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About

About

I graduated in Mathematics Applied to Computer Science, from Faculty of Sciences (UP) in 1995, and took my MSc in Foundations of Advanced Information Technology, from Imperial College, London, in 1997. In 2004 I concluded my PhD in Computer Science in concurrent and distributed programming.

I am currently an Assistant Professor, with tenure, at Faculty of Sciences in University of Porto. My research interests are in the areas of text and web mining, community detection, e-learning and web-based learning and standards in education.

I'm also a researcher in the CRACS Research Unit where I have been leading international projects involving University of University of Porto, Texas at Austin, University of Coimbra and University of Aveiro, regarding the automatic detection of relevance in social networks.

Interest
Topics
Details

Details

002
Publications

2022

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Authors
Paiva, JC; Leal, JP; Figueira, Á;

Publication
ACM Transactions on Computing Education

Abstract
Practical programming competencies are critical to the success in computer science education and go-to-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deeply, and fairly fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state-of-the-art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.

2022

What Makes a Movie Get Success? A Visual Analytics Approach

Authors
Vaz, B; Barros, MD; Lavoura, MJ; Figueira, A;

Publication
MARKETING AND SMART TECHNOLOGIES, VOL 1

Abstract

2022

On Creation of Synthetic Samples from GANs for Fake News Identification Algorithms

Authors
Vaz, B; Bernardes, V; Figueira, Á;

Publication
Lecture Notes in Networks and Systems

Abstract

2022

Survey on Synthetic Data Generation, Evaluation Methods and GANs

Authors
Figueira, A; Vaz, B;

Publication
MATHEMATICS

Abstract
Synthetic data consists of artificially generated data. When data are scarce, or of poor quality, synthetic data can be used, for example, to improve the performance of machine learning models. Generative adversarial networks (GANs) are a state-of-the-art deep generative models that can generate novel synthetic samples that follow the underlying data distribution of the original dataset. Reviews on synthetic data generation and on GANs have already been written. However, none in the relevant literature, to the best of our knowledge, has explicitly combined these two topics. This survey aims to fill this gap and provide useful material to new researchers in this field. That is, we aim to provide a survey that combines synthetic data generation and GANs, and that can act as a good and strong starting point for new researchers in the field, so that they have a general overview of the key contributions and useful references. We have conducted a review of the state-of-the-art by querying four major databases: Web of Sciences (WoS), Scopus, IEEE Xplore, and ACM Digital Library. This allowed us to gain insights into the most relevant authors, the most relevant scientific journals in the area, the most cited papers, the most significant research areas, the most important institutions, and the most relevant GAN architectures. GANs were thoroughly reviewed, as well as their most common training problems, their most important breakthroughs, and a focus on GAN architectures for tabular data. Further, the main algorithms for generating synthetic data, their applications and our thoughts on these methods are also expressed. Finally, we reviewed the main techniques for evaluating the quality of synthetic data (especially tabular data) and provided a schematic overview of the information presented in this paper.

2021

Profiling Accounts Political Bias on Twitter

Authors
Guimaraes, N; Figueira, A; Torgo, L;

Publication
PROCEEDINGS OF 2021 16TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI'2021)

Abstract

Supervised
thesis

2021

A machine learning approach to money laundering detection inspired by GANs

Author
Ricardo Ribeiro Pereira

Institution
UP-FCUP

2021

Recommendation System for the News Market

Author
Miguel Ângelo Rebelo

Institution
UP-FCUP

2021

Generic Lock-Free Memory Reclamation

Author
Pedro Carvalho Moreno

Institution
UP-FCUP

2021

Application of Intelligent Systems to the Energy Area

Author
Daniel Carlos do Vale Ramos

Institution
UP-FEP

2020

Prevenção de más classificações por análise de padrões de interação

Author
Andreia Cunha

Institution
UP-FCUP