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About

Rui Portocarrero Sarmento has a degree in Electrical Engineering in the Faculty of Engineering, University of Porto and a MSc in Data Analysis and Decision Support Systems in the Faculty of Economics of the University of Porto. He has worked in several areas from an international technical support centre to software development companies focusing on Communications and Intranet solutions with Linux-based Enterprise Operating Systems. Finally, he has also worked for the main public transportation company in his hometown, Porto, as a Project Management engineer in the IT area. He is currently also collaborating with LIAAD (Laboratory of Artificial Intelligence and Decision Support) in INESC TEC researching on Large Scale Social Networks Analysis and Visualization.

Interest
Topics
Details

Details

  • Name

    Rui Portocarrero Sarmento
  • Cluster

    Computer Science
  • Role

    Research Assistant
  • Since

    23rd February 2013
Publications

2018

Incremental TextRank - Automatic Keyword Extraction for Text Streams

Authors
Sarmento, RP; Cordeiro, M; Brazdil, P; Gama, J;

Publication
Proceedings of the 20th International Conference on Enterprise Information Systems, ICEIS 2018, Funchal, Madeira, Portugal, March 21-24, 2018, Volume 1.

Abstract

2018

Incremental TextRank - Automatic Keyword Extraction for Text Streams

Authors
Sarmento, RP; Cordeiro, M; Brazdil, P; Gama, J;

Publication
Proceedings of the 20th International Conference on Enterprise Information Systems, ICEIS 2018, Funchal, Madeira, Portugal, March 21-24, 2018, Volume 1.

Abstract
Text Mining and NLP techniques are a hot topic nowadays. Researchers thrive to develop new and faster algorithms to cope with larger amounts of data. Particularly, text data analysis has been increasing in interest due to the growth of social networks media. Given this, the development of new algorithms and/or the upgrade of existing ones is now a crucial task to deal with text mining problems under this new scenario. In this paper, we present an update to TextRank, a well-known implementation used to do automatic keyword extraction from text, adapted to deal with streams of text. In addition, we present results for this implementation and compare them with the batch version. Major improvements are lowest computation times for the processing of the same text data, in a streaming environment, both in sliding window and incremental setups. The speedups obtained in the experimental results are significant. Therefore the approach was considered valid and useful to the research community. Copyright

2018

Evolving Networks and Social Network Analysis Methods and Techniques

Authors
Cordeiro, M; Sarmento, RP; Brazdil, P; Gama, J;

Publication
Social Media and Journalism - Trends, Connections, Implications

Abstract

2017

Comparative approaches to using R and Python for statistical data analysis

Authors
Sarmento, R; Costa, V;

Publication
Comparative Approaches to Using R and Python for Statistical Data Analysis

Abstract
The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to Using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.

2017

Efficient Incremental Laplace Centrality Algorithm for Dynamic Networks

Authors
Sarmento, RP; Cordeiro, M; Brazdil, P; Gama, J;

Publication
Complex Networks & Their Applications VI - Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), COMPLEX NETWORKS 2017, Lyon, France, November 29 - December 1, 2017.

Abstract
Social Network Analysis (SNA) is an important research area. It originated in sociology but has spread to other areas of research, including anthropology, biology, information science, organizational studies, political science, and computer science. This has stimulated research on how to support SNA with the development of new algorithms. One of the critical areas involves calculation of different centrality measures. The challenge is how to do this fast, as many increasingly larger datasets are available. Our contribution is an incremental version of the Laplacian Centrality measure that can be applied not only to large graphs but also to dynamically changing networks. We have conducted several tests with different types of evolving networks. We show that our incremental version can process a given large network, faster than the corresponding batch version in both incremental and full dynamic network setups. © Springer International Publishing AG 2018.