Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Factos & Números
000
Apresentação

Centro de Sistemas de Computação Avançada

A  missão do CRACS é procurar a excelência científica nas áreas de linguagens de programação, computação paralela e distribuída, segurança e privacidade, mineração de informação e sistemas web baseados no desenvolvimento de sistemas de software escaláveis para aplicações desafiadoras e multidisciplinares.

O nosso ambiente de investigação é enriquecido com jovens e talentosos investigadores que, em conjunto com investigadores seniores, constituem a massa crítica necessária e dotam a instituição das competências científicas para cumprir a sua missão.

Últimas Notícias

INESC TEC com 5 projetos exploratórios FCT aprovados em 4 áreas de I&D

Telecomunicações e multimédia, fotónica aplicada, software confiável e sistemas de computação avançada – são estas as quatro áreas que os investigadores do INESC TEC vão trabalhar no âmbito dos cinco projetos que foram aprovados através do Concurso de Projetos Exploratórios da Fundação para a Ciência e a Tecnologia (FCT).

02 outubro 2024

Ciência e Engenharia dos Computadores

Falou-se de segurança e privacidade em evento internacional organizado pela primeira vez em Portugal

Criptografia, software malicioso, privacidade de dados, segurança na web e em dispositivos móveis, controlo de acesso e autenticação seguros – estes foram alguns dos tópicos discutidos na 14ª edição da Conferência ACM sobre segurança e privacidade de dados e aplicações. Organizada pelo INESC TEC e pela Faculdade de Ciências da Universidade do Porto (FCUP), esta foi a primeira vez que a Conferência decorreu noutro país que não os Estados Unidos da América.

27 junho 2024

A privacidade nas redes 6G pode ser um desafio: INESC TEC integra projeto europeu com foco na “proteção”

As futuras redes 6G devem fazer da privacidade dos dados uma das prioridades. O INESC TEC integra o PRIVATEER, um projeto europeu que quer fazer uma análise de segurança robusta e descentralizada, baseada em Inteligência Artificial, para redes 6G. “Privacidade” é a palavra-chave.  

13 junho 2023

Investigadores do INESC TEC premiados por trabalho de investigação que visa a proteção de privacidade em telemóveis

Um grupo de Investigadores do INESC TEC foi distinguido por um trabalho de investigação sobre a gestão de permissões em dispositivos móveis. A equipa desenvolveu um conjunto de técnicas para automatizar a resposta a pedidos de permissões por parte das aplicações de smartphones com uma fiabilidade de 90%. Este trabalho recebeu o prémio de melhor artigo científico na conferência ACM CODASPY que teve lugar nos Estados Unidos da América.

08 julho 2022

INESC TEC integra projeto que vai tornar veículos autónomos mais seguros

  No âmbito do projeto THEIA - Automated Perception Driving, uma parceria entre a Universidade do Porto e a Bosch, que tem como objetivo tornar os veículos autónomos mais seguros através de uma melhor perceção da envolvente exterior, o INESC TEC irá contribuir para o desenvolvimento de algoritmos de perceção, computação e arquiteturas baseadas em inteligência artificial.

07 junho 2022

Equipa
Publicações

CRACS Publicações

Ler todas as publicações

2025

GANs in the Panorama of Synthetic Data Generation Methods

Autores
Vaz, B; Figueira, A;

Publicação
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS

Abstract
This article focuses on the creation and evaluation of synthetic data to address the challenges of imbalanced datasets in machine learning (ML) applications, 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.

2025

Post, Predict, and Rank: Exploring the Relationship Between Social Media Strategy and Higher Education Institution Rankings

Autores
Bruna Rocha; Álvaro Figueira;

Publicação
Informatics

Abstract
In today’s competitive higher education sector, institutions increasingly rely on international rankings to secure financial resources, attract top-tier talent, and elevate their global reputation. Simultaneously, these universities have expanded their presence on social media, utilizing sophisticated posting strategies to disseminate information and boost recognition and engagement. This study examines the relationship between higher education institutions’ (HEIs’) rankings and their social media posting strategies. We gathered and analyzed publications from 18 HEIs featured in a consolidated ranking system, examining various features of their social media posts. To better understand these strategies, we categorized the posts into five predefined topics—engagement, research, image, society, and education. This categorization, combined with Long Short-Term Memory (LSTM) and a Random Forest (RF) algorithm, was utilized to predict social media output in the last five days of each month, achieving successful results. This paper further explores how variations in these social media strategies correlate with the rankings of HEIs. Our findings suggest a nuanced interaction between social media engagement and the perceived prestige of HEIs.

2025

Incremental Repair Feedback on Automated Assessment of Programming Assignments

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

Publicação
ELECTRONICS

Abstract
Automated assessment tools for programming assignments have become increasingly popular in computing education. These tools offer a cost-effective and highly available way to provide timely and consistent feedback to students. However, when evaluating a logically incorrect source code, there are some reasonable concerns about the formative gap in the feedback generated by such tools compared to that of human teaching assistants. A teaching assistant either pinpoints logical errors, describes how the program fails to perform the proposed task, or suggests possible ways to fix mistakes without revealing the correct code. On the other hand, automated assessment tools typically return a measure of the program's correctness, possibly backed by failing test cases and, only in a few cases, fixes to the program. In this paper, we introduce a tool, AsanasAssist, to generate formative feedback messages to students to repair functionality mistakes in the submitted source code based on the most similar algorithmic strategy solution. These suggestions are delivered with incremental levels of detail according to the student's needs, from identifying the block containing the error to displaying the correct source code. Furthermore, we evaluate how well the automatically generated messages provided by AsanasAssist match those provided by a human teaching assistant. The results demonstrate that the tool achieves feedback comparable to that of a human grader while being able to provide it just in time.

2025

Multilayer horizontal visibility graphs for multivariate time series analysis

Autores
Silva, VF; Silva, ME; Ribeiro, P; Silva, F;

Publicação
DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Multivariate time series analysis is a vital but challenging task, with multidisciplinary applicability, tackling the characterization of multiple interconnected variables over time and their dependencies. Traditional methodologies often adapt univariate approaches or rely on assumptions specific to certain domains or problems, presenting limitations. A recent promising alternative is to map multivariate time series into high-level network structures such as multiplex networks, with past work relying on connecting successive time series components with interconnections between contemporary timestamps. In this work, we first define a novel cross-horizontal visibility mapping between lagged timestamps of different time series and then introduce the concept of multilayer horizontal visibility graphs. This allows describing cross-dimension dependencies via inter-layer edges, leveraging the entire structure of multilayer networks. To this end, a novel parameter-free topological measure is proposed and common measures are extended for the multilayer setting. Our approach is general and applicable to any kind of multivariate time series data. We provide an extensive experimental evaluation with both synthetic and real-world datasets. We first explore the proposed methodology and the data properties highlighted by each measure, showing that inter-layer edges based on cross-horizontal visibility preserve more information than previous mappings, while also complementing the information captured by commonly used intra-layer edges. We then illustrate the applicability and validity of our approach in multivariate time series mining tasks, showcasing its potential for enhanced data analysis and insights.

2025

Extending the Quantitative Pattern-Matching Paradigm

Autores
Alves, S; Kesner, D; Ramos, M;

Publicação
PROGRAMMING LANGUAGES AND SYSTEMS, APLAS 2024

Abstract
We show how (well-established) type systems based on non-idempotent intersection types can be extended to characterize termination properties of functional programming languages with pattern matching features. To model such programming languages, we use a (weak and closed) lambda-calculus integrating a pattern matching mechanism on algebraic data types (ADTs). Remarkably, we also show that this language not only encodes Plotkin's CBV and CBN lambda-calculus as well as other subsuming frameworks, such as the bang-calculus, but can also be used to interpret the semantics of effectful languages with exceptions. After a thorough study of the untyped language, we introduce a type system based on intersection types, and we show through purely logical methods that the set of terminating terms of the language corresponds exactly to that of well-typed terms. Moreover, by considering non-idempotent intersection types, this characterization turns out to be quantitative, i.e. the size of the type derivation of a term t gives an upper bound for the number of evaluation steps from t to its normal form.

Factos & Números

17Docentes do Ensino Superior

2020

1Capítulos de livros

2020

7Artigos em conferências indexadas

2020