Abstract
In this paper we describe a methodology for heart sound classification and results obtained at PASCAL Classifying Heart Sounds Challenge. The results of competing methodologies are shown. The approach has two steps: segmentation and classification of heart sounds. We also describe the data collection procedure.

Abstract
In this paper we introduce a new conceptualization for the reduction of the number of support vectors (SVs) for an efficient design of support vector machines. The techniques here presented provide a good balance between SVs reduction and generalization capability. Our proposal explores concepts from classification with reject option. These methods output a third class (the rejected instances) for a binary problem when a prediction cannot be given with sufficient confidence. Rejected instances along with misclassified ones are discarded from the original data to give rise to a classification problem that can be linearly solved. Our experimental study on two benchmark datasets show significant gains in terms of SVs reduction with competitive performances.

Abstract
The increasing capabilities of smartphones is paving way to novel applications through the crowd-sourcing of these untapped resources, to form hyperlocal meshes commonly known as edge-clouds. While a relevant body-of-work is already available for the underlying networking, computing and storage facilities, security and privacy remain second class citizens. In this paper we present Panoptic, an edge-cloud system that enables the search for missing people, similar to the commonly known Amber alert system, in high density scenarios where wireless infrastructure might be limited (WiFi and LTE), e.g. concerts, while featuring privacy and security by design. Since the limited resources present in the mobile devices, namely battery capacity, Panoptic offers a computing offloading that tries to minimize data leakage while offering acceptable levels of performance. Our results show that it is achievable to run these algorithms in an edge-cloud configuration and that it is beneficial to use this architecture to lower data transfer through the wireless infrastructure while enforcing privacy. Results from our experimental evaluation show that the security layer does not impose a significant overhead, and only accounts for 2% of the total execution time for an edge cloud comprised by, but not limited to, 8 devices.

Abstract
The teaching process of auscultation is complex in itself, and difficult to operate since it requires a wide spectrum of patients with the most diverse cardiopulmonary pathologies, readily available during teaching and assessment hours, for an ever-growing number of medical students. In this paper we will focus on how virtual patient technologies can promote the evolution of the current teaching methodologies, promoting better learning. The chosen methodology was: a) a review of available medical simulation technologies for auscultation teaching; b) a case study illustrating how a virtual patient simulation technology has been successfully used to teach and certify auscultation skills. Results show the positive impact and high acceptability of virtual patient simulation technologies in the teaching of auscultation to medical students.

Abstract
Recently, soft attention mechanisms have been successfully used in a wide variety of applications such as the generation of image captions, text translation, etc. This mechanism attempts to mimic the visual cortex of a human brain by not analyzing all the objects in a scene equally, but by looking for clues (or salient features) which might give a more compact representation of the environment. In doing so, the human brain can process information more quickly and without overloading. Having learned this lesson, in this paper, we try to make a bridge from the visual to the audio scene classification problem, namely the classification of heart sound signals. To do so, a novel approach merging soft attention mechanisms and recurrent neural nets is proposed. Using the proposed methodology, the algorithm can successfully learn automatically significant audio segments when detecting and classifying abnormal heart sound signals, both improving these classification results and somehow creating a simple justification for them.

Abstract
Most work done in Heart Sound Segmentation approaches use a threshold-based approach to correctly identify S1 and S2 segments in a given signal. We propose a new method that uses the Stationary Wavelet Transform to segment the signal and hierarchical clustering to distinguish the S1 and S2 heart sound from noise. This approach was tested in the Classifying Heart Sounds PASCAL Challenge datasets and achieved better results than the winning approach of this contest, with a total error redcution of 21% and 43% for Digiscope and iStethoscope in test sets, respectively.