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Factos & Números
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Apresentação

Laboratório de Software Confiável

O HASLab dedica-se à criação e à implementação de sistemas de software confiável, i.e., software correto e resiliente perante falhas e ataques.

De forma a cumprir este grande objetivo, o HASLab opera em três grandes áreas - Cibersegurança, Sistemas Distribuídos e Engenharia de Software.

Engenharia de Software - são explorados métodos, técnicas e ferramentas para o desenvolvimento de software, podendo este ser integrado nas funcionalidades internas de determinados componentes, na sua configuração junto de outros componentes, e também na interação com o utilizador.

Sistemas Distribuídos - com vista a melhorar a confiabilidade e a escalabilidade de software, explorando as propriedades inerentes à distribuição e à replicação de sistemas computacionais.

Cibersegurança - de forma a minimizar a vulnerabilidade dos componentes de software a ataques, com recurso à implementação de estruturas e de protocolos criptográficos com propriedades de segurança formalmente comprovadas.

Através de uma abordagem multidisciplinar que assenta em princípios teóricos comprovados, o HASLab visa disponibilizar soluções - fundamentos teóricos, métodos, linguagens, ferramentas - para o desenvolvimento de sistemas TIC abrangentes, dando garantias aos seus proprietários e utilizadores. Os grandes domínios de aplicação da investigação desenvolvida no HASLab incluem o desenvolvimento de sistemas de software cruciais para garantir a segurança e a proteção, a operacionalização de infraestruturas da nuvem seguras, e a gestão e o tratamento de big data, tendo em conta as questões da privacidade.

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Projetos Selecionados

exaSIMPLE

exaSIMPLE: A Hybrid ML-CFD SIMPLE Algorithm for the Exascale Era

2024-2025

Saude24GB

Linha de Saúde 24h da Guiné-Bissau

2024-2024

EPICURE

High-level specialised application support service in High-Performance Computing (HPC)

2024-2028

TwinEU

Digital Twin for Europe

2024-2026

HANAMI

Hpc AlliaNce for Applications and supercoMputing Innovation: the Europe - Japan collaboration

2024-2026

ENSCOMP3

Ensino de Ciência da Computação nas Escolas 3

2023-2025

AzDIH

Azores Digital Innovation Hub on Tourism and Sustainability

2023-2025

PFAI4_4eD

Programa de Formação Avançada Industria 4 - 4a edição

2023-2023

ATE

Aliança para a Transição Energética

2023-2025

Green_Dat_AI

Energy-efficient AI-ready Data Spaces

2023-2025

EuroCC2

National Competence Centres in the framework of EuroHPC Phase 2

2023-2025

fMP

Formação de Introdução à utilização de recursos HPC (Técnicas básicas de Programação Paralela)

2022-2022

AURORA

Deteção de atividade no interior do veículo

2022-2023

NewSpacePortugal

Agenda New Space Portugal

2022-2025

ATTRACT_DIH

Digital Innovation Hub for Artificial Intelligence and High-Performance Computing

2022-2025

BeFlexible

Boosting engagement to increase flexibility

2022-2026

ENERSHARE

European commoN EneRgy dataSpace framework enabling data sHaring-driven Across- and beyond- eneRgy sErvices

2022-2025

Gridsoft

Parecer sobre a implementação de software para redes elétricas inteligentes

2022-2022

PFAI4_3ed

Programa de Formação Avançada Industria 4 - 3a edição

2022-2022

THEIA

Automated Perception Driving

2022-2023

SpecRep

Constraint-based Specification Repair

2022-2023

IBEX

Métodos quantitativos para a programação ciber-física: Uma abordagem precisa para racicionar sobre imprecisões na computação ciber-física

2022-2024

FLEXCOMM

Towards Energy-aware Communications: Connecting the power grid and communication infrastructure

2022-2023

STDCNCS

Desenvolvimento de estudo sobre a comunidade de cibersegurança em Portugal, no âmbito do Observatório de Cibersegurança

2021-2023

Sustainable HPC

Computação de elevado desempenho sustentável

2021-2025

CircThread

Building the Digital Thread for Circular Economy Product, Resource & Service Management

2021-2025

PassCert

Investigação do Impacto de Verificação Formal na Adopção de Software para Segurança de Passwords

2021-2022

IoT4Distribuicao

Análise de Requisitos e Especificação Funcional de uma Arquitetura Distribuída baseada em soluções IoT para a Gestão e Controlo da Rede de Distribuição

2021-2023

RISC2

A network for supporting the coordination of High-Performance Computing research between Europe and Latin America

2021-2023

CloudAnalytics4Dams

Gestão de Grandes Quantidades de Dados em Barragens da EDP Produção

2021-2021

PAStor

Programmable and Adaptable Storage for AI-oriented HPC Ecosystems

2020-2021

PFAI4.0

Programa de Formação Avançada Industria 4.0

2020-2021

Collaboration

Collaborative Visual Development

2020-2021

AIDA

Adaptive, Intelligent and Distributed Assurance Platform

2020-2023

BigHPC

A Management Framework for Consolidated Big Data and HPC

2020-2023

SLSNA

Prestação de Serviços no ambito do projeto SKORR

2020-2021

AppOwl

Deteção de Mutações Maliciosas no Browser

2020-2021

InterConnect

Interoperable Solutions Connecting Smart Homes, Buildings and Grids

2019-2024

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

CLOUD4CANDY

Cloud for CANDY

2019-2019

HADES

Sistemas descentralizados confiáveis e escaláveis suportados por hardware

2018-2022

MaLPIS

Aprendizagem Automática para Deteção de Ataques e Identificação de Perfis Segurança na Internet

2018-2022

SKORR

Advancing the Frontier of Social Media Management Tools

2018-2021

DaVinci

Architecturas distribuídas: variabilidade e interação de sistemas ciber-físicos

2018-2022

SAFER

Verificação de segurança para software robótico

2018-2021

KLEE

Modelação coalgébrica e análise para biologia sintética computacional

2018-2021

InteGrid

Demonstration of INTElligent grid technologies for renewables INTEgration and INTEractive consumer participation enabling INTEroperable market solutions and INTErconnected stakeholders

2017-2020

Lightkone

Lightweight Computation for Networks at the Edge

2017-2019

CloudDBAppliance

European Cloud In-Memory Database Appliance with Predictable Performance for Critical Applications

2016-2019

GSL

GreenSoftwareLab: Computação Verde como uma Disciplina de Engenharia

2016-2019

Cloud-Setup

PLATAFORMA DE PREPARAÇÃO DE CONTEÚDOS AUDIOVISUAIS PARA INGEST NA CLOUD

2016-2019

CORAL-TOOLS

CORAL - Sustainable Ocean Exploitation: Tools and Sensors

2016-2018

SafeCloud

Secure and Resilient Cloud Architecture

2015-2018

NanoStima-RL1

NanoSTIMA - Macro-to-Nano Human Sensing Technologies

2015-2019

NanoStima-RL3

NanoSTIMA - Health data infrastructure

2015-2019

SMILES

TEC4Growth - RL SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics

2015-2019

UPGRID

Real proven solutions to enable active demand and distributed generation flexible integration, through a fully controllable LOW Voltage and medium voltage distribution grid

2015-2017

LeanBigData

Análise Integrada e Visual de Big Data Ultra-escalável e Ultra-eficiente

2014-2017

Practice

Ferramentas de Preservação de Privacidade na Cloud

2013-2016

CoherentPaaS

PaaS Rica e Coerente com um Modelo de Programação Comum

2013-2016

Equipa
001

Laboratório

CLOUDinha

Publicações

HASLab Publicações

Ler todas as publicações

2020

Data governance: Organizing data for trustworthy Artificial Intelligence

Autores
Janssen, M; Brous, P; Estevez, E; Barbosa, LS; Janowski, T;

Publicação
GOVERNMENT INFORMATION QUARTERLY

Abstract
The rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements. However, they all rely on data which is not only big, open and linked but varied, dynamic and streamed at high speeds in real-time. Managing such data is challenging. To overcome such challenges and utilize opportunities for BDAS, organizations are increasingly developing advanced data governance capabilities. This paper reviews challenges and approaches to data governance for such systems, and proposes a framework for data governance for trustworthy BDAS. The framework promotes the stewardship of data, processes and algorithms, the controlled opening of data and algorithms to enable external scrutiny, trusted information sharing within and between organizations, risk-based governance, system-level controls, and data control through shared ownership and self-sovereign identities. The framework is based on 13 design principles and is proposed incrementally, for a single organization and multiple networked organizations.

2020

Towards a register-based census in Oman

Autores
Al Lawati, AH; Barbosa, LS;

Publicação
ICEGOV 2020: 13th International Conference on Theory and Practice of Electronic Governance, Athens, Greece, 23-25 September, 2020

Abstract
A national census is an official count of a country's population that aims to motivate and measure sustainable development. Traditionally, a census is a cumbersome manual operation that involves distributing surveys to all households in the country through field agents or by mail. Recently, some countries have utilized voluntary electronic submissions in addition to the manual work to reduce costs and increase efficiency. However, an increasing number of countries are resorting to a register-based census that uses pre-existing official registers to derive its data. This paper describes Oman's upcoming register-based census, e-Census 2020, and analyses it against the European Commission's necessary conditions that facilitate a successful transition from a traditional to a register-based census [1]. © 2020 ACM.

2020

Software engineering for 'quantum advantage'

Autores
Barbosa, LS;

Publicação
ICSE '20: 42nd International Conference on Software Engineering, Workshops, Seoul, Republic of Korea, 27 June - 19 July, 2020

Abstract
Software is a critical factor in the reliability of computer systems. While the development of hardware is assisted by mature science and engineering disciplines, software science is still in its infancy. This situation is likely to worsen in the future with quantum computer systems. Actually, if quantum computing is quickly coming of age, with potential groundbreaking impacts on many different fields, such benefits come at a price: quantum programming is hard and finding new quantum algorithms is far from straightforward. Thus, the need for suitable formal techniques in quantum software development is even bigger than in classical computation. A lack of reliable approaches to quantum computer programming will put at risk the expected quantum advantage of the new hardware. This position paper argues for the need for a proper quantum software engineering discipline benefiting from precise foundations and calculi, capable of supporting algorithm development and analysis. © 2020 ACM.

2020

Quantum Bayesian decision-making

Autores
Oliveira, Md; Barbosa, LS;

Publicação
CoRR

Abstract

2020

Sequence Mining for Automatic Generation of Software Tests from GUI Event Traces

Autores
Oliveira, A; Freitas, R; Jorge, A; Amorim, V; Moniz, N; Paiva, ACR; Azevedo, PJ;

Publicação
Intelligent Data Engineering and Automated Learning - IDEAL 2020 - 21st International Conference, Guimaraes, Portugal, November 4-6, 2020, Proceedings, Part II

Abstract
In today’s software industry, systems are constantly changing. To maintain their quality and to prevent failures at controlled costs is a challenge. One way to foster quality is through thorough and systematic testing. Therefore, the definition of adequate tests is crucial for saving time, cost and effort. This paper presents a framework that generates software test cases automatically based on user interaction data. We propose a data-driven software test generation solution that combines the use of frequent sequence mining and Markov chain modeling. We assess the quality of the generated test cases by empirically evaluating their coverage with respect to observed user interactions and code. We also measure the plausibility of the distribution of the events in the generated test sets using the Kullback-Leibler divergence. © 2020, Springer Nature Switzerland AG.

Factos & Números

0Capítulos de livros

2020

68Investigadores

2016

4Artigos em revistas indexadas

2020

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