Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
About

About

Benedita Malheiro holds a PhD. and a MSc. in Electrical Engineering and Computers and a five year graduation degree in Electrical Engineering from the University of Porto (Faculty of Engineering). She is an Adjunct Professor at the Electrical Engineering Department of the Instituto Superior de Engenharia do Porto (ISEP) where she is responsible for two modules of the MSc. in Electrical and Computers Engineering degree and supervises undergraduate (Final Project/Internship) and postgraduate (Thesis/Dissertation) students. As a researcher she is specialized in problems of a distributed, dynamic and decentralized nature.

Interest
Topics
Details

Details

001
Publications

2018

APASail—An Agent-Based Platform for Autonomous Sailing Research and Competition

Authors
Alves, B; Veloso, B; Malheiro, B;

Publication
Robotic Sailing 2017

Abstract

2018

Personalised Dynamic Viewer Profiling for Streamed Data

Authors
Veloso, B; Malheiro, B; Burguillo, JC; Foss, JD; Gama, J;

Publication
Advances in Intelligent Systems and Computing - Trends and Advances in Information Systems and Technologies

Abstract

2018

Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing

Authors
Leal, F; Malheiro, B; Burguillo, JC;

Publication
Advances in Intelligent Systems and Computing - Trends and Advances in Information Systems and Technologies

Abstract

2018

Collaborative Learning with Sustainability-driven Projects: A Summary of the EPS@ISEP Programme

Authors
Silva, MF; Malheiro, B; Guedes, P; Duarte, AJ; Ferreira, P;

Publication
International Journal of Engineering Pedagogy (iJEP)

Abstract
This paper describes the collaborative learning environment, aligned with the United Nations Millennium Development Goals, provided by the European Project Semester (EPS). EPS is a one semester capstone project programme offered by eighteen European engineering schools as part of their student ex-change programme portfolio. In this international programme, students are organized in teams, grouping individuals from diverse academic backgrounds and nationalities. The teams, after choosing a project proposal, become fully responsible for the conduction of their projects. By default, project proposals refer to open multidisciplinary real problems. The purpose of the project is to expose students to problems of a greater dimension and complexity than those faced throughout the degree programme as well as to put them in con-tact with the so-called real world, in opposition to the academic world. EPS provides an integrated framework for undertaking capstone projects, which is focused on multicultural and multidisciplinary teamwork, communication, problem-solving, creativity, leadership, entrepreneurship, ethical reasoning and global contextual analysis. Specifically, the design and development of sustainable systems for growing food allow students not only to reach the described objectives, but to foster sustainable development practices. As a re-sult, we recommend the adoption of this category of projects within EPS for the benefit of engineering students and of the society as a whole.

2018

Scalable data analytics using crowdsourced repositories and streams

Authors
Veloso, B; Leal, F; Gonzalez Velez, H; Malheiro, B; Carlos Burguillo, JC;

Publication
Journal of Parallel and Distributed Computing

Abstract
The scalable analysis of crowdsourced data repositories and streams has quickly become a critical experimental asset in multiple fields. It enables the systematic aggregation of otherwise disperse data sources and their efficient processing using significant amounts of computational resources. However, the considerable amount of crowdsourced social data and the numerous criteria to observe can limit analytical off-line and on-line processing due to the intrinsic computational complexity. This paper demonstrates the efficient parallelisation of profiling and recommendation algorithms using tourism crowdsourced data repositories and streams. Using the Yelp data set for restaurants, we have explored two different profiling approaches: entity-based and feature-based using ratings, comments, and location. Concerning recommendation, we use a collaborative recommendation filter employing singular value decomposition with stochastic gradient descent (SVD-SGD). To accurately compute the final recommendations, we have applied post-recommendation filters based on venue suitability, value for money, and sentiment. Additionally, we have built a social graph for enrichment. Our master–worker implementation shows super-linear scalability for 10, 20, 30, 40, 50, and 60 concurrent instances. © 2018 Elsevier Inc.

Supervised
thesis

2017

Media Content Personalisation Brokerage Platform

Author
Bruno Miguel Delindro Veloso

Institution
Outra

2017

Recommendation of Personalised Tourist Resources

Author
Fátima Manuela da Silva Leal

Institution
Outra

2016

Plataforma de Competição de Veleiros Autónomos

Author
BRUNO MIGUEL FERREIRA ALVES

Institution
IPP-ISEP

2016

Renegociação de Contratos de Intermediação da Comercialização Eletrónica de Recursos de Cloud Computing

Author
RÚBEN DE CASTRO RODRIGUES MOREIRA DA CUNHA

Institution
IPP-ISEP

2016

Automatic Fluid Sampler

Author
PEPIJN DE WINTER

Institution
IPP-ISEP