Integration of CompTIA Cloud+ into Universiti Teknologi MARA’s Computer Engineering Special Topics Syllabus

Cloud computing is one of the frontier technologies in computer engineering. Therefore, it is important to prepare the students with the knowledge and skills required in this field. This paper describes the integration of the renowned CompTIA Cloud+ professional training course into the ECE648: Special Topics in Computer Networking subject. The subject is a final year elective for the Faculty of Electrical Engineering, Universiti Teknologi MARA, Malaysia undergraduate computer engineering students. We first begin by assessing the current teaching syllabus of a variety of international universities to establish fundamental topics that should be covered in a cloud computing course. We then proceed to describe our implementation, which is done in accordance with the CompTIA Cloud+ certification syllabus. Among the items described are how the Cloud+ course contents are adjusted to be more suitable for the course, as well as additional practical elements (not originally available in the Cloud+ course) added to the course syllabus. Implementation results are described

Developing sustainability pathways for social simulation tools and services

The use of cloud technologies to teach agent-based modelling and simulation (ABMS) is an interesting application of a nascent technological paradigm that has received very little attention in the literature. This report fills that gap and aims to help instructors, teachers and demonstrators to understand why and how cloud services are appropriate solutions to common problems they face delivering their study programmes, as well as outlining the many cloud options available. The report first introduces social simulation and considers how social simulation is taught. Following this factors affecting the implementation of agent-based models are explored, with attention focused primarily on the modelling and execution platforms currently available, the challenges associated with implementing agent-based models, and the technical architectures that can be used to support the modelling, simulation and teaching process. This sets the context for an extended discussion on cloud computing including service and deployment models, accessing cloud resources, the financial implications of adopting the cloud, and an introduction to the evaluation of cloud services within the context of developing, executing and teaching agent-based models

Survey and Analysis of Production Distributed Computing Infrastructures

This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures

Performance Evaluation of Serverless Applications and Infrastructures

Context. Cloud computing has become the de facto standard for deploying modern web-based software systems, which makes its performance crucial to the efficient functioning of many applications. However, the unabated growth of established cloud services, such as Infrastructure-as-a-Service (IaaS), and the emergence of new serverless services, such as Function-as-a-Service (FaaS), has led to an unprecedented diversity of cloud services with different performance characteristics. Measuring these characteristics is difficult in dynamic cloud environments due to performance variability in large-scale distributed systems with limited observability.Objective. This thesis aims to enable reproducible performance evaluation of serverless applications and their underlying cloud infrastructure.Method. A combination of literature review and empirical research established a consolidated view on serverless applications and their performance. New solutions were developed through engineering research and used to conduct performance benchmarking field experiments in cloud environments.Findings. The review of 112 FaaS performance studies from academic and industrial sources found a strong focus on a single cloud platform using artificial micro-benchmarks and discovered that most studies do not follow reproducibility principles on cloud experimentation. Characterizing 89 serverless applications revealed that they are most commonly used for short-running tasks with low data volume and bursty workloads. A novel trace-based serverless application benchmark shows that external service calls often dominate the median end-to-end latency and cause long tail latency. The latency breakdown analysis further identifies performance challenges of serverless applications, such as long delays through asynchronous function triggers, substantial runtime initialization for coldstarts, increased performance variability under bursty workloads, and heavily provider-dependent performance characteristics. The evaluation of different cloud benchmarking methodologies has shown that only selected micro-benchmarks are suitable for estimating application performance, performance variability depends on the resource type, and batch testing on the same instance with repetitions should be used for reliable performance testing.Conclusions. The insights of this thesis can guide practitioners in building performance-optimized serverless applications and researchers in reproducibly evaluating cloud performance using suitable execution methodologies and different benchmark types

Student Research Symposium Schedule 2023

Student Research Symposium Schedule 2023, including abstracts and faculty advisor information

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

Identificação da valência emocional em sentenças de contos infantis

Orientador: Paula Dornhofer Paro CostaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A análise de sentimentos em textos tem sido amplamente explorada recentemente, principalmente usando técnicas de processamento de linguagem natural e aprendizado de máquina. No entanto, apesar dos avanços alcançados, ainda existem desafios significativos. Nosso trabalho explora a análise de sentimentos em textos narrativos, identificando as valências emocionais em sentenças pertencentes a contos infantis, que podem ser usadas, por exemplo, como recurso para aplicações destinadas a sintetizar narradores e atores virtuais no idioma português do Brasil. Usando técnicas de processamento de linguagem natural e um banco de dados afetivo chamado Anew-Br, criamos nosso algoritmo EMONT V1, que atribui valência emocional às frases do corpus desenvolvido. Foram adotadas duas abordagens diferentes para obter resultados comparáveis, aumentando assim a confiabilidade do nosso sistema: uma avaliação subjetiva que visa rotular frases por um grupo de 100 voluntários, que assumimos ser grund truth, e uma avaliação objetiva comparando os rótulos fornecidos por algumas plataformas comerciais que prometem fornecer funcionalidades semelhantes. Nosso algoritmo alcançou um desempenho de precisão equivalente a setores importantes de serviços de análise de sentimentos, como IBM Watson, API do Google Cloud Natural Language e Microsoft Azure Text Analytics. Os resultados dessa metodologia podem ser estendidos para outras frases infantis ou textos semelhantes, por exemplo, romance, história curta, crônica, fábula, parábola, anedota ou lendaAbstract: Sentiment analysis in texts has been widely explored recently, mainly using natural language processing and machine learning techniques. However, despite the advances achieved, there are still significant challenges. Our work explores the analysis of sentiments in narrative texts by identifying the emotional valences in sentences belonging to children's tales, which can be used, for example, as a resource for applications aimed at synthesizing narrators and virtual actors in the Brazilian Portuguese language. Using Natural Language Processing techniques and an affective database called Anew-Br, we created our EMONT V1 algorithm, which attributes emotional valence to the phrases of the developed corpus. Two different approaches were taken to obtain comparable results, thereby increasing the reliability of our system: a subjective assessment that aims to label sentences by a group of 100 volunteers, which we assume to be grund truth, and an objective assessment comparing the labels provided by some commercial platforms that promise to provide similar functionality. Our algorithm has achieved precision performance equivalent to significant industries of sentiment analysis services, such as IBM Watson, Google Cloud Natural Language API, and Microsoft Azure Text Analytics. The results of this methodology can be extended to other children sentences or similar texts, for instance, romance, short story, chronicle, fable, parable, anecdote, or legendMestradoEngenharia de ComputaçãoMestra em Engenharia Elétrica149147/2016-3CNP

ICSEA 2022: the seventeenth international conference on software engineering advances

The Seventeenth International Conference on Software Engineering Advances (ICSEA 2022), held between October 16th and October 20th, 2022, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. Several tracks were proposed to treat the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learned. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. Other advanced aspects are related to on-time practical aspects, such as run-time vulnerability checking, rejuvenation process, updates partial or temporary feature deprecation, software deployment and configuration, and on-line software updates. These aspects trigger implications related to patenting, licensing, engineering education, new ways for software adoption and improvement, and ultimately, to software knowledge management. There are many advanced applications requiring robust, safe, and secure software: disaster recovery applications, vehicular systems, biomedical-related software, biometrics related software, mission critical software, E-health related software, crisis-situation software. These applications require appropriate software engineering techniques, metrics and formalisms, such as, software reuse, appropriate software quality metrics, composition and integration, consistency checking, model checking, provers and reasoning. The nature of research in software varies slightly with the specific discipline researchers work in, yet there is much common ground and room for a sharing of best practice, frameworks, tools, languages and methodologies. Despite the number of experts we have available, little work is done at the meta level, that is examining how we go about our research, and how this process can be improved. There are questions related to the choice of programming language, IDEs and documentation styles and standard. Reuse can be of great benefit to research projects yet reuse of prior research projects introduces special problems that need to be mitigated. The research environment is a mix of creativity and systematic approach which leads to a creative tension that needs to be managed or at least monitored. Much of the coding in any university is undertaken by research students or young researchers. Issues of skills training, development and quality control can have significant effects on an entire department. In an industrial research setting, the environment is not quite that of industry as a whole, nor does it follow the pattern set by the university. The unique approaches and issues of industrial research may hold lessons for researchers in other domains. We take here the opportunity to warmly thank all the members of the ICSEA 2022 technical program committee, as well as all the reviewers. The creation of such a high-quality conference program would not have been possible without their involvement. We also kindly thank all the authors who dedicated much of their time and effort to contribute to ICSEA 2022. We truly believe that, thanks to all these efforts, the final conference program consisted of top-quality contributions. We also thank the members of the ICSEA 2022 organizing committee for their help in handling the logistics of this event. We hope that ICSEA 2022 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in software engineering advances