Bernardo Pereira Nunes

Australian National University

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Co-Authors

Marco A. Casanova

Pontifical Catholic University of Rio de Janeiro

Sean Wolfgand Matsui Siqueira

Universidade Federal do Estado do Rio de Janeiro

Stefan Dietze

Heinrich Heine University Düsseldorf

Giseli Rabello Lopes

Universidade Federal do Rio de Janeiro

Luiz André P. Paes Leme

Universidade Federal Fluminense

Ricardo Kawase

L3S Research Center

Besnik Fetahu

Amazon (United States)

Marcelo Tibau

Universidade Federal do Estado do Rio de Janeiro

Terhi Nurmikko-Fuller

Australian National University

Rubén Manrique

Universidad de Los Andes

Cooperative Institutions

Pontifical Catholic University of Rio de Janeiro

Universidade Federal do Estado do Rio de Janeiro

Australian National University

Universidade Federal do Rio de Janeiro

Universidade Federal Fluminense

L3S Research Center

Leibniz University Hannover

Universidade de São Paulo

Universidade do Estado do Rio de Janeiro

Universidad de Los Andes

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Citation

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Not Another Hardcoded Solution to the Student Dropout Prediction Problem: A Novel Approach Using Genetic Algorithms for Feature Selection

Lecture notes in computer science (2022)

Yixin Cheng Bernardo Pereira Nunes Rubén Manrique

Dropout (neural networks)

Feature (linguistics)

10.1007/978-3-031-09680-8_23

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Citations (3)

Automatic Classification and Taxonomy Generation for Semi-structured Data

Bernardo Pereira Nunes Giseli Rabello Lopes Marco A. Casanova

The problem of data classification goes back to the definition of taxonomies covering knowledge areas. With the advent of the Web, the amount of data available increased several orders of magnitude, making manual data classification impossible. This work presents an approach based on the prototype theory to automatically classify semi-structured data, represented by frames, without any previous knowledge about structured classes. Our approach uses a variation of the K-Means algorithm that organizes a set of frames into classes, structured as a strict hierarchy.

Semi-structured data

10.1109/cit/iucc/dasc/picom.2015.30

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Citations (1)

Automatic classification of documents in cold-start scenarios

Ricardo Kawase Marco Fisichella Bernardo Pereira Nunes Kyung-Hun Ha Markus Bick

Document classification is key to ensuring quality of any digital library. However, classifying documents is a very time-consuming task. In addition, few or none of the documents in a newly created repository are classified. The non-classification of documents not only prevents users from finding information but also hinders the system's aptitude to recommend relevant items. Moreover, the lack of classified documents prevents any kind of machine learning algorithm to automatically annotate these items. In this work, we propose a novel approach to automatically classifying documents that differs from previous works in the sense that it exploits the wisdom of the crowds available on the Web. Our proposed strategy adapts an automatic tagging approach combined with a straightforward matching algorithm to classify documents in a given domain classification. To validate our findings, we compared our methods against the existing and performed a user evaluation with 61 participants to estimate the quality of the classifications. Results show that, in 72% of the cases, the automatic classification is relevant and well accepted by participants. In conclusion, automatic classification can facilitate access to relevant documents.

Document classification

10.1145/2479787.2479789

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Citations (6)

SCS Connector - Quantifying and Visualising Semantic Paths Between Entity Pairs

Lecture notes in computer science (2014)

Bernardo Pereira Nunes José Herrera Davide Taibi Giseli Rabello Lopes Marco A. Casanova

Interface (matter)

10.1007/978-3-319-11955-7_67

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Citations (13)

An Educational Game Based on Images and Semantic Web Technologies

Bernardo Pereira Nunes Giseli Rabello Lopes Terhi Nurmikko-Fuller Marco A. Casanova Sean Wolfgand Matsui Siqueira

ISCOOL is an interactive educational game for text analysis and interpretation. It draws from several reference datasets, providing users with information about people, organisations and locations, as well as word definitions and historical facts that serve as the basis for reading comprehension and provide a wider context for information to be accessed, interpreted and understood. In game-play, users choose images to best illustrate a read text. The interactivity of the game encourages users to test their knowledge, and critically analyse what was read. ISCOOL was assessed by students with low literacy levels in English. The results show high levels of acceptance and applicability to genuine learning activities.

Interactivity

Educational game

Game-Based Learning

10.1109/icalt.2016.18

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Citations (0)

Learning in Communities: How Do Outstanding Users Differ From Other Users?

Thiago Baesso Procaci Sean Wolfgand Matsui Siqueira Bernardo Pereira Nunes

This paper reports on an investigation into outstanding and ordinary users of two Question & Answer (Q&A) communities. Considering some learning-related perspectives such as participation, linguistic traits, social ties, influence, and focus, we found that outstanding users are (i) more likely to engage in discussions; (ii) they tend to use more sophisticated linguistic traits; (iii) their inclusion into a discussion results in longer debates; (iv) they value the diversity of their connections; (v) they participate in several topics, rather than one specialist niche. These findings allow us to use behavioral patterns to predict whether a given user is outstanding and also predict which answer gives a definitive solution for a question.

Value (mathematics)

10.1109/icalt.2018.00048

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Citations (6)

MFI-TransSW+: Efficiently Mining Frequent Itemsets in Clickstreams

Lecture notes in business information processing (2017)

Franklin A. de Amorim Bernardo Pereira Nunes Giseli Rabello Lopes Marco A. Casanova

10.1007/978-3-319-53676-7_7

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Citations (2)

Annotation tool for enhancing e-learning courses

Lecture notes in computer science (2012)

Bernardo Pereira Nunes Ricardo Kawase Stefan Dietze Gilda Helena Bernardino de Campos Wolfgang Nejdl

Digital Learning

10.15488/1330

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Citations (8)

Cite4Me: Semantic Retrieval and Analysis of Scientific Publications

Bernardo Pereira Nunes Besnik Fetahu Marco A. Casanova

This paper presents the Cite4Me Web application and its features created for the LAK Challenge 2013. The Web application focuses on two main directions: (i) interlinking of the LAK dataset with related data sources from the Linked Open Data cloud; and (ii) providing innovative search, visualization, retrieval and recommendation of scientific publications from the LAK dataset and related interlinked resources. Our approach is based on semantic and cooccurrence relations to provide new browsing experiences to Web users and an overview of scientific data available. Furthermore, we present a detailed analysis of the LAK dataset along with applications which contributes to the development of the learning analytics field.

Linked Data