Toward a Formal Semantics for Autonomic Components

Autonomic management can improve the QoS provided by parallel/ distributed applications. Within the CoreGRID Component Model, the autonomic management is tailored to the automatic - monitoring-driven - alteration of the component assembly and, therefore, is defined as the effect of (distributed) management code. This work yields a semantics based on hypergraph rewriting suitable to model the dynamic evolution and non-functional aspects of Service Oriented Architectures and component-based autonomic applications. In this regard, our main goal is to provide a formal description of adaptation operations that are typically only informally specified. We contend that our approach makes easier to raise the level of abstraction of management code in autonomic and adaptive applications.Comment: 11 pages + cover pag

Machine Learning for Survival Prediction in Breast Cancer

In the last few years, machine learning revealed an important instrument to support decision making in oncology. In this manuscript, an application is presented about the use of several machine learning algorithms for the prediction of the survival rate of breast cancer patients. Before presenting the results, the manuscript contains a rather basic introduction to the foundations of machine learning, that can be useful for medical doctors that are not expert in the area. The experiments were carried on using the well-known 70-gene signature dataset for breast cancer. The presented results highlight that genetic programming has interesting advantages compared to other machine learning algorithms, both in terms of prediction accuracy and in terms of model interpretability.info:eu-repo/semantics/publishedVersio

Sequential Symbolic Regression with Genetic Programming

This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression. The SSR method is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transformation is performed according to the semantic distances between the desired and obtained outputs and a geometric semantic operator. The rationale behind SSR is that, after generating a suboptimal function f via symbolic regression, the output errors can be approximated by another function in a subsequent iteration. The method was tested in eight polynomial functions, and compared with canonical genetic programming (GP) and geometric semantic genetic programming (SGP). Results showed that SSR significantly outperforms SGP and presents no statistical difference to GP. More importantly, they show the potential of the proposed strategy: an effective way of applying geometric semantic operators to combine different (partial) solutions, avoiding the exponential growth problem arising from the use of these operators

Understanding over-indebtedness in Portugal: descriptive and predictive models.

Over-indebtedness is a recurring problem in Portugal. After facing different economic cycles, between financial crises and prosperity periods, Portuguese consumers have been striving to keep their household finances stable and avoid being over-indebted. This project aims to gain insights on over-indebtedness, from different perspectives that range from the social to the economic point of view. It examines over-indebtedness from a psychological and from a data science perspective. In particular, we suggest that the systemic impact of financial crisis in Portugal not only promotes over-indebtedness, but it crafts a specific profile of over-indebted consumers which may be distinguished from other profiles, ranging from the emphasis on lack of self-regulation and careless management of one’s budget to other causal factors such as consumerism, crisis, and unemployment. Given this scenario, this project proposes the use of Machine Learning (ML) for developing descriptive and predictive models, to understand the influencing factors of over-indebtedness on Portuguese consumers and will be used for establishing consumer clusters and guidelines for over-indebtedness regulation and consumer financial empowerment.info:eu-repo/semantics/publishedVersio