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Advanced Computing Systems

At CRACS, our mission is to pursue scientific excellence in the areas of programming languages, parallel and distributed computing, security and privacy, information mining, and Web based systems with a focus on developing scalable software systems for challenging, multidisciplinary applications.

Our research environment is enriched with junior talented researchers that together with senior researchers build the necessary critical mass and scientific competences to fulfill the institution’s mission.

Latest News

INESC TEC with five FCT exploratory projects approved in four R&D areas

Telecommunications and Multimedia, Applied Photonics, High-assurance Software and Advanced Computing Systems – these are the four domains that INESC TEC researchers will explore within the scope of the five projects that were approved through the Call for Exploratory Projects promoted by the Foundation for Science and Technology (FCT).

02nd October 2024

Computer Science and Engineering

A discussion about security and privacy at an international event organised in Portugal for the first time

Encryption, malicious software, data privacy, web and mobile security, secure access control and authentication – these were some of the topics discussed at the 14th edition of the ACM Conference on data and application security and privacy. Organised by INESC TEC and the Faculty of Sciences of the University of Porto (FCUP), this was the first time that the Conference took place in a country other than the United States of America.

27th June 2024

Privacy in 6G networks can be a challenge: INESC TEC integrates European project focusing on protection

Future 6G networks should make data privacy a top priority. INESC TEC is part of PRIVATEER, a European project that aims to create a robust and decentralised AI-based security analysis for 6G networks. "Privacy" is the key word. 

13th June 2023

INESC TEC researchers acknowledged for research work aimed at protecting the privacy of mobile phones

A group of INESC TEC researchers was acknowledged due to their research work on the management of permissions on mobile devices. The team developed a set of techniques to automate the response to requests for permissions by smartphone applications, with a reliability of 90%. This work received the award for best scientific paper at the ACM CODASPY conference, which took place in the United States of America.

08th July 2022

INESC TEC part of project that will make autonomous vehicles safer

INESC TEC will contribute to the development of perception algorithms, computing and architectures based on artificial intelligence, within the scope of the project THEIA - Automated Perception Driving, a partnership between the University of Porto and Bosch - which aims to make autonomous vehicles safer through a better perception of the outside environment.

07th June 2022

Team
Publications

CRACS Publications

View all Publications

2025

GANs in the Panorama of Synthetic Data Generation Methods

Authors
Vaz, B; Figueira, A;

Publication
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

Authors
Bruna Rocha; Álvaro Figueira;

Publication
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

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

Publication
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

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

Publication
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

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

Publication
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.

Facts & Figures

59Researchers

2016

7Proceedings in indexed conferences

2020

16Senior Researchers

2016