Abstract
In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MITBIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the "class-oriented" evaluation and an accuracy of 86.4% in the "subject-oriented" evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.

Abstract

Abstract
The quantity and complexity that end-users are increasingly demanding from their applications and devices makes it impractical for a software developer to "foresee" every possible combination and explore every valid alternative. One solution is to empower end-users with tools that allows them to explore their necessities in a collaborative framework, where novices and experts can co-exist and share. We believe that such a tool could not only reduce the number of "small", specific-tailored applications, but also foster discovery and experimentation.

Abstract
This paper reports the application of a new kernel density estimation model based on the Nadaraya-Watson estimator, for the problem of wind power uncertainty forecasting. The new model is described, including the use of kernels specific to the wind power problem. A novel time-adaptive approach is presented. The quality of the new model is benchmarked against a splines quantile regression model currently in use in the industry. The case studies refer to two distinct wind farms in the United States and show that the new model produces better results, evaluated with suitable quality metrics such as calibration, sharpness, and skill score.

Abstract
The emergence of multi-agent systems in the past years has led to the development of new methodologies to assist in the requirements and architectural analysis, as well as in the design phases of such systems. Consequently, several Agent Oriented Software Engineering (AOSE) methodologies have been proposed. In this paper, we analyze some AOSE methodologies, including Gaia, which supports the architectural design stage, and some proposed extensions. We then use an adapted version of this methodology to design an abstract generic system meta-model for a multi-robot application, which can be used as a basis for the design of these systems, avoiding or shortening repetitive tasks common to most systems. Based on the proposed Generic Robotic Agent Meta-Model (GRAMM), two distinct models for two different applications are derived, demonstrating the versatility and adaptability of the meta-model. By adapting the Gaia methodology to the design of open systems, this work makes the designers' job faster and easier, decreasing the time needed to complete several tasks, while at the same time maintaining a high-level overview of the system.

Abstract
Programing paradigms define how to think and design while creating software. Object-Oriented and Functional paradigms are two of the most adopted for synthesizing it. Modern languages, attempting to provide higher abstractions, are increasingly supporting native multi-paradigm programming styles. The Object-functional approach still uses classes for information and high-level structure, but allows algorithms to be implemented functionally. New challenges now exist and there is a general lack of knowledge on best practices for adopting this paradigm. This research proposes the systematic usage of software patterns to capture these new recurring problems and their solutions, though not discarding the identification of new algorithms and designs. We will use Scala as a base language, and will attempt to validate our hypothesis through multiple methodologies, including quasi-experiments and case studies. We expect to provide a basis for improvement for programming languages (through pattern absorption) and for software engineering professionals.