Adriano C. M. Pereira

• PhD
• Professor (Associate) at Federal University of Minas Gerais

In order to understand the conditions of school inclusion in Brazil for children with disabilities in light of the impact of the COVID-19 pandemic in 2020, a survey was carried out with 1044 caregivers of children enrolled in Basic Education in special or regular schools, private or private, from Elementary to High School, in Brazil. Of these, 568...

In order to understand the conditions of school inclusion in Brazil for children with disabilities in light of the impact of the COVID-19 pandemic in 2020, a survey was carried out with 1044 caregivers of children enrolled in Basic Education in special or regular schools, private or private, from Elementary to High School, in Brazil. Of these, 568...

Special education, from an inclusive perspective, should be present at all levels of education, from early childhood education to higher education. To ensure the inclusion of students with disabilities, it is essential to adapt the curriculum and monitor the development of each individual through the Individual Development Plan. This article presen...

Entender e prever resultados em futebol é um desafio pela complexidade do jogo em si, pelo número e diversidade dos jogadores envolvidos e mesmo por fatores externos que nem sempre são qualificáveis ou quantificáveis. Por outro lado, há todo um esforço no sentido de treinar os times e prepará-los para agir e reagir adequadamente a uma variedade de...

Nowadays, most e-commerce and entertainment services have adopted interactive Recommender Systems (RS) to guide the entire journey of users into the system. This task has been addressed as a Multi-Armed Bandit problem where systems must continuously learn and recommend at each iteration. However, despite the recent advances, there is still a lack o...

Online recommendation task has been recognized as a Multi-Armed Bandit (MAB) problem. Despite the recent advances, there still needs to be more consensus on the best practices to evaluate such bandit solutions. Recently, we observed two complementary frameworks that allow us to evaluate bandit solutions more accurately: iRec and OBP. The first has...

Portfolio optimisation is a core problem in quantitative finance and scenario generation techniques play a crucial role in simulating the future behaviour of the assets that can be used in allocation strategies. In the literature, there are different approaches to generating scenarios, from historical observations to models that predict the volatil...

Nowadays, Recommender Systems have played a crucial role in several entertainment scenarios by making personalised recommendations and guiding the entire users’ journey from their first interaction. Recent works have addressed it as a Contextual Bandit by providing a sequential decision model to explore items not tried yet (or not tried enough) or...

O mercado de capitais tem crescido a cada dia e desempenhado um importante papel entre as pessoas físicas que querem investir e as empresas que precisam capitalizar-se para expandir o seu negócio. Em especial, o investimento no mercado de ações é uma das formas mais rápidas e atrativas de obter lucros consideráveis em um curto espaço de tempo. Poré...

This work is a companion reproducibility paper that presents a framework to reproduce our previous experiments and results reported in Werneck et al. (2021). In that previous paper, we introduced a systematic mapping process of points-of-interest (POI) recommendation methods and provided a uniform evaluation methodology based on metrics covering di...

Recommender Systems (RSs) have assumed a crucial role in several digital companies by directly affecting their key performance indicators. Nowadays, in this era of big data, the information available about users and items has been continually updated and the application of traditional batch learning paradigms has become more restricted. In this sen...

The recent success of distinct e-commerce systems has driven many fashion companies into the online marketplace, allowing consumers to quickly and easily access a worldwide network of brands. However, to succeed in this scenario, it is necessary to provide a tailored, personalized, and reliable fashion shopping experience. Moreover, unfortunately,...

The World Wide Web emerged more than three decades ago, and even after all this time, accessibility is still challenging and a major obstacle for it to be universal and inclusive. Despite W3C eff orts to standardize and publish best practices towards a more accessible Web, we still lack methodologies and tools to collect Web content, measure and as...

The emergence of Location-based social networks (LBSNs) in recent years has boosted improvements in Recommender Systems for a new and specific task: the recommendation of points-of-interest (POI). Despite all the blunt advances recently observed, the area still lacks an updated and consolidated view about the main limitations, common assumptions, a...

Nowadays, recommender systems play an important role in several Location-Based Social Networks (LBSNs). The current advances have considered the trade-off between accuracy and diversity to help users to discover and explore new points-of-interest (POI). However, differently from traditional recommendation scenarios, other equally relevant dimension...

Forecasting financial time series is a problem studied by researchers from different fields, who are looking for effective ways to achieve financial gains. Over time, many authors conducted studies on the possible predictability of the series through different statistical tests, and recently several papers explore the application of machine learnin...

Automated stock trading is now the de-facto way that investors have chosen to obtain high profits in the stock market while keeping risk under control. One of the approaches is to create agents employing Reinforcement Learning (RL) algorithms to learn and decide whether or not to operate in the market in order to achieve maximum profit. Automated f...

Nowadays, Recommender Systems (RS) have been applied in most of Location-Based Social Networks (LBSNs). In general, these RSs aim to provide the best points-of-interest (POIs) to users, encouraging them to visit new places or explore more of their preferences. Despite the researches advances in this scenario, there is an opportunity for improvement...

Neste trabalho foi desenvolvido um metaclassificador baseado em métodos de inteligência computacional para prever tendências em séries temporais financeiras. O kernel do metaclassificador foi baseado na ferramenta (Weka). Sete classificadores foram combinados para realizar a metaclassificação. Testes foram realizados com nove ativos da Bolsa de Val...

A financial asset price-forecasting model based on damped driven harmonic oscillator is presented. It is inspired on the idea that in the price fluctuations a restoring force to a supposed fair price, besides inertia and dissipation, could take place. The model is evaluated in an emerging market, which is of the Brazilian stock exchange BM&FBovespa...

One of the solutions for handling and treating the diverse data related to the sustainability of an agroecosystem is the use of Information Systems and Internet of Things. In this work, we adopt a methodology called Indicators of Sustainability in Agroecosystems (Indicadores de Sustentabilidade em Agroecossistemas – ISA), implement an information s...

Este trabalho realiza a caracterização e análise dos dados de séries temporais de cotações históricas de 9 ativos (i.e., BBAS3, PETR4, JBSS3, KROT3, LAME4, MRVE4, NATU3, RADL3 e TIMP3) de segmentos distintos do índice Bovespa (Ibovespa) com a proposta de avaliar 8 modelos de classificação. Além disso, propõe a utilização da combinação de modelos de...

Abstract The competitive dynamics of the globalized market demand information on the internal and external reality of corporations. Information is a precious asset and is responsible for establishing key advantages to enable companies to maintain their leadership. However, reliable, rich information is no longer the only goal. The time frame to ext...

Online social networks provide a bunch of useful information that can help to solve different problems. In this context, we present a data characterization and analysis of Stocktwits, a financial online social network, in order to get insights and views that can be applied to financial markets and algorithmic trading (e-commerce). Furthermore, we c...

Nowadays, Recommender Systems (RSs) have been used to help users to discover relevant Points Of Interest (POI) in Location Based Social Network (LBSN), such as Yelp and FourSquare. Due to the main challenges of data sparsity and the geographic influence in this scenario, most of works about POI recommendations has only focused on improving the syst...

Recommender Systems (RS) have been applied in several scenarios due to their ability to satisfy the user's interest. Traditionally, they have been applied in scenarios such as entertainment and e-commerce, and nowadays, in Location Based Social Network (LBSN) to recommend points-of-interest (POIs). Despite the advances in traditional scenarios, the...

Nowadays, streaming companies, such as YouTube and Netflix, have prospered in Web multimedia market. In addition to presenting creative and engaging content, these companies have attracted users with a new standard to watch videos. Contrary to the traditional model, these services have used a nonlinear model, which users decide what, when and where...

Sentiment analysis has become a key tool for several social media applications, including, analysis of user’s opinions about products and services, support for politics during campaigns and even identification of market trending. Multiple existing sentiment analysis methods explore different techniques, usually relying on lexical resources or learn...

In this paper, we present a novel multimodal approach to estimate tension levels in news videos. The news media constitute a particular type of discourse and has become a central part of the modern-day lives of millions of people. In this context, it is important to study how the news industry affects human life and how it works. To support such a...

The competitive dynamics of the globalized market demand information on the internal and external reality of corporations. Information is a precious asset and is responsible for establishing key advantages to enable companies to maintain their leadership. However, reliable, rich information is no longer the only goal. The time frame to extract info...

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. It is estimated that by 2050 we will have a pop...

Background: The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to i...

Background:The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to inv...

Converting first-time users into recurring ones is key to the success of Web-based applications. This problem is known as Pure Cold-Start and it refers to the capability of Recommender Systems (RSs) to provide useful recommendations to users without historical data. Traditionally, RSs assume that non-personalized recommendation can mitigate this pr...

Atualmente grandes volumes de dados são gerados e coletados por meio de sensores, dispositivos e redes sociais. A capacidade de lidar com grandes massas de dados tornou-se um importante fator para o sucesso de muitas organizações, exigindo, cada vez mais, a utilização de processamento paralelo e distribuído. Para auxiliar os desenvolvedores a proje...

Nowadays, the non-linear multimedia services, such as Netflix and YouTube, have become increasingly popular. In this scenario, the users deciding what, where and when to consume the contents. This behavioral change led the content-producing companies outsource tasks related to video distribution to content-management companies, which are specialize...

The investment market has been growing every day, performing an important role in the lives of individuals and corporations. Therefore, there is a need to better understand the situations that occur in the capital market, by means of strategies and indicators that can assist in pattern recognition, analisys and investiment decisions. This work perf...

The success of Web-based applications depends on their ability to convert first-time users into recurring ones. This problem is known as Pure Cold-Start and it refers to the capability of Recommender Systems (RSs) providing useful recommendations to users without historical data. Traditionally, the systems assume that items biased by popularity, re...

O StockTwits é um microblog social cada vez mais popular e voltado para o público interessado em mercado financeiro. Neste trabalho, o porquê de empresas distintas serem citadas juntas no StockTwits é investigado. Além disso, analisamos como utilizar essa informação para auxiliar na tomada de decisão no mercado de ações. Especificamente, é proposta...

This paper proposes a combined approach of two machine learning techniques for financial time series classification. Boltzmann Restricted Machines (RBM) were used as the latent features extractor and Support Vector Machines (SVM) as the classifier. Tests were performed with real data of five assets from Brazilian Stock Market. The results of the co...

This paper presents Fraud-BNC, a customized Bayesian Network Classifier (BNC) algorithm for a real credit card fraud detection problem. The task of creating Fraud-BNC was automatically performed by a Hyper-Heuristic Evolutionary Algorithm (HHEA), which organizes the knowledge about the BNC algorithms into a taxonomy and searches for the best combin...

Recommender systems (RSs) have become essential tools in e-commerce applications, helping users in the decision-making process. Evaluation on these tools is, however, a major divergence point nowadays, since there is no consensus regarding which metrics are necessary to consolidate new RSs. For this reason, distinct frameworks have been developed t...

Desambiguar entidades em fluxos de mensagens extraídos de mídias sociais é um novo desafio na área de processamento de linguagem natural. O baixo teor informacional e a má formação sintática desse tipo de texto prejudicam a aplicação de abordagens clássicas para resolver este problema. Neste trabalho, é proposta uma modelagem contextual das mensage...

Recommender Systems (RSs) have assumed a prominent role in e-commerce domains, affecting decisively distinct business phases, such as convert new users into customers. The total absence of information about new users is one of the main challenges in this area, and it is known in the literature as Ramp-up Problem. In this scenario, non-personalized...

Recently, Multimedia Web content has gotten so much popular worldwide (e.g., YouTube and Netflix). Traditional corporations are also contracting online multimedia platforms to provide content to their users. These services are called non-UGC (non User Generated Content), where these companies produce and publish content for client consumption. In t...

This work addresses the development of a computational model of visual attention to perform the automatic summarization of digital videos from television archives. Although the television system represents one of the most fascinating media phenomena ever created, we still observe the absence of effective solutions for content-based information retr...

This work addresses the development of a novel computer-aided methodology for discourse analysis of TV newscasts. A TV newscast constitutes a particular type of discourse and has become a central part of the modern-day lives of millions of people. It is important to understand how this media content works and how it affects human life. To support t...

Due to the large amount of data generated in electronic transactions, to find the best set of features is an essential task to identify frauds. Fraud detection is a specific application of anomaly detection, characterized by a large imbalance between the classes, which can be a detrimental factor for feature selection techniques. In this work we ev...

A new differential equation based model for stock price trend forecast is proposed as a tool to investigate efficiency in an emerging market. Its predictive power showed statistically to be higher than the one of a completely random model, signaling towards the presence of arbitrage opportunities. Conditions for accuracy to be enhanced are investig...

It has been broadly discussed over the last years about the growth and popularity of the Internet and, more specifically, about the World Wide Web and its services and applications. Despite being common sense, acquiring indicators about this growth and characteristics of the whole Web, or event parts of it, is a big challenge, which can be explaine...

This paper presents a novel approach to perform sentiment analysis of news videos, based on the fusion of audio, textual and visual clues extracted from their contents. The proposed approach aims at contributing to the semiodiscoursive study regarding the construction of the ethos (identity) of this media universe, which has become a central part o...

Sistemas de informação que tem por objetivo realizar previsões em séries temporais financeiras e negociar a partir destas estão sujeitos a diversos riscos, pois o mercado acionário sofre influências de diferentes origens continuamente. O estudo das finanças quantitativas aborda métodos para tratamento de problemas desta natureza, fato que se dá, pr...

This paper presents a novel approach to perform sentiment analysis of news videos, based on the fusion of audio, textual and visual clues extracted from their contents. The proposed approach aims at contributing to the semiodiscoursive study regarding the construction of the ethos (identity) of this media universe, which has become a central part o...

This paper presents a novel approach to perform sentiment analysis of news videos, based on the fusion of audio, textual and visual clues extracted from their contents. The proposed approach aims at contributing to the semiodiscoursive study regarding the construction of the ethos (identity) of this media universe, which has become a central part o...

Sentiment analysis has become a key tool for several social media applications, including analysis of user's opinions about products and services, support to politics during campaigns and even for market trending. There are multiple existing sentiment analysis methods that explore different techniques, usually relying on lexical resources or learni...

This paper presents a multimodal approach to perform content-based sentiment analysis in TV newscasts videos in order to assist in the automatic estimation of polarity tension of TV news. The proposed approach aims to contribute to the semiodiscoursive study relative to the construction of ethos of those TV shows. In order to achieve this goal, it...

Sentiment analysis has been applied in many contexts, including user reviews analysis on products and services, trends and financial market moods. Established methods for sentiment analysis present a behavior that varies according to the application and its lexical base, generating different results among them. In this paper, a new sentiment analys...

The use of social networks and the Web is growing every day, generating a lot of data that can aggregate value to different applications. In financial market, there is a need to better understand the situations that occur in the capital market, through negotiation strategies and technical indicators that can assist in analysis and investment decisi...

In this paper, we present a view of the Brazilian stock exchange market based on a large characterization and analysis of Twitter data. In our analysis, we show that events and news about the stock market are capable of generating peaks of publications by Twitter users and that the frequency of posts follows the starting of the exchange trading day...

One topic of great interest in the literature is time series prediction. This kind of prediction, however, does not have to provide the exact future values every time: in some cases, knowing only time series future tendency is enough. In this paper, we propose a neural network ensemble that receives as input the last values from a time series and r...

There is an increase in the volume of electronic transactions in the Web in recent years, mainly due to the significant growth that has been observed in e-commerce. This scenario makes the frauds in electronic transactions a matter of high importance. This work proposes a comprehensive approach to address the problem of fraud in the emerging market...

Neste artigo apresentamos uma visão do mercado de ações brasileiro a partir da caracterização e análise de dados coletados do Twitter no período de julho de 2013 a julho de 2014. Este trabalho tem como principal diferencial ser o primeiro a apontar as ações da BOVESPA que podem ter seu volume financeiro e número de ordens de compra e venda monitora...

A análise de sentimentos tornou-se uma ferramenta essencial para aplicação em diversos contextos, incluindo análise de opinião do usuário sobre produtos e serviços, previsão durante campanhas políticas e até mesmo em tendências do mercado financeiro. Apesar do grande interesse neste tema e na quantidade de pesquisas na área, a maioria dos métodos f...

The aim of this study was to model and use machine learning techniques to maximize the chance of a market maker be executed successfully in a stock market, that is, when their bid and ask orders are filled at the desired prices. In this context, a binary ensemble classifier was created to decide whether, at a specific time, is or not propitious to...

The volume of electronic transactions has raised significantly in last years, mainly due to the popularization of electronic commerce (e-commerce), such as online retailers (e.g., Amazon.com, eBay, Ali Express.com). We also observe a significant increase in the number of fraud cases, resulting in billions of dollars losses each year worldwide. Ther...

The volume of online transactions has increased considerably in the recent years. Consequently, the number of fraud cases has also increased, causing billion dollar losses each year worldwide. Therefore, it is mandatory to employ mechanisms that are able to assist in fraud detection. In this work, it is proposed the use of Genetic Programming (GP)...

Recently, there has been a large popularization of location-based social networks, such as Foursquare and Apontador, in which users can share their current locations, upload tips and make comments about places. Part of this popularity is due to facility access to the Internet through mobile devices with GPS. Despite the various efforts towards unde...

The volume of electronic transactions has raised a lot in last years, mainly due to the popularization of e-commerce. We also observe a significant increase in the number of fraud cases, resulting in billions of dollars losses each year worldwide. Therefore, it is essential to develop and apply techniques that can assist in fraud detection. In this...

Currently, it has been observed an increasing popularization of e-commerce. Thus, in areas such as tourism, online retailers are dealing with different challenges such as performance, scalability and personalization. In this context, it is fundamental to understand the characteristics of the user requests, and the access patterns of users on these...

With the rise ofWeb 2.0 applications, most people started consuming information and sharing opinions and ideas about most aspects of their lives on a variety of social media platforms, creating massive and continuous streams of valuable data. While this opened the door for information extraction and mining techniques that can help us understand dif...

This paper presents a novel multimedia information system, called SAPTE, for supporting the discourse analysis and information retrieval of television programs from their corresponding video recordings. Unlike most common systems, SAPTE uses both content independent and dependent metadata, which are determined by the application of discourse analys...

The amount of online transactions has increased a lot in last years, mainly due to the popularization of e-commerce, such as Web retailers. As a consequence, the number of fraud cases has grown considerably, causing billion of dollars losses each year worldwide. Thus, it is important and necessary to develop and apply techniques that can assist in...

Recommender systems play a key role in the decision making process of users in Web systems. In tourism, it is widely used to recommend hotels, tourist attractions, accommodations, etc. In this paper, we present a personalized neighborhood-based method to recommend cities. This is a fundamental problem whose solution support other tourism recommenda...

Artificial Neural Networks (ANN) have been frequently applied to reduce risks and maximize the net returns in different types of algorithm trading. Using a real dataset, and aiming to support the Market Making process in High-Frequency Trading, this work investigates the use of a multilayer perceptron (MLP) to predict positive oscillations in short...