Myriam Delgado’s research while affiliated with Federal University of Technology of Paraná and other places

Location-Based Social Networks (LBSNs) can help model users’ interests in urban areas in several ways. In the present work, we focus on Interest Networks (iNETs), which result from modeling LBSN data into graphs. The present study provides insights into which areas are frequently visited together by getting data from two distinct LBSNs, Foursquare and Google Places. Although the studied LBSNs differ in nature, with data varying in regularity and purpose, both modeled iNETs revealed similar urban behavior patterns and were likewise impacted by socioeconomic and geographic factors. Also, we discuss the development of a tool to empower urban studies and the by-products of this research.

This study investigates methods using a global data source, Google Places, to identify culturally similar urban areas without relying on difficult-to-access data like user preferences shown through check-ins. We propose and assess a simple method requiring only information about place types and their frequency in the studied areas, and a more advanced method that enhances venue categories using Scenes Theory-it helps us understand the cultural significance of everyday urban life. We tested our methods in 14 cities worldwide and all US states. The results suggest that a straightforward approach based on category frequencies can highlight major cultural differences. However, the Scenes Theory-based method provides a better understanding of cultural nuances, as the ones supported by survey data.

O conhecimento a respeito das características dos diferentes grupos culturais que existem no mundo e a identificação de similaridades culturais entre suas respectivas áreas de ocupação podem trazer diversos benefícios econômicos e sociais, como a recomendação de locais sob critérios culturais. Pesquisas referentes ao estudo dessas diferentes culturas são realizadas, em grande parte, de maneira tradicional, as quais são caras e não escalam. Dessa forma, este trabalho consiste em obter características relevantes de áreas urbanas utilizando dados geolocalizados de fontes da web, e aplicar uma metodologia que enriquece esses dados obtidos para a geração de uma assinatura cultural de áreas urbanas. Em uma aplicação prática da proposta, o resultado se mostra muito coerente, separando os bairros de Curitiba em clusters com características culturais distintas.

As Redes Sociais Baseadas em Localização (LBSNs) são úteis na compreensão do comportamento urbano, oferecendo dados valiosos sobre preferências dos usuários. A modelagem desses dados em grafos, como as Redes de Interesse, permite percepções relevantes. Essas redes podem ser úteis para, por exemplo, recomendações de áreas urbanas, previsões de mobilidade e formulação de políticas públicas. Este estudo compara redes de interesse de duas LBSNs distintas, Foursquare e Google Places, usando dados de check-ins e avaliações de estabelecimentos. Embora as LBSNs estudadas sejam diferentes em natureza, com dados diferindo em regularidade e propósito, ambas as redes de interesse modeladas revelaram padrões similares de comportamento urbano. Fatores socioeconômicos e geográficos também mostraram impacto semelhante nas redes de interesse estudadas.

As Artificial Neural Network (ANN) smart applications become highly popular, some drawbacks of traditional cloud-based deployment emerge. Issues like high monetary cost, for storing and running applications, low privacy on data and models, and high latency experienced by cloud-based neural networks, might difficult their use, leading to poor user experiences. This paper explores, in the context of smart homes, ANN-based models running offline and a fog topology as an alternative to online learning methods. The work proposes four different approaches and compares their performance when solving eight distinct classification problems. Each problem regards activities performed in one out of eight rooms in the addressed smart home. Results show that hybridization of an autoencoder model with MLP-based classifiers can detect rare activities and provide good results for almost all rooms particularly when encompassing suitable pipelines. In the online context, although the performance of the hybrid approach decreases, as expected, some interesting insights result from experiments, particularly the fact that fog computing provides results not too far from cloud systems, yet demanding less resources. The online fog-based proposal appears therefore as an alternative for restricted computing on streaming data.KeywordsMultilayer perceptronAutoencoderClassification problemsUser activities

A better understanding of the behavior of tourists is strategic for improving services in the competitive and important economic segment of global tourism. Critical studies in the literature often explore the issue using traditional data, such as questionnaires or interviews. Traditional approaches provide precious information; however, they impose challenges to obtaining large-scale data, making it hard to study worldwide patterns. Location-based social networks (LBSNs) can potentially mitigate such issues due to the relatively low cost of acquiring large amounts of behavioral data. Nevertheless, before using such data for studying tourists' behavior, it is necessary to verify whether the information adequately reveals the behavior measured with traditional data -- considered the ground truth. Thus, the present work investigates in which countries the global tourism network measured with an LBSN agreeably reflects the behavior estimated by the World Tourism Organization using traditional methods. Although we could find exceptions, the results suggest that, for most countries, LBSN data can satisfactorily represent the behavior studied. We have an indication that, in countries with high correlations between results obtained from both datasets, LBSN data can be used in research regarding the mobility of the tourists in the studied context.

Since its inception in 2013, the Travelling Thief Problem (TTP) has been widely studied as an example of problems with multiple interconnected sub-problems. The dependency in this model arises when tying the travelling time of the "thief" to the weight of the knapsack. However, other forms of dependency as well as combinations of dependencies should be considered for investigation, as they are often found in complex real-world problems. Our goal is to study the impact of different forms of dependency in the TTP using a simple local search algorithm. To achieve this, we use Local Optima Networks, a technique for analysing the fitness landscape.

During the evolutionary process, algorithms based on probability distributions for generating new individuals suffer from computational burden due to the intensive computation of probability distribution estimations, particularly when using Probabilistic Graph Models (PGMs). In the Bayesian Optimisation Algorithm (BOA), for instance, determining the optimal Bayesian network structure by a given solution sample is an NP-hard problem. To overcome this issue, we consider a new BOA-based optimisation approach (FBOA) which explores the fact that patterns of PGM adjustments can be used as a guide to reduce the frequency of PGM updates because significant changes in PGM structure might not occur so frequently, and because they can be particularly sparse at the end of evolution. In the present paper, this new approach is scrutinised in the search space of an NK-landscape optimisation problem for medium and large-size instances. Average gaps and success rates as well as the correlation between the landscape ruggedness of the problem and the expected runtime of FBOA and BOA are presented for medium-size instances. For large-size instances, optimisation results from FBOA and BOA are compared. The experiments show that, despite our FBOA being of almost three times faster than BOA, it still produces competitive optimisation results.

Collaborative Filtering (CF) can be understood as the process of predicting the preferences of users and deriving useful patterns by studying their activities. In the survey context, it can be used to predict answers to questions as combinations of other available answers. In this paper, we aim to test five CF-based algorithms (item-item, iterative matrix factorization, neural collaborative filtering, logistic matrix factorization, and an ensemble of them) to estimate scores in four survey applications (checkpoints) composed of 700,000 employee's ratings. These data have been collected from 2019 to 2020 by a large Brazilian tech company with more than 10,000 employees. The results show that collaborative filtering approaches provide relevant alternatives to score questions of surveys. They provided good quality estimates. This result can be further explored to eventually reduce the size of questionnaires, avoiding burden phenomena faced by respondents when dealing with large surveys.