Master Theses authored by students of the Master's Degree in Data Science, a program jointly organized by UAB-UPF and the Barcelona School of Economics (BSE)
This research explores the use of LoRA (Low-Rank Adaptation) and DreamBooth fine-tuning techniques on Stable Diffusion models to generate new versions of an original comic book character. By addressing the challenge of ...
Di Gianvito, Angelo; Gatland, Oliver; Yuzkiv, Viktoriia(2024-07)
This study aims to analyse the evolution of visual reporting on the Russia-Ukraine war in Spanish news broadcasts. We investigate how the depiction of the war changed from December 2022 to April 2024, focusing on the ...
Advances in data and computing techniques have opened possibilities for real-time and cost-efficient conflict prediction and early warning capabilities, with news-based data being utilized to generate relevant forecasts. ...
De los Santos, Daniela; Frey, Eric; Vassallo, Renato(2023-08-18)
This study presents a novel forecasting framework for global refugee flows,
incorporating non-conventional data sources such as Google Trends, the GDELT project event dataset, conflict forecasts, among others. Our main ...
This study delves into understanding and predicting user engagement in Enhance VR, a virtual reality cognitive training application, through data-driven approaches. The dataset encompasses de-identified user data including ...
We attempt to build a road quality classifier to detect bad roads using satellite imagery in the province of Sud-Kivu in the Democratic Republic of the Congo (DRC). Using 60 cm/pixel resolution from Google Earth, paired ...
This thesis presents a general-purpose corpus construction methodology with Twitter data for a given political topic in a given country. It applies the methodology to immigration in Chile from November 2021 to April 2022, ...
Financial institutions are beginning to integrate cryptocurrencies into their payment systems but must ensure to comply with anti-money laundering regulations to avoid facilitating transactions linked to criminal activities. ...
Bayesian optimization has emerged as an effective and efficient approach for finding the global optimum of highly complex derivative-free black-box functions. It typically models the objective function with Gaussian processes ...
Vector autoregression (VAR) models are a popular choice for forecasting of macroeconomic time series data. Due to their simplicity and success at modelling the monetary economic indicators VARs have become a standard tool ...
Normalizing flows are an elegant approximation to generative modelling. It can be shown that learning a probability distribution of a continuous variable X is equivalent to learning a mapping f from the domain where X is ...
In this study, we propose an approach for the extraction of a low-dimensional signal from a collection of text documents ordered over time. The proposed framework foresees the application of Latent Dirichlet Allocation ...
In this thesis project I analyse labour flow networks and company control networks in the UK. I observe that these networks exhibit characteristics that are typical of empirical networks, such as heavy-tailed degree ...
In order to apply statistical learning in the framework of crossed random effects models it is necessary to efficiently compute the Cholesky factor L of the models precision matrix. In this paper we show that for the case ...
In this thesis we develop a traffic light control agent that can manage traffic lights with the objective to reduce traffic jams, trip time and other traffic metrics in a given network using reinforcement learning. To this ...
The main goal of our Master Project is to predict intraday stock market movements using two different kinds of input features: financial indicators and sentiments from news and tweets. While the former are part of the ...
Hierarchical modeling is a practical approach with proven results in modeling real world data. This paper studies Gaussian hierarchical models and methods which exploit the sparse conditional independence structure of such ...
Data scientific questions face the fundamental trade-off between complexity, generalizability and computational feasibility. The need for quick estimation and evaluation of a vast amount of statistical models has given ...
This paper studies a novel particle filter method proposed by Brownlees and Kristensen (2017) for parameter estimation of nonlinear state space models. The particle filter, named Importance Sampling Particle Filter, is tested ...
Garriga Calleja, Roger; Mas Adell, Javier; Poudel, Saurav(2017)
Internet has seen a tremendous growth in the last few years. Because of that, we have a lot of information about most of the things in web. And the usage of Recommendation system has become more important than ever.
Rec ...