Papers by Gabriel Tavares
iSys, 2017
Online Social Networks (OSNs), such as Twitter, offer attractive means of social interactions and... more Online Social Networks (OSNs), such as Twitter, offer attractive means of social interactions and communications, but also raise privacy and security issues. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside a huge volume of data from several themes, topics, and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for TopicModeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph community architecture (density and degree) allows the indication of a topic strength and the classification of it as natural or artificial. The later created by the spammers on OSNs.
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This dissertation investigates the Quadratic Unconstrained Binary Optimization (QUBO) problem, i.... more This dissertation investigates the Quadratic Unconstrained Binary Optimization (QUBO) problem, i.e. the problem of minimizing a quadratic function in binary variables. QUBO is studied at two complementary levels. First, there is an algorithmic aspect that tells how to preprocess the problem, how to find heuristics, how to get improved bounds and how to solve the problem with all the above ingredients. Second, there is a practical aspect that uses QUBO to solve various applications from the engineering and social sciences fields including: via minimization, 2D/3D Ising models, 1D Ising chain models, image binarization, hierarchical clustering, greedy graph coloring/partitioning, MAX–2–SAT, MIN–VC, MAX–CLIQUE, MAX–CUT, graph stability and minimum k–partition. Several families of fast heuristics for QUBO are analyzed, which include a novel probabilistic based class of methods. It is shown that there is a unique maximal set of persistencies for the linearization model for QUBO. This set...
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ArXiv, 2021
Process discovery methods have obtained remarkable achievements in Process Mining, delivering com... more Process discovery methods have obtained remarkable achievements in Process Mining, delivering comprehensible process models to enhance management capabilities. However, selecting the suitable method for a specific event log highly relies on human expertise, hindering its broad application. Solutions based on Meta-learning (MtL) have been promising for creating systems with reduced human assistance. This paper presents a MtL solution for recommending process discovery methods that maximize model quality according to complementary dimensions. Thanks to our MtL pipeline, it was possible to recommend a discovery method with 92% of accuracy using light-weight features that describe the event log. Our experimental analysis also provided significant insights on the importance of log features in generating recommendations, paving the way to a deeper understanding of the discovery algorithms.
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In this study, an analysis of public telecommunications services in OECD countries is carried out... more In this study, an analysis of public telecommunications services in OECD countries is carried out. This evaluation is based on the relative efficiency calculated by the Data Envelopment Analysis (DEA) technique. In particular, the variable returns to scale DEA model under the radial outputs maximization is used. Several approaches, including reference frequency, cross-efficiency, super-efficiency and level efficiency, are used to evaluate the performance of OECD countries regarding the telecommunications services they provide.
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2020 2nd International Conference on Process Mining (ICPM), 2020
Assuring anomaly-free business process executions is a key challenge for many organizations. Trad... more Assuring anomaly-free business process executions is a key challenge for many organizations. Traditional techniques address this challenge using prior knowledge about anomalous cases that is seldom available in real-life. In this work, we propose the usage of word2vec encoding and One-Class Classification algorithms to detect anomalies by relying on normal behavior only. We investigated 6 different types of anomalies over 38 real and synthetics event logs, comparing the predictive performance of Support Vector Machine, One-Class Support Vector Machine, and Local Outlier Factor. Results show that our technique is viable for real-life scenarios, overcoming traditional machine learning for a wide variety of settings where only the normal behavior can be labeled.
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Operations Research Letters, 2013
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Socio-Economic Planning Sciences, 2008
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Discrete Optimization, 2008
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Computers & Operations Research, 2010
The paper presents a generic labeling algorithm for finding all nondominated outcomes of the mult... more The paper presents a generic labeling algorithm for finding all nondominated outcomes of the multiple objective integer knapsack problem (MOIKP). The algorithm is based on solving the multiple objective shortest path problem on an underlying network. Algorithms for ...
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Rutcor Research Report, 2005
Title: Local Search Heuristics for Unconstrained Quadratic Binary Optimization Authors: Endre Bor... more Title: Local Search Heuristics for Unconstrained Quadratic Binary Optimization Authors: Endre Boros, Peter L. Hammer, and Gabriel Tavares Abstract: We study and analyze a family of local search based heuristics for unconstrained quadratic binary optimization. These types of ...
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RUTCOR Research Report, …, 2006
TITLE: Preprocessing of Unconstrained Quadratic Binary Optimization AUTHORS: Endre Boros, Peter L... more TITLE: Preprocessing of Unconstrained Quadratic Binary Optimization AUTHORS: Endre Boros, Peter L. Hammer, Gabriel Tavares ABSTRACT: We propose several efficient preprocessing techniques for Unconstrained Quadratic Binary Optimization (QUBO), including the direct ...
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Journal of Heuristics, 2007
Abstract We present a family of local-search-based heuristics for Quadratic Uncon-strained Binary... more Abstract We present a family of local-search-based heuristics for Quadratic Uncon-strained Binary Optimization (QUBO), all of which start with a (possibly fractional) initial point, sequentially improving its quality by rounding or switching the value of one variable, until arriving to a local ...
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Papers by Gabriel Tavares