Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About

About

Associate Professor with Habilitation at University of Trás-os-Montes e Alto Douro (UTAD) and Senior Researcher at INESC TEC.

He earned a doctorate in UTAD in 2002 in Electrical Engineering and held in 2008 the Habilitation in Informatics/Accessibility. I was Associate Professor in December 2012.

He was Pro-Rector for Innovation and Information Management at UTAD, from 23 July 2010 to 29 July 2013.

He produced over 150 scientific papers, including book chapters, journal articles and articles in proceedings of scientific events. He supervised 40 postgraduate students (masters and doctorates).
He was member of the research team in 35 research and development projects.

He was member of several organizing committees of the international scientific meetings. In 2006 he directed the team that created the conference "Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (www.dsai.ws/2016) and in 2016 the conference Technology and Innovation is Sports, Health and Wellbeing (www.tishw.ws/2016).
The main research interests are: Digital Image Processing, Accessibility and Human Computer Interaction.

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

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

Interest
Topics
Details

Details

  • Name

    João Barroso
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st October 2012
011
Publications

2023

A Model for Cognitive Personalization of Microtask Design

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

Publication
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

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

Publication
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

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

Publication
ENGINEERING FAILURE ANALYSIS

Abstract

2022

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

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

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

Abstract

2022

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

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

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

Abstract

Supervised
thesis

2022

Internet of Things 4 Seniors

Author
Luís Filipe Jesus Correia

Institution
UTAD

2022

Tecnologias e aplicações da Interface Cérebro-Computador (BCI)

Author
Pedro Alexandre santos Letra

Institution
UTAD

2022

Software-Defened things no apoio a idosos

Author
Daniel Alexander Lopes Fuentes

Institution
UTAD

2021

Balcão único do aluno

Author
Yasmine Alexandra Nóbrega de Sales Gomes Amraoui

Institution
UTAD

2021

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

Author
Rui Miguel Simões de Oliveira Pinheiro

Institution
UTAD