ISR-WN: Integration of semantic resources based on WordNet

La presente herramienta informática constituye un software que es capaz concebir una red semántica con los siguientes recursos: WordNet versión 1.6 y 2.0, WordNet Affects versión 1.0 y 1.1, WordNet Domain versión 2.0, SUMO, Semantic Classes y Senti WordNet versión 3.0, todos integrados y relacionados en una única base de conocimiento. Utilizando estos recursos, ISR-WN cuenta con funcionalidades añadidas que permiten la exploración de dicha red de un modo simple aplicando funciones tanto como de recorrido como de búsquedas textuales. Mediante la interrogación de dicha red semántica es posible obtener información para enriquecer textos, como puede ser obtener las definiciones de aquellas palabras que son de uso común en determinados Dominios en general, dominios emocionales, y otras conceptualizaciones, además de conocer de un determinado sentido de una palabra su valoración proporcionada por el recurso SentiWordnet de positividad, negatividad y objetividad sentimental. Toda esta información puede ser utilizada en tareas de procesamiento del lenguaje natural como: • Desambiguación del Sentido de las Palabras, • Detección de la Polaridad Sentimental • Análisis Semántico y Léxico para la obtención de conceptos relevantes en una frase según el tipo de recurso implicado. Esta herramienta tiene como base el idioma inglés y se encuentra disponible como una aplicación de Windows la cual dispone de un archivo de instalación el cual despliega en el ordenador de residencia las librerías necesarias para su correcta utilización. Además de la interfaz de usuario ofrecida, esta herramienta puede ser utilizada como API (Application Programming Interface) por otras aplicaciones

There are no rigid filiform Lie algebras of low dimension

We prove that there are no rigid complex filiform Lie algebras in the variety of (filiform) Lie algebras of dimension less than or equal to 11. More precisely we show that in any Euclidean neighborhood of a filiform Lie bracket (of low dimension), there is a non-isomorphic filiform Lie bracket. This follows by constructing non-trivial linear deformations in a Zariski open dense set of the variety of filiform Lie algebras of dimension 9, 10 and 11 (in lower dimensions this is well known.)Fil: Vera, Sonia Vanesa. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; ArgentinaFil: Tirao, Paulo Andres. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Estudios de Matemática. Universidad Nacional de Córdoba. Centro de Investigación y Estudios de Matemática; Argentin

Effect of Sunflower and Marine Oils on Ruminal Microbiota, In vitro Fermentation and Digesta Fatty Acid Profile

Funding This work has been funded by Consejería de Educación, Junta de Castilla y León (research project LE007A07). Acknowledgments We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI). Support received from CICYT project AGL2005-04760-C02-02 is gratefully acknowledged.Peer reviewedPublisher PD

Resilient and Survivable Ring Star Problems

In this paper, we consider both the Resilient Ring Star Problem, in which a solution should be easy to fix when a single hub fails, and the Survivable Ring Star Problem, in which a solution guarantees that a Ring Star topology is available at no cost when a single hub fails. An ILP formulation is proposed for both problems, as well as a Benders decomposition. The solution provided by both problems are also compared in order to determine which problem returns the most appropriate solutions, when the failure rate varies.Comment: ROADEF 2023, ROADEF, Feb 2023, Rennes, Franc

Classification of Cognitive Evoked Potentials for ADHD Detection in Children using Recurrence Plots and CNNs

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood-onset condition characterized by difficulty paying attention and hyperactivity. The diagnosis of ADHD is made from psychological tests and electroencephalography (EEG). However, patient cooperation is necessary, which is a challenge with ADHD children. This work proposes a method for classification of ADHD and control cases from cognitive event-related potentials using recurrence plots and deep learning. A total of 44 children were included in this study (22 children with ADHD and 22 case controls). The signals were processed by a high-pass filter to eliminate DC components, wavelets transform with six decomposition levels, and synchronized averaging for each of the six channels (F3, AF3, F4, AF4, F7 and F8). Subsequently, the recurrence plot of each of the processed signals was obtained and used as inputs for two convolutional neural networks (CNN). The proposed models showed accuracies of 69.44% and 77,78%. © 2021 IEE

Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs

The convolutional neural networks (CNNs) as tools for ultrasound image segmentation often have their performance affected by the low signal-to-noise ratio of the images. This prevents a correct classification and extraction of relevant information and therefore affects clinical diagnosis. We propose a study of the effect of different speckle filtering methods on CNN performance. For the proposed metrics (Jaccard coefficient and BF-Score), it was obtained that the SRAD filter exhibited the best behavior even in the lowest quality data. In addition, the lowest values were obtained for the standard deviation and variance, which translates into lower data dispersion, better repeatability, and, therefore, greater confidence in its accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG

Spreading semantic information by Word Sense Disambiguation

This paper presents an unsupervised approach to solve semantic ambiguity based on the integration of the Personalized PageRank algorithm with word-sense frequency information. Natural Language tasks such as Machine Translation or Recommender Systems are likely to be enriched by our approach, which includes semantic information that obtains the appropriate word-sense via support from two sources: a multidimensional network that includes a set of different resources (i.e. WordNet, WordNet Domains, WordNet Affect, SUMO and Semantic Classes); and the information provided by word-sense frequencies and word-sense collocation from the SemCor Corpus. Our series of results were analyzed and compared against the results of several renowned studies using SensEval-2, SensEval-3 and SemEval-2013 datasets. After conducting several experiments, our procedure produced the best results in the unsupervised procedure category taking SensEval campaigns rankings as reference.This research work has been partially funded by the University of Alicante, Generalitat Valenciana , Spanish Government, Ministerio de Educación, Cultura y Deporte and ASAP - Ayudas Fundación BBVA a equipos de investigación científica 2016(FUNDACIONBBVA2-16PREMIO) through the projects, TIN2015- 65100-R, TIN2015-65136-C2-2-R, PROMETEOII/2014/001, GRE16- 01: “Plataforma inteligente para recuperación, análisis y representación de la información generada por usuarios en Internet” and PR16_SOC_0013

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government and the European Commission through the Projects, TIN2015-65136-C2-2- R, TIN2015-65100-R, SAM (FP7-611312), and PROMETEOII/2014/001