Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
About

About

Adjunt professor at Computers Engineering Department, ESTG-Leiria (Polytechnic of Leiria) and reseracher at CRACS.

Holds a PHD in Computer Science by Universidade do Porto; MSc in Informatics, branch of systems and networks, also by Universidade do Porto; Degree in Computers Enginnering by Instituto Superior de Engenharia do Porto (Polytechnic of Porto).

Coordinates a post-graduation on security and digital forensics at Polytechnic of Leiria and is responsible by classes on networking, systems administration, cloud technology and datacenters infrastrucutres.

Main areas of research include immune-inspired algorithms applied to automatic detection of anomalies, ensemble based algorithms for classification and anomaly detection, learning on dynamic systems in a temporal basis.

Previously he was algo ICT project manager and system administrator in companies.

Interest
Topics
Details

Details

  • Name

    Mário João Antunes
  • Cluster

    Computer Science
  • Role

    Project Leader
  • Since

    01st January 2009
Publications

2017

Adaptive learning for dynamic environments: A comparative approach

Authors
Costa, J; Silva, C; Antunes, M; Ribeiro, B;

Publication
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Abstract
Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn + +.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn + +.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).

2016

Choice of Best Samples for Building Ensembles in Dynamic Environments

Authors
Costa, J; Silva, C; Antunes, M; Ribeiro, B;

Publication
ENGINEERING APPLICATIONS OF NEURAL NETWORKS, EANN 2016

Abstract
Machine learning approaches often focus on optimizing the algorithm rather than assuring that the source data is as rich as possible. However, when it is possible to enhance the input examples to construct models, one should consider it thoroughly. In this work, we propose a technique to define the best set of training examples using dynamic ensembles in text classification scenarios. In dynamic environments, where new data is constantly appearing, old data is usually disregarded, but sometimes some of those disregarded examples may carry substantial information. We propose a method that determines the most relevant examples by analysing their behaviour when defining separating planes or thresholds between classes. Those examples, deemed better than others, are kept for a longer time-window than the rest. Results on a Twitter scenario show that keeping those examples enhances the final classification performance.

2016

A telemedicine application using WebRTC

Authors
Antunes, M; Silva, C; Barranca, J;

Publication
INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS/INTERNATIONAL CONFERENCE ON PROJECT MANAGEMENT/INTERNATIONAL CONFERENCE ON HEALTH AND SOCIAL CARE INFORMATION SYSTEMS AND TECHNOLOGIES, CENTERIS/PROJMAN / HCIST 2016

Abstract
ICT in healthcare businesses has been growing in Portugal in the past few decades. The implementation of large scale information systems in hospitals, the deployment of electronic prescription and electronic patient records applications are just a few examples. Telemedicine is another emergent and widely used ICT solution to smooth the communication between patients and healthcare professionals, by allowing video and voice transfer over the Internet. Although there are several implementations of telemedicine solutions, they usually have some drawbacks, namely: i) too specific for a purpose; ii) based on proprietary applications; iii) require additional software installation; iv) and usually have associated costs. In this paper we propose a telemedicine solution based on WebRTC Application Programming Interface (API) to transmit video and voice in real time over the Internet, through a web browser. Besides microphone and webcam control, we have also included two additional functionalities that may be useful to both patients and healthcare professionals during the communication, namely i) bidirectional sending files capability and ii) shared whiteboard which allows free drawing. The proposed solution uses exclusively open source software components and requires solely a WebRTC compatible web browser, like Google Chrome or Firefox. We have made two types of tests in healthcare environment: i) a bidirectional patient-doctor communication; ii) and connecting at one end an external USB medical device with an integrated webcam. The results were promising, since they revealed the potential of using WebRTC API to control microphone and webcam in a telemedicine application, as well as the appropriateness and acceptance of the features included. (C) 2016 Published by Elsevier B.V.

2015

Active Manifold Learning with Twitter Big Data

Authors
Silva, C; Antunes, M; Costa, J; Ribeiro, B;

Publication
INNS CONFERENCE ON BIG DATA 2015 PROGRAM

Abstract
The data produced by Internet applications have increased substantially. Big data is a flaring field that deals with this deluge of data by using storage techniques, dedicated infrastructures and development frameworks for the parallelization of defined tasks and its consequent reduction. These solutions however fall short in online and highly data demanding scenarios, since users expect swift feedback. Reduction techniques are efficiently used in big data online applications to improve classification problems. Reduction in big data usually falls in one of two main methods: (i) reduce the dimensionality by pruning or reformulating the feature set; (ii) reduce the sample size by choosing the most relevant examples. Both approaches have benefits, not only of time consumed to build a model, but eventually also performance-wise, usually by reducing overfitting and improving generalization capabilities. In this paper we investigate reduction techniques that tackle both dimensionality and size of big data. We propose a framework that combines a manifold learning approach to reduce dimensionality and an active learning SVM-based strategy to reduce the size of labeled sample. Results on Twitter data show the potential of the proposed active manifold learning approach.

2015

Automatic network configuration in virtualized environment using GNS3

Authors
Emiliano, R; Antunes, M;

Publication
10TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION (ICCSE 2015)

Abstract
Computer networking is a central topic in computer science courses curricula offered by higher education institutions. Network virtualization and simulation tools, like GNS3, allows students and practitioners to test real world networking configuration scenarios and to configure complex network scenarios by configuring virtualized equipments, such as routers and switches, through each one's virtual console. The configuration of advanced network topics in GNS3 requires that students have to apply basic and very repetitive IP configuration tasks in all network equipments. As the network topology grows, so does the amount of network equipments to be configured, which may lead to logical configuration errors. In this paper we propose an extension for GNS3 network virtualizer, to automatically generate a valid configuration of all the network equipments in a GNS3 scenario. Our implementation is able to automatically produce an initial IP and routing configuration of all the Cisco virtual equipments by using the GNS3 specification files. We tested this extension against a set of networked scenarios which proved the robustness, readiness and speedup of the overall configuration tasks. In a learning environment, this feature may save time for all networking practitioners, both beginners or advanced, who aim to configure and test network topologies, since it automatically produces a valid and operational configuration for all the equipments designed in a GNS3 environment.