Abstract
Road transportation emissions have increased in the last few decades and have been the primary source of pollutants in urban areas with ever-growing populations. In this context, it is important to have effective measures to monitor road emissions in regions. Creating an emission inventory over a region that can map the road emission based on the vehicle trips can be helpful for this. In this work, we show that it is possible to use raw GPS data to measure levels of pollution in a region. By transforming the data using feature engineering and calculating the vehicle-specific power (VSP), we show the areas with higher emissions levels made by a fleet of taxis in Porto, Portugal. The Uber H3 grid system is used to decompose the city into hexagonal grids to sample nearby data points into a region. We validate our experiments on real-world sensor datasets deployed in several city regions, showing the correlation with VSP and true values for several pollutants attesting to the method's usefulness.

Abstract
Analyzing the way individuals move is fundamental to understand the dynamics of humanity. Transportation mode plays a significant role in human behavior as it changes how individuals travel, how far, and how often they can move. The identification of transportation modes can be used in many applications and it is a key component of the internet of things (IoT) and the Smart Cities concept as it helps to organize traffic control and transport management. In this paper, we propose the use of ensemble methods to infer the transportation modes using raw GPS data. From latitude, longitude, and timestamp we perform feature engineering in order to obtain more discriminative fields for the classification. We test our features in several machine learning algorithms and among those with the best results we perform feature selection using the Boruta method in order to boost our accuracy results and decrease the amount of data, processing time, and noise in the model. We assess the validity of our approach on a real-world dataset with several different transportation modes and the results show the efficacy of our approach.

Abstract

Abstract
Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a density-based clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.

Abstract
We present a solution submitted to the Social Media and Harassment Competition held in collaboration with ECML PKDD 2019 Conference. The dataset used is as set of tweets and the first task was on the detection of harassment tweets. To deal with this problem, we proposed a solution based on a gradient tree-boosting algorithm. The second task was categorization harassment tweets according to the type of harassment, a multiclass classification problem. For this problem we proposed a LSTM network model. The solutions proposed for these tasks presented good predictive accuracy.