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Heitor Ramos

UFMG - The Federal University of Minas Gerais, DCC - Departamento de Ciência da Computação, Faculty Member

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I am a passionate Computer Science Professor currently working at the Federal University of Minas Gerais, Brazil, with 10 years of experience in teaching Computer Science classes, advising graduate students, and coordinating R

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Chiara Flora Bassignana

Helga Gruberg Cazon

Universidad Catolica Boliviana

AGNES GISBERT KAPINGA

André Gonçalves

Penn State University

Nina Isabella Moeller

Coventry University

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Papers by Heitor Ramos

Agro 4.0: A Green Information System for Sustainable Agroecosystem Management

Cornell University - arXiv, Jul 11, 2019

Agriculture is one of the most critical activities developed today by humankind and is in constan... more Agriculture is one of the most critical activities developed today by humankind and is in constant technical evolution to supply food and other essential products to everlasting and increasing demand. New machines, seeds, and fertilizers were developed to increase the productivity of cultivated areas. The idea of sustainable development became widely known after the United Nations Conference on Environment and Development in Rio de Janeiro, Brazil, in 1992 (ECO-92). It is estimated that by 2050 we will have a population of 9 billion people and the production of food to meet this demand must occur sustainably. To achieve this goal, it is paramount the adoption of sustainable management techniques for agroecosystems. However, this is a complex task due to a large number of variables involved. One of the solutions for the handling and treatment of such diverse data is the use of Green IS. In this work, we adopt a methodology called Indicators of Sustainability in Agroecosystems (Indicadores de Sustentabilidade em Agroecossistemas-ISA), implement an information system based on it and apply Data Science techniques over the gathered data-from 100 real rural properties-to compute which are the most relevant ISA Indicators for the final ISA Sustainability Index Score. As a result, we have developed a set of tools for data collection, processing, visualization, and analysis of the sustainability of a rural property or region, following the ISA methodology. We also have that with only 7 of the 21 Indicators present in ISA we can identify the level of sustainability in more than 90% of cases, allowing for a new discussion about shrinking the amount of data needed for the computation of ISA, or remodelling the final computation of the Sustainability Index so other Indicators can be more expressive. Users of the solutions developed in this work can identify best practices for sustainability in participating agroecosystems.

CALT: Uma Ferramenta Automática para Cobrança do ICMS em Operações Interestaduais

Anais do Workshop de Computação Aplicada em Governo Eletrônico (WCGE 2021)

Devido a mudanças constantes na legislação e a complexidade do sistema tributário brasileiro, det... more Devido a mudanças constantes na legislação e a complexidade do sistema tributário brasileiro, determinar a tributação de um produto não é uma tarefa trivial. Com isso, este artigo apresenta uma proposta de ferramenta para o cálculo automático dos tributos devidos nas operações interestaduais destinadas ao estado de Alagoas. Mediante o uso técnicas de agrupamento e classificação, o trabalho proposto pretende aprender os padrões de tributação em notas fiscais previamente calculadas e, deste modo, fornecer um modelo capaz de predizer a tributação de um produto. Dessa forma, o cálculo automático oferece uma maneira facilitada de obter a cobrança do ICMS com uma melhora imensurável no tempo de processamento das notas eletrônicas em relação ao cálculo manual, que de forma prática não era realizado em todas as notas.

The Quest for Sense: Physical phenomena Classification in the Internet of things

2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS)

This paper investigates the precise identification of physical phenomena in the Internet of Thing... more This paper investigates the precise identification of physical phenomena in the Internet of Things (IoT) context, which is one of the main challenges when dealing with the massive scale of IoT data. For this, we use information theory quantifiers in the characterization and classification of physical phenomena to minimize the effects of the lack of proper descriptions and the high heterogeneity of IoT sensors. Thus, by understanding the dynamics behind physical phenomena, we perform the classification of sensor data based on their expected behavior, not their data points. By using a simple classification algorithm, we show that the behavioral dynamics of some physical phenomena are more affected by different geographical regions than others. This gives a classification accuracy of 75% when all phenomena are considered and of 93% when considering only the invariant ones, with a worst case of false positives of 12%. This result indicates the high potential of our technique to correctly identify physical phenomena from sensor data, a fundamental issue for several applications, even in an unreliable IoT environment.

A New Similarity Space Tailored for Supervised Deep Metric Learning

ACM Transactions on Intelligent Systems and Technology, Nov 16, 2020

We propose a novel deep metric learning method. Differently from many works on this area, we defi... more We propose a novel deep metric learning method. Differently from many works on this area, we defined a novel latent space obtained through an autoencoder. The new space, namely S-space, is divided into different regions that describe the positions where pairs of objects are similar/dissimilar. We locate makers to identify these regions. We estimate the similarities between objects through a kernel-based t-student distribution to measure the markers' distance and the new data representation. In our approach, we simultaneously estimate the markers' position in the S-space and represent the objects in the same space. Moreover, we propose a new regularization function to avoid similar markers to collapse altogether. We present evidences that our proposal can represent complex spaces, for instance, when groups of similar objects are located in disjoint regions. We compare our proposal to 9 different distance metric learning approaches (four of them are based on deep-learning) on 28 real-world heterogeneous datasets. According to the four quantitative metrics used, our method overcomes all the nine strategies from the literature.

Aprendizado Federado aplicado à Internet das Coisas

Minicursos do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, 2021

The main objective of this short course is to present the main fundamentals of Federated Learning... more The main objective of this short course is to present the main fundamentals of Federated Learning (FL), covering the tools and steps necessary for the development of applications and services aimed at the Internet of Things (IoT). The concepts covered during this short course include introducing Machine Learning (centralized and distributed), the state-of-the-art in FL, an overview of existing work, challenges, and future perspectives for advancing the field. FL is a recent research field that still has a lot to be explored. Therefore, it has research fronts that remain open, with non-trivial challenges. Thus, this short course intends to deepen the following questions: (i) How does FL differ from centralized and decentralized Machine Learning? (ii) What are the main characteristics of FL that enable the development of applications in the context of IoT. (iii) What are the technological challenges for implementing FL? (iv) What are the main methodologies used for the development of IoT applications based on FL? (v) What are the main LF research problems? (vi) What are the representative applications of this area? Resumo O objetivo principal deste minicurso é apresentar os principais fundamentos do Aprendizado Federado (Federated Learning-FL), abrangendo as ferramentas e passos necessários para o desenvolvimento de aplicações e serviços voltados à Internet das Coisas (Internet of Things-IoT). Os conceitos abordados durante o presente minicurso incluem uma introdução à Aprendizado de Máquina (centralizada e distribuída), o estado-da-arte

Uma Abordagem para Geração de Séries Temporais de Mobilidade Urbana Baseada em Aprendizado Profundo

Anais do V Workshop de Computação Urbana (CoUrb 2021), 2021

Um dos grandes desafios na coleta e divulgação de dados de mobilidade urbana está no fato de que ... more Um dos grandes desafios na coleta e divulgação de dados de mobilidade urbana está no fato de que esses dados possuem informações que podem comprometer a privacidade dos usuários. Uma alternativa a esse problema é a geração de dados sintéticos que possam preservar as características dos dados reais. Este trabalho analisa a eficácia da utilização de um modelo estatístico clássico e propõe o uso de algoritmos de aprendizado profundo, como as Redes Generativas Adversarias (GANs, em inglês) para geração de séries temporais baseadas em dados de mobilidade urbana. As séries geradas foram comparadas com os dados reais por meio de uma análise visual e uma análise quantitativa. Os resultados mostraram que os modelos baseados em aprendizado profundo são capazes de gerar dados com as mesmas características dos dados reais.

Effects of population mobility on the COVID-19 spread in Brazil

PLOS ONE, 2021

This article proposes a study of the SARS-CoV-2 virus spread and the efficacy of public policies ... more This article proposes a study of the SARS-CoV-2 virus spread and the efficacy of public policies in Brazil. Using both aggregated (from large Internet companies) and fine-grained (from Departments of Motor Vehicles) mobility data sources, our work sheds light on the effect of mobility on the pandemic situation in the Brazilian territory. Our main contribution is to show how mobility data, particularly fine-grained ones, can offer valuable insights into virus propagation. For this, we propose a modification in the SENUR model to add mobility information, evaluating different data availability scenarios (different information granularities), and finally, we carry out simulations to evaluate possible public policies. In particular, we conduct a case study that shows, through simulations of hypothetical scenarios, that the contagion curve in several Brazilian cities could have been milder if the government had imposed mobility restrictions soon after reporting the first case. Our result...

IoT Botnet Detection based on Anomalies of Multiscale Time Series Dynamics

IEEE Transactions on Knowledge and Data Engineering, 2022

White Noise Test from Ordinal Patterns in the Entropy–Complexity Plane

International Statistical Review, 2022

Enriching Traffic Information with a Spatiotemporal Model based on Social Media

2018 IEEE Symposium on Computers and Communications (ISCC), 2018

In this work, we argue that Location-Based Social Media (LBSM) feeds may offer a new layer to imp... more In this work, we argue that Location-Based Social Media (LBSM) feeds may offer a new layer to improve traffic and transit comprehension. Initially, we showed the significant correlation between Twitter's feed and traditional traffic sensors. Then, we presented the Twitter MAPS (T-MAPS) a low-cost spatiotemporal model to improve the description of traffic conditions through tweets. T-MAPS enhance traditional traffic sensors by carrying the human lens into the transportation system. We conducted a case study by running T-MAPS and Google Maps route recommendation, in which, we showed T-MAPS viability, as an additional traffic descriptor. As a result, we noticed the median of route similarity reached 62%, and for a quarter of the evaluated trajectories, the similarity achieved between 75% and 100%. Also, we presented three route description services, based on natural language analyzes, Route Sentiment (RS), Route Information (RI), and Area' Tags (AT) aiming to enhance the route information.

Event Detection in Social Media Through Phase Transition of Bigrams Entropy

2018 IEEE Symposium on Computers and Communications (ISCC), 2018

A social network is a valuable source of data that is useful to understand a wide range of events... more A social network is a valuable source of data that is useful to understand a wide range of events happening around the world, with each user being a potential contributor to accomplish this task. This work proposes a novel method to detect events in Twitter based on the calculation of entropy of the content of tweets in order to classify the most shared topic as an event or not. We observed that the entropy of the bigrams extracted from tweets are subject to a continuous phase transition when social media users start to react and interact with an event that is taking place. Hence, we propose a method to detect this phase transition, and consequently detect an event, and extract the keywords related to the corresponding event. We compared the performance of our method to other approaches of the literature and we observed that our method is the more regular among three metrics and reached the best overall performance. Furthermore, we present evidence that our method is very sensitive to correctly detect events that occur almost at same time.

Transition Graphs for SAR Image Texture Characterization: An Exploratory Study

2021 2nd China International SAR Symposium (CISS), 2021

We investigate the use of ordinal pattern transition graphs in the characterization of PolSAR ima... more We investigate the use of ordinal pattern transition graphs in the characterization of PolSAR image textures. Chagas et al. [4] proposed WATG, a transition graph analysis which includes the amplitude information of texture samples in the graph edges. This approach led to good results in the characterization and classification of data from urban, oceanic, and vegetation regions. In this work, we study how this approach behaves when considering the three intensity channels of PolSAR data, and its behavior when the samples are not homogeneous.

Exploiting Daily Trajectories for Efficient Routing in Vehicular Ad Hoc Networks

2018 IEEE International Conference on Communications (ICC), 2018

Vehicular ad hoc network (VANET) is a fundamental building block in the design of an Intelligent ... more Vehicular ad hoc network (VANET) is a fundamental building block in the design of an Intelligent Transportation System (ITS). Considering the various applications in ITS, a VANET must provide communication solutions in different situations. In particular, we are interested in dealing with situations where the unicast communication problem occurs in sparse network scenarios. In this paper, we shed light on the need for mechanisms that take into account the vehicles' trajectories. We present a characterization that shows the spatiotemporal regularity of the vehicle movement. We propose a new methodology for identifying the spatiotemporal relationship between vehicle trajectories. We create a novel method named ROSTER for unicast routing in sparse VANETs. Simulations results show that the proposed solution considerably reduces message overhead in the network by maintaining compatible levels of delivery rate in comparison with other protocols.

Dynamic Temporal Mix-Zone Placement Approach for Location-Based Services Privacy

2019 IEEE Latin-American Conference on Communications (LATINCOM), 2019

This paper proposes the Dynamic Temporal Mix-Zone Placement (DTMZP), a novel heuristic approach f... more This paper proposes the Dynamic Temporal Mix-Zone Placement (DTMZP), a novel heuristic approach for the optimal mix-zone placement problem in Location-Based Services privacy. Mix-zone is a privacy model that changes the pseudonym of $n$ mobile entities (e.g., vehicles) that cross a specific geographic region at a given time interval. We present a formal definition of DTMZP and evaluate it through simulations using the Cab-spotting dataset. We evaluate DTMZP considering the mix-zones coverage and pseudonymisation rates. The obtained results reveal a coverage rate of 88.56% and a pseudonymisation rate of 77.86% in the best scenario.

A non intrusive low cost Arduino-based three phase sensor kit for electric power measuring

2017 2nd International Multidisciplinary Conference on Computer and Energy Science (SpliTech), 2017

This article presents a kit to collect data of electric loads using a device that measures single... more This article presents a kit to collect data of electric loads using a device that measures single-phase data and and second device that measures the input of three-phase main power supply of a house. To collect the data, we used current sensors based on an open magnetic core to measure the electromagnetic field induced by the current in the electric conducting wire in a non intrusive way. In particular, each sensor from the three-phase device wraps/encloses each phase without alignment. We conducted a data analysis for validation. Initially, we carried out a statistical analysis of the data on the three-phase device resulting an error of 9, 2% constituted by the noise of the sensors and Arduino, which required a calibration. The calibration was made by software. We present a method to calculate the neutral RMS without complex numbers using (Analysis of Variance) ANOVA and post hoc Tukey's multiple comparison test to assert the differences of measures among phases. We managed to ...

A MS-Assisted GPS System For Low Power Location-Based Mobile Applications

Location based applications have gained much attention in recent years mainly with the advent of ... more Location based applications have gained much attention in recent years mainly with the advent of mobile devices such as smart-phones, PC-tablets, and netbooks. These devices facilitate the position data collection and allows some applications to make use of this data. For instance, mobile search is taking advantage of the local information to improve the user experience. Among the most common location sensors, GPS is the most accurate providing very small location error and allowing, for instance, very high accurate applications such as turn-by-turn navigation. Therefore, the sensors commonly used to collect location data are very energy hungry and tend to deplete the device’s battery very quickly. Although GPS is the most accurate current location sensor, it is also the most energy hungry, thus, nowadays the use of GPS sensor is very restricted in mobile application scenarios. Bearing in mind this constrained scenario, this work aim at studying the GPS theory and proposing some alt...

Author Correction: A detailed characterization of complex networks using Information Theory

Scientific Reports, 2021

An amendment to this paper has been published and can be accessed via a link at the top of the pa... more

On the Comparison of the Social Dynamic of Large Cities through Participatory Sensing

IEEE Latin America Transactions, 2017

The dynamics of cities have been studied over the years with various applications such as urban p... more The dynamics of cities have been studied over the years with various applications such as urban planning, disease propagation, traffic forecasting, local recommendations and studies of human social behavior. However, with the technological evolution, especially the popularization of smartphones and the Internet, a new opportunity is presented to carry out such studies: the use of social media data for the population study. In this context, this paper aims to present a new way to compare cities, using as similarity measure of the pattern of social mobility of its inhabitants. To validate the study, 1,601,323 Foursquare check-ins were used spread over 10 cities in a period of 33 days.

Social Mix-zones

Proceedings of the 18th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, 2021

In many different contexts, the encounter between two or more individuals opens a window in which... more In many different contexts, the encounter between two or more individuals opens a window in which information can be exchanged. Considering Mobile Ad hoc Networks (MANETs) scenarios, encounters-also called contacts-are used to transfer data between nodes (the users). In more recent cases, tracing contacts between individuals has shown to be a strong strategy in mapping the transmission of contagious diseases, such as COVID-19. However, sharing contact data can impose threats to the safety of participants regarding their social and mobility behavior. As an example, we can infer acquaintances, as well as home and work locations. This work presents a strategy to anonymize contact tracing data by utilizing mix-zones, a well-defined concept to anonymize data in a given region. Called social mix-zones, it considers the number of contacts happening in a location, producing anonymized data and protecting the personal integrity of the individuals. We validate the proposal using two real contact tracing data, showing that social mix-zones can cover a large portion of contacts, reducing the risk of malicious location attacks.

GIN: Better going safe with personalized routes

2020 IEEE Symposium on Computers and Communications (ISCC), 2020

Contextual data characterize distinct regions of the city, allowing them to differentiate them ac... more Contextual data characterize distinct regions of the city, allowing them to differentiate them according to security, entertainment, services, among others. Using contextual data to suggest routes helps to understand new aspects of a city that can change users’ perceptions of different routes. The impact of each type of contextual data may vary according to the user’s profile, which is not taken into account in most of the systems proposed by the literature. Besides, it is necessary to consider the behavior of contextual data, which changes according to the type of data. To tackle the problems mentioned above, we propose a route suggestion system with space-time risk, called GIN. The system consists of three modules, namely: identification of contextual windows, context mapping, and route personalization. Moreover, we propose a strategy to decrease the number of route requests to improve system scalability. The results show that the system adapts to sensitive changes in user’s profiles. We obtained promising by using the behavior of contextual data to avoid unnecessary requests. This strategy allowed a reduction of up to 50% of requests made to the system.

Agro 4.0: A Green Information System for Sustainable Agroecosystem Management

Cornell University - arXiv, Jul 11, 2019

Agriculture is one of the most critical activities developed today by humankind and is in constan... more Agriculture is one of the most critical activities developed today by humankind and is in constant technical evolution to supply food and other essential products to everlasting and increasing demand. New machines, seeds, and fertilizers were developed to increase the productivity of cultivated areas. The idea of sustainable development became widely known after the United Nations Conference on Environment and Development in Rio de Janeiro, Brazil, in 1992 (ECO-92). It is estimated that by 2050 we will have a population of 9 billion people and the production of food to meet this demand must occur sustainably. To achieve this goal, it is paramount the adoption of sustainable management techniques for agroecosystems. However, this is a complex task due to a large number of variables involved. One of the solutions for the handling and treatment of such diverse data is the use of Green IS. In this work, we adopt a methodology called Indicators of Sustainability in Agroecosystems (Indicadores de Sustentabilidade em Agroecossistemas-ISA), implement an information system based on it and apply Data Science techniques over the gathered data-from 100 real rural properties-to compute which are the most relevant ISA Indicators for the final ISA Sustainability Index Score. As a result, we have developed a set of tools for data collection, processing, visualization, and analysis of the sustainability of a rural property or region, following the ISA methodology. We also have that with only 7 of the 21 Indicators present in ISA we can identify the level of sustainability in more than 90% of cases, allowing for a new discussion about shrinking the amount of data needed for the computation of ISA, or remodelling the final computation of the Sustainability Index so other Indicators can be more expressive. Users of the solutions developed in this work can identify best practices for sustainability in participating agroecosystems.

CALT: Uma Ferramenta Automática para Cobrança do ICMS em Operações Interestaduais

Anais do Workshop de Computação Aplicada em Governo Eletrônico (WCGE 2021)

Devido a mudanças constantes na legislação e a complexidade do sistema tributário brasileiro, det... more Devido a mudanças constantes na legislação e a complexidade do sistema tributário brasileiro, determinar a tributação de um produto não é uma tarefa trivial. Com isso, este artigo apresenta uma proposta de ferramenta para o cálculo automático dos tributos devidos nas operações interestaduais destinadas ao estado de Alagoas. Mediante o uso técnicas de agrupamento e classificação, o trabalho proposto pretende aprender os padrões de tributação em notas fiscais previamente calculadas e, deste modo, fornecer um modelo capaz de predizer a tributação de um produto. Dessa forma, o cálculo automático oferece uma maneira facilitada de obter a cobrança do ICMS com uma melhora imensurável no tempo de processamento das notas eletrônicas em relação ao cálculo manual, que de forma prática não era realizado em todas as notas.

The Quest for Sense: Physical phenomena Classification in the Internet of things

2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS)

This paper investigates the precise identification of physical phenomena in the Internet of Thing... more This paper investigates the precise identification of physical phenomena in the Internet of Things (IoT) context, which is one of the main challenges when dealing with the massive scale of IoT data. For this, we use information theory quantifiers in the characterization and classification of physical phenomena to minimize the effects of the lack of proper descriptions and the high heterogeneity of IoT sensors. Thus, by understanding the dynamics behind physical phenomena, we perform the classification of sensor data based on their expected behavior, not their data points. By using a simple classification algorithm, we show that the behavioral dynamics of some physical phenomena are more affected by different geographical regions than others. This gives a classification accuracy of 75% when all phenomena are considered and of 93% when considering only the invariant ones, with a worst case of false positives of 12%. This result indicates the high potential of our technique to correctly identify physical phenomena from sensor data, a fundamental issue for several applications, even in an unreliable IoT environment.

A New Similarity Space Tailored for Supervised Deep Metric Learning

ACM Transactions on Intelligent Systems and Technology, Nov 16, 2020

We propose a novel deep metric learning method. Differently from many works on this area, we defi... more We propose a novel deep metric learning method. Differently from many works on this area, we defined a novel latent space obtained through an autoencoder. The new space, namely S-space, is divided into different regions that describe the positions where pairs of objects are similar/dissimilar. We locate makers to identify these regions. We estimate the similarities between objects through a kernel-based t-student distribution to measure the markers' distance and the new data representation. In our approach, we simultaneously estimate the markers' position in the S-space and represent the objects in the same space. Moreover, we propose a new regularization function to avoid similar markers to collapse altogether. We present evidences that our proposal can represent complex spaces, for instance, when groups of similar objects are located in disjoint regions. We compare our proposal to 9 different distance metric learning approaches (four of them are based on deep-learning) on 28 real-world heterogeneous datasets. According to the four quantitative metrics used, our method overcomes all the nine strategies from the literature.

Aprendizado Federado aplicado à Internet das Coisas

Minicursos do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, 2021

The main objective of this short course is to present the main fundamentals of Federated Learning... more The main objective of this short course is to present the main fundamentals of Federated Learning (FL), covering the tools and steps necessary for the development of applications and services aimed at the Internet of Things (IoT). The concepts covered during this short course include introducing Machine Learning (centralized and distributed), the state-of-the-art in FL, an overview of existing work, challenges, and future perspectives for advancing the field. FL is a recent research field that still has a lot to be explored. Therefore, it has research fronts that remain open, with non-trivial challenges. Thus, this short course intends to deepen the following questions: (i) How does FL differ from centralized and decentralized Machine Learning? (ii) What are the main characteristics of FL that enable the development of applications in the context of IoT. (iii) What are the technological challenges for implementing FL? (iv) What are the main methodologies used for the development of IoT applications based on FL? (v) What are the main LF research problems? (vi) What are the representative applications of this area? Resumo O objetivo principal deste minicurso é apresentar os principais fundamentos do Aprendizado Federado (Federated Learning-FL), abrangendo as ferramentas e passos necessários para o desenvolvimento de aplicações e serviços voltados à Internet das Coisas (Internet of Things-IoT). Os conceitos abordados durante o presente minicurso incluem uma introdução à Aprendizado de Máquina (centralizada e distribuída), o estado-da-arte

Uma Abordagem para Geração de Séries Temporais de Mobilidade Urbana Baseada em Aprendizado Profundo

Anais do V Workshop de Computação Urbana (CoUrb 2021), 2021

Um dos grandes desafios na coleta e divulgação de dados de mobilidade urbana está no fato de que ... more Um dos grandes desafios na coleta e divulgação de dados de mobilidade urbana está no fato de que esses dados possuem informações que podem comprometer a privacidade dos usuários. Uma alternativa a esse problema é a geração de dados sintéticos que possam preservar as características dos dados reais. Este trabalho analisa a eficácia da utilização de um modelo estatístico clássico e propõe o uso de algoritmos de aprendizado profundo, como as Redes Generativas Adversarias (GANs, em inglês) para geração de séries temporais baseadas em dados de mobilidade urbana. As séries geradas foram comparadas com os dados reais por meio de uma análise visual e uma análise quantitativa. Os resultados mostraram que os modelos baseados em aprendizado profundo são capazes de gerar dados com as mesmas características dos dados reais.

Effects of population mobility on the COVID-19 spread in Brazil

PLOS ONE, 2021

This article proposes a study of the SARS-CoV-2 virus spread and the efficacy of public policies ... more This article proposes a study of the SARS-CoV-2 virus spread and the efficacy of public policies in Brazil. Using both aggregated (from large Internet companies) and fine-grained (from Departments of Motor Vehicles) mobility data sources, our work sheds light on the effect of mobility on the pandemic situation in the Brazilian territory. Our main contribution is to show how mobility data, particularly fine-grained ones, can offer valuable insights into virus propagation. For this, we propose a modification in the SENUR model to add mobility information, evaluating different data availability scenarios (different information granularities), and finally, we carry out simulations to evaluate possible public policies. In particular, we conduct a case study that shows, through simulations of hypothetical scenarios, that the contagion curve in several Brazilian cities could have been milder if the government had imposed mobility restrictions soon after reporting the first case. Our result...

IoT Botnet Detection based on Anomalies of Multiscale Time Series Dynamics

IEEE Transactions on Knowledge and Data Engineering, 2022

White Noise Test from Ordinal Patterns in the Entropy–Complexity Plane

International Statistical Review, 2022

Enriching Traffic Information with a Spatiotemporal Model based on Social Media

2018 IEEE Symposium on Computers and Communications (ISCC), 2018

In this work, we argue that Location-Based Social Media (LBSM) feeds may offer a new layer to imp... more In this work, we argue that Location-Based Social Media (LBSM) feeds may offer a new layer to improve traffic and transit comprehension. Initially, we showed the significant correlation between Twitter's feed and traditional traffic sensors. Then, we presented the Twitter MAPS (T-MAPS) a low-cost spatiotemporal model to improve the description of traffic conditions through tweets. T-MAPS enhance traditional traffic sensors by carrying the human lens into the transportation system. We conducted a case study by running T-MAPS and Google Maps route recommendation, in which, we showed T-MAPS viability, as an additional traffic descriptor. As a result, we noticed the median of route similarity reached 62%, and for a quarter of the evaluated trajectories, the similarity achieved between 75% and 100%. Also, we presented three route description services, based on natural language analyzes, Route Sentiment (RS), Route Information (RI), and Area' Tags (AT) aiming to enhance the route information.

Event Detection in Social Media Through Phase Transition of Bigrams Entropy

2018 IEEE Symposium on Computers and Communications (ISCC), 2018

A social network is a valuable source of data that is useful to understand a wide range of events... more A social network is a valuable source of data that is useful to understand a wide range of events happening around the world, with each user being a potential contributor to accomplish this task. This work proposes a novel method to detect events in Twitter based on the calculation of entropy of the content of tweets in order to classify the most shared topic as an event or not. We observed that the entropy of the bigrams extracted from tweets are subject to a continuous phase transition when social media users start to react and interact with an event that is taking place. Hence, we propose a method to detect this phase transition, and consequently detect an event, and extract the keywords related to the corresponding event. We compared the performance of our method to other approaches of the literature and we observed that our method is the more regular among three metrics and reached the best overall performance. Furthermore, we present evidence that our method is very sensitive to correctly detect events that occur almost at same time.

Transition Graphs for SAR Image Texture Characterization: An Exploratory Study

2021 2nd China International SAR Symposium (CISS), 2021

We investigate the use of ordinal pattern transition graphs in the characterization of PolSAR ima... more We investigate the use of ordinal pattern transition graphs in the characterization of PolSAR image textures. Chagas et al. [4] proposed WATG, a transition graph analysis which includes the amplitude information of texture samples in the graph edges. This approach led to good results in the characterization and classification of data from urban, oceanic, and vegetation regions. In this work, we study how this approach behaves when considering the three intensity channels of PolSAR data, and its behavior when the samples are not homogeneous.

Exploiting Daily Trajectories for Efficient Routing in Vehicular Ad Hoc Networks

2018 IEEE International Conference on Communications (ICC), 2018

Vehicular ad hoc network (VANET) is a fundamental building block in the design of an Intelligent ... more Vehicular ad hoc network (VANET) is a fundamental building block in the design of an Intelligent Transportation System (ITS). Considering the various applications in ITS, a VANET must provide communication solutions in different situations. In particular, we are interested in dealing with situations where the unicast communication problem occurs in sparse network scenarios. In this paper, we shed light on the need for mechanisms that take into account the vehicles' trajectories. We present a characterization that shows the spatiotemporal regularity of the vehicle movement. We propose a new methodology for identifying the spatiotemporal relationship between vehicle trajectories. We create a novel method named ROSTER for unicast routing in sparse VANETs. Simulations results show that the proposed solution considerably reduces message overhead in the network by maintaining compatible levels of delivery rate in comparison with other protocols.

Dynamic Temporal Mix-Zone Placement Approach for Location-Based Services Privacy

2019 IEEE Latin-American Conference on Communications (LATINCOM), 2019

This paper proposes the Dynamic Temporal Mix-Zone Placement (DTMZP), a novel heuristic approach f... more This paper proposes the Dynamic Temporal Mix-Zone Placement (DTMZP), a novel heuristic approach for the optimal mix-zone placement problem in Location-Based Services privacy. Mix-zone is a privacy model that changes the pseudonym of $n$ mobile entities (e.g., vehicles) that cross a specific geographic region at a given time interval. We present a formal definition of DTMZP and evaluate it through simulations using the Cab-spotting dataset. We evaluate DTMZP considering the mix-zones coverage and pseudonymisation rates. The obtained results reveal a coverage rate of 88.56% and a pseudonymisation rate of 77.86% in the best scenario.

A non intrusive low cost Arduino-based three phase sensor kit for electric power measuring

2017 2nd International Multidisciplinary Conference on Computer and Energy Science (SpliTech), 2017

This article presents a kit to collect data of electric loads using a device that measures single... more This article presents a kit to collect data of electric loads using a device that measures single-phase data and and second device that measures the input of three-phase main power supply of a house. To collect the data, we used current sensors based on an open magnetic core to measure the electromagnetic field induced by the current in the electric conducting wire in a non intrusive way. In particular, each sensor from the three-phase device wraps/encloses each phase without alignment. We conducted a data analysis for validation. Initially, we carried out a statistical analysis of the data on the three-phase device resulting an error of 9, 2% constituted by the noise of the sensors and Arduino, which required a calibration. The calibration was made by software. We present a method to calculate the neutral RMS without complex numbers using (Analysis of Variance) ANOVA and post hoc Tukey's multiple comparison test to assert the differences of measures among phases. We managed to ...

A MS-Assisted GPS System For Low Power Location-Based Mobile Applications

Location based applications have gained much attention in recent years mainly with the advent of ... more Location based applications have gained much attention in recent years mainly with the advent of mobile devices such as smart-phones, PC-tablets, and netbooks. These devices facilitate the position data collection and allows some applications to make use of this data. For instance, mobile search is taking advantage of the local information to improve the user experience. Among the most common location sensors, GPS is the most accurate providing very small location error and allowing, for instance, very high accurate applications such as turn-by-turn navigation. Therefore, the sensors commonly used to collect location data are very energy hungry and tend to deplete the device’s battery very quickly. Although GPS is the most accurate current location sensor, it is also the most energy hungry, thus, nowadays the use of GPS sensor is very restricted in mobile application scenarios. Bearing in mind this constrained scenario, this work aim at studying the GPS theory and proposing some alt...

Author Correction: A detailed characterization of complex networks using Information Theory

Scientific Reports, 2021

An amendment to this paper has been published and can be accessed via a link at the top of the pa... more

On the Comparison of the Social Dynamic of Large Cities through Participatory Sensing

IEEE Latin America Transactions, 2017

The dynamics of cities have been studied over the years with various applications such as urban p... more The dynamics of cities have been studied over the years with various applications such as urban planning, disease propagation, traffic forecasting, local recommendations and studies of human social behavior. However, with the technological evolution, especially the popularization of smartphones and the Internet, a new opportunity is presented to carry out such studies: the use of social media data for the population study. In this context, this paper aims to present a new way to compare cities, using as similarity measure of the pattern of social mobility of its inhabitants. To validate the study, 1,601,323 Foursquare check-ins were used spread over 10 cities in a period of 33 days.

Social Mix-zones

Proceedings of the 18th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, 2021

In many different contexts, the encounter between two or more individuals opens a window in which... more In many different contexts, the encounter between two or more individuals opens a window in which information can be exchanged. Considering Mobile Ad hoc Networks (MANETs) scenarios, encounters-also called contacts-are used to transfer data between nodes (the users). In more recent cases, tracing contacts between individuals has shown to be a strong strategy in mapping the transmission of contagious diseases, such as COVID-19. However, sharing contact data can impose threats to the safety of participants regarding their social and mobility behavior. As an example, we can infer acquaintances, as well as home and work locations. This work presents a strategy to anonymize contact tracing data by utilizing mix-zones, a well-defined concept to anonymize data in a given region. Called social mix-zones, it considers the number of contacts happening in a location, producing anonymized data and protecting the personal integrity of the individuals. We validate the proposal using two real contact tracing data, showing that social mix-zones can cover a large portion of contacts, reducing the risk of malicious location attacks.

GIN: Better going safe with personalized routes

2020 IEEE Symposium on Computers and Communications (ISCC), 2020

Contextual data characterize distinct regions of the city, allowing them to differentiate them ac... more Contextual data characterize distinct regions of the city, allowing them to differentiate them according to security, entertainment, services, among others. Using contextual data to suggest routes helps to understand new aspects of a city that can change users’ perceptions of different routes. The impact of each type of contextual data may vary according to the user’s profile, which is not taken into account in most of the systems proposed by the literature. Besides, it is necessary to consider the behavior of contextual data, which changes according to the type of data. To tackle the problems mentioned above, we propose a route suggestion system with space-time risk, called GIN. The system consists of three modules, namely: identification of contextual windows, context mapping, and route personalization. Moreover, we propose a strategy to decrease the number of route requests to improve system scalability. The results show that the system adapts to sensitive changes in user’s profiles. We obtained promising by using the behavior of contextual data to avoid unnecessary requests. This strategy allowed a reduction of up to 50% of requests made to the system.