SHõWA: A Self-healing Framework for Web-based Applications

The complexity of systems is considered an obstacle to the progress of the IT industry. Autonomic computing is presented as the alternative to cope with the growing complexity. It is a holistic approach, in which the systems are able to configure, heal, optimize, and protect by themselves. Web-based applications are an example of systems where the complexity is high. The number of components, their interoperability, and workload variations are factors that may lead to performance failures or unavailability scenarios. The occurrence of these scenarios affects the revenue and reputation of businesses that rely on these types of applications. In this article, we present a self-healing framework for Web-based applications (SHõWA). SHõWA is composed by several modules, which monitor the application, analyze the data to detect and pinpoint anomalies, and execute recovery actions autonomously. The monitoring is done by a small aspect-oriented programming agent. This agent does not require changes to the application source code and includes adaptive and selective algorithms to regulate the level of monitoring. The anomalies are detected and pinpointed by means of statistical correlation. The data analysis detects changes in the server response time and analyzes if those changes are correlated with the workload or are due to a performance anomaly. In the presence of per- formance anomalies, the data analysis pinpoints the anomaly. Upon the pinpointing of anomalies, SHõWA executes a recovery procedure. We also present a study about the detection and localization of anomalies, the accuracy of the data analysis, and the performance impact induced by SHõWA. Two benchmarking applications, exercised through dynamic workloads, and different types of anomaly were considered in the study. The results reveal that (1) the capacity of SHõWA to detect and pinpoint anomalies while the number of end users affected is low; (2) SHõWA was able to detect anomalies without raising any false alarm; and (3) SHõWA does not induce a significant performance overhead (throughput was affected in less than 1%, and the response time delay was no more than 2 milliseconds).

An automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference

In the last few years the number of systems and devices that use voice based interaction has grown significantly. For a continued use of these systems the interface must be reliable and pleasant in order to provide an optimal user experience. However there are currently very few studies that try to evaluate how good is a voice when the application is a speech based interface. In this paper we present a new automatic voice pleasantness classification system based on prosodic and acoustic patterns of voice preference. Our study is based on a multi-language database composed by female voices. In the objective performance evaluation the system achieved a 7.3% error rate.

Tutor Inteligente para o Ensino em Medicina

O objectivo deste trabalho consistiu no desenvolvimento de um protótipo que possibilita a adaptação do conteúdo disponibilizado de acordo com as características pessoais e psicológicas do aluno, aplicado no ensino da Medicina, nomeadamente na componente de Desenho de Estudos da disciplina de Introdução à Medicina. Para o protótipo desenvolvido foi definida uma arquitectura constituída por três componentes: um Modelo de Aluno que engloba as características pessoais e psicológicas do aluno, um Modelo de Domínio constituído por um grafo de conceitos e um Modelo Pedagógico formado pelas regras de adaptação e mecanismos de interação utilizados para obter uma solução adaptativa. Os diferentes componentes desenvolvidos para este protótipo permitem que este apresente as seguintes funcionalidades: Acesso ao conceito adequado, tendo em consideração o nível de conhecimento do aluno; Visualização de conte udos adequados ao estilo de aprendizagem do aluno; Adaptação do percurso do aluno de acordo com os resultados obtidos; Atualização das preferências de aprendizagem, com base no comportamento demonstrado pelo aluno na interação com o sistema. A primeira versão da ferramenta j a foi implementada. No entanto ainda será realizada a avaliação do protótipo em ambiente de aprendizagem, com a maior brevidade possível.