Abstract

Abstract
In healthcare domains, dealing with missing data is crucial since absent observations compromise the reliability of decision support models. K-nearest neighbours imputation has proven beneficial since it takes advantage of the similarity between patients to replace missing values. Nevertheless, its performance largely depends on the distance function used to evaluate such similarity. In the literature, k-nearest neighbours imputation frequently neglects the nature of data or performs feature transformation, whereas in this work, we study the impact of different heterogeneous distance functions on k-nearest neighbour imputation for biomedical datasets. Our results show that distance functions considerably impact the performance of classifiers learned from the imputed data, especially when data is complex. © 2020, Springer Nature Switzerland AG.

Abstract
The current proposal for the next revision of the Ada language considers the possibility to map the language parallel features to an underlying OpenMP runtime. As previously presented, and discussed in previous workshops, the works on fine-grain parallelism in Ada map well to the OpenMP tasking model for parallelism. Nevertheless, and although the general model of integration, and the semantic constructs are already reflected in the proposed revision of the standard, the integration of these new features with the Real-Time Systems Annex of Ada is still not complete. This paper presents an overview of what is supported and the still open issues.

Abstract
Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a density-based clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.

Abstract
The use of sensors in the real world is on the rise, providing information on medical diagnostics for healthcare and improving quality of life. Optical fiber sensors, as a result of their unique properties (small dimensions, capability of multiplexing, chemical inertness, and immunity to electromagnetic fields) have found wide applications, ranging from structural health monitoring to biomedical and point-of-care instrumentation. Furthermore, these sensors usually have good linearity, rapid response for real-time monitoring, and high sensitivity to external perturbations. Optical fiber sensors, thus, present several features that make them extremely attractive for a wide variety of applications, especially biomedical applications. This paper reviews achievements in the area of temperature optical fiber sensors, different configurations of the sensors reported over the last five years, and application of this technology in biomedical applications.

Abstract
The optic disc (OD) and the fovea are relevant landmarks in fundus images. Their localization and segmentation can facilitate the detection of some retinal lesions and the assessment of their importance to the severity and progression of several eye disorders. Distinct methodologies have been developed for detecting these structures, mainly based on color and vascular information. The methodology herein described combines the entropy of the vessel directions with the image intensities for finding the OD center and uses a sliding band filter for segmenting the OD. The fovea center corresponds to the darkest point inside a region defined from the OD position and radius. Both the Messidor and the IDRiD datasets are used for evaluating the performance of the developed methods. In the first one, a success rate of 99.56% and 100.00% are achieved for OD and fovea localization. Regarding the OD segmentation, the mean Jaccard index and Dice’s coefficient obtained are 0.87 and 0.94, respectively. The proposed methods are also amongst the top-3 performing solutions submitted to the IDRiD online challenge. © Springer Nature Switzerland AG 2020.