Abstract
We are developing a new method for the identification of rib boundaries in chest x-ray images. The identification of rib boundaries is important for radiologist diagnosis of lung diseases as TB. The radiologists use the ribs as reference for location and can be used to eliminate false positives in the detection of abnormalities. Our method automatically identifies rib boundaries from raw images through a sequence of steps using a combination of image processing techniques. Radiographs are still very relevant in practice because in Portugal and many other countries it is the first step for TB detection. We have access a large database of x-ray images provided by the pneumological screening centre (CDP) of Vila Nova de Gaia, in Portugal.

Abstract
Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio-annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which make their real-time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains. The goal of this work is to propose a methodology to identify bird sounds. In this paper, we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs). We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.

Abstract
Learning programming is a complex task that poses significant challenges. Students face different kinds of difficulties at complex levels that traditional teaching/learning methods are nor able to cope with. For this reason, several authors have researched the pedagogical effectiveness of program visualization and animation, and developed some tools. Animation can help students on the analysis and understanding of given programs, and can also guide on the development of new ones. It is very important to give students the opportunity to practice solving programming exercises by themselves. Receiving feedback is essential for knowledge acquisition. New tools arose ( especially in the area of programming contests) to allow for the submission of solutions ( programs developed by the students) to the problem statements presented by the teacher and to assess them, returning immediately information about the submitted answer. These tools can be incorporated into teaching activities, allowing students to test their work getting immediate feedback. Automatic evaluation systems significantly improve students performance. In this article are shown these two approaches, animation and automatic assessment, and proposed a new pedagogical practice resulting from the combination of both.