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About

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.

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Publications

2021

Panel Data

Authors
Costa, V; Sarmento, RP;

Publication
Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management

Abstract
Panel data is a regression analysis type that uses time data and spatial data. Thus, the behavior of groups, for example, enterprises or communities, is analyzed through a time scale. Panel data allows exploring variables that cannot be observed or measured or variables that evolve over time but not across groups or communities. In this chapter, two different techniques used in panel data analysis is explored: fixed effects (FE) and random effects (RE). First, theoretical concepts of panel data are presented. Additionally, a case study example of the use of this type of regression is provided. Panel data analysis is performed with R language, and a step-by-step approach is presented.

2019

Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks

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

Publication
Complex Networks and Their Applications VIII - Volume 1 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019, Lisbon, Portugal, December 10-12, 2019.

Abstract
Discovering communities in a network is a fundamental and important problem to complex networks. Find the most influential actors among its peers is a major task. If on one side, studies on community detection ignore the influence of actors and communities, on the other hand, ignoring the hierarchy and community structure of the network neglect the actor or community influence. We bridge this gap by combining a dynamic community detection method with a dynamic centrality measure. The proposed enhanced dynamic hierarchical community detection method computes centrality for nodes and aggregated communities and selects each community representative leader using the ranked centrality of every node belonging to the community. This method is then able to unveil, track, and measure the importance of main actors, network intra and inter-community structural hierarchies based on a centrality measure. The empirical analysis performed, using two temporal networks shown that the method is able to find and tracking community leaders in evolving networks. © 2020, Springer Nature Switzerland AG.

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

2018

A comprehensive workflow for enhancing business bankruptcy prediction

Authors
Sarmento, R; Trigo, L; Fonseca, L;

Publication
Intelligent Systems: Concepts, Methodologies, Tools, and Applications

Abstract
Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.