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Detalhes

Detalhes

  • Nome

    Bruno Miguel Veloso
  • Cargo

    Investigador Sénior
  • Desde

    01 março 2013
  • Nacionalidade

    Portugal
  • Contactos

    +351220402963
    bruno.m.veloso@inesctec.pt
003
Publicações

2024

SWINN: Efficient nearest neighbor search in sliding windows using graphs

Autores
Mastelini, SM; Veloso, B; Halford, M; de Carvalho, ACPDF; Gama, J;

Publicação
INFORMATION FUSION

Abstract
Nearest neighbor search (NNS) is one of the main concerns in data stream applications since similarity queries can be used in multiple scenarios. Online NNS is usually performed on a sliding window by lazily scanning every element currently stored in the window. This paper proposes Sliding Window-based Incremental Nearest Neighbors (SWINN), a graph-based online search index algorithm for speeding up NNS in potentially never-ending and dynamic data stream tasks. Our proposal broadens the application of online NNS-based solutions, as even moderately large data buffers become impractical to handle when a naive NNS strategy is selected. SWINN enables efficient handling of large data buffers by using an incremental strategy to build and update a search graph supporting any distance metric. Vertices can be added and removed from the search graph. To keep the graph reliable for search queries, lightweight graph maintenance routines are run. According to experimental results, SWINN is significantly faster than performing a naive complete scan of the data buffer while keeping competitive search recall values. We also apply SWINN to online classification and regression tasks and show that our proposal is effective against popular online machine learning algorithms.

2024

Detecting and Explaining Anomalies in the Air Production Unit of a Train

Autores
Davari, N; Veloso, B; Ribeiro, RP; da Gama, JMP;

Publicação
Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, SAC 2024, Avila, Spain, April 8-12, 2024

Abstract

2024

From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning

Autores
Alcoforado, A; Okamura, LH; Fama, IC; Dias Bueno, BF; Lavado, AM; Ferraz, TP; Veloso, B; Reali Costa, AH;

Publicação
Proceedings of the 16th International Conference on Computational Processing of Portuguese, PROPOR 2024, Santiago de Compostela, Galicia/Spain, 12-15 March, 2024

Abstract

2024

Super-Resolution Analysis for Landfill Waste Classification

Autores
Molina, M; Ribeiro, RP; Veloso, B; Gama, J;

Publicação
Advances in Intelligent Data Analysis XXII - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Stockholm, Sweden, April 24-26, 2024, Proceedings, Part I

Abstract
Illegal landfills are a critical issue due to their environmental, economic, and public health impacts. This study leverages aerial imagery for environmental crime monitoring. While advances in artificial intelligence and computer vision hold promise, the challenge lies in training models with high-resolution literature datasets and adapting them to open-access low-resolution images. Considering the substantial quality differences and limited annotation, this research explores the adaptability of models across these domains. Motivated by the necessity for a comprehensive evaluation of waste detection algorithms, it advocates cross-domain classification and super-resolution enhancement to analyze the impact of different image resolutions on waste classification as an evaluation to combat the proliferation of illegal landfills. We observed performance improvements by enhancing image quality but noted an influence on model sensitivity, necessitating careful threshold fine-tuning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

2023

Online Anomaly Explanation: A Case Study on Predictive Maintenance

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

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

Abstract
Predictive Maintenance applications are increasingly complex, with interactions between many components. Black-box models are popular approaches due to their predictive accuracy and are based on deep-learning techniques. This paper presents an architecture that uses an online rule learning algorithm to explain when the black-box model predicts rare events. The system can present global explanations that model the black-box model and local explanations that describe why the black-box model predicts a failure. We evaluate the proposed system using four real-world public transport data sets, presenting illustrative examples of explanations.

Teses
supervisionadas

2023

A Pink Tax no Brasil e em Portugal: um estudo comparativo

Autor
Beatriz Azevedo Paredes

Instituição
UP-FEP

2023

Analysis of EU Countries' Development through Statis Methodology

Autor
Emanuel Fernandes Pais

Instituição
UP-FEP

2023

A benchmark of stock assessment models for Octopus vulgaris in Portugal

Autor
Alberto Jorge Machado de Almeida de Sousa Rocha

Instituição
UP-FCUP

2023

Customers' revenue fluctuation in a Telecommunication company: Data Warehouse Construction and Visualization

Autor
Cândido Rafael Toledo Rocha

Instituição
UP-FEP

2023

Deep Neural Networks in Medical Microbiology - Bacterial Colonies Classification

Autor
José Duarte Pinho Pereira

Instituição
UP-FEP