Search

Your search keyword '"Santos, Guto Leoni"' showing total 109 results
Start Over Author "Santos, Guto Leoni" Publication Year Range Last 10 years
109 results on '"Santos, Guto Leoni"'

1. Kicking Prejudice: Large Language Models for Racism Classification in Soccer Discourse on Social Media

Author

Santos, Guto Leoni, dos Santos, Vitor Gaboardi, Kearns, Colm, Sinclair, Gary, Black, Jack, Doidge, Mark, Fletcher, Thomas, Kilvington, Dan, Endo, Patricia Takako, Liston, Katie, Lynn, Theo, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Guizzardi, Giancarlo, editor, Santoro, Flavia, editor, Mouratidis, Haralambos, editor, and Soffer, Pnina, editor

Published
2024
Full Text
View/download PDF

2. Identifying Citizen-Related Issues from Social Media Using LLM-Based Data Augmentation

Author

dos Santos, Vitor Gaboardi, Santos, Guto Leoni, Lynn, Theo, Benatallah, Boualem, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Guizzardi, Giancarlo, editor, Santoro, Flavia, editor, Mouratidis, Haralambos, editor, and Soffer, Pnina, editor

Published
2024
Full Text
View/download PDF

4. Predicting Short-term Mobile Internet Traffic from Internet Activity using Recurrent Neural Networks

Author

Santos, Guto Leoni, Rosati, Pierangelo, Lynn, Theo, Kelner, Judith, Sadok, Djamel, and Endo, Patricia Takako

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Mobile network traffic prediction is an important input in to network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non-linear patterns or other important correlations in time series mobile network data. We compare the performance of two deep learning architectures - Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) - for predicting mobile Internet traffic using two months of Telecom Italia data for the metropolitan area of Milan. K-Means clustering was used a priori to group cells based on Internet activity and the Grid Search method was used to identify the best configurations for each model. The predictive quality of the models was evaluated using root mean squared error. Both Deep Learning algorithms were effective in modeling Internet activity and seasonality, both within days and across two months. We find variations in performance across clusters within the city. Overall, the LSTM outperformed the GRU in our experiments.

Published
2020

5. The Greatest Teacher, Failure is: Using Reinforcement Learning for SFC Placement Based on Availability and Energy Consumption

Author

Santos, Guto Leoni, Lynn, Theo, Kelner, Judith, and Endo, Patricia Takako

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning
Abstract

Software defined networking (SDN) and network functions virtualisation (NFV) are making networks programmable and consequently much more flexible and agile. To meet service level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO2), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa (RNP) Network, Brazil's National Teaching and Research Network backbone. The simulation results showed that PPO2 generally outperformed A2C and a greedy approach both in terms of acceptance rate and energy consumption. A2C outperformed PPO2 only in the scenario where network servers had a greater number of computing resources.

Published
2020

6. Using Reinforcement Learning to Allocate and Manage Service Function Chains in Cellular Networks

Author

Santos, Guto Leoni and Endo, Patricia Takako

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

It is expected that the next generation cellular networks provide a connected society with fully mobility to empower the socio-economic transformation. Several other technologies will benefits of this evolution, such as Internet of Things, smart cities, smart agriculture, vehicular networks, healthcare applications, and so on. Each of these scenarios presents specific requirements and demands different network configurations. To deal with this heterogeneity, virtualization technology is key technology. Indeed, the network function virtualization (NFV) paradigm provides flexibility for the network manager, allocating resources according to the demand, and reduces acquisition and operational costs. In addition, it is possible to specify an ordered set of network virtual functions (VNFs) for a given service, which is called as service function chain (SFC). However, besides the advantages from service virtualization, it is expected that network performance and availability do not be affected by its usage. In this paper, we propose the use of reinforcement learning to deploy a SFC of cellular network service and manage the VNFs operation. We consider that the SFC is deployed by the reinforcement learning agent considering a scenarios with distributed data centers, where the VNFs are deployed in virtual machines in commodity servers. The NFV management is related to create, delete, and restart the VNFs. The main purpose is to reduce the number of lost packets taking into account the energy consumption of the servers. We use the Proximal Policy Optimization (PPO) algorithm to implement the agent and preliminary results show that the agent is able to allocate the SFC and manage the VNFs, reducing the number of lost packets., Comment: 9 pages, 6 figures, 2 tables

Published
2020

9. Internet of Medical Things (IoMT) Applications in E-Health Systems Context : An Overview and Research Challenges

Author

Monteiro, Kayo, Silva, Élisson, Remigio, Émerson, Santos, Guto Leoni, Endo, Patricia Takako, Pisello, Anna Laura, Editorial Board Member, Hawkes, Dean, Editorial Board Member, Bougdah, Hocine, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Boemi, Sofia-Natalia, Editorial Board Member, Mohareb, Nabil, Editorial Board Member, Mesbah Elkaffas, Saleh, Editorial Board Member, Bozonnet, Emmanuel, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Mahgoub, Yasser, Editorial Board Member, De Bonis, Luciano, Editorial Board Member, Kostopoulou, Stella, Editorial Board Member, Pradhan, Biswajeet, Editorial Board Member, Abdul Mannan, Md., Editorial Board Member, Alalouch, Chaham, Editorial Board Member, O. Gawad, Iman, Editorial Board Member, Nayyar, Anand, Editorial Board Member, Amer, Mourad, Series Editor, Alja’am, Jihad, editor, Al-Maadeed, Somaya, editor, and Halabi, Osama, editor

Published
2021
Full Text
View/download PDF

11. A Methodology for Automating the Cloud Data Center Availability Assessment

Author

Santos, Guto Leoni, Rosendo, Daniel, Gomes, Demis, Ferreira, Leylane, Moreira, Andre, Sadok, Djamel, Kelner, Judith, Goncalves, Glauco, Wilderman, Mattias, Endo, Patricia Takako, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Takizawa, Makoto, editor, Xhafa, Fatos, editor, and Enokido, Tomoya, editor

Published
2020
Full Text
View/download PDF

14. 'Best run club in the world': Manchester City fans and the legitimation of sportswashing?

Author

Kearns, Colm, Sinclair, Gary, Black, Jack, Doidge, Mark, Fletcher, Thomas, Kilvington, Daniel, Liston, Katie, Lynn, Theo, and Santos, Guto Leoni

Subjects
VIRTUAL communities, SPORTSWASHING, PRIVATE equity funds, SPORTS events, ATHLETIC clubs, MASS media
Abstract

The term sportswashing has been discussed and analysed within academic circles, as well as the mainstream media. However, the majority of existing research has focused on one-off event-based sportswashing strategies (such as autocratic states hosting major international sports events) rather than longer term investment-based strategies (such as state actors purchasing sports clubs and teams). Furthermore, little has been written about the impact of this latter strategy on the existing fanbase of the purchased team and on their relationship with sportswashing and the discourses surrounding it. This paper addresses this lacuna through analysis of a popular Manchester City online fan forum, which illustrates the manner in which this community of dedicated City fans have legitimated the actions of the club's ownership regime, the Abu Dhabi United Group – a private equity group operated by Abu Dhabi royalty and UAE politicians. The discursive strategies of the City fans are discussed, in addition to the wider significance of these strategies on the issue of sportswashing and its coverage by the media. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

23. Illusion of truth: analysing and classifying COVID-19 fake news in Brazilian Portuguese language

Author

Endo, Patricia Takako, Santos, Guto Leoni, Eduarda de Lima Xavier, Maria, Nascimento Campos, Gleyson Rhuan, Conceição de Lima, Luciana, Silva, Ivanovitch, Egli, Antonia, Lynn, Theo, Endo, Patricia Takako, Santos, Guto Leoni, Eduarda de Lima Xavier, Maria, Nascimento Campos, Gleyson Rhuan, Conceição de Lima, Luciana, Silva, Ivanovitch, Egli, Antonia, and Lynn, Theo

Abstract

Public health interventions to counter the COVID-19 pandemic have accelerated and increased digital adoption and use of the Internet for sourcing health information. Unfortunately, there is evidence to suggest that it has also accelerated and increased the spread of false information relating to COVID-19. The consequences of misinformation, disinformation and misinterpretation of health information can interfere with attempts to curb the virus, delay or result in failure to seek or continue legitimate medical treatment and adherence to vaccination, as well as interfere with sound public health policy and attempts to disseminate public health messages. While there is a significant body of literature, datasets and tools to support countermeasures against the spread of false information online in resource-rich languages such as English and Chinese, there are few such resources to support Portuguese, and Brazilian Portuguese specifically. In this study, we explore the use of machine learning and deep learning techniques to identify fake news in online communications in the Brazilian Portuguese language relating to the COVID-19 pandemic. We build a dataset of 11,382 items comprising data from January 2020 to February 2021. Exploratory data analysis suggests that fake news about the COVID-19 vaccine was prevalent in Brazil, much of it related to government communications. To mitigate the adverse impact of fake news, we analyse the impact of machine learning to detect fake news based on stop words in communications. The results suggest that stop words improve the performance of the models when keeping them within the message. Random Forest was the machine learning model with the best results, achieving 97.91% of precision, while Bi-GRU was the best deep learning model with an F1 score of 94.03%.

Published
2022

26. Prediction of malaria using deep learning models: A case study on city clusters in the state of Amazonas, Brazil, from 2003 to 2018

Author

Barboza, Matheus Félix Xavier, primary, Monteiro, Kayo Henrique de Carvalho, additional, Rodrigues, Iago Richard, additional, Santos, Guto Leoni, additional, Monteiro, Wuelton Marcelo, additional, Figueira, Elder Augusto Guimaraes, additional, Sampaio, Vanderson de Souza, additional, Lynn, Theo, additional, and Endo, Patricia Takako, additional

Published
2022
Full Text
View/download PDF

29. SPIDER: An availability‐aware framework for the service function chain placement in distributed scenarios.

Author

Santos, Guto Leoni, Endo, Patricia Takako, Sadok, Djamel, and Kelner, Judith

Subjects
SPIDERS, VIRTUAL networks, INFRASTRUCTURE (Economics), NETWORK performance, COMMUNICATION infrastructure
Abstract

The network function virtualization (NFV) paradigm replaces hardware‐dependent network functions by virtual network functions (VNFs) that can be deployed in commodity hardware, including legacy servers. Consequently, the use of NFV is expected to reduce operating and capital expenses, as well as improve service deployment operation and management flexibility. For many use cases, the VNFs must be visited and invoked following a specific order of execution in order to compose a complete network service, named service function chain (SFC). Nonetheless, despite the benefits from NFV and SFC virtualization technologies, their introduction must not harm network performance and service availability. On the one hand, redundancy is seen by network service planners as a mechanism well established to combat availability issues. At same time, there is a goal to optimize resource utilization in order to reduce operational expenditure. In this article, we share our experience in the design use of a framework, named SPIDER, focused on SFC placement that considers the network infrastructure condition and the required SFC availability to define the placement strategy. The SPIDER monitors the status of infrastructure nodes and links and defines which servers the VNFs should be placed on and the number of redundant replicas needed. We present a proof‐of‐concept of SPIDER using Kubernetes to launch the VNFs as containers. We also use Kubernetes to forward the traffic between the VNFs, composing the service chain. We perform experiments to evaluate the runtime of SPIDER and the SFC delay under different network conditions. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

32. Service Function Chain Placement in Distributed Scenarios: A Systematic Review

Author

Santos, Guto Leoni, primary, Bezerra, Diego de Freitas, additional, Rocha, Élisson da Silva, additional, Ferreira, Leylane, additional, Moreira, André Luis Cavalcanti, additional, Gonçalves, Glauco Estácio, additional, Marquezini, Maria Valéria, additional, Recse, Ákos, additional, Mehta, Amardeep, additional, Kelner, Judith, additional, Sadok, Djamel, additional, and Endo, Patricia Takako, additional

Published
2021
Full Text
View/download PDF

35. Predicting short‐term mobile Internet traffic from Internet activity using recurrent neural networks.

Author

Santos, Guto Leoni, Rosati, Pierangelo, Lynn, Theo, Kelner, Judith, Sadok, Djamel, and Endo, Patricia Takako

Subjects
WIRELESS Internet, RECURRENT neural networks, INTERNET traffic, STANDARD deviations, DEEP learning, K-means clustering, RANDOM forest algorithms
Abstract

Summary: Mobile network traffic prediction is an important input into network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non‐linear patterns, or other important correlations in time series mobile network data. We compare the performance of two deep learning (DL) architectures, long short‐term memory (LSTM) and gated recurrent unit (GRU), and two conventional machine learning (ML) architectures—Random Forest and Decision Tree—for predicting mobile Internet traffic using 2 months of Telecom Italia data for Milan. K‐Means clustering was used a priori to group cells based on Internet activity, and the Grid Search method was used to identify the best configurations for each model. The predictive quality of the models was evaluated using root mean squared error and mean absolute error. Both DL algorithms were effective in modeling Internet activity and seasonality, both within days and across 2 months. We find variations in performance across clusters within the city. Overall, the DL models outperformed the conventional ML models, and the LSTM outperformed the GRU in our experiments. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

39. Standardization Efforts for Traditional Data Center Infrastructure Management: The Big Picture

Author

Ferreira, Leylane, primary, Endo, Patricia Takako, additional, Rosendo, Daniel, additional, Santos, Guto Leoni, additional, Gomes, Demis, additional, Moreira, Andre Luis Cavalcanti, additional, Goncalves, Glauco Estacio, additional, Kelner, Judith, additional, Sadok, Djamel, additional, Mehta, Amardeep, additional, and Wildeman, Mattias, additional

Published
2020
Full Text
View/download PDF

45. Data set for automatic detection of online misogynistic speech

Author

Lynn, Theo, Endo, Patricia Takako, Rosati, Pierangelo, Silva, Ivanovitch, Santos, Guto Leoni, Ging, Debbie, Lynn, Theo, Endo, Patricia Takako, Rosati, Pierangelo, Silva, Ivanovitch, Santos, Guto Leoni, and Ging, Debbie

Abstract

The data set is composed of 2285 definitions posted on the Urban Dictionary platform from 1999 to May 2016. The data was classified as misogynistic and non-misogynistic by three independent researchers with domain knowledge. The data set is available in public repository in a table containing two columns: the text-based definition from Urban Dictionary and its respective classification (1 for misogynistic and 0 for non-misogynistic).

Published
2019

46. A comparison of machine learning approaches for detecting misogynistic Speech in urban dictionary

Author

Lynn, Theo, Endo, Patricia Takako, Rosati, Pierangelo, Silva, Ivanovitch, Santos, Guto Leoni, Ging, Debbie, Lynn, Theo, Endo, Patricia Takako, Rosati, Pierangelo, Silva, Ivanovitch, Santos, Guto Leoni, and Ging, Debbie

Abstract

—Recent moves to consider misogyny as a hate crime have refocused efforts for owners of web properties to detect and remove misogynistic speech. This paper considers the use of deep learning techniques for detection of misogyny in Urban Dictionary, a crowdsourced online dictionary for slang words and phrases. We compare the performance of two deep learning techniques, Bi-LSTM and Bi-GRU, to detect misogynistic speech with the performance of more conventional machine learning techniques, logistic regression, Naive-Bayes classification, and Random Forest classification. We find that both deep learning techniques examined have greater accuracy in detecting misogyny in the Urban Dictionary than the other techniques examined. Dublin, Ireland debbie.ging@dcu.ie it was announced that the UK Law Commission would review whether misogynistic conduct should be treated as a hate crime [6]. Index Terms—misogyny, hate speech, recurrent neural networks, deep learning, LSTM, machine learning, urban dictionary

Published
2019
Full Text
View/download PDF

47. Modeling the availability and performance of the integration be-tween edge, fog and cloud infrastructures

Author

SANTOS, Guto Leoni, KELNER, Judith, and ENDO, Patricia Takako

Subjects
Redes de computadores, Computação em Nuvem, Computação nas Bordas
Abstract

CAPES The Internet of Things has the potential of transforming health systems through the collection and analysis of patient physiological data via wearable devices and sensor networks. Such systems can offer assisted living services in real-time and offer a range of multimedia-based health services. However, service downtime, particularly in the case of emergencies, can lead to adverse outcomes and in the worst case, to death. In this dissertation, we propose an e-health monitoring architecture based on sensors that relies on cloud and fog infrastructures to handle and store patient data. Furthermore, we propose stochastic models to analyze availability and performance of such systems including models to understand how failures across the Cloud-to-Thing continuum impact on ehealth system availability and to identify potential bottlenecks. To feed our models with real data, we designed and built a prototype and executed performance experiments. Our results identify that the sensors and fog devices are the components that have the most significant impact on the availability of the e-health monitoring system, as a whole, in the scenarios analyzed. Our findings suggest that in order to identify the best architecture to host the e-health monitoring system, there is a trade-off between performance and delays that must be resolved. A Internet das Coisas tem o potencial de transformar sistemas e-health através da coleta e análise de dados fisiológicos do paciente através de dispositivos vestíveis e redes de sensores. Tais sistemas podem oferecer serviços de monitoramento em tempo real e oferecer serviços de saúde baseados em multimídia. No entanto, o tempo de inatividade do serviço, particularmente no caso de emergências, pode levar a resultados adversos e, no pior dos casos, à morte. Nesta dissertação, foi proposta uma arquitetura de monitoramento de e-health baseada em sensores que dependem de infra-estruturas de cloud e fog para manipular e armazenar dados de pacientes. Além disso, modelos estocásticos foram propostos para analisar a disponibilidade e o desempenho de tais sistemas, incluindo modelos para entender como as falhas desde os dispositivos edge até a nuvem afetam a disponibilidade do sistema e-health e para identificar possíveis gargalos. Para alimentar os modelos com dados reais, um protótipo foi projetado e construído com o intuito de executar experimentos acerca do desempenho do sistema e-health. A partir dos resultados, é possível identificar que os sensores e dispositivos fog são, de maneira geral, os componentes que mais impactam na disponibilidade do sistema e-health nos cenários analisados. Além disso, é possível concluir que para identificar a melhor arquitetura para hospedar um sistema e-health, é necessário encontrar um equilíbrio entre o desempenho e a latência.

Published
2018

48. Optimizing Resource Availability in Composable Data Center Infrastructures

Author

Ferreira, Leylane, primary, Lynn, Theo, additional, Endo, Patricia Takako, additional, da Silva Rocha, Elisson, additional, Monteiro, Kayo Henrique C., additional, Santos, Guto Leoni, additional, Silva, Francisco Airton, additional, Kelner, Judith, additional, Sadok, Djamel, additional, Filho, Carmelo J. A. Bastos, additional, and Rosati, Pierangelo, additional

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results