Abstract
The aging process causes physical and psychological changes, as well as social changes. It is one of the major risk factors for the onset of diseases and introduces restrictions on people's lifestyle. Although it constitutes a natural process undergone by every human being, the consequences of aging may be intensified by the deterioration of the social bonds and the loss of contact with family and friends, particularly when the elderly are permanently moved to an elderly care residence center. The usage of telepresence devices has been suggested to promote social interactions between older people and their social groups, allowing people to be in touch even though they are not close. This paper reviews four cases of telepresence robots being used to support the elderly and concludes that this type of solution and technology has made considerable progress, currently finding itself in its maturity stage, as shown by the cases described.

Abstract

Abstract
Well logs are records of petro-physical data acquired along a borehole, providing direct information about what is in the subsurface. The data collected by logging wells can have significant economic consequences in oil and gas exploration, not only because it has a direct impact on the following decisions, but also due to the subsequent costs inherent to drilling wells, and the potential return of oil deposits. These logs frequently present gaps of varied sizes in the sensor recordings, that happen for diverse reasons. These gaps result in less information used by the interpreter to build the stratigraphic models, and consequently larger uncertainty regarding what will be encountered when the next well is drilled. The main goal of this work is to compare Gradient Tree Boosting, Random Forests, Artificial Neural Networks, and three algorithms of Linear Regression on the prediction of the gaps in well log data. Given the logs from a specific well, we use the intervals with complete information as the training data to learn a regression model of one of the sensors for that well. The algorithms are compared with each other using a few individual example wells with complete information, on which we build artificial gaps to cross validate the results. We show that the ensemble algorithms tend to perform significantly better, and that the results hold when addressing the different examples individually. Moreover, we performed a grid search over the ensembles parameters space, but did not find a statistically significant difference in any situation.

Abstract
Guerrilla marketing is an innovative approach to communicate with customers and to capture their attention essentially due to its inherent creativity, unconventional media, and low cost. Despite the interesting contributions in the literature on this topic, most of what is known about guerrilla marketing is confined to its use and impact on consumers. This study aims to fill a gap identified in the guerrilla marketing literature by conducting an exploratory research study on the propensity of performing guerrilla marketing campaigns in a Business-to-Business (B2B) context. The research objectives of this paper are (i) identifying the perceptions of B2B managers on guerrilla marketing campaigns and (ii) exploring determinants of the adoption of guerrilla marketing campaigns targeted at corporate customers.We present the results of a qualitative research study comprising 12 semi-structured interviews with managers of different business areas. A content analysis was performed using Nvivo software.Participants in this study demonstrated that B2B managers recognize and value the advantages associated with guerrilla communication, which is in many instances seen as useful and viable for the B2B sector. The propensity for adoption is dependent on internal factors such as corporate culture, managers’ and collaborators’ profiles, risk-proneness, market share, and product innovativeness, but also on the sector’s usual practices of innovation and communication. Guerrilla marketing campaigns are more appropriate for attracting new B2B customers and need to be carefully adapted to the targets' profiles and preferences.

Abstract
Data imbalance is characterized by a discrepancy in the number of examples per class of a dataset. This phenomenon is known to deteriorate the performance of classifiers, since they are less able to learn the characteristics of the less represented classes. For most imbalanced datasets, the application of sampling techniques improves the classifier's performance. For small datasets, oversampling has been shown to be the most appropriate strategy since it augments the original set of samples. Although several oversampling strategies have been proposed and tested over the years, the work has mostly focused on binary or multi-class tasks. Motivated by medical applications, where there is often an order associated with the classes (increasing likelihood of malignancy, for instance), the present work tests some existing oversampling techniques in ordinal contexts. Moreover, four new oversampling techniques are proposed. Experiments were made both on private and public datasets. Private datasets concern the assessment of response to treatment on oncologic diseases. The 15 public datasets were chosen since they are widely used in the literature. Results show that data balance techniques improve classification results on ordinal imbalanced datasets, even when these techniques are not specifically designed for ordinal problems. With our pipeline, better or equal to published results were obtained for 10 out of the 15 public datasets with improvements upon a decrease of 0.43 on MMAE.

Abstract
Case-based reasoning enables solving new problems using past experience, by reusing solutions for past problems. The simplicity of this technique has made it very popular in several domains. However, the use of this type of approach to support decisions in the power and energy domain is still rather unexplored, especially regarding the flexibility of consumption in buildings in response to recent environmental concerns and consequent governmental policies that envisage the increase of energy efficiency. In order to determine the amount of consumption reduction that should be applied in a building, this article proposes a methodology that adapts the past results of similar cases in order to achieve a decision for the new case. A clustering methodology is used to identify the most similar previous cases, and an expert system is developed to refine the final solution after the combination of the similar cases results. The proposed CBR methodology is evaluated using a set of real data from a residential building. Results prove the advantages of the proposed methodology, demonstrating its applicability to enhance house energy management systems by determining the amount of reduction that should be applied in each moment, thus allowing such systems to carry out the reduction through the different loads of the building.