Abstract
A large amount of information is continuously generated in intensive health care. An analysis of these data streams can supply valuable insights to improve the monitoring of the patients. The volume, frequency and complexity of data, which come unlabeled, make their analysis a challenging task. Machine learning (ML) techniques have been successfully employed for mining data streams to extract useful knowledge for health care monitoring. It includes the detection of changes in the behavior of sensors, failures on machines or systems, and data anomalies. Anomaly (or outlier) detection is a ML task that aims to find exceptions or abnormalities in a dataset. These exceptions, in a medical context, can represent a new disease pattern, an event to be further investigated, behavior changes or potential health complications. Despite of its analysis in data streams is a challenging task, temporal abstractions techniques should help due to they deal with the management and abstraction of time based data, offering high level of visualization of each data object in its context. The aim of this paper is to review recent research in anomaly detection and temporal abstractions and discuss the application of their combination to intensive care data streams.

Abstract
Airport systems adapted to the influx of Low-Cost Carriers (LCC) as the segment grew in prominence in the European market during the last decades. The generalised perspective that LCCs are attached to remote secondary airports is being increasingly challenged by recent moves of the largest European LCC. The reality is that the impact of LCCs has spread to most commercial airports in Europe, primary and secondary alike. Yet, despite valuable insights on the evolution of airline networks, the existing literature lacks a clear understanding of why this has occurred. This paper explains the dynamics in the evolution of airports systems that resulted in significant growth for the low-cost segment in Europe. A multiple case study involving 42 European airports was used to identify the mechanisms that triggered the traffic patterns leading to the ascendency of LCCs in their respective airport systems. Understanding these mechanisms may prove valuable for supporting airport strategic planning.

Abstract
The adoption of robotics technology has the potential to advance quality, efficiency and safety for manufacturing enterprises, in particular small and medium-sized enterprises. This paper presents a human-robot interaction (HRI) system that enables a robot to receive commands, provide information to a human teammate and ask them a favor. In order to build a robust HRI system based on gesture recognition, three key issues are addressed: richness, multiple feature fusion and failure verification. The developed system has been tested and validated in a realistic lab with a real mobile manipulator and a human teammate to solve a puzzle game. © 2017 IEEE.

Abstract

Abstract
The goal of the SimProgramming approach is to help students overcome their learning difficulties in the transition from entry-level to advanced computer programming, developing an appropriate set of learning strategies. We implemented it at the University of Trás-os-Montes e Alto Douro (Portugal), in two courses (PM3 and PM4) of the bachelor programmes in Informatics Engineering and ICT. We conducted semi-structured interviews with students (n=38) at the end of the courses, to identify the students' strategies for self-regulation of learning in the assignment. We found that students changed some of their strategies from one course edition to the following one and that changes are related to the SimProgramming approach. We believe that changes to the educational approach were appropriate to support the assignment goals. We recommend applying the SimProgramming approach in other educational contexts, to improve educational practices by including techniques to help students in their learning. © 2018 Production.

Abstract
Research in compiler pass phase ordering (i.e., selection of compiler analysis/transformation passes and their order of execution) has been mostly performed in the context of CPUs and, in a small number of cases, FPGAs. In this paper we present experiments regarding compiler pass phase ordering specialization of OpenCL kernels targeting NVIDIA GPUs using Clang/LLVM 3.9 and the libclc OpenCL library. More specifically, we analyze the impact of using specialized compiler phase orders on the performance of 15 PolyBench/GPU OpenCL benchmarks. In addition, we analyze the final NVIDIA PTX assembly code generated by the different compilation flows in order to identify the main reasons for the cases with significant performance improvements. Using specialized compiler phase orders, we were able to achieve performance improvements over the CUDA version and OpenCL compiled with the NVIDIA driver. Compared to CUDA, we were able to achieve geometric mean improvements of 1.54× (up to 5.48×). Compared to the OpenCL driver version, we were able to achieve geometric mean improvements of 1.65× (up to 5.70×). © Springer International Publishing AG, part of Springer Nature 2018.