Abstract
In the latest years, the number of elderly people that has been living alone and need regular support has highly increased. Meal intake monitoring is a well-known strategy that enables premature detection of health problems. There are several attempts to develop automatic meal intake monitoring systems, but they are inadequate to monitor elderly people at home. In this context, we propose an automatic meal intake monitoring system that helps tracking people's eating behaviors, and is adequate for elderly remote monitoring at home due to its nonintrusive features. The system uses the MS Kinect sensor that provides the coordinates of the user's sitting skeleton during his meals. It analyzes the coordinates, detects eating gestures, and classifies them using Hidden Markov Models (HMMs) to estimate the user's eating behavior. A demonstrative prototype for detection and classification of gestures was implemented and tested. The detection module got satisfactory percentages of sensitivity, having a minimum of 72.7% and a maximum of 90%. The Classification module was tested with 3 proposed methods and the best method had a good average percentage of success (approximately 83%) in the classification of Soup and Main dish; regarding the left hand transporting Liquids, the results were less successful. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
Lung cancer is the world's most lethal type of cancer, being crucial that an early diagnosis is made in order to achieve successful treatments. Computer-aided diagnosis can play an important role in lung nodule detection and on establishing the nodule malignancy likelihood. This paper is a contribution in the design of a learning approach, using computed tomography images. Our methodology involves the measurement of a set of features in the nodular image region, and train classifiers, as K-nearest neighbor or support vector machine (SVM), to compute the malignancy likelihood of lung nodules. For this purpose, the Lung Image Database Consortium and image database resource initiative database is used due to its size and nodule variability, as well as for being publicly available. For training we used both radiologist's labels and annotations and diagnosis data, as biopsy, surgery and follow-up results. We obtained promising results, as an Area Under the Receiver operating characteristic curve value of 0.962 +/- 0.005 and 0.905 +/- 0.04 was achieved for the Radiologists' data and for the Diagnosis data, respectively, using an SVM with an exponential kernel combined with a correlation-based feature selection method.

Abstract
People facing threats of mobility loss have their self-confidence shaken and tend to reduce their physical activity. As is well-known, the decreased physical activity, particularly for the elderly, is one of the factors that contribute to accelerating the deterioration of their health with consequent loss of autonomy and quality of life. Today, GPS-based technologies available on mobile devices offer many solutions to help guide users around much of the world. However, there are several known factors that act as barriers to the use of these technologies, such as user unfamiliarity with these devices, the complexity of geographical information and the difficulty of typing the origin and destination locations. In this paper we propose a solution for mobile devices that seeks to promote user confidence in daily mobility, especially among the elderly. We present the main system functionalities and the interface design. © 2014 Springer International Publishing.

Abstract
In lung cancer diagnosis, the design of robust Computer Aided Diagnosis (CAD) systems needs to include an adequate differentiation of benign from malignant nodules. This paper presents a CAD system for the classification of lung nodules in chest Computed Tomography (CT) scans as the way to diagnose lung cancer. The proposed method measures a set of 295 heterogeneous characteristics, including morphology, intensity or texture features, that were used as input of different KNN and SVM classifiers. The system was modeled and trained using a groundtruth provided by specialists taken from a public lung image dataset, the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). This image dataset includes chest CT scans with lung nodule location together with information about the degree of malignancy, among other properties, provided by multiple expert clinicians. In particular, the computed degree of malignancy try to follow the manual labeling by the different radiologists. Promising results were obtained with a first order SVM with an exponential kernel achieving an area under the receiver operating characteristic curve of 96.2 +/- 0.5% when compared with the groundtruth provided in the public CT lung image dataset.

Abstract
Over the last decades the role of information systems (IS) has evolved and has become truly important within organizations. Despite this evidence, not all organizations have the internal resources and know-how required to develop it. The outsourcing of IS services have been presented as an alternative and strategic option in the management of this function in many organizations and various sectors of activity. Developing a better understanding of the main IS services outsourcing practices in the banking sector, given their particularities (eg with regard to confidentiality requirements and secrecy), is the central objective of this work. To this end, a survey was conducted based on a questionnaire in order to characterize, among other things, the services subject to outsourcing, the main motivations, risks, barriers and impacts associated with the outsourcing as well as contracting aspects, customer-supplier relationship and satisfaction. The main results are presented in this paper.

Abstract
The Fluvial Ecology Laboratory at the University of Tras-os-Montes and Alto Douro (LEF-CITAB) uses the River Habitat Survey (RHS) methodology a Water Framework Directive accepted method for assessing the character and habitat quality of rivers, which involves the use of a paper questionnaire, GPS and photographic camera for the collection of data in the field, which can be very cumbersome. In order to make this a more efficient and rapid process LEF-CITAB suggested the creation of a mobile application to record field data. This paper outlines the development of the proposed mobile application - Mobile RHS. (C) 2015 The Authors. Published by Elsevier B.V.