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Dr. Dianne Medeiros
Universidade Federal Fluminense - UFF

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0 Cloud Communications
0 Machine Learning
0 Smart Cities
0 IoT
0 Vehicular Networks

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Machine Learning

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Short Biography

Dianne Scherly Varela de Medeiros is a professor at the Universidade Federal Fluminense (UFF). Dianne received her Master’s degree on Telecommunications Engineering from UFF in 2013, and her D.Sc. degree on Electric Engineering from the Universidade Federal do Rio de Janeiro in 2017. Between 2015 and 2016, she had a sandwich scholarship to work on her Ph.D. Thesis on the LIP6 (Laboratoire d’Informatique de Paris 6) at Sorbonne Université, Paris, France.

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Review
Published: 18 January 2021 in Information
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The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities.

ACS Style

Nicollas de Oliveira; Pedro Pisa; Martin Lopez; Dianne de Medeiros; Diogo Mattos. Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges. Information 2021, 12, 38 .

AMA Style

Nicollas de Oliveira, Pedro Pisa, Martin Lopez, Dianne de Medeiros, Diogo Mattos. Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges. Information. 2021; 12 (1):38.

Chicago/Turabian Style

Nicollas de Oliveira; Pedro Pisa; Martin Lopez; Dianne de Medeiros; Diogo Mattos. 2021. "Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges." Information 12, no. 1: 38.

Conference paper
Published: 07 December 2020 in Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2020)
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A alocação eficiente do tráfego em nuvens é desafiadora devido ao compartilhamento de recursos entre os clientes. Isso pode implicar recursos ociosos caso os clientes estejam limitados a utilizar somente a banda contratada. O uso da nuvem pode ser otimizado provendo recursos aos clientes dinamicamente de acordo com a demanda. Agentes de aprendizado por reforço promovem respostas adaptáveis a ambientes variantes no tempo. Este artigo propõe um mecanismo baseado em Q-learning com múltiplos agentes para gerenciar o acesso aos recursos por cada cliente da nuvem. A proposta é analisada em um ambiente emulado, no qual um controlador é responsável pela alocação de tráfego aos clientes. Os resultados mostram que o mecanismo reduz a ociosidade da nuvem, permitindo que clientes com baixa priorização utilizem a banda disponível, ao mesmo tempo que garante a banda contratada a clientes prioritários. O mecanismo exige baixo comprometimento de processamento total, mesmo variando o número de estados e espaço de ações, ao passo que o custo em memória por agente aumenta, alcançando um máximo de 300 kB para 200 estados e ações.

ACS Style

Reiner Henrique Dos Santos Filho; Diogo Menezes Ferrazani Mattos; Dianne Scherly Varela De Medeiros. Agentes Inteligentes baseados em Aprendizado por Reforço para Alocação Dinâmica de Tráfego em Nuvens. Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2020) 2020, 141 -154.

AMA Style

Reiner Henrique Dos Santos Filho, Diogo Menezes Ferrazani Mattos, Dianne Scherly Varela De Medeiros. Agentes Inteligentes baseados em Aprendizado por Reforço para Alocação Dinâmica de Tráfego em Nuvens. Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2020). 2020; ():141-154.

Chicago/Turabian Style

Reiner Henrique Dos Santos Filho; Diogo Menezes Ferrazani Mattos; Dianne Scherly Varela De Medeiros. 2020. "Agentes Inteligentes baseados em Aprendizado por Reforço para Alocação Dinâmica de Tráfego em Nuvens." Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC 2020) , no. : 141-154.

Journal article
Published: 19 October 2020 in Journal of Internet Services and Applications
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In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

ACS Style

Dianne S. V. Medeiros; Helio N. Cunha Neto; Martin Andreoni Lopez; Luiz Claudio S. Magalhães; Natalia C. Fernandes; Alex B. Vieira; Edelberto F. Silva; Diogo M. F. Mattos. A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges. Journal of Internet Services and Applications 2020, 11, 1 -48.

AMA Style

Dianne S. V. Medeiros, Helio N. Cunha Neto, Martin Andreoni Lopez, Luiz Claudio S. Magalhães, Natalia C. Fernandes, Alex B. Vieira, Edelberto F. Silva, Diogo M. F. Mattos. A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges. Journal of Internet Services and Applications. 2020; 11 (1):1-48.

Chicago/Turabian Style

Dianne S. V. Medeiros; Helio N. Cunha Neto; Martin Andreoni Lopez; Luiz Claudio S. Magalhães; Natalia C. Fernandes; Alex B. Vieira; Edelberto F. Silva; Diogo M. F. Mattos. 2020. "A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges." Journal of Internet Services and Applications 11, no. 1: 1-48.

Preprint content
Published: 16 September 2020
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In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

ACS Style

Dianne Scherly Varela De Medeiros; Helio Do Nascimento Cunha Neto; Martin Andreoni Lopez; Luiz Claudio Schara Magalhães; Natalia Castro Fernandes; Alex Borges Vieira; Edelberto Franco Silva; Diogo Menezes Ferrazani Mattos. A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges. 2020, 1 .

AMA Style

Dianne Scherly Varela De Medeiros, Helio Do Nascimento Cunha Neto, Martin Andreoni Lopez, Luiz Claudio Schara Magalhães, Natalia Castro Fernandes, Alex Borges Vieira, Edelberto Franco Silva, Diogo Menezes Ferrazani Mattos. A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges. . 2020; ():1.

Chicago/Turabian Style

Dianne Scherly Varela De Medeiros; Helio Do Nascimento Cunha Neto; Martin Andreoni Lopez; Luiz Claudio Schara Magalhães; Natalia Castro Fernandes; Alex Borges Vieira; Edelberto Franco Silva; Diogo Menezes Ferrazani Mattos. 2020. "A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges." , no. : 1.

Journal article
Published: 08 July 2020 in IEEE Signal Processing Letters
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Human inefficiency to distinguish between true and false facts poses fake news as a threat to logical truth, which deteriorates democracy, journalism, and credibility in governmental institutions. In this letter, we propose a computational-stylistic analysis based on natural language processing, efficiently applying machine learning algorithms to detect fake news in texts extracted from social media. The analysis considers news from Twitter, from which approximately 33,000 tweets were collected, assorted between real and proven false. In assessing the quality of detection, 86% accuracy, and 94% precision stand out even employing a dimensional reduction to one-sixth of the number of original features. Our approach introduces a minimum overhead, while it has the potential of providing a high confidence index on discriminating fake from real news.

ACS Style

Nicollas Oliveira; Dianne S. V. Medeiros; Diogo M. F. Mattos. A Sensitive Stylistic Approach to Identify Fake News on Social Networking. IEEE Signal Processing Letters 2020, 27, 1250 -1254.

AMA Style

Nicollas Oliveira, Dianne S. V. Medeiros, Diogo M. F. Mattos. A Sensitive Stylistic Approach to Identify Fake News on Social Networking. IEEE Signal Processing Letters. 2020; 27 ():1250-1254.

Chicago/Turabian Style

Nicollas Oliveira; Dianne S. V. Medeiros; Diogo M. F. Mattos. 2020. "A Sensitive Stylistic Approach to Identify Fake News on Social Networking." IEEE Signal Processing Letters 27, no. : 1250-1254.

Journal article
Published: 12 June 2020 in Computer Networks
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Consensus mechanisms in blockchain applications allow mistrusting peers to agree on the global state of the chain. Most of the existing consensus mechanisms, however, are constrained by low efficiency and high energy consumption. In this paper, we propose the Blockchain Reputation-Based Consensus (BRBC) mechanism in which a node must have the reputation score higher than a given network trust threshold before being allowed to insert a new block in the chain. A randomly-selected set of judges monitors the behaviour of each node involved in the consensus and updates the node reputation score. Every cooperative behaviour results in a reward, and a non-cooperative or malicious behaviour results in a punishment. BRBC also uses the reputation score to revoke access to nodes with a reputation score below a given threshold. We present a security analysis, and we demonstrate that BRBC resists against a set of known attacks in the blockchain network. Finally, we simulate a blockchain network to assert the mechanism scalability and resilience to malicious actions in various network scenarios and different rates of malicious actions. The results show BRBC to be efficient to expel all nodes that acted with more than 50% of malicious actions.

ACS Style

Marcela T. de Oliveira; Lúcio H.A. Reis; Dianne S.V. Medeiros; Ricardo C. Carrano; Sílvia D. Olabarriaga; Diogo M.F. Mattos. Blockchain reputation-based consensus: A scalable and resilient mechanism for distributed mistrusting applications. Computer Networks 2020, 179, 107367 .

AMA Style

Marcela T. de Oliveira, Lúcio H.A. Reis, Dianne S.V. Medeiros, Ricardo C. Carrano, Sílvia D. Olabarriaga, Diogo M.F. Mattos. Blockchain reputation-based consensus: A scalable and resilient mechanism for distributed mistrusting applications. Computer Networks. 2020; 179 ():107367.

Chicago/Turabian Style

Marcela T. de Oliveira; Lúcio H.A. Reis; Dianne S.V. Medeiros; Ricardo C. Carrano; Sílvia D. Olabarriaga; Diogo M.F. Mattos. 2020. "Blockchain reputation-based consensus: A scalable and resilient mechanism for distributed mistrusting applications." Computer Networks 179, no. : 107367.

Research article
Published: 03 June 2020 in Transactions on Emerging Telecommunications Technologies
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Attacks on cyber‐physical systems, such as nuclear and water treatment plants, have physical consequences that impact the lives of thousands of citizens. In such systems, it is mandatory to monitor the field network and detect potential threats before a problem occurs. This work proposes a hybrid approach that orchestrates unsupervised and incremental learning methods to detect threats that impact the control loops in a plant. We use online data processing to identify new attack vectors. We train the online incremental learning method as new attacks arrive. We also apply a one‐class support vector machine to each monitored sensor or actuator to retrieve abnormal behaviors of their closed control loop. The proposed solution orchestrates the outputs from the two machine learning methods and alerts the system operators when it detects a threat. We evaluate the proposal on the Secure Water Treatment testbed dataset, and the results reveal that our proposal detects threats at more than 90% precision and with accuracy higher than 95%.

ACS Style

Lúcio Henrik A. Reis; Andrés Murillo Piedrahita; Sandra Rueda; Natália C. Fernandes; Dianne S. V. Medeiros; Marcelo Dias De Amorim; Diogo M. F. Mattos. Unsupervised and incremental learning orchestration for cyber‐physical security. Transactions on Emerging Telecommunications Technologies 2020, 31, 1 .

AMA Style

Lúcio Henrik A. Reis, Andrés Murillo Piedrahita, Sandra Rueda, Natália C. Fernandes, Dianne S. V. Medeiros, Marcelo Dias De Amorim, Diogo M. F. Mattos. Unsupervised and incremental learning orchestration for cyber‐physical security. Transactions on Emerging Telecommunications Technologies. 2020; 31 (7):1.

Chicago/Turabian Style

Lúcio Henrik A. Reis; Andrés Murillo Piedrahita; Sandra Rueda; Natália C. Fernandes; Dianne S. V. Medeiros; Marcelo Dias De Amorim; Diogo M. F. Mattos. 2020. "Unsupervised and incremental learning orchestration for cyber‐physical security." Transactions on Emerging Telecommunications Technologies 31, no. 7: 1.

Article
Published: 13 April 2020 in Journal of Network and Systems Management
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Inferring the quality of service experienced by wireless users is challenging, as network monitoring does not capture the service perception for each user individually. In this paper, we propose an unsupervised machine learning approach to infer the quality of service experienced by wireless users, based on the different usage profiles of a large-scale wireless network. To this end, our approach correlates the usage data of access points, and the summaries of connection flows passing through the access points in the network. Then, we apply the k-means clustering algorithm to infer different network usage profiles. We evaluate our proposed approach to infer QoS on a real large-scale wireless network, and the results show that discriminating the flows into five clusters allows identifying prevalent usage profiles of the degraded state of the network and overload conditions in access points, considering only the flow summaries.

ACS Style

Lucio Henrik A. Reis; Luiz Claudio S. Magalhães; Dianne Scherly V. De Medeiros; Diogo M. F. Mattos. An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking. Journal of Network and Systems Management 2020, 28, 1228 -1247.

AMA Style

Lucio Henrik A. Reis, Luiz Claudio S. Magalhães, Dianne Scherly V. De Medeiros, Diogo M. F. Mattos. An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking. Journal of Network and Systems Management. 2020; 28 (4):1228-1247.

Chicago/Turabian Style

Lucio Henrik A. Reis; Luiz Claudio S. Magalhães; Dianne Scherly V. De Medeiros; Diogo M. F. Mattos. 2020. "An Unsupervised Approach to Infer Quality of Service for Large-Scale Wireless Networking." Journal of Network and Systems Management 28, no. 4: 1228-1247.

Preprint content
Published: 18 March 2020
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In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

ACS Style

Dianne Scherly Varela De Medeiros; Helio Do Nascimento Cunha Neto; Martin Andreoni Lopez; Luiz Claudio Schara Magalhães; Natalia Castro Fernandes; Alex Borges Vieira; Edelberto Franco Silva; Diogo Mezenes Ferrazani Mattos. A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges. 2020, 1 .

AMA Style

Dianne Scherly Varela De Medeiros, Helio Do Nascimento Cunha Neto, Martin Andreoni Lopez, Luiz Claudio Schara Magalhães, Natalia Castro Fernandes, Alex Borges Vieira, Edelberto Franco Silva, Diogo Mezenes Ferrazani Mattos. A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges. . 2020; ():1.

Chicago/Turabian Style

Dianne Scherly Varela De Medeiros; Helio Do Nascimento Cunha Neto; Martin Andreoni Lopez; Luiz Claudio Schara Magalhães; Natalia Castro Fernandes; Alex Borges Vieira; Edelberto Franco Silva; Diogo Mezenes Ferrazani Mattos. 2020. "A Survey on Data Analysis on Large-Scale Wireless Networks: Online Stream Processing, Trends, and Challenges." , no. : 1.

Journal article
Published: 13 March 2020 in Vehicular Communications
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Participatory Sensing (PS) is a known paradigm of collaborative networks which provides incentives for users to participate in sensing tasks of Regions of Interest (RoIs). A challenge in wireless networking, however, is to balance the amount of data collected by users without imposing excessive load to the network. In this direction, this paper proposes a centralized system to adapt the sampling rate assigned to each crowdsourcing participant sensor. The sampling rate is computed based on the standard deviation of samples collected from a given RoI. The results obtained via simulations show a tradeoff between the sampling rate and the number of crowdsourcing participants. The more crowdsourcing participants, the lower must be the individual sampling rate and the amount of data transferred. This strategy can increase the data delivery rate taking into account the available short contact times, even though it requires a larger number of sensors.

ACS Style

Carlos Henrique De O.M. André; Dianne S.V. Medeiros; Miguel Elias M. Campista. Towards participatory sensing of regions of interest with adaptive sampling rate. Vehicular Communications 2020, 25, 100254 .

AMA Style

Carlos Henrique De O.M. André, Dianne S.V. Medeiros, Miguel Elias M. Campista. Towards participatory sensing of regions of interest with adaptive sampling rate. Vehicular Communications. 2020; 25 ():100254.

Chicago/Turabian Style

Carlos Henrique De O.M. André; Dianne S.V. Medeiros; Miguel Elias M. Campista. 2020. "Towards participatory sensing of regions of interest with adaptive sampling rate." Vehicular Communications 25, no. : 100254.

Article
Published: 28 February 2020 in Annals of Telecommunications
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Blockchain is a disruptive technology that relies on the distributed nature of the peer-to-peer network while performing an agreement, or consensus, a mechanism to achieve an immutable, global, and consistent registry of all transactions. Thus, a key challenge in developing blockchain solutions is to design the consensus mechanism properly. As a consequence of being a distributed application, any consensus mechanism is restricted to offer two of three properties: consistency, availability, and partition tolerance. In this paper, we survey the main consensus mechanisms on blockchain solutions, and we highlight the properties of each one. Moreover, we differentiate both deterministic and probabilistic consensus mechanisms, and we highlight coordination solutions that facilitate the data distribution on the blockchain, without the need for a sophisticated consensus mechanism.

ACS Style

Gabriel R. Carrara; Leonardo M. Burle; Dianne S. V. Medeiros; Célio Vinicius N. De Albuquerque; Diogo M. F. Mattos. Consistency, availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking. Annals of Telecommunications 2020, 75, 163 -174.

AMA Style

Gabriel R. Carrara, Leonardo M. Burle, Dianne S. V. Medeiros, Célio Vinicius N. De Albuquerque, Diogo M. F. Mattos. Consistency, availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking. Annals of Telecommunications. 2020; 75 (3-4):163-174.

Chicago/Turabian Style

Gabriel R. Carrara; Leonardo M. Burle; Dianne S. V. Medeiros; Célio Vinicius N. De Albuquerque; Diogo M. F. Mattos. 2020. "Consistency, availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking." Annals of Telecommunications 75, no. 3-4: 163-174.