdas financeiras estão entre os 10 efeitos mais citados e de maior impacto em um projeto com a presença da DT. Diagramas probabilísticos de causa e efeito da DT para os diferentes tipos de DT foram elaborados. Os resultados também indicaram que o tipo de modelo de processo (ágil, híbrido ou tradicional) impacta nos efeitos da DT sentidos por equipes de desenvolvimento. Conclusão: Com InsighTD, pretende-se reduzir o problema de investigações isoladas em DT que ainda não são representativas e, assim, construir um conjunto aberto e generalizável de dados experimentais para a compreensão de problemas práticos e desafios da área. Parte do conhecimento organizado será estruturado em diagramas probabilísticos de causa e efeito que permitem apoiar atividades de gestão da DT.
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2
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CLÍCIA DOS SANTOS PINTO
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Exploiting heterogeneous computing techniques to address probabilistic big data linkage
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Líder : MARCOS ENNES BARRETO
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MIEMBROS DE LA BANCA :
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ESBEL TOMÁS VALERO ORELLANA
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GEORGE MARCONI DE ARAUJO LIMA
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MARCOS ENNES BARRETO
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MAYCON LEONE MACIEL PEIXOTO
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RODRIGO DA ROSA RIGHI
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Data: 28-jul-2020
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Resumen Espectáculo
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Although heterogeneous computing is a powerful approach to solve computationally intensive problems, its performance and efficiency highly depend on the workload to which they are exposed. Managing large volumes of data in heterogeneous environments involves choosing efficient scheduling and partitioning algorithms that minimize the response time and the volume of communication among processing units while ensuring scalability. This requirement has become more urgent as the devices composing such heterogeneous platforms become more numerous and diversified. This work presents a methodology for using heterogeneous computing techniques over hybrid CPU+GPU environments to allow for data and task distribution within big data linkage applications. This methodology was integrated into the AtyImo tool, which was partially developed during this research to provide probabilistic record linkage. As proof of concept, the implemented solution was used to integrate a large-scale (100 million records) socioeconomic database with public health data from disparate governmental sources. The proposed methodology is able to perform 1x10ˆ12 pairwise comparison in around one hour, which is a quite prominent result amongst existing data linkage tools. Observed results evidence that the developed solution achieves good performance and can be an alternative to solve scalability issues in data linkage contexts. The possibility of probabilistically linking massive datasets using hybrid architectures and exploring the heterogeneous nature of available resources with an efficient execution time are the main contributions of this work.
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3
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MICHELLE LARISSA LUCIANO CARVALHO
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ToffA-DAS: An Approach to conduct Trade-off Analysis for Dynamically Adaptable Software
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Líder : EDUARDO SANTANA DE ALMEIDA
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MIEMBROS DE LA BANCA :
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EDUARDO SANTANA DE ALMEIDA
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RAFAEL AUGUSTO DE MELO
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RITA SUZANA PITANGUEIRA MACIEL
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CECILIA MARY FISCHER RUBIRA
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PAULO CESAR MASIERO
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ROSSANA MARIA DE CASTRO ANDRADE
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Data: 24-sep-2020
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Resumen Espectáculo
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The Dynamic Software Product Lines (DSPL) engineering processes aim to design Dynamically Adaptable Software (DAS) by increasing the flexibility for the generation of a huge number of configurations. It results in a software configuration space explosion making the analysis more difficult and complicating the developer’s work. In this sense, software engineers need to find a combination of systems features that can simultaneously satisfy constraints specified in feature and context models, Non-functional Requirements (NFRs), and stakeholder’s preferences. It means that they have to measure many configurations until finding the optimal ones, characterizing the product configuration process in a complex optimization problem. Most of the existing studies do not focus on the interactions between contextual information and NFRs when dealing with feature selection to meet the desired quality objectives in DAS. In addition, such studies do not use any planning strategy to support the configuration selection process. Based on these research gaps, we propose an approach that (i) manages the system’s features and contexts; (ii) facilitates the understanding of how DSPL applications can behave from a certain context change, and (iii) enables to conduct trade-off analysis in order to find valid and optimal configurations, which meet the constraints and the interactions between contextual information and NFRs. Aiming to support the context variability modeling of DAS, we proposed the Extended Context-aware Feature Modeling (eCFM) technique to deal with constraints among contexts. Next, we defined the DAS Trade-off Analysis (ToffA-DAS) approach to deal with the configuration selection process embracing interactions between contextual information and NFRs. We also proposed a strategy to analyze context changes in order to define adaptation models for each prioritization of the system’s features, contexts, and NFRs. Finally, we evolved our approach and named it as DAS Trade-off Analysis PLUS (ToffA-DAS+). ToffA-DAS is based on the integer linear programming technique, whereas ToffA-DAS+ uses a genetic algorithm. We performed a set of empirical studies in order to evaluate the proposal for this thesis. First, we conducted a survey to evaluate eCFM from the viewpoint of expressiveness to model the context constraints and easiness of use. Indeed, the analysis was focused on the comprehensibility of contextual variability modeling. Next, we performed a study based on simulations to gather initial evidence about the feasibility of using ToffA-DAS. It is based on how to conduct trade-off analysis and define adaptation models from optimal configurations found in the analysis. We also conducted an exploratory study to evaluate how the configurations obtained by the execution of ToffA-DAS affect the overall satisfaction level of NFRs. Finally, we evaluated the evolution of our approach in comparison with the previous release. As a result of the first study, the eCFM was considered a technique with a great expressiveness to represent adaptation rules among contexts and system features, besides the easiness of use and organization with the grouping of contexts. Therefore, we argue that the software engineers may take into account the use of eCFM technique to model DAS. In the second study, ToffA-DAS presented consistent results in accordance with the real-world scenarios and satisfied the estimated utility values and model constraints. The third study showed that the set of configurations generated by ToffA-DAS execution provide high satisfaction levels of NFRs. In the last study, we collected evidence that ToffA-DAS+ suggests more solutions from then possible valid configurations of the model. Based on the aforementioned studies, we evidenced that our approach can be handy when software engineers need assistance in the understanding of how to design a variety of configurable options for DSPL applications. It is based on the principle that each configuration option must be optimal to meet certain contextual changes without losing service quality. With the usage of our approach, software engineers can exhaustively analyze and simulate a solution before implementing it.
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4
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LUIS PAULO DA SILVA CARVALHO
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Identifying and Analyzing Software Concerns from Third-Party Components' Metadata
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Líder : MANOEL GOMES DE MENDONCA NETO
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MIEMBROS DE LA BANCA :
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CLAUDIO NOGUEIRA SANT ANNA
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LAIS DO NASCIMENTO SALVADOR
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MANOEL GOMES DE MENDONCA NETO
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PAULO CAETANO DA SILVA
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SÉRGIO CASTELO BRANCO SOARES
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Data: 16-nov-2020
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Resumen Espectáculo
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Modularity is a key concept in software development. Well-modularized systems are easier to maintain and evolve. However, it is difficult to achieve good modularity in software systems, because developers must keep systems modular with respect to several perspectives. Frequently, this involves dealing with concerns that scatter and tangle through several modules, the crosscutting concerns as they are called. Logging, Database Access, and Testing Automation are examples of crosscutting concerns. They are important, but many of them are not central to the systems' business rules. As a consequence, often, they are not subject to modularization. Additionally, the analysis of crosscutting concerns tends to be effort-intensive and rapidly grows in complexity in large systems.
Studies on concerns often resort to manual identification of interests. Unfortunately, manual identification tends to be subjective, imprecise, and effort-intensive. Software Architecture Documents (SADs) can be used as auxiliary resources to identify and analyze concerns, but SADs are not common assets. And, when they are available, there is no guarantee that they contain relevant information about concerns of particular interest. As consequence, ideally, developers should rely on automation to identify and process information about concerns over the source code. Automatic approaches are a must have when the codebase is extensive and one wants to analyze how concerns evolve throughout the system's history. In this context, this work takes advantage of the injection of components in software projects to define a method for locating information about crosscutting concerns in software projects.
On modern systems, developers implement modules to address central business rules, but they usually inject third-party components in the codebase to materialize concerns related to secondary aspects of the system. For instance, the previously mentioned, Logging, Database Access, and Tests Automation are concerns that are often implemented with the help of external components. As these are the type of concerns that most scatter and interrelate through systems' modules, we saw an opportunity to propose a method to identify and analyzed them using injection data and metadata.
Our method first identifies concerns from the metadata that developers use to inject third-party components in their systems. Then, it evaluates how those concerns spreads, and evolve through time, over the codebase. We developed a tool named Architectural Knowledge Suite (AKS) to automate the method. We used this tool to conduct an action research study with the help of software development specialists to evaluate the reliability of our method and to refine it. We also ran three other studies using our method to process real information systems' source code, characterizing and analyzing how developers implement concerns in the real world.
Among our findings, we highlight that our method met the expectations of the specialists to a moderate degree. We perceived that grouping software projects according to their contexts of use can optimize the identification and analyses of concerns. We noticed that developers tend to mix concerns by joining references to different components through the lines of source code artifacts, but we spotted some exceptional cases. We also saw opportunities to adapt our method to expand the identification of concerns toward varied contexts of adopted software development technologies.
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5
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ERASMO LEITE MONTEIRO
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A MATURITY MODEL PROPOSAL FOR INTEROPERABILITY IN SYSTEMS: FROM SINTATIC TO ORGANIZATIONAL
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Líder : RITA SUZANA PITANGUEIRA MACIEL
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MIEMBROS DE LA BANCA :
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DANIELA BARREIRO CLARO
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IVAN DO CARMO MACHADO
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JOSÉ MARIA NAZAR DAVID
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PAULO CESAR MASIERO
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RITA SUZANA PITANGUEIRA MACIEL
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Data: 04-dic-2020
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Resumen Espectáculo
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Interoperability is the ability of heterogeneous systems to communicate transparently and
can be achieved through levels, such as: (i) syntactic, (ii) semantic, (iii) pragmatic, and
(iv) organizational. However, there are several challenges for systems to provide interoperability
such as what requirements are necessary to achieve the desire interoperability.
Several proposals have been made to discuss aspects of a specific level of interoperability
or for a given domain. However, the interoperability of a system is an aspect that can
evolve throughout its life cycle, so support is needed to aid this process. Maturity models
can help in this case as they have been used in several domains to assess the maturity of
the system according to specific aspects (e.g, interoperability). This work presents AMortisse
(mAturity Model fOR inTeroperability In Software SystEms), a maturity model to
check interoperability in software systems towards the specification of a methodology for
maturity model definition. This methodology aims to aid the Maturity Model (MM) developers
by presenting a MM development life-cycle applicable for different domains. We aim
to systematize tasks involved in MM development, such as MM domain requirements,
organization of related concepts into levels, dimensions, and the path to maturity, therefore
leveraging the produced MM quality. Despite their popularity, maturity models
have been criticized due to lack of empirical validation and effective methods to aid in
their definition. A validation was performed by applying it to an organization's system to
attest Amortisse's ability to indicate the system's maturity. As a result, Amortisse was
able to measure the level of interoperability of the system and show which requirements
must be met to evolve between levels of maturity. The results of this investigation show
that the Amortisse and the methodology are feasible. We hope the presented methodology
provides clarity while obtaining this model and may help the development of new
maturity models in different domains. We hope that Amortisse will be able to show
systems interoperability level and indicate missing requirements.
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6
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ELIVALDO LOZER FRACALOSSI RIBEIRO
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Defining and Providing Pragmatic Interoperability - The MIDAS Middleware Case.
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Líder : DANIELA BARREIRO CLARO
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MIEMBROS DE LA BANCA :
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DANIELA BARREIRO CLARO
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ELISA YUMI NAKAGAWA
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FRANK AUGUSTO SIQUEIRA
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IVAN DO CARMO MACHADO
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LAIS DO NASCIMENTO SALVADOR
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Data: 10-dic-2020
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Resumen Espectáculo
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Modern information systems are becoming increasingly complex. This complexity is related to the need to combine heterogeneous software. Since a system may contain many software programs, and each software may be developed independently, providing transparent communication between heterogeneous systems is not a trivial task. The lack of standardization causes a problem known as lock-in. Lock-in situations occur when users are dependent on a system due to the lack of interoperability among different providers. Interoperability is heterogeneous systems’ ability to communicate transparently, and it is classified into three levels: syntactic, semantic, and pragmatic. The syntactic level enables systems to exchange information based on standard coding. Semantic interoperability is concerned with ensuring that systems to share the same data meaning. Finally, pragmatic interoperability ensures that systems understand the message intention so that the result is within common expectations. Despite the various levels, solutions for interoperability among systems focus on a specific layer. The absence of a pragmatic interoperability model hinders transparent communication among systems because the mandatory information to interoperate is not explicit. The pragmatic level requires the semantic level that, in turn, requires the syntactic level. In addition to the need to interoperate heterogeneous systems, current technologies evidence the challenges of storing, processing, and making available the data generated by this communication. Cloud Computing aims to fulfill some of these requirements. Cloud Computing is a new paradigm that enables access to a ubiquitous network of applications, platforms, and hardware as services. These services are organized in levels, and they are accessed with a pay-per-use policy. Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Data as a Service (DaaS) are examples of cloud services. Therefore, this work presents a Conceptual frAmework for Pragmatic InTeroperAbiLity (CAPITAL). Although focused on pragmatic interoperability, the CAPITAL framework considers the syntactic and semantic levels. We performed experiments on each model to validate our CAPITAL framework. The syntactic model provides a detailed description of the syntactic interoperability of MIDAS (Middleware for DaaS and SaaS). The semantic model provides an ontology to formalize the communication between SaaS and DaaS. This ontology assists the semantic interoperability of MIDAS. The CAPITAL framework describes the model for pragmatic interoperability. We perform three studies to evaluate our CAPITAL framework. In the first study, we simulated the CAPITAL framework in four distinct scenarios that aim to provide a modeling and coding guide. The second study is a controlled experiment that investigates whether our framework eases to understand the concept and interpret scenarios with pragmatic interoperability. In the third study, we incorporated our framework into MIDAS as a proof of concept to discuss and present a middleware version for pragmatic interoperability. The three studies suggest that the CAPITAL framework positively influences the understanding, modeling, and standardization of scenarios with pragmatic interoperability. Our findings evidence that models for syntactic, semantic, and pragmatic interoperability describe mandatory elements to provide transparent communication.
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7
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LEANDRO JOSE SILVA ANDRADE
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Data Interplay: a model to improve performance efficiency in the Internet of Things data
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Líder : CASSIO VINICIUS SERAFIM PRAZERES
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MIEMBROS DE LA BANCA :
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CASSIO VINICIUS SERAFIM PRAZERES
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DANILO BARBOSA COIMBRA
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FLÁVIA COIMBRA DELICATO
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MAYCON LEONE MACIEL PEIXOTO
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PAULO DE FIGUEIREDO PIRES
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Data: 11-dic-2020
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Resumen Espectáculo
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The Internet of Things (IoT) has been playing an important role in the technology scenario due to its high potential and impact on different society segments. Estimates suggest a trend for an increase in the number of IoT devices connected to the Internet for the next few years. As a consequence, the volume of data produced by IoT devices will follow this growth perspective, and there will be a demand for systems that are able to process, store and promote access to large amounts of data. In the typical IoT system, the data collected from sensors is stored and processed in cloud servers; however, some IoT solutions use edge devices to perform actions, such as processing, storage and access, using only local infrastructure for low latency requirements. Fog Computing has been used to improve IoT solutions with the aim to transfer some of the complexity from the cloud to the edge of the network, i.e., closer to devices, applications and/or users, working as a kind of ``local and private cloud''. The cooperation of devices and applications between edge and cloud creates a need for an interplay to allow data flow among the layers of IoT systems deployed in the edge and the cloud. Thus, it is necessary to support the data life cycle since its collection, analysis and use. Performance efficiency is a quality factor of systems and software engineering, which measures ``performance relative to the amount of resources used under stated conditions''. In special, in IoT systems which involve a large volume of data, the performance efficiency of data interplay is a relevant requirement. This thesis proposes a data interplay model of Internet of Things to provide the definition and deployment of the IoT data life cycle, in collection, analytics and data use stages. This data interplay proposal aims to improve performance efficiency in the operations regarding the IoT data life cycle: collection, analytics and use among devices and applications in edge and cloud infrastructures.
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8
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FLAVIO DUSSE
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A Computational Reference Model to Support Decision-Making for Emergency Management Based on Visual Analytics
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Líder : MANOEL GOMES DE MENDONCA NETO
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MIEMBROS DE LA BANCA :
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JOSÉ CARLOS MALDONADO
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LAIS DO NASCIMENTO SALVADOR
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MANOEL GOMES DE MENDONCA NETO
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MARCOS ROBERTO DA SILVA BORGES
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VANINHA VIEIRA DOS SANTOS
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Data: 15-dic-2020
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Resumen Espectáculo
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Background: The number of emergencies around the world has been increasing in recent years. No emergency is the same as the previous and the next. Emergency Management (EM) refers to the activity of dealing with emergency tasks in different phases and iterations. People working in an emergency are generally under stress to make the right decision at the right time. They have to process large amounts of data and to assimilate the received information in an intuitive and visual way. We found that information overload as well as non-dedicated information are problems in Emergency Management (EM). Visual Analytics (VA) is potentially useful to analyze and understand the huge amount of data produced in an emergency. However, we found that the full potential of VA is not being exploited in EM. Objective: We seek to develop a conceptual model for using VA in EM. This model incorporates themes that are currently under-exploited, to better support decision-making in EM. The model aims to help visualization designers to create effective VA interfaces that in turn help emergency managers to make quick and assertive decisions with these interfaces. Methods: We performed a long-term multi-method study. First, we carried out a systematic mapping study to analyze the available visualization tools and their applications in EM. To complement this information, we carried out appraisal of official documents, ethnographic studies, questionnaires and focus groups during large events held in Brazil (e.g., Soccer World Cup and Olympics Games). Then, we analyzed actual tools that produces emergency information visualization and we interviewed professionals experienced in EM. We crosschecked and analyzed this data qualitatively using the coding technique. We identified the relationships between the visual needs and other major themes of influence for EM. We used our findings aligned with VA concepts to develop our model for EM visualization. Results: We evaluated our proposal using an exploratory study in a Brazilian Command and Control Center, comparing the available tools against our model. The visualizations that were designed with the support of the model had 73.4\% higher scores, 25\% equal scores and only 1.6\% lower scores than the ones designed without it. We believe that the main contribution of this work is to introduce the model to conceive and evaluate the effectiveness of VA in EM scenarios. The results of the dissemination of this model will foment the research on the use of VA in EM. We hope that C2 Centers incorporate the use of the proposed model in their routine; if it helps in timely and assertive decision-making, the quality of the service provided to society will improve. The ultimate contribution of our work is the potential reduction of financial and, above all, human losses in emergencies.
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9
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KALYF ABDALLA BUZAR LIMA
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"From modeling perceptions to evaluating video summarizers"
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Líder : LUCIANO REBOUCAS DE OLIVEIRA
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MIEMBROS DE LA BANCA :
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GECYNALDA SOARES DA SILVA GOMES
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JOAO PAULO PAPA
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LUCIANO REBOUCAS DE OLIVEIRA
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PAULO JORGE CANAS RODRIGUES
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RICARDO DA SILVA TORRES
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Data: 18-dic-2020
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Resumen Espectáculo
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Hours of video are uploaded to streaming platforms every minute, with recommender systems suggesting popular and relevant videos that can help users save time in the searching process. Video summarizers have been developed to detect the video's most relevant parts, automatically condensing them into a shorter video. Currently, the evaluation of this type of method is challenging since the metrics do not assess user annotations' subjective criteria, such as conciseness. To address the conciseness criterion, we propose a novel metric to evaluate video summarizers at multiple compression rates. Our metric, called Compression Level of USer Annotation (CLUSA), assesses the video summarizers' performance by matching the predicted relevance scores directly. To do so, CLUSA generates video summaries by gradually discarding video segments from the relevance scores annotated by users. After grouping the generated video summaries by the compression rates, CLUSA matches them to the predicted relevance scores. To preserve relevant information in concise video summaries, CLUSA weighs the video summarizers' performance in each compression range to compute an overall performance score. As CLUSA weighs all compression ranges even that user annotations do not span some compression rates, the baseline changes with each video summarization data set. Hence, the interpretation of the video summarizers' performance score is not as straightforward as other metrics.
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10
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BABACAR MANE
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Evolving the interoperability from SaaS and DaaS/DBaaS: the MIDAS case
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Líder : DANIELA BARREIRO CLARO
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MIEMBROS DE LA BANCA :
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DANIELA BARREIRO CLARO
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GLAUCO DE FIGUEIREDO CARNEIRO
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JOSÉ MARIA NAZAR DAVID
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MARLO VIEIRA DOS SANTOS E SOUZA
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VANINHA VIEIRA DOS SANTOS
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Data: 21-dic-2020
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Resumen Espectáculo
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The provider of both Data as a Service (DaaS) and Database as a Service (DBaaS) stores and manages a high volume of heterogeneous data. Such data are produced by mobile computing, ubiquitous devices, social networks, and they are distributed geographically
and available to consumers and organizations as services through an API. Users face a challenge when accessing similar distributed data from distinct DaaS/DBaaS providers due to the lack of a standard API and tools. Consequently, cloud users face interoperability and integration issues for consumption, provisioning, management, and supervision resources among distinct clouds. In such a heterogeneous environment, organizations desiring to exchange their data among clouds or move their applications to distinct clouds will face a lock-in situation due to the lack of a standard solution. Middleware has been employed to deal with interoperability issues to minimize the effort to overpass lock-in problems. Thus, this thesis introduces the middleware MIDAS to minimize the effort to interoperate SaaS and DaaS. We can summarize three main contributions from this work. Initially, the MIDAS Middleware provides (i) syntactic interoperability among SaaS and DaaS. MIDAS allows a cloud query from SaaS to request data from distinct DaaS/DBaaS. Data attributes and conditions are described in SQL or MongoDB queries. Therefore, a SaaS request may be affected by the evolution of the DaaS attributes. As a second contribution, MIDAS was evolved to provide (ii) semantic interoperability to ensure DaaS consistency and maintain SaaS’s original query. Currently, MIDAS runtime implementations rely on Cloud Foundry, Amazon Web Services, OpenShift, and Heroku providers. Some cloud development and runtime environment diversities (.e.g., application framework and programming languages) prevent implementing, running, or deploying applications on a large scale. To avoid ambiguity in the development and seamless deployment of MIDAS in different cloud providers, we present the third contribution of this thesis, a Domain-Specific Modeling Language (DSML), which is (iii) the metamodel of MIDAS architecture and a Unified Modeling Language (UML) profile. This metamodel should guide the instantiation of MIDAS platform-independent models and other implementations. It is validated by executing three types of user queries to measure the constructor’s coverage levels to define the middleware and implement it in a specific cloud provider.
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