Catálogo de publicaciones - libros

Object-oriented software development has become the de-facto standard programming paradigm used in modern software systems. Additionally genericity has grown more popular since the enhancement of Java and C#. This paper attempts to reconsider the principles of compiler construction from this modern, object-oriented point of view. We present a multi-paradigm, mainly object-oriented and generic approach for creating a compiler generator using a combination of the Interpreter pattern and the Visitor pattern. A prototype of such an object-oriented and generic compiler generator has also been developed using C# 2.0 and will serve as a reference to explain the design throughout this paper.

A perception scheme for Reinforcement Learning (RL) is developed as a function approximator. The main motivation for the development of this scheme is the need for generalization when the problem to be solved has continuous state variables. We propose a solution to the generalization problem in RL algorithms using a k-nearest-neighbor pattern classification (k-NN). By means of the k-NN technique we investigate the effect of collective decision making as a mechanism of perception and action-selection and a sort of back-propagation of its proportional influence in the action-selection process as the factor that moderate the learning of each decision making unit. A very well known problem is presented as a case study to illustrate the results of this k-NN based perception scheme.

A detailed analysis is carried to show that a Brownian motor driven by suitable double-well potentials randomly alternating with realistically chosen parameters can account for the experimentally evaluated relevant quantities.

Missing values in genetic data are a common issue. In this paper we explore several machine learning techniques for creating models that can be used to impute the missing genotypes using multiple genetic markers. We map the machine learning techniques to different patterns of transmission and, in particular, we contrast the effect of short and long range disequilibrium between markers. The assumption of short range disequilibrium implies that only physically close genetic variants are informative for reconstructing missing genotypes, while this assumption is relaxed in long range disequilibrium and physically distant genetic variants become informative for imputation. We evaluate the accuracy of a flexible feature selection model that fits both patterns of transmission using six real datasets of single nucleotide polymorphisms (SNP). The results show an increased accuracy compared to standard imputation models. [Supplementary material] http://bios.ugr.es/missingGenotypes

The paper deals with the application of the EEG temporal profiles for the neonatal sleep stages modelling. The temporal profiles created by adaptive segmentation and cluster analysis reflect the time structure of the EEG during different periods of sleep. They can be used for neonatal EEG quantification and for the detection of sleep stage changes.

Agile access to the huge amount of information published by the thousands of news sites available on-line leads to the application of Information Retrieval techniques to this problem. The aim of this paper is to present , a news retrieval system that obtains the articles from different on-line sources providing news searching and browsing. The main points solved during the development of were: article recognition and extraction, redundancy detection and text summarization. For these points we provided effective solutions that put all them together had risen to a system that satisfies, in a reasonable way, the daily information needs of the user.

Having access to large quantities of storage space has always been a problem. Traditionally, large storage capacity has been expensive, using dedicated storage systems (NAS), or more complex networked storage (SAN). In many cases these solutions are overkill both in price and in features. Many small offices and research labs with limited budget have computers with unused disk space that could be used as shared storage.

A distributed filesystem can take advantage of the storage space of several computers to provide a larger storage. For a small office or lab this filesystem would have to provide easy integration into the infrastructure and reasonable scalability.

In this work we propose a filesystem that provides access to a large storage using the unused space in a network of workstations.

Parsing schemata provide a formal, simple and uniform way to describe, analyze and compare different parsing algorithms. The notion of a parsing schema comes from considering parsing as a deduction process which generates intermediate results called . An initial set of items is directly obtained from the input sentence, and the parsing process consists of the application of inference rules (called ) which produce new items from existing ones. Each item contains a piece of information about the sentence’s structure, and a successful parsing process will produce at least one containing a full parse tree for the sentence or guaranteeing its existence. Their abstraction of low-level details makes parsing schemata useful to define parsers in a simple and straightforward way. Comparing parsers, or considering aspects such as their correction and completeness or their computational complexity, also becomes easier if we think in terms of schemata. However, when we want to actually use a parser by running it on a computer, we need to implement it in a programming language, so we have to abandon the high level of abstraction and worry about implementation details that were irrelevant at the schema level. In particular, we study in this article how the source parsing schema should be analysed to decide what kind of indexes need to be generated in order to obtain an efficient parser.

In this paper, we present a new approximation in Natural Language Processing () aimed at knowledge representation and acquisition using a formal syntactic frame. In practice, we introduce our implementation on an encyclopedic corpus in a botanic domain, illustrating the algorithm on a set of preliminary tests.

Sentence word segmentation is an important task in robust part-of-speech (POS) tagging systems. In some cases this is relatively simple, since each textual word (or token) corresponds to one linguistic component. However, there are many others where segmentation can be very hard, such as those of contractions, verbal forms with enclitic pronouns, etc., where the same token contains information about two or more linguistic components.