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Presentation

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

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

INESC TEC developed a tool to identify biological species

INESC TEC researchers developed Biolens, a web application that allows the classification of biological species through the submission of photographs. Currently, the platform is able to recognise a significant subset of the Portuguese species of dragonflies, butterflies and moths.

03rd June 2022

INESC TEC researcher publishes book dedicated to gamification

Ricardo Queirós, researcher at INESC TEC, and professor at the School of Media Arts and Design of the Polytechnic of Porto (ESMAD-P.Porto), is one of the authors of the book Gamificação Aplicada às Organizações e ao Ensino, along with Mário Pinto, also a professor at ESMAD-P.Porto and a researcher at uniMAD.

24th March 2022

043

Featured Projects

FGPEPlusPlus

FGPE++ Gamified Programming Learning at Scale

2023-2025

PRIVATEER

Privacy-first Security Enablers for 6G Networks

2023-2025

BLOCKCHAINPT

BLOCKCHAIN.PT - AGENDA “DESCENTRALIZAR PORTUGAL COM BLOCKCHAIN”

2023-2025

THEIA

Automated Perception Driving

2022-2023

AI4DM

AI predictive modeling Services

2021-2022

FGPEPlus

Learning tools interoperability for gamified programming education

2021-2023

JuezLTI

Automatic assessment of computing exercises using LTI standard

2021-2023

PANDORA

Cyber Defence Platform for Real-time Threat Hunting, Incident Response and Information Sharing

2020-2022

Cortaderia

Desenvolvimento de Software para Monitorização da Espécie Invasora Cortaderia selloana

2020-2020

Authenticus19_20

Consultoria Tecnológica em Sistemas CRIS e Cálculo de APC

2019-2020

T4CDTKC

Training 4 Cotec, Digital Transformation Knowledge Challenge - Elaboração de Programa de Formação “CONHECER E COMPREENDER O DESAFIO DAS TECNOLOGIAS DE TRANSFORMAÇÃO DIGITAL”

2019-2021

Angerona

Privacy preserving IOT middleware

2018-2019

FGPE

Framework for Gamified Programming Education

2018-2021

AuthenticusNF

Desenvolvimento de Indicadores de Produção Científica Baseados no Authenticus

2018-2018

PGODISSEIA

Serviço de instalação e configuração de uma plataforma de autenticação, implementação de solução de gestão centralizada de certificados digitais, auditoria de segurança (pen-testing) e análise de impacto de privacidade dos tratamentos de dados pessoais das plataformas de integração e autenticação

2018-2020

CRADLE

Deep learning in cancer drug discovery: a pipeline for the generation of new therapies

2018-2021

Authenticus2019

Apoio Técnico ao CINTESIS para extração de indicadores de produção científica baseados no Authenticus

2018-2018

ELVEN

Elven - Expressive Logics for VErifying the Net

2016-2019

Digi-NewB

Non-invasive monitoring of perinatal health through multiparametric digital representation of clinically relevant functions for improving clinical intervention in neonatal units (Digi-NewB)

2016-2020

FOUREYES

TEC4Growth - RL FourEyes - Intelligence, Interaction, Immersion and Innovation for media industries

2015-2019

NanoStima-RL5

NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis

2015-2019

NanoStima-RL3

NanoSTIMA - Health data infrastructure

2015-2019

NanoStima-RL4

NanoSTIMA - Health Data Analysis & Decision

2015-2019

SMILES

SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics

2015-2019

FOTOCATGRAF

Graphene-based semiconductor photocatalysis for a safe and sustainable water supply: an advanced technology for emerging pollutants removal

2015-2018

REMINDS

Relevance Mining and Detection System (REMINDS)

2015-2017

PANF

Methods to retrieve and communicate data from Sifarma

2015-2016

SEA

SEA-Sistema de ensino autoadaptativo

2015-2015

MGI

Contrato de Aquisição de serviços de produção e desenvolvimento de módulo para gestão de iterações para integrar no sistema de informação da UP (SIGARRA)

2015-2015

Hyrax

Crowd-Sourcing Mobile Devices to Develop Edge Clouds

2014-2018

DAT

Curation and intelligent data analysis

2014-2015

ABLe

Advice-Based Learning for Health Care

2013-2015

Authenticus

Authenticus - System to Identify and Validate Portuguese Scientific Publications

2013-2016

SIBILA

Towards Smart Interacting Blocks that Improve Learned Advice

2013-2015

ADE

Adverse Drug Effects Detection

2012-2015

e-Policy

Engineering for the Policy-making Life Cycle (ePolicy)

2011-2014

Leap

Logic environments with Advanced Paralelism

2011-2014

MACAW

Macroprogramming for Wireless Sensor Networks

2011-2014

Breadcrumbs

Social network based on personal libraries of news fragments

2010-2012

Ofelia

Open Federated Environments Leveraging Identity and Authorization

2010-2013

Horus

Horn Representations of Uncertain Systems

2010-2013

DIGISCOPE

DIGItally enhanced stethosCOPE for clinical usage

2010-2013

Palco3.0

Intelligent Web system to support the management of a social network on music

2008-2011

Team
Publications

CRACS Publications

View all Publications

2024

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

Authors
Mendonça, M; Figueira, Á;

Publication
Informatics

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

Uncovering Manipulated Files Using Mathematical Natural Laws

Authors
Fernandes, P; Ciardhuáin, SO; Antunes, M;

Publication
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I

Abstract
The data exchange between different sectors of society has led to the development of electronic documents supported by different reading formats, namely portable PDF format. These documents have characteristics similar to those used in programming languages, allowing the incorporation of potentially malicious code, which makes them a vector for cyberattacks. Thus, detecting anomalies in digital documents, such as PDF files, has become crucial in several domains, such as finance, digital forensic analysis and law enforcement. Currently, detection methods are mostly based on machine learning and are characterised by being complex, slow and mainly inefficient in detecting zero-day attacks. This paper aims to propose a Benford Law (BL) based model to uncover manipulated PDF documents by analysing potential anomalies in the first digit extracted from the PDF document's characteristics. The proposed model was evaluated using the CIC Evasive PDFMAL-2022 dataset, consisting of 1191 documents (278 benign and 918 malicious). To classify the PDF documents, based on BL, into malicious or benign documents, three statistical models were used in conjunction with the mean absolute deviation: the parametric Pearson and the non-parametric Spearman and Cramer-Von Mises models. The results show a maximum F1 score of 87.63% in detecting malicious documents using Pearson's model, demonstrating the suitability and effectiveness of applying Benford's Law in detecting anomalies in digital documents to maintain the accuracy and integrity of information and promoting trust in systems and institutions.

2024

GERF - Gamified Educational Virtual Escape Room Framework for Innovative Micro-Learning and Adaptive Learning Experiences

Authors
Queirós, R;

Publication
Communications in Computer and Information Science

Abstract
This paper introduces GERF, a Gamified Educational Virtual Escape Room Framework designed to enhance micro-learning and adaptive learning experiences in educational settings. The framework incorporates a user taxonomy based on the user type hexad, addressing the preferences and motivations of different learners profiles. GERF focuses on two key facets: interoperability and analytics. To ensure seamless integration of Escape Room (ER) platforms with Learning Management Systems (LMS), the Learning Tools Interoperability (LTI) specification is used. This enables smooth and efficient communication between ERs and LMS platforms. Additionally, GERF uses the xAPI specification to capture and transmit experiential data in the form of xAPI statements, which are then sent to a Learning Record Store (LRS). By leveraging these learning analytics, educators gain valuable insights into students’ interactions within the ER, facilitating the adaptation of learning content based on individual learning needs. Ultimately, GERF empowers educators to create personalized learning experiences within the ER environment, fostering student engagement and learning outcomes. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2024

Comparing semantic graph representations of source code: The case of automatic feedback on programming assignments

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

Publication
Comput. Sci. Inf. Syst.

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 fo-cus on source code aspects rather than requiring effective code execution. To this end, syntactic and semantic information encoded in textual data is typically rep-resented 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. Conse-quently, 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, ComSIS Consortium. All rights reserved.

2024

Hardware Security for Internet of Things Identity Assurance

Authors
Cirne A.; Sousa P.R.; Resende J.S.; Antunes L.;

Publication
IEEE Communications Surveys and Tutorials

Abstract
With the proliferation of Internet of Things (IoT) devices, there is an increasing need to prioritize their security, especially in the context of identity and authentication mechanisms. However, IoT devices have unique limitations in terms of computational capabilities and susceptibility to hardware attacks, which pose significant challenges to establishing strong identity and authentication systems. Paradoxically, the very hardware constraints responsible for these challenges can also offer potential solutions. By incorporating hardware-based identity implementations, it is possible to overcome computational and energy limitations, while bolstering resistance against both hardware and software attacks. This research addresses these challenges by investigating the vulnerabilities and obstacles faced by identity and authentication systems in the IoT context, while also exploring potential technologies to address these issues. Each identified technology underwent meticulous investigation, considering known security attacks, implemented countermeasures, and an assessment of their pros and cons. Furthermore, an extensive literature survey was conducted to identify instances where these technologies have effectively supported device identity. The research also includes a demonstration that evaluates the effectiveness of hardware trust anchors in mitigating various attacks on IoT identity. This empirical evaluation provides valuable insights into the challenges developers encounter when implementing hardware-based identity solutions. Moreover, it underscores the substantial value of these solutions in terms of mitigating attacks and developing robust identity frameworks. By thoroughly examining vulnerabilities, exploring technologies, and conducting empirical evaluations, this research contributes to understanding and promoting the adoption of hardware-based identity and authentication systems in secure IoT environments. The findings emphasize the challenges faced by developers and highlight the significance of hardware trust anchors in enhancing security and facilitating effective identity solutions.

Facts & Figures

17Academic Staff

2020

59Researchers

2016

1Book Chapters

2020