Abstract
Built-in sensors in most modern smartphones open multiple opportunities for novel context-aware applications. Although the Human Activity Recognition field seized such opportunity, many challenges are yet to be addressed, such as the differences in movement by people doing the same activities. This paper exposes empirical research on Online Semi-supervised Learning (OSSL), an under-explored incremental approach capable of adapting the classification model to the user by continuously updating it as data from the user's own input signals arrives. Ultimately, we achieved an average accuracy increase of 0.18 percentage points (PP) resulting in a 82.76% accuracy model with Naive Bayes, 0.14 PP accuracy increase resulting in a 83.03% accuracy model with a Democratic Ensemble, and 0.08 PP accuracy increase resulting in a 84.63% accuracy model with a Confidence Ensemble. These models could detect 3 stationary activities, 3 active activities, and all transitions between the stationary activities, totaling 12 distinct activities.

Abstract
Many water boiler manufacturers are not able to detect the occurrence of failures in the machines they produce before they can pose inconvenience and sometimes danger for costumers and workers. Moreover, the number of boilers that have to be monitored, are many times in the range of the thousands or even millions, proportionaly to the number of costumers a company possesses. The detection of these failures in real time, would provide a significant improvement to the perception that consumers have of a certain company, since, if these failures occur, maintenance services can be deployed almost as soon as a failure happens. In this paper, an application prototype capable of monitoring and preventing failures in domestic water boilers, on the y, is presented. This application evaluates measurements which are performed by sensors within the boilers, and identifies the ones that greatly differ from those received previously, as new data arrives, detecting tendencies which might illustrate the occurrence of a failure. The incremental local outlier factor is used with an approach based on the interquatile range measure to detect the outlier factors that should be analysed.

Abstract
Transportation systems are very complex systems due to the characteristics of their components such as buses. Nowadays, buses are set up to follow a particular schedule that is very sensitive to the changes that occur inside the system. These schedules must frequently be updated, if necessary, due to many reasons. Among these reasons, we have the population growth inside the cities as well as traffic and congestions caused by unforeseen events. To solve the problem of system variability, companies such as the Public Transport Company in the city of Porto (STCP) usually fixes bus schedules with headways adapted to each type of bus lines (i.e., high/low-frequency bus lines). In this work, we adopt a robust optimization model from literature to improve the bus schedules using Automatic Vehicle Location Data collected along the year in the city of Porto. We apply the model to a high-frequency bus line case study. We present the model imperfections and propose new updates.

Abstract
The agribusiness volatility is related to the uncertainty of the environment, rising demand, falling prices and new technologies. However, generation of agriculture data has increased over past years and can be used for a growing number of applications of data mining techniques in agriculture. The multidisciplinary approach of integrating computer science with agriculture will support the necessary decisions to be taken in order to mitigate risks and maximize profits. The present study analyzes different methods of regression applied in the study case of grapes production forecast. The selected methods were multivariate linear regression, regression trees, lasso and random forest. Their performance were compared against the predictions obtained by the company through the mean squared error and the coefficient of variation. The four regression methods used obtained better predictive results than the method used by the company with statistical significance < 0.5%. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic disease that propagates from one family generation to the next. The disease can have severe effects on the life of patients after the first symptoms (onset) appear. Accurate prediction of the age of onset for these patients can help the management of the impact. This is, however, a challenging problem since both familial and non-familial characteristics may or may not affect the age of onset. In this work, we assess the importance of sets of genealogical features used for Predicting the Age of Onset of TTR-FAP Patients. We study three sets of features engineered from clinical and genealogical data records obtained from Portuguese patients. These feature sets, referred to as Patient, First Level and Extended Level Features, represent sets of characteristics related to each patient's attributes and their familial relations. They were compiled by a Medical Research Center working with TTR-FAP patients. Our results show the importance of genealogical data when clinical records have no information related with the ancestor of the patient, namely its Gender and Age of Onset. This is suggested by the improvement of the estimated predictive error results after combining First and Extended Level with the Patients Features.

Abstract
This work describes a problem oriented approach to analyze and predict the Age of Onset of Patients diagnosed with Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP). We constructed, from a set of clinical and familial records, three sets of features which represent different characteristics of a patient, before becoming symptomatic. Using those features, we tested a set of machine learning regression methods, namely Decision Tree (Regression Tree), Elastic Net, Lasso, Linear Regression, Random Forest Regressor, Ridge Regression and Support Vector Machine Regressor (SVM). Later, we defined a baseline model that represents the current medical practice to serve as a guideline for us to measure the accuracy of our approach. Our results show a significant improvement of machine learning methods when compared with the current baseline. © 2018 IEEE.