Abstract
There is no standard definition of outliers, but most authors agree that outliers are points far from other data points. Several outlier detection techniques have been developed mainly with two different purposes. On one hand, outliers are the interesting observations, like in fraud detection, on the other side, outliers are considered measurement observations that should be removed from the analysis, e.g. robust statistics. In this work, we start from the observation that outliers are effected by the so called Simpson paradox: a trend that appears in different groups of data but disappears or reverses when these groups are combined. Given a dataset, we learn a regression tree. The tree grows by partitioning the data into groups more and more homogeneous of the target variable. At each partition defined by the tree, we apply a box plot on the target variable to detect outliers. We would expected that deeper nodes of the tree contain less and less outliers. We observe that some points previously signaled as outliers are no more signaled as such, but new outliers appear. The identification of outliers depends on the context considered. Based on this observation, we propose a new method to quantify the level of outlierness of data points. © Springer Nature Switzerland AG 2018.

Abstract
The paper describes the use of computer applications on education for industrial robotic systems. Robotics evolved as a central issue in teaching for scientific and engineering courses and inherently encompasses a spectrum of sciences and technologies and qualification levels. However, most current teaching approaches, related to robotics, concentrate on individual aspects or small student groups and do not involve dangerous situations. With a mixed-reality robotics teaching, a true interdisciplinary setup can be reached, this approach has been used in a robotics course at higher education, to prepare students for future activities in industry and also in research. This active learning experience approach in the field of Industrial Robotics was implemented in a Master degree on Electrical Engineering.

Abstract
In this work we reviewed the current state-of-the art regarding the usage of robots, in particular telepresence robots, on educational related activities. We also researched the current consumer and corporate grade telepresence robotic equipment and tested three of these devices. Lastly, we reviewed the problem of disabled students, including students with special education needs, which fail at accessing and staying on higher education. One of the reasons for such problem is the impossibility to physically attend all the classes due to temporary or permanent limitations. As a conclusion of this work, and considering the ongoing positive cases with robotics and the current equipment availability, we propose the creation of telepresence services on the higher education institutions as a solution for those students that can’t attend classes. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
The choroid is the middle layer of the eye globe located between the retina and the sclera. It is proven that choroidal thickness is a sign of multiple eye diseases. Optical Coherence Tomography (OCT) is an imaging technique that allows the visualization of tomographic images of near surface tissues like those in the eye globe. The automatic calculation of the choroidal thickness reduces the subjectivity of manual image analysis as well as the time of large scale measurements. In this paper, a method for the automatic estimation of the choroidal thickness from OCT images is presented. The pre-processing of the images is focused on noise reduction, shadow removal and contrast adjustment. The inner and outer boundaries of the choroid are delineated sequentially, resorting to a minimum path algorithm supported by new dedicated cost matrices. The choroidal thickness is given by the distance between the two boundaries. The data are then interpolated and mapped to an infrared image of the eye fundus. The method was evaluated by calculating the error as the distance from the automatically estimated boundaries to the boundaries delineated by an ophthalmologist. The error of the automatic segmentation was low and comparable to the differences between manual segmentations from different ophthalmologists.

Abstract
In this paper, we introduce the streaming graph canonization problem. Its goal is finding a canonical representation of a sequence of graphs in a stream. Our model of a stream fixes the graph's vertices and allows for fully dynamic edge changes, meaning it permits both addition and removal of edges. Our focus is on small graphs, since small graph isomorphism is an important primitive of many subgraph-based metrics, like motif analysis or frequent subgraph mining. We present an efficient data structure to approach this problem, namely a graph isomorphism discrete finite automaton and showcase its efficiency when compared to a non-streaming-aware method that simply recomputes the isomorphism information from scratch in each iteration.