Horário: 14h
Palestrante:Lucas Maciel Vieira (doutorando)
Orientadora: Profa Maria Emilia M. T. Walter
Título: Competing Endogenous RNA in Colorectal Cancer: an analysis for colon, rectum and rectosigmoid junction
Resumo: Colorectal cancer (CRC) is a heterogeneous cancer. Its treatment depends on its anatomical site and distinguishes between colon, rectum, and rectosigmoid junction cancer. This study aimed to identify diagnostic and prognostic biomarkers using networks of CRC-associated transcripts that can be built based on competing endogenous RNAs (ceRNA).
Horário: 14h20
Palestrante:Jefferson Viana Fonseca Abreu (mestrando)
Orientadora: Profa Célia Ghedini Ralha
Título: A Multi-agent Approach for Online Twitter Bot Detection
Evento:Proceedings of the VI Jornadas Nacionales de Investigación en Ciberseguridad (JNIC 2021 Live), virtualmente 9 al 10 de junio de 2021
Resumo: Online social networks are tools that allow interaction between human beings with a large number of users. Platforms like Twitter present the problem of social bots which are controlled by automated agents potentially used for malicious activities. Thus, social bot detection is important to keep people safe from harmful effects. In this work, we approach the Twitter bot online detection problem with a multi-agent system (MAS). It is based on supervised classification with three machine learning algorithms and a reduced set of features. The MAS performance compared to the three algorithms applied separately - Random Forest, Support Vector Machine, and Na¨ıve Bayes - presented similar results. Besides, interesting results for online bot detection with the MAS prototype suggested that 88.19% of bots detected were correctly labeled. The results indicate that the approach used is feasible and promising for the real-time bot detection problem.
Horário: 14h40
Palestrante:Yuri Barcellos Galli (mestrando)
Orientadora: Prof Bruno L. Macchiavello
Título: Deep Neural Networks as an Aid in Detecting Signs of Osteoporosis by Analyzing Panoramic Radiographs
Resumo: Osteoporosis is a synonym to bone fragility, and it is a silent disease that is only detected commonly after it has already caused damages to the person that has it. This bone fragility disease makes fracture more common and more damaging to its holders, and for that reason is a matter of public health. Identifying the disease in an early stage is essential to help avoid its damages, and in that task artificial intelligence has proven to be of great aid in recent years. Machine learning algorithms can predict the risk of osteoporosis by analyzing patient’s images coming from routine exams such as panoramic radiographs. This work proposes a Convolutional Neural Network (CNN) architecture that aims to identify signs of osteoporosis in that type of image using machine learning techniques.
Horário: 15h00
Palestrante:Harley Vera Oliveira (doutorando)
Orientadora: Profa Maristela T. Holanda
Título: Data Modeling and NoSQL Databases - a Systematic Mapping Review
Resumo: Modeling is one of the most important steps in developing a database. In traditional databases, the Entity Relationship (ER) andUnified Modeling Language (UML) models are widely used. But how are NoSQL databases being modeled? We performed a systematicmapping review to answer three research questions to identify and analyze the levels of representation, models used, and contextswhere the modeling process occurred in the main categories of NoSQL databases. We found 54 primary studies where we identifiedthat conceptual and logical levels received more attention than the physical level of representation. The UML, ER, and new notationbased on ER and UML were adapted to model NoSQL databases, in the same way, formats such as JSON, XML, and XMI were used togenerate schemas through the three levels of representation. New contexts such as benchmark, evaluations, migration, and schemageneration were identified, as well as new features to be considered for modeling NoSQL databases, such as the number of recordsby entities, CRUD operations, system requirements (availability, consistency, or scalability). Additionally, a coupling and cocitationanalysis was carried out to identify relevant works and researchers.
Horário: 15h20
Palestrante:Breno Gustavo Soares da Costa (doutorando)
Orientadora: Profa Aletéia Favacho
Título: Orchestration in Fog computing
Resumo: Fog computing is a paradigm that brings computational resources and services to the network edge, in the vicinity of user devices, lowering latency and connecting with cloud computing resources. Unlike cloud computing, fog resources are based on constrained and heterogeneous nodes whose connectivity can be unstable. In this complex scenario, there is a need to define and implement orchestration processes to ensure that applications and services can be provided, considering the settled agreements. Although some publications have dealt with orchestration in fog computing, there are still some diverse definitions and functional intersection with other areas, such as resource management and monitoring. We present a generic architecture of fog orchestration, created from the consolidation of 50 analyzed papers, bringing to light the essential functionalities addressed in the literature.
Local: Teams MS - Equipe PPGI-316415 Seminário, Canal Seminários 1-2021
Profa Célia Ghedini Ralha (Este endereço de email está sendo protegido de spambots. Você precisa do JavaScript ativado para vê-lo.)
Coordenadora Seminários de Pós-Graduação em Informática 1-2021