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High-Assurance Software

At HASLab, we anchor our research on a rigorous approach to three areas of Computer Science: Software Engineering, Distributed Systems, and Cryptography and Information Security.

Our contributions to these areas range from fundamental research on formal methods and algorithms, to applied research on the development of tools and middleware that address real-world demands stemming from long-term collaborations with industry.

Latest News
Computer Science

INESC TEC researcher joins Redis Labs' Technical Advisory Board

Carlos Baquero, senior researcher of the High-Assurance Software Laboratory (HASLab) of INESC TEC and lecturer at the University of Minho, joins the Redis Labs’s Technical Advisory Board. 

09th February 2018

Computer Science

Goal: to reduce mistakes associated with the use of medical devices

Saving lives through the reduction of mistakes in the use of medical devices. That is the purpose that moved researchers from the High-Assurance Software Laboratory (HASLab) of INESC TEC to participate in an international programme that intends to reduce the number of mistakes associated with the use of equipment such as glucose monitor or hemodialysis machine, improving its safety and resulting performance.

01st February 2018

Computer Science

INESC TEC researcher nominated for the College of Assessors of the New Zealand Government

José Creissac Campos, senior researcher of High-Assurance Software Laboratory (HASLab) of INESC TEC and professor at the University of Minho, was nominated a member of the College of Assessors of New Zealand Ministry of Business, Innovation and Employment (MBIE).

31st January 2018

Computer Science

European project SafeCloud receives positive evaluation in second year

The European project SafeCloud, Secure and Resilient Cloud Architecture, received a positive assessment in its second year of activity, as part of the European Commission's evaluation, in November, in Brussels.

17th January 2018

Computer Science

INESC TEC’s collaborator wins a Gulbenkian research grant

Afonso Rodrigues, INESC TEC’s collaborator and student in the Integrated Master's in Physics Engineering at University of Minho, was one of the eight winners of a Gulbenkian scientific and technological research grant, in the context of Quantum Technologies.

04th January 2018

Interest Topics
018

Featured Projects

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: Towards an Engineering Discipline for Green Software

2016-2019

Cloud-Setup-1

PLATAFORMA DE PREPARAÇÃO DE CONTEÚDOS AUDIOVISUAIS PARA INGEST NA CLOUD

2016-2018

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

2015-2018

NanoStima-RL3

NanoSTIMA – Health data infrastructure

2015-2018

SMILES

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

2015-2018

UPGRID

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

2015-2018

PaaS2

Integrated Management of PaaS services

2014-2015

LeanBigData

Ultra-Scalable and Ultra-Efficient Integrated and Visual Big Data Analytics

2014-2017

Practice

Privacy-Preserving Computation in the Cloud

2013-2016

CoherentPaaS

A Coherent and Rich PaaS with a Common Programming Model

2013-2016

PaaS

Integrated Management of PaaS services

2013-2015

Cloud

Integrated Management of Cloud Services with Operations Support Systems

2012-2013

WEB2Economy

Web 2.0 Platform - WEB2Economy

2011-2012

Team
001

Laboratories

CLOUDinha Laboratory

Publications

HASLAB Publications

View all Publications

2018

Delta State replicated data types

Authors
Almeida, PS; Shoker, A; Baquero, C;

Publication
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING

Abstract
Conflict-free Replicated Data Types (CRDTs) are distributed data types that make eventual consistency of a distributed object possible and non ad-hoc. Specifically, state-based CRDTs ensure convergence through disseminating the entire state, that may be large, and merging it to other replicas. We introduce Delta State Conflict-Free Replicated Data Types (delta-CRDT) that can achieve the best of both operation-based and state-based CRDTs: small messages with an incremental nature, as in operation-based CRDTs, disseminated over unreliable communication channels, as in traditional state-based CRDTs. This is achieved by defining delta-mutators to return a delta-state, typically with a much smaller size than the full state, that to be joined with both local and remote states. We introduce the delta-CRDT framework, and we explain it through establishing a correspondence to current state-based CRDTs. In addition, we present an anti-entropy algorithm for eventual convergence, and another one that ensures causal consistency. Finally, we introduce several delta-CRDT specifications of both well-known replicated datatypes and novel datatypes, including a generic map composition.

2018

Delta state replicated data types

Authors
Almeida, PS; Shoker, A; Baquero, C;

Publication
J. Parallel Distrib. Comput.

Abstract

2018

Preference rules for label ranking: Mining patterns in multi-target relations

Authors
de Sa, CR; Azevedo, P; Soares, C; Jorge, AM; Knobbe, A;

Publication
INFORMATION FUSION

Abstract
In this paper, we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.

2018

A taxonomy for planning and designing smart mobility services

Authors
Cledou, G; Estevez, E; Soares Barbosa, L;

Publication
Government Information Quarterly

Abstract

2018

Data Leakage in Java Applets with Exception Mechanism

Authors
Bernardeschi, C; Masci, P; Santone, A;

Publication
Proceedings of the Second Italian Conference on Cyber Security, Milan, Italy, February 6th - to - 9th, 2018.

Abstract

Supervised Theses

2016

Modelação de consumo de energia em Linux

Author
Carlos Gustavo Ferreira Araújo Portela

Institution
UM

2016

Safety Verification for ROS Applications

Author
André Filipe Faria dos Santos

Institution
UM

2016

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Author
Chong liu

Institution
UM

2016

Holistic performance and scalability analysis for large scale distributed systems

Author
Francisco Nuno Teixeira Neves

Institution
UM

2016

Optimizing Operation-based Conflict-free Replicated Data Types

Author
Georges Younes

Institution
UM

Facts & Figures

0R&D Employees

2017

21Academic Staff

2017