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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
Computer Science

INESC TEC is part of the Strategic Council for the Digital Economy

The Confederation of Portuguese Business (CIP) created the Strategic Council for the Digital Economy, an advisory body to be coordinated by the former Secretary of State for Youth, and current Director of Corporate and Legal Affairs of Microsoft Portugal, Pedro Duarte. It will be composed of 35 representatives of the sector. One of these elements will be Luís Filipe Antunes, researcher at the Centre for Research in Advanced Computing Systems (CRACS) of INESC TEC and President of the Department of Computer Science of the Faculty of Sciences of the University of Porto.

18th January 2018

Computer Science

Team with INESC TEC researchers wins the 2017 ICDM best paper award

The Carnegie Mellon University (CMU) Database Group and the University of Porto won the 2017 IEEE International Conference on Data Mining (ICDM) Best Paper award for the paper “TensorCast: Forecasting with Context using Coupled Tensors”, a novel method that forecasts time-evolving networks like Twitter, for example. The conference will be held between on November 18-21 in New Orleans, US.

10th November 2017

Protocol between INESC TEC and National Institute of Informatics takes researchers to Japan

Following the signing of a memorandum of understanding between INESC TEC and the National Institute of Informatics (NII), in Tokyo (Japan) in 2014, five INESC TEC researchers had the opportunity to do an internship at the Japanese institution.

02nd February 2016

Computer Science

INESC TEC develops software to diagnose breast and prostate cancer

ExpertBayes is the name of the software created by INESC TEC that can help health professionals, more specifically doctors, to diagnose illnesses such as breast and prostate cancer.

04th January 2016

Computer Science

Project on the detection of pollutants approved as part of call UT Austin – Portugal

INESC TEC’s Centre for Research in Advanced Computing Systems (CRACS) is involved in a multidisciplinary project that was approved as part of a call of the University of Texas/Austin – Portugal Programme.

08th April 2015

Interest Topics
026

Featured Projects

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

2015-2018

NanoStima-RL5

NanoSTIMA – Advanced Methodologies for Computer-Aided Detection and Diagnosis

2015-2018

NanoStima-RL3

NanoSTIMA – Health data infrastructure

2015-2018

NanoStima-RL4

NanoSTIMA – Health Data Analysis & Decision

2015-2018

SMILES

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

2015-2018

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

2017

A LEARNING AND SOCIAL MANAGEMENT SYSTEM – VERSION 3.0

Authors
Figueira, A; Oliveira, L;

Publication
INTED2017 Proceedings

Abstract

2017

Detecting Journalistic Relevance on Social Media: A two-case study using automatic surrogate features

Authors
Figueira, A; Guimarães, N;

Publication
Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, Sydney, Australia, July 31 - August 03, 2017

Abstract

2017

Journalistic Relevance Classification in Social Network Messages: an Exploratory Approach

Authors
Sandim, M; Fortuna, P; Figueira, A; Oliveira, L;

Publication
COMPLEX NETWORKS & THEIR APPLICATIONS V

Abstract
Social networks are becoming a wide repository of information, some of which may be of interest for general audiences. In this study we investigate which features may be extracted from single posts propagated throughout a social network, and that are indicative of its relevance, from a journalistic perspective. We then test these features with a set of supervised learning algorithms in order to evaluate our hypothesis. The main results indicate that if a text fragment is pointed out as being interesting, meaningful for the majority of people, reliable and with a wide scope, then it is more likely to be considered as relevant. This approach also presents promising results when validated with several well-known learning algorithms.

2017

Predicting the Relevance of Social Media Posts Based on Linguistic Features and Journalistic Criteria

Authors
Pinto, A; Oliveira, HG; Figueira, A; Alves, AO;

Publication
NEW GENERATION COMPUTING

Abstract
An overwhelming quantity of messages is posted in social networks every minute. To make the utilization of these platforms more productive, it is imperative to filter out information that is irrelevant to the general audience, such as private messages, personal opinions or well-known facts. This work is focused on the automatic classification of public social text according to its potential relevance, from a journalistic point of view, hopefully improving the overall experience of using a social network. Our experiments were based on a set of posts with several criteria, including the journalistic relevance, assessed by human judges. To predict the latter, we rely exclusively on linguistic features, extracted by Natural Language Processing tools, regardless the author of the message and its profile information. In our first approach, different classifiers and feature engineering methods were used to predict relevance directly from the selected features. In a second approach, relevance was predicted indirectly, based on an ensemble of classifiers for other key criteria when defining relevance-controversy, interestingness, meaningfulness, novelty, reliability and scope-also in the dataset. The first approach achieved a F (1)-score of 0.76 and an Area under the ROC curve (AUC) of 0.63. But the best results were achieved by the second approach, with the best learned model achieving a F (1)-score of 0.84 with an AUC of 0.78. This confirmed that journalistic relevance can indeed be predicted by the combination of the selected criteria, and that linguistic features can be exploited to classify the latter.

2017

Communication and resource usage analysis in online environments: An integrated social network analysis and data mining perspective

Authors
Figueira, Alvaro;

Publication
2017 IEEE Global Engineering Education Conference, EDUCON 2017, Athens, Greece, April 25-28, 2017

Abstract

Supervised Theses

2016

Communities and Anomaly Detection in Large Edge-Labeled Graphs

Author
Miguel Ramos de Araújo

Institution
UP-FCUP

2016

Scheduling computations over high-churn networks of mobile devices

Author
Joaquim Magalhães Esteves da Silva

Institution
UP-FCUP

2016

Towards a Middleware for Mobile-Edge-Cloud Applications

Author
João Filipe Rodrigues

Institution
UP-FCUP

2016

Long term goal oriented recommender system

Author
Amir Hossein Nabizadeh Rafsanjani

Institution
UP-FCUP

2016

Pattern Discovery in Complex Networks

Author
David Oliveira Aparício

Institution
UP-FCUP

Facts & Figures

2R&D Employees

2017

14Academic Staff

2017