Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Interest
Topics
Details

Details

  • Name

    Vítor Santos Costa
  • Role

    Senior Researcher
  • Since

    01st January 2009
006
Publications

2024

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

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

Publication
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

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

Publication
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

Authors
Guimaraes, V; Costa, VS;

Publication
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

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

Publication
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

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

Publication
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.

Supervised
thesis

2023

Towards Early detection of faults and failures in complex systems

Author
Christopher David Harrison

Institution
UP-FCUP

2023

Towards Early detection of faults and failures in complex systems

Author
Christopher David Harrison

Institution
UP-FCUP

2023

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

Author
Diogo Roberto de Melo e Diogo Machado

Institution
UP-FCUP

2023

Overcoming the current limitations of Reinforcement Learning towards Artificial General Intelligence

Author
Filipe Emanuel dos Santos Marinho da Rocha

Institution
UP-FCUP

2023

Type Assignment in Logic Programming

Author
João Luis Alves Barbosa

Institution
UP-FCUP