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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 Researcher in Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciência and Assistant Professor in Universidade do Porto Faculdade de Economia. Published 21 articles in journals. Has 19 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 3 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 87 collaborator(s) co-authorship of scientific papers.

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    Details

    Details

    • Name

      Bruno Miguel Veloso

    • Cluster

      Computer Science

    • Role

      Senior Researcher

    • Since

      01st March 2013
    002
    Publications
    View all Publications

    2023

    Online Anomaly Explanation: A Case Study on Predictive Maintenance

    Authors
    Ribeiro, RP; Mastelini, SM; Davari, N; Aminian, E; Veloso, B; Gama, J;

    Publication
    MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

    Abstract

    2023

    An Online Data-Driven Predictive Maintenance Approach for Railway Switches

    Authors
    Tome, ES; Ribeiro, RP; Veloso, B; Gama, J;

    Publication
    MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

    Abstract

    2023

    Ethical and Technological AI Risks Classification: A Human Vs Machine Approach

    Authors
    Teixeira, S; Veloso, B; Rodrigues, JC; Gama, J;

    Publication
    MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I

    Abstract

    2023

    Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set

    Authors
    Davari, N; Veloso, B; Ribeiro, RP; Gama, J;

    Publication
    MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II

    Abstract

    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.

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