Abstract

Abstract

Abstract
In the last years researchers in the field of intelligent transportation systems have made several efforts to extract valuable information from social media streams. However, collecting domain-specific data from any social media is a challenging task demanding appropriate and robust classification methods. In this work we focus on exploring geolocated tweets in order to create a travel-related tweet classifier using a combination of bag-of-words and word embeddings. The resulting classification makes possible the identification of interesting spatio-temporal relations in Sao Paulo and Rio de Janeiro.

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.