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About

About

Bruno Veloso. Completed the Mestrado integrado in Engenharia Eletrotécnica e de Computadores in 2012/10/31 by Instituto Politécnico do Porto Instituto Superior de Engenharia do Porto, Licenciatura in Engenharia Eletrotécnica e de Computadores in 2010/07/31 by Instituto Politécnico do Porto Instituto Superior de Engenharia do Porto and Doctor in Telematics Engineering in 2017/09/11 by Universidade de Vigo. Is Assistant Professor in Universidade Portucalense Infante Dom Henrique, Researcher in Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciência, Coordinator of the Bsc in Informatics Engineering in Universidade Portucalense Infante Dom Henrique and Coordinator of the Msc Data Science in Universidade Portucalense Infante Dom Henrique. Published 10 articles in journals. Has 8 section(s) of books and 2 book(s). Organized 5 event(s). Participated in 5 event(s). Supervised 1 MSc dissertation(s) e co-supervised 5. Has received 1 awards and/or honors. Participates and/or participated as Master Student Fellow in 1 project(s), Other in 1 project(s), PhD Student Fellow in 1 project(s) and Researcher in 4 project(s). Works in the area(s) of Engineering and Technology with emphasis on Electrotechnical Engineering, Electronics and Informatics. In their professional activities interacted with 41 collaborator(s) co-authorship of scientific papers.

Interest
Topics
Details

Details

  • Name

    Bruno Miguel Veloso
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st March 2013
003
Publications

2022

Stream-based explainable recommendations via blockchain profiling

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

Publication
INTEGRATED COMPUTER-AIDED ENGINEERING

Abstract
Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters - Memory-based and Model-based - using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.

2022

The Importance of Digital Transformation in International Business

Authors
Pereira, CS; Durao, N; Moreira, F; Veloso, B;

Publication
Sustainability

Abstract
This study was developed under the scope of a Portuguese project focused on the entrepreneur’s perspective and perception on the internationalization process of his company: more specifically, about the factors that enhanced the company entry into foreign markets as well as the constraints found in this process. This work focuses on the importance of using digital transformation to integrate technological tools in international business practice and strategy and the obstacles encountered with introducing these new technologies. This study aims to determine the relationships between technology categories and obstacles. The final goal is to assess the impact of these characteristics of the companies by the sector of economic activity, size, and percentage of profits resulting from international expansion. A questionnaire was designed and sent by email to 8183 companies from the AICEP database, distributed by three main activity sectors. A total of 310 valid answers were gathered from the Portuguese internationalized companies. The research limitations are related to the reduced number of interviews. These interviews showed that managers were not aware of the concept of digital transformation and misunderstood the use of digital technologies in the internationalization process of the business. This limitation can add some bias to the qualitative results. In addition to these limitations, the number of responses per sector was also not homogeneous. The practical implications of this study are that managers and top-level executives can use that to better understand how companies could use digital tools and what obstacles they should avoid when they want to internationalize their business. This paper is one of the first research contributions to analyze the impact of digital transformation in the internalization of Portuguese companies.

2022

ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling

Authors
Alcoforado, A; Ferraz, TP; Gerber, R; Bustos, E; Oliveira, AS; Veloso, BM; Siqueira, FL; Costa, AHR;

Publication
Lecture Notes in Computer Science - Computational Processing of the Portuguese Language

Abstract

2022

A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set

Authors
Davari, N; Pashami, S; Veloso, B; Nowaczyk, S; Fan, Y; Pereira, PM; Ribeiro, RP; Gama, J;

Publication
Lecture Notes in Computer Science - Advances in Intelligent Data Analysis XX

Abstract

2021

Classification and Recommendation With Data Streams

Authors
Veloso, B; Gama, J; Malheiro, B;

Publication
Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management

Abstract
Nowadays, with the exponential growth of data stream sources (e.g., Internet of Things [IoT], social networks, crowdsourcing platforms, and personal mobile devices), data stream processing has become indispensable for online classification, recommendation, and evaluation. Its main goal is to maintain dynamic models updated, holding the captured patterns, to make accurate predictions. The foundations of data streams algorithms are incremental processing, in order to reduce the computational resources required to process large quantities of data, and relevance model updating. This article addresses data stream knowledge processing, covering classification, recommendation, and evaluation; describing existing algorithms/techniques; and identifying open challenges.