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Detalhes

Detalhes

  • Nome

    Vítor Santos Costa
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2009
006
Publicações

2023

Using Balancing Methods to Improve Glycaemia-Based Data Mining

Autores
Machado, D; Costa, VS; Brandão, P;

Publicação
Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023, Volume 5: HEALTHINF, Lisbon, Portugal, February 16-18, 2023.

Abstract

2022

Data Type Inference for Logic Programming

Autores
Barbosa, J; Florido, M; Costa, VS;

Publicação
LOGIC-BASED PROGRAM SYNTHESIS AND TRANSFORMATION (LOPSTR 2021)

Abstract
In this paper we present a new static data type inference algorithm for logic programming. Without the need for declaring types for predicates, our algorithm is able to automatically assign types to predicates which, in most cases, correspond to the data types processed by their intended meaning. The algorithm is also able to infer types given data type definitions similar to data definitions in Haskell and, in this case, the inferred types are more informative, in general. We present the type inference algorithm, prove it is decidable and sound with respect to a type system, and, finally, we evaluate our approach on example programs that deal with different data structures.

2022

Online Learning of Logic Based Neural Network Structures

Autores
Guimaraes, V; Costa, VS;

Publicação
INDUCTIVE LOGIC PROGRAMMING (ILP 2021)

Abstract
In this paper, we present two online structure learning algorithms for NeuralLog, NeuralLog+OSLR and NeuralLog+OMIL. NeuralLog is a system that compiles first-order logic programs into neural networks. Both learning algorithms are based on Online Structure Learner by Revision (OSLR). NeuralLog+OSLR is a port of OSLR to use NeuralLog as inference engine; while NeuralLog+OMIL uses the underlying mechanism from OSLR, but with a revision operator based on Meta-Interpretive Learning. We compared both systems with OSLR and RDN-Boost on link prediction in three different datasets: Cora, UMLS and UWCSE. Our experiments showed that NeuralLog+OMIL outperforms both the compared systems on three of the four target relations from the Cora dataset and in the UMLS dataset, while both NeuralLog+OSLR and NeuralLog+OMIL outperform OSLR and RDNBoost on the UWCSE, assuming a good initial theory is provided.

2022

Impact of the glycaemic sampling method in diabetes data mining

Autores
Machado, D; Costa, VS; Brandao, P;

Publicação
2022 27TH IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (IEEE ISCC 2022)

Abstract
Finger-pricking is the traditional procedure for glycaemia monitoring. It is an invasive method where the person with diabetes is required to prick their finger. In recent years, continuous-glucose monitoring (CGM), a new and more convenient method of glycaemia monitoring, has become prevalent. CGM provides continuous access to glycaemic values without the need of finger-pricking. Data mining can be used to understand glycaemic values, and to ideally warn users of abnormal situations. CGM provides significantly more data than finger-pricking. Thus, the amount and value of CGM data ultimately questions the role of finger-pricking for glycaemic studies. In this work we use the OhioT1DM data set in order to study the importance of finger-prick-based data. We use Random Forest as a classification method, a robust method that tends to obtain quality results. Our results indicate that, although more demanding and scarcer, finger-prick-based glycaemic values have a significant role on diabetes management and on data mining.

2022

Typed SLD-Resolution: Dynamic Typing for Logic Programming

Autores
Barbosa, J; Florido, M; Costa, VS;

Publicação
LOGIC-BASED PROGRAM SYNTHESIS AND TRANSFORMATION (LOPSTR 2022)

Abstract
The semantic foundations for logic programming are usually separated into two different approaches. The operational semantics, which uses SLD-resolution, the proof method that computes answers in logic programming, and the declarative semantics, which sees logic programs as formulas and its semantics as models. Here, we define a new operational semantics called TSLD-resolution, which stands for Typed SLD-resolution, where we include a value wrong, that corresponds to the detection of a type error at run-time. For this we define a new typed unification algorithm. Finally we prove the correctness of TSLD-resolution with respect to a typed declarative semantics.

Teses
supervisionadas

2022

Hypoglycaemia Prediction on Type 1 Diabetes Patients using Continuous Glucose Monitoring and Health Record Data

Autor
Guilherme Lucas Peralta

Instituição
UP-FEUP

2022

Finding patterns that predict hyper and hypoglycaemia

Autor
Ricardo Bruno Ferreira de Faria

Instituição
UP-FCUP

2022

Advising Diabetes’ self-management supported by user data in a mobile platform

Autor
Diogo Roberto de Melo e Diogo Machado

Instituição
UP-FCUP

2022

Modelos espaciais de previsão de preços de transação habitacionais

Autor
João Lucas Faria de Pires

Instituição
UP-FCUP

2022

Overcoming the current limitations of Reinforcement Learning towards Artificial General Intelligence

Autor
Filipe Emanuel dos Santos Marinho da Rocha

Instituição
UP-FCUP