Abstract
There is a lot of work in text categorization using only the title and abstract of the papers. However, in a full paper there is a much larger amount of information that could be used to improve the text classification performance. The potential benefits of using full texts come with an additional problem: the increased size of the data sets. To overcome the increased the size of full text data sets we performed an assessment study on the use of feature selection methods for full text classification. We have compared two existing feature selection methods (Information Gain and Correlation) and a novel method called k-Best-Discriminative-Terms. The assessment was conducted using the Ohsumed corpora. We have made two sets of experiments: using title and abstract only; and full text. The results achieved by the novel method show that the novel method does not perform well in small amounts of text like title and abstract but performs much better for the full text data sets and requires a much smaller number of attributes.

Abstract
The tourism sector has become one of the most important engines of economic growth for many countries. With the evolution of Information and Communication Technologies, new opportunities have arisen for a reengineering in the way the various tourism and hospitality industries interact with tourists. Equally, new possibilities open to the entities that vision to promote the patrimony and endogenous products of their regions. Information and Communication Technologies are today one of the most critical areas for the success of tourism and how tourist destinations can be promoted. Tourists, increasingly a digital generation, expect that the access to information and services be done in an innovative, immersive and contextualized way, through the use of technologies that are embedded in their daily lives. This article discusses the touristic routes creation process and the role of Information and Communication Technologies in the creation and support of touristic routes. Also, a technological model is presented in its conceptual perspective, as well the functional modelling of its main components and features. © 2019 International Business Information Management Association (IBIMA).

Abstract
Diagnosis of Fabry disease (FD) remains a challenge mostly due to its rare occurrence and phenotipical variability, with considerable delay between onset and clinical diagnosis. It is then of extreme importance to explore biomarkers capable of assisting the earlier diagnosis of FD. There is growing evidence supporting the use of gait assessment in the diagnosis and management of several neurological diseases. In fact, gait abnormalities have previously been observed in FD, justifying further investigation. The aim of this study is to evaluate the effectiveness of different machine learning strategies when distinguishing patients with FD from healthy controls based on normalized gait features. Gait features of an individual are affected by physical characteristics including age, height, weight, and gender, as well as walking speed or stride length. Therefore, in order to reduce bias due to inter-subject variations a multiple regression (MR) normalization approach for gait data was performed. Four different machine learning strategies Support Vector Machines (SVM), Random Forest (RF), Multiple Layer Perceptrons (MLPs), and Deep Belief Networks (DBNs) - were employed on raw and normalized gait data. Wearable sensors positioned on both feet were used to acquire the gait data from 36 patients with FD and 34 healthy subjects. Gait normalization using MR revealed significant differences in percentage of stance phase spent in foot flat and pushing (p < 0.05), with FD presenting lower percentages in foot flat and higher in pushing. No significant differences were observed before gait normalization. Support Vector Machine was the superior classifier achieving an FD classification accuracy of 78.21 % after gait normalization, compared to 71.96% using raw gait data. Gait normalization improved the performance of all classifiers. To the best of our knowledge, this is the first study on gait classification that includes patients with FD, and our results support the use of gait assessment on the clinical assessment of FD.

Abstract
Touch screens are nowadays one of the major interfaces in the interaction between humans and technology, mostly due to the significant growth in the use of smartphones and tablets in the last years. This broad use, that reaches people from all strata of society, makes touch screens a relevant tool to study the mechanisms that influence the way we interact with electronic devices. In this paper we collect data regarding the interaction patterns of different users with mobile devices. We present a way to formalize these interaction patterns and analyze how aspects such as age and gender influence them. The results of this research may be relevant for developing mobile applications that identify and adapt to the users or their characteristics, including impairments in fine motor skills or in cognitive function.

Abstract

Abstract
A sensor based on 2 hollow core microspheres is proposed. Each microsphere was produced separately through fusion splicing and then joined. The resultant structure is a Fabry-Perot interferometer with multiple interferences that can be approximated to a 4-wave interferometer. Strain characterization was attained for a maximum of 1350 mu epsilon, achieving a linear response with a sensitivity of 3.39 +/- 0.04 pm/mu epsilon. The fabrication technique, fast and with no chemical hazards, as opposed to other fabrication techniques, makes the proposed sensor a compelling solution for strain measurements in hash environments.