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Detalhes

Detalhes

  • Nome

    Vítor Santos Costa
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2009
006
Publicações

2024

Yet Another Lock-Free Atom Table Design for Scalable Symbol Management in Prolog

Autores
Moreno, P; Areias, M; Rocha, R; Costa, VS;

Publicação
INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING

Abstract
Prolog systems rely on an atom table for symbol management, which is usually implemented as a dynamically resizeable hash table. This is ideal for single threaded execution, but can become a bottleneck in a multi-threaded scenario. In this work, we replace the original atom table implementation in the YAP Prolog system with a lock-free hash-based data structure, named Lock-free Hash Tries (LFHT), in order to provide efficient and scalable symbol management. Being lock-free, the new implementation also provides better guarantees, namely, immunity to priority inversion, to deadlocks and to livelocks. Performance results show that the new lock-free LFHT implementation has better results in single threaded execution and much better scalability than the original lock based dynamically resizing hash table.

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

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

2023

Towards Early detection of faults and failures in complex systems

Autor
Christopher David Harrison

Instituição
UP-FCUP

2023

Towards Early detection of faults and failures in complex systems

Autor
Christopher David Harrison

Instituição
UP-FCUP

2023

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

Autor
Diogo Roberto de Melo e Diogo Machado

Instituição
UP-FCUP

2023

Overcoming the current limitations of Reinforcement Learning towards Artificial General Intelligence

Autor
Filipe Emanuel dos Santos Marinho da Rocha

Instituição
UP-FCUP

2023

Type Assignment in Logic Programming

Autor
João Luis Alves Barbosa

Instituição
UP-FCUP