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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

  • Name

    Álvaro Figueira
  • Role

    Area Manager
  • Since

    01st March 2009
002
Publications

2024

Topic Extraction: BERTopic's Insight into the 117th Congress's Twitterverse

Authors
Mendonça, M; Figueira, A;

Publication
INFORMATICS-BASEL

Abstract
As social media (SM) becomes increasingly prevalent, its impact on society is expected to grow accordingly. While SM has brought positive transformations, it has also amplified pre-existing issues such as misinformation, echo chambers, manipulation, and propaganda. A thorough comprehension of this impact, aided by state-of-the-art analytical tools and by an awareness of societal biases and complexities, enables us to anticipate and mitigate the potential negative effects. One such tool is BERTopic, a novel deep-learning algorithm developed for Topic Mining, which has been shown to offer significant advantages over traditional methods like Latent Dirichlet Allocation (LDA), particularly in terms of its high modularity, which allows for extensive personalization at each stage of the topic modeling process. In this study, we hypothesize that BERTopic, when optimized for Twitter data, can provide a more coherent and stable topic modeling. We began by conducting a review of the literature on topic-mining approaches for short-text data. Using this knowledge, we explored the potential for optimizing BERTopic and analyzed its effectiveness. Our focus was on Twitter data spanning the two years of the 117th US Congress. We evaluated BERTopic's performance using coherence, perplexity, diversity, and stability scores, finding significant improvements over traditional methods and the default parameters for this tool. We discovered that improvements are possible in BERTopic's coherence and stability. We also identified the major topics of this Congress, which include abortion, student debt, and Judge Ketanji Brown Jackson. Additionally, we describe a simple application we developed for a better visualization of Congress topics.

2024

Comparing Semantic Graph Representations of Source Code: The Case of Automatic Feedback on Programming Assignments

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

Publication
COMPUTER SCIENCE AND INFORMATION SYSTEMS

Abstract
Static source code analysis techniques are gaining relevance in automated assessment of programming assignments as they can provide less rigorous evaluation and more comprehensive and formative feedback. These techniques focus on source code aspects rather than requiring effective code execution. To this end, syntactic and semantic information encoded in textual data is typically represented internally as graphs, after parsing and other preprocessing stages. Static automated assessment techniques, therefore, draw inferences from intermediate representations to determine the correctness of a solution and derive feedback. Consequently, achieving the most effective semantic graph representation of source code for the specific task is critical, impacting both techniques' accuracy, outcome, and execution time. This paper aims to provide a thorough comparison of the most widespread semantic graph representations for the automated assessment of programming assignments, including usage examples, facets, and costs for each of these representations. A benchmark has been conducted to assess their cost using the Abstract Syntax Tree (AST) as a baseline. The results demonstrate that the Code Property Graph (CPG) is the most feature -rich representation, but also the largest and most space -consuming (about 33% more than AST).

2024

GANs in the Panorama of Synthetic Data Generation Methods

Authors
Vaz, B; Figueira, Á;

Publication
ACM Transactions on Multimedia Computing, Communications, and Applications

Abstract
This paper focuses on the creation and evaluation of synthetic data to address the challenges of imbalanced datasets in machine learning applications (ML), using fake news detection as a case study. We conducted a thorough literature review on generative adversarial networks (GANs) for tabular data, synthetic data generation methods, and synthetic data quality assessment. By augmenting a public news dataset with synthetic data generated by different GAN architectures, we demonstrate the potential of synthetic data to improve ML models’ performance in fake news detection. Our results show a significant improvement in classification performance, especially in the underrepresented class. We also modify and extend a data usage approach to evaluate the quality of synthetic data and investigate the relationship between synthetic data quality and data augmentation performance in classification tasks. We found a positive correlation between synthetic data quality and performance in the underrepresented class, highlighting the importance of high-quality synthetic data for effective data augmentation.

2024

Clustering source code from automated assessment of programming assignments

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

Publication
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS

Abstract
Clustering of source code is a technique that can help improve feedback in automated program assessment. Grouping code submissions that contain similar mistakes can, for instance, facilitate the identification of students' difficulties to provide targeted feedback. Moreover, solutions with similar functionality but possibly different coding styles or progress levels can allow personalized feedback to students stuck at some point based on a more developed source code or even detect potential cases of plagiarism. However, existing clustering approaches for source code are mostly inadequate for automated feedback generation or assessment systems in programming education. They either give too much emphasis to syntactical program features, rely on expensive computations over pairs of programs, or require previously collected data. This paper introduces an online approach and implemented tool-AsanasCluster-to cluster source code submissions to programming assignments. The proposed approach relies on program attributes extracted from semantic graph representations of source code, including control and data flow features. The obtained feature vector values are fed into an incremental k-means model. Such a model aims to determine the closest cluster of solutions, as they enter the system, timely, considering clustering is an intermediate step for feedback generation in automated assessment. We have conducted a twofold evaluation of the tool to assess (1) its runtime performance and (2) its precision in separating different algorithmic strategies. To this end, we have applied our clustering approach on a public dataset of real submissions from undergraduate students to programming assignments, measuring the runtimes for the distinct tasks involved: building a model, identifying the closest cluster to a new observation, and recalculating partitions. As for the precision, we partition two groups of programs collected from GitHub. One group contains implementations of two searching algorithms, while the other has implementations of several sorting algorithms. AsanasCluster matches and, in some cases, improves the state-of-the-art clustering tools in terms of runtime performance and precision in identifying different algorithmic strategies. It does so without requiring the execution of the code. Moreover, it is able to start the clustering process from a dataset with only two submissions and continuously partition the observations as they enter the system.

2023

PROGpedia: Collection of source-code submitted to introductory programming assignments

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

Publication
DATA IN BRIEF

Abstract
Learning how to program is a difficult task. To acquire the re-quired skills, novice programmers must solve a broad range of programming activities, always supported with timely, rich, and accurate feedback. Automated assessment tools play a major role in fulfilling these needs, being a common pres-ence in introductory programming courses. As programming exercises are not easy to produce and those loaded into these tools must adhere to specific format requirements, teachers often opt for reusing them for several years. There-fore, most automated assessment tools, particularly Mooshak, store hundreds of submissions to the same programming ex-ercises, as these need to be kept after automatically pro-cessed for possible subsequent manual revision. Our dataset consists of the submissions to 16 programming exercises in Mooshak proposed in multiple years within the 2003-2020 timespan to undergraduate Computer Science students at the Faculty of Sciences from the University of Porto. In particular, we extract their code property graphs and store them as CSV files. The analysis of this data can enable, for instance, the generation of more concise and personalized feedback based on similar accepted submissions in the past, the identifica-tion of different strategies to solve a problem, the under -standing of a student's thinking process, among many other findings.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Supervised
thesis

2023

Analysis of Higher Education Institutions' Social Media Posting Using NLP Techniques

Author
Lirielly Ruela Vitorugo Nascimento

Institution
UP-FCUP

2023

Reasoning on Semantic Representations of Source Code to Support Programming Education

Author
José Carlos Costa Paiva

Institution
UP-FCUP

2023

Predictive Geovisual Analytics, using data streams fusion, for Risk Monitoring and Early Warning Systems optimization

Author
Pedro Miguel Tavares da Silva Gonçalves

Institution
UP-FCUP

2023

Dashboard NLP para análise de obras literárias

Author
Arina de Jesus Amador Monteiro Sanches

Institution
UP-FCUP

2023

Imbalanced MultiClass Classification with Concept Drift

Author
José Gabriel Moreira Pinto

Institution
UP-FCUP