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Sobre

Sobre

Professor Associado com Agregação da UTAD e Investigador Sénior do INESC TEC.

Doutorou-se, na UTAD, em 2002, em Engenharia Eletrotécnica e realizou, em 2007, as provas Públicas de Agregação em Informática/Acessibilidade. Passou a Professor Associado da UTAD em dezembro de 2012.

Foi Pró-reitor para a Inovação e Gestão da Informação da UTAD, de 23 Julho de 2010 a 29 Julho de 2013.

Produziu mais de 150 trabalhos académicos, entre capítulos de livros, artigos em revistas e artigos em atas de eventos ciêntificos. Orientou 40 trabalhos de pós-graduação (mestrados e doutoramentos).

Participou em 35 projetos de investigação e desenvolvimento (foi investigador principal em 15 destes projetos).

Participou na organização de vários encontros científicos de natureza internacional, em 2006 coordenou a equipa que criou a conferência “Software Development for Enhancing Accessibility and Fighting Info-exclusion (www.dsai.ws/2016) e em 2016 a conferência Technology and Innovation is Sports, Health and Wellbeing (www.tishw.ws/2016).

As áreas principais de investigação são: Processamento Digital de Imagem, Acessibilidade e Interação pessoa Computador.

Google Scholar: http://scholar.google.com/citations?user=HBVvNYQAAAAJ&hl=en

SCOPUS: http://www.scopus.com/authid/detail.url?authorId=20435746800

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Barroso
  • Cluster

    Informática
  • Cargo

    Investigador Coordenador
  • Desde

    01 outubro 2012
011
Publicações

2023

A Model for Cognitive Personalization of Microtask Design

Autores
Paulino, D; Guimaraes, D; Correia, A; Ribeiro, J; Barroso, J; Paredes, H;

Publicação
SENSORS

Abstract
The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker’s cognitive profile. There are two common methods for assessing a crowd worker’s cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model’s performance.

2023

A Machine Learning Tool to Monitor and Forecast Results from Testing Products in End-of-Line Systems

Autores
Nunes, C; Nunes, R; Pires, EJS; Barroso, J; Reis, A;

Publicação
APPLIED SCIENCES-BASEL

Abstract
The massive industrialization of products in a factory environment requires testing the product at a stage before its exportation to the sales market. For example, the end-of-line tests at Continental Advanced Antenna contribute to the validation of an antenna's functionality, a product manufactured by this organization. In addition, the storage of information from the testing process allows the data manipulation through automated machine learning algorithms in search of a beneficial contribution. Studies in this area (automatic learning/machine learning) lead to the search and development of tools designed with objectives such as preventing anomalies in the production line, predictive maintenance, product quality assurance, forecast demand, forecasting safety problems, increasing resources, proactive maintenance, resource scalability, reduced production time, and anomaly detection, isolation, and correction. Once applied to the manufacturing environment, these advantages make the EOL system more productive, reliable, and less time-consuming. This way, a tool is proposed that allows the visualization and previous detection of trends associated with faults in the antenna testing system. Furthermore, it focuses on predicting failures at Continental's EOL.

2022

Reliability Analysis Based Improved Directional Simulation Using Harris Hawks Optimization Algorithm for Engineering Systems

Autores
Jafari Asl, J; Ben Seghier, ME; Ohadi, S; Correia, J; Barroso, J;

Publicação
ENGINEERING FAILURE ANALYSIS

Abstract

2022

My Buddy: A 3D Game for Children Based on Voice Commands

Autores
Carvalho, D; Rocha, T; Barroso, J;

Publicação
INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021

Abstract

2022

Automated Evaluation Tools for Web and Mobile Accessibility: A Systematic Literature Review

Autores
Dias, J; Carvalho, D; Paredes, H; Martins, P; Rocha, T; Barroso, J;

Publicação
INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021

Abstract

Teses
supervisionadas

2022

Internet of Things 4 Seniors

Autor
Luís Filipe Jesus Correia

Instituição
UTAD

2022

Tecnologias e aplicações da Interface Cérebro-Computador (BCI)

Autor
Pedro Alexandre santos Letra

Instituição
UTAD

2022

Software-Defened things no apoio a idosos

Autor
Daniel Alexander Lopes Fuentes

Instituição
UTAD

2021

Balcão único do aluno

Autor
Yasmine Alexandra Nóbrega de Sales Gomes Amraoui

Instituição
UTAD

2021

Detection of new constructions using artificial intelligence techniques applied to images captured by unmanned aerial vehicles

Autor
Rui Miguel Simões de Oliveira Pinheiro

Instituição
UTAD