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In pandemic times or due to patient’s motor disabilities, the visit to health care centers can be difficult. Fortunately, multiple solutions have emerged in the last decades to provide remote medical care. In this scenario, the monitoring of vital signals is crucial for a number of diseases. Technologies of Internet of Things (IoT) are improving the collection, digitalization, transmission, and storage of such data. Some works in this field present measurement systems using of-the-shelf sensors and actuators. This paper aims to check the effectiveness, ease, and robustness of the AD8232 cardiac sensor for its use in telemedicine. Twenty experiments were performed using different devices and sensors and the results show that although it is an efficient sensor for studying or practicing with micro-controllers and sensors, it is not an effective solution for professional use. It presents problems with the position of the electrodes and the sensitivity of the sensor. Also, the device creates noise signals (impairing the collection of data), doesn’t allow the control of multiple parameters, and has limitations in technology. Most of the graphs made by the AD8232 module did not form curves that would allow their interpretation. The conclusion, considering the difficulty in obtaining good graphics, is that this sensor cannot be applied professionally. En épocas de pandemia o debido a las discapacidades motrices de los pacientes, la visita a los centros sanitarios puede resultar difícil. Afortunadamente, en las últimas décadas han surgido múltiples soluciones para proporcionar atención médica a distancia. En este escenario, la monitorización de las señales vitales es crucial para varias enfermedades. Las tecnologías del Internet de las Cosas (IoT) están mejorando la recogida, digitalización, transmisión y almacenamiento de dichos datos. Algunos trabajos en este campo presentan sistemas de medición que utilizan sensores y actuadores estándar. Este trabajo pretende comprobar la eficacia, facilidad y robustez del sensor cardíaco AD8232 para su uso en telemedicina. Se han realizado 20 experimentos utilizando diferentes dispositivos y sensores y los resultados muestran que, aunque es un sensor eficiente para estudiar o practicar con microcontroladores y sensores, no es una solución eficaz para el uso profesional. Presenta problemas con la posición de los electrodos y la sensibilidad del sensor. Además, el dispositivo crea señales de ruido (perjudicando la recogida de datos), no permite el control de múltiples parámetros y tiene limitaciones en la tecnología. La mayoría de los gráficos realizados por el módulo AD8232 no formaban curvas que permitieran su interpretación. La conclusión, teniendo en cuenta la dificultad para obtener buenas gráficas, es que este sensor no puede aplicarse profesionalmente. Em tempos de pandemia ou devido a deficiências motoras do paciente, a visita a centros de saúde pode ser difícil. Felizmente, nas últimas décadas têm surgido múltiplas soluções para fornecer cuidados médicos à distância. Neste cenário, a monitorização de sinais vitais é crucial para uma série de doenças. As tecnologias da Internet das Coisas (IoT) estão a melhorar a recolha, digitalização, transmissão e armazenamento de tais dados. Alguns trabalhos neste campo apresentam sistemas de medição que utilizam sensores e atuadores. Este trabalho visa verificar a eficácia, facilidade e robustez do sensor cardíaco AD8232 para a sua utilização em telemedicina. Vinte experiências foram realizadas utilizando diferentes dispositivos e sensores e os resultados mostram que, embora seja um sensor eficiente para estudar ou praticar com micro-controladores e sensores, não é uma solução eficaz para uso profissional. Apresenta problemas com a posição dos eletrodos e a sensibilidade do sensor. Além disso, o dispositivo cria sinais de ruído (prejudicando o recolhimento de dados), não permite o controle de múltiplos parâmetros e tem limitações na tecnologia. A maioria dos gráficos feitos pelo módulo AD8232 não formaram curvas que permitissem a sua interpretação. A conclusão, considerando a dificuldade em obter bons gráficos, é que este sensor não pode ser aplicado de forma profissional.

The volume of e-mails has been increasing in recent years. However, since 2005, at least half of these e-mails have been made up of spam. This massive traffic of unwanted messages causes losses to users, such as the excessive and unnecessary use of the bandwidth of their networks, loss of productivity, exposure of inappropriate content to inappropriate audiences etc. This paper proposes the study and the application of machine learning models to the classification of e-mails in existing anti-spam systems and, in particular, in the new anti-spam system Open-MaLBAS. After carrying out many experiments on different data sets, it was possible both to prove the feasibility of the proposal and to develop a powerful combination of techniques, methods, and models that can be successfully applied to the classification of e-mails in anti-spam systems.

Spam e-mails are unsolicited e-mails received by users of the e-mail service. Spam e-mails cause serious harm to organizations, for they waste, among other things, their computational and networking resources. To reduce the damage caused by them, organizations use anti-spams. Anti-spams are software systems that classify e-mails in order to separate legitimate from spam e-mails. The best current commercial and open-source anti-spams, and in particular the well-known commercial anti-spam CanIt-PRO, make use of various techniques, such as blacklists and/or SMTP extensions, to classify e-mails. Unfortunately, both blacklists and SMTP extensions have serious drawbacks, such as low scalability and high computational and network costs. This paper introduces the Open Machine-Learning-Based Anti-Spam (Open-MaLBAS). Unlike the best current anti-spams, Open-MaLBAS does not make use of blacklists and SMTP extensions, but only of machine learning models for e-mail classification. Open-MaLBAS was compared to CanIt-PRO in a series of experiments on a database composed of 862,227 real e-mails, collected over three months at the Federal University of Itajubá, Brazil. The e-mails were previously classified by CanIt-PRO. From the experiments, it was observed that Open-MaLBAS was able to correctly classify 81.48% and 98.13% of the e-mails in the database, using, respectively, the two models — Multi-Layer Perceptron and Random Forest — evaluated. In addition, it managed to obtain times of up to 88% shorter than those of CanIt-PRO to classify all e-mails in the database. Open-MaLBAS is implemented in Java language, under free software license, for free use. It is available on GitHub.

A predição de estruturas de proteínas é um campo de pesquisa que busca simular o enovelamento de cadeias de aminoácidos de forma a descobrir as funções das proteínas na natureza, um processo altamente dispendioso por meio de métodos in vivo. Inserida no contexto da Bioinformática, é uma das tarefas mais computacionalmente custosas e desafiadoras da atualidade. Devido à complexidade, muitas pesquisas se utilizam de gateways científicos para disponibilização de ferramentas de execução e análise desses experimentos, aliado ao uso de workflows científicos para organização de tarefas e disponibilização de informações. No entanto, esses gateways podem enfrentar gargalos de desempenho e falhas estruturais, produzindo resultados de baixa qualidade. Para atuar nesse contexto multifacetado e oferecer alternativas para algumas das limitações, esta tese propõe uma arquitetura modular baseada nos conceitos de Service Oriented Architecture (SOA) para oferta de recursos computacionais em gateways científicos, com foco nos experimentos de Protein Structure Prediction (PSP). A Arquitetura Modular Distribuída para auxílio à Predição de Estruturas de Proteínas (MDAPSP) é descrita conceitualmente e validada em um modelo de simulação computacional, no qual se pode identificar suas capacidades, detalhar o funcionamento de seus módulos e destacar seu potencial. A avaliação experimental demonstra a qualidade dos algoritmos propostos, ampliando a capacidade de atendimento de um gateway científico, reduzindo o tempo necessário para experimentos de predição e lançando as bases para o protótipo de uma arquitetura funcional. Os módulos desenvolvidos alcançam boa capacidade de otimização de experimentos de PSP em ambientes distribuídos e constituem uma novidade no modelo de provisionamento de recursos para gateways científicos. PSP is a scientific process that simulates the folding of amino acid chains to discover the function of a protein in live organisms, considering that its an expensive process to be done by in vivo methods. PSP is a computationally demanding and challenging effort in the Bioinformatics stateof- the-art. Many works use scientific gateways to provide tools for execution and analysis of such experiments, along with scientific workflows to organize tasks and to share information. However, these gateways can suffer performance bottlenecks and structural failures, producing low quality results. With the goal of offering alternatives to some of the limitations and considering the complexity of the topics involved, this thesis proposes a modular architecture based on SOA concepts to provide computing resources to scientific gateways, with focus on PSP experiments. The Modular Distributed Architecture to support Protein Structure Prediction (MDAPSP) is described conceptually and validated in a computer simulation model that explain its capabilities, detail the modules operation and highlight its potential. The performance evaluation presents the quality of the proposed algorithms, a reduction of response time in PSP experiments and prove the benefits of the novel algorithms, establishing the basis for a prototype. The new modules can optmize the PSP experiments in distributed environments and are a innovation in the resource provisioning model for scientific gateways.

Science Gateways have been widely accepted as an important tool in academic research, due to their flexibility, simple use and extension. However, such systems may yield performance traps that delay work progress and cause waste of resources or generation of poor scientific results. This paper addresses an investigation on some of the failures in a Galaxy system and analyses of their impacts. The use case is based on protein structure prediction experiments performed. A novel science gateway component is proposed towards the definition of the relation between general parameters and capacity of machines. The machine-learning strategies used appoint the best machine setup in a heterogeneous environment and the results show a complete overview of Galaxy, a diverse platform organization, and the workload behavior. A Support Vector Regression (SVR) model generated and based on a historic data-set provided an excellent learning module and proved a varied platform configuration is valuable as infrastructure in a science gateway. The results revealed the advantages of investing in local cluster infrastructures as a base for scientific experiments.

Scientific Gateways are one of the most important tools for designing and running experiments. Despite the possibility of local operation they are mainly available via online interfaces based on cloud computing instances. Our studies show that cloud machines may not be the best solution to every situation and that the advantages of heterogeneous cluster machines should be considered in scheduling experiments, saving both financial and computational resources, avoiding network delays and managing the infrastructure as needed. We run a variety of scenarios of bioinformatic experiments in three different sets of machines, a workstation, a cluster and cloud platform. Then, using Support Vector Machines (SVM), a nonlinear regression technique over the results, we can define the best machine configuration in terms of processing time according to the input parameters. The results show an approximation with a small error, that can define with good confidence the proper infrastructure to host a instance of the framework Galaxy, used as study case. With a system based on diverse environments, the researchers can properly schedule each set of experiments.

Esta dissertação de mestrado tem como objetivo avaliar a integração de políticas de tolerância a falhas em uma arquitetura de Web Services com múltiplos módulos. A arquitetura utilizada é denominada WSARCH, e foi desenvolvida para o estudo das relações e interoperabilidade entre serviçcos. Os mecanismos de tolerência a falhas foram integrados aos módulos da arquitetura, testados, comparados e avaliados. A avaliação de desempenho mostrou que os mecanismos de tolerância a falhas introduzidos foram eficientes e apresentaram resultados adequados. As técnicas de reputação utilizadas na seleção de serviço atuaram satisfatoriamente e foram consideradas um importante avanço nos mecanismos da arquitetura This master\'s thesis aims to evaluate the integration of fault tolerance mechanisms in a Web Services architecture with multiple modules. The architecture used is named WSARCH and was developed for the study of interactions and interoperability of services. WSARCH is an architecture conceived to receive tests and experiments involving concepts of Web Services. The fault tolerance tools were integrated in the architecture, tested, evaluated and comparated. The performance evaluation showed that the fault tolerance mechanisms introduced were ecient and presented appropriate results. The reputation techniques utilized in service selection operated successfully and were considered an important advance in the mechanisms of the architecture

Green computing has emerged as a hot topic leading to a need to understand energy consumption of computations. This need also extends to devices with limited resources as are common in the internet of things. RESTful services have shown their potential on such devices, but there are many choices of frameworks for their development and execution. Current research has analysed performance of the frameworks but no attention has been given to systematically studying their power consumption. In this paper we analyse the execution behaviour and power consumption of web services on devices with limited resources and make initial observations that should influence future development of web service frameworks. Specifically, we conduct experiments comparing web services in the Axis2 and CXF frameworks analysing the respective performance and power consumption. Bringing together the best features of small devices and SoC, it is possible to provide diverse, mobile and green applications — however careful selection of development environments can make significant differences in performance and energy consumption.

Fault tolerance techniques can improve the trust of users in service oriented architectures as they can ensure data availability. This paper presents an implementation of a novel fault tolerance mechanism in a SOA architecture which simultaneously provides increased availability and better quality of service. In addition to this mechanism, a service selector using reputation ratings of the architecture components is discussed. The selection is based on information from past transactions of the components of the architecture, which allows to identify the best web services able to meet the requests of customers. The mechanisms are tested and a performance evaluation is presented to validate the results.

This paper analyzes the execution behavior of web services on devices with limited resources. The experiments compare web services in the Axis2 and CXF frameworks analyzing performance and power consumption. To determine which framework is better suited for service provision, a testing environment and a performance and energy evaluation between them are presented. We show that the Raspberry Pi can be useful in service-oriented applications for different types of tasks. Bringing together the best features of small devices and SoC, it is possible to provide diverse, mobile and green applications.

Performing functionality testing in serviceoriented architectures is not a trivial task. The difficulty is especially the large number of components that may be present in a SOA such as brokers, providers, service registries, clients, monitoring tools, data storage tools, etc. Thus, in order to facilitate the process of conducting functional testing and capacity planning in service-oriented systems, we present PEESOS. This first version is a functional prototype that offers facilities to assist researchers and industry to test their new applications, allowing collaborations that can be done between the participants to achieve an appropriate objective when developing a new application. The first results show that it is possible to make a planning environment easier to operate and to readily obtain results for performance evaluation of a target architecture. Since this is a first version of the prototype, it has interface and scalability limitations as well as needing improvements in performance of the logs repository and also in a core engine. We hope that such limitations can be corrected in the near future, including gathering information from the scientific community to make the prototype a useful and accessible tool. PEESOS is on-line and available at http://peesos.wsarch.lasdpc.icmc.usp.br.