Las perífrasis verbales en contraste

ReseñaReviewObra ressenyada: L. GARCÍA FERNÁNDEZ; G.GABRIEL KRIVOCHE, Las perífrasis verbales en contraste. Madrid, Arco-Libros, 2019

"Otrosí digo": La variación gramatical en el español jurídico para extranjeros

Presentación de la comunicación presentada en la I Jornadas de Español para Fines Específicos de la Universidad de Viena

Traceability for trustworthy AI: a review of models and tools

Traceability is considered a key requirement for trustworthy artificial intelligence (AI), related to the need to maintain a complete account of the provenance of data, processes, and artifacts involved in the production of an AI model. Traceability in AI shares part of its scope with general purpose recommendations for provenance as W3C PROV, and it is also supported to different extents by specific tools used by practitioners as part of their efforts in making data analytic processes reproducible or repeatable. Here, we review relevant tools, practices, and data models for traceability in their connection to building AI models and systems. We also propose some minimal requirements to consider a model traceable according to the assessment list of the High-Level Expert Group on AI. Our review shows how, although a good number of reproducibility tools are available, a common approach is currently lacking, together with the need for shared semantics. Besides, we have detected that some tools have either not achieved full maturity, or are already falling into obsolescence or in a state of near abandonment by its developers, which might compromise the reproducibility of the research trusted to them

Comparing the Performance of Deep Learning Methods to Predict Companies' Financial Failure

This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades under Project RTI2018-102002-A-I00, in part by the Ministerio de Economia y Competitividad under Project TIN2017-85727-C4-2-P and Project PID2020-115570GB-C22, in part by the Fondo Europeo de Desarrollo Regional (FEDER) and Junta de Andalucia under Project B-TIC-402-UGR18, and in part by the Junta de Andalucia under Project P18-RT-4830.One of the most crucial problems in the eld of business is nancial forecasting. Many companies are interested in forecasting their incoming nancial status in order to adapt to the current nancial and business environment to avoid bankruptcy. In this work, due to the effectiveness of Deep Learning methods with respect to classi cation tasks, we compare the performance of three well-known Deep Learning methods (Long-Short Term Memory, Deep Belief Network and Multilayer Perceptron model of 6 layers) with three bagging ensemble classi ers (Random Forest, Support Vector Machine and K-Nearest Neighbor) and two boosting ensemble classi ers (Adaptive Boosting and Extreme Gradient Boosting) in companies' nancial failure prediction. Because of the inherent nature of the problem addressed, three extremely imbalanced datasets of Spanish, Taiwanese and Polish companies' data have been considered in this study. Thus, ve oversampling balancing techniques, two hybrid balancing techniques (oversamplingundersampling) and one clustering-based balancing technique have been applied to avoid data inconsistency problem. Considering the real nancial data complexity level and type, the results show that the Multilayer Perceptron model of 6 layers, in conjunction with SMOTE-ENN balancing method, yielded the best performance according to the accuracy, recall and type II error metrics. In addition, Long-Short Term Memory and ensemble methods obtained also very good results, outperforming several classi ers used in previous studies with the same datasets.Ministerio de Ciencia, Innovacion y Universidades RTI2018-102002-A-I00Spanish Government TIN2017-85727-C4-2-P PID2020-115570GB-C22European Commission B-TIC-402-UGR18Junta de Andalucia B-TIC-402-UGR18 P18-RT-483

Authority-based conversation tracking in Twitter: an unattended methodological approach

Twitter is undoubtedly one of the most widely used data sources to analyze human communication. The literature is full of examples where Twitter is accessed, and data are downloaded as the previous step to a more in-depth analysis in a wide variety of knowledge areas. Unfortunately, the extraction of relevant information from the opinions that users freely express in Twitter is complicated, both because of the volume generated—more than 6000 tweets per second—and the difficulties related to filtering out only what is pertinent to our research. Inspired by the fact that a large part of users use Twitter to communicate or receive political information, we created a method that allows for the monitoring of a set of users (which we will call authorities) and the tracking of the information published by them about an event. Our approach consists of dynamically and automatically monitoring the hottest topics among all the conversations where the authorities are involved, and retrieving the tweets in connection with those topics, filtering other conversations out. Although our case study involves the method being applied to the political discussions held during the Spanish general, local, and European elections of April/May 2019, the method is equally applicable to many other contexts, such as sporting events, marketing campaigns, or health crises

Evolution and prospects of the Comprehensive R Archive Network (CRAN) package ecosystem

Free and open source software package ecosystems have existed for a long time, but such collaborative development practice has surged in recent years. One of the oldest and most popular package ecosystems is Comprehensive R Archive Network (CRAN), the repository of packages of the statistical language R, a popular statistical computing environment. CRAN stores a large number of packages that are updated regularly and depend on many other packages in a complex graph of relations. As the repository grows, its sustainability could be threatened by that complexity or nonuniform evolution of some packages. This paper provides an empirical analysis of the evolution of the CRAN repository in the last 20 years, considering the laws of software evolution and the effect of CRAN's policies on such development. Results show how the progress of CRAN is consistent with the laws of continuous growth and change and how there seems to be a relevant increase in complexity in recent years. Significant challenges are raising related to the scale and scope of software package managers and the services they provide; understanding how they change over time and what might endanger their sustainability are key factors for their future improvement, maintenance, policies, and, eventually, sustainability of the ecosystem

Integration of a wearable mobile mapping solution and advance numerical simulations for the structural analysis of historical constructions: a case of study in San Pedro church (Palencia, Spain)

This work aims at enhancing the current methodologies used for generating as-built CAD models suitable for advanced numerical simulations. To this end, this paper proposes the use of a wearable mobile mapping system that allows one to improve the digitalization stage in terms of flexibility and time required. The noise showed by the resulting point cloud, based on the simultaneous location and mapping (SLAM) solution, demands a post-processing stage that introduces the use of a parameter-free noise reduction filter. This filter improves the quality of the point cloud, allowing for the adjustment of surfaces by means of parametric and non-parametric shapes. These shapes are created by using reverse engineering procedures. The results showed during this investigation highlight a novel application of this sensor: the creation of as-built CAD models for advanced numerical simulations. The results of this investigation are complemented by a valuable contribution with respect to the use of an advanced restoration solution, by means of textile reinforced mortar. To this end, the CAD model is used as the geometrical base for several numerical simulations by means of the finite element method. All this procedure is applied in a construction with structural problems.European Commission | Ref. SOE1 / P5 / P0258Junta de Castilla y León | Ref. SA075P17Junta de Castilla y León | Ref. EDU / 1100/2017European Commission | Ref. H2020-MSCA-IF-2019, n. 679 894785; proyecto AVATA

Aplicación de modelos de redes neuronales artificiales a la determinación de movimientos en una presa bóveda.

The complexity of the dam-foundation makes difficult to interpret the records of auscultation. It is not easy to make a prognosis of the behavior of the dam in accordance with the different situations that may occur over the life of the dam. N or is it easy to interpret the deviations observed from the values estimated by numerical or statistical modeling. Neural networks offer the possibility of treating the whole dam foundation as a complex system which rules of behavior are established solely on the basis of the observed behavior without making simplifying assumptions. It has shaped the radial movement in different seasons of the pendulums available in a dam vault, which has been used as test case. We analyzed the response of the dam complex models growing. T he simplest considered only as pa r ame t e rs determining the behavior reservoir level and a moving average of ambient temperatures (simple model). In order to model applicable to any pendulum of any dam, has raised the consideration of various means mobile temperature, so that is the model itself that determines the weight of each moving average (model general). Have also been raised as da ta models that incorporate the values of the movements measured immediately prior (short-term models). The comparison of results obtained through each of the previous models of neural networks with statistical and numerical models commonly used that allows the use of neural network models useful for the interpretation of auscultation. The submission details the results obtained and the accuracy of the prognosis of each of the models studied. Models are expected to nalyze incorporate additional time variable (dynamic models), to c apture the effect of drift. This research is pa rt of the R&D financed by the Ministerio de Ciencia e Innovación: estudio de la seguridad de presas e identificación de escenarios de riesgo mediante sistemas inteligentes, reference number 048/RN08/04.5

Uso de la reacción en cadena de la polimerasa para el control terapéutico de la infección crónica por trypanosoma cruzi

En las normas actuales de tratamiento etiológico de la fase crónica tardía de la enfermedad de Chagas en Argentina, Brasil y la Organización Mundial de la Salud, se recomienda controlar la eficacia terapéutica con pruebas serológicas y parasitológicas convencionales. Sin embargo las primeras suelen continuar positivas 10 años o más luego del tratamiento, y las segundas son, en general, de baja sensibilidad en esta etapa de la enfermedad. La Reacción en Cadena de la Polimerasa (PCR)al ser más sensible que los exámenes parasitológicos convencionales, podría informar con una cobertura mayor si hubo falla terapéutica. Hemos ofrecido tratamiento con benznidazol (5 mg/kg/día, por 60 días) a 138 pacientes de 16 a 35 años de edad, infectados crónicamente con Trypanosoma cruzi. La eficacia terapéutica se controló con PCR periódicas, hemocultivo y serología convencional en dos grupos de pacientes: uno (GT, 57 pacientes) que aceptó y cumplió el tratamiento y otro (GNT, 37 pacientes) que lo rechazó. Antes de la administración de benznidazol la PCR mostró una sensibilidad diagnóstica de 41% (57/138 pacientes) y el hemocultivo 7,2% (10/138). Sesenta meses postratamiento el grupo GT mostró una positividad de PCR acumulada de 28,1% (16/57) y el grupo GNT 54,1% (20/37; p=0.0016). A pesar de que la sensibilidad diagnóstica de PCR es limitada, la negatividad de pruebas repetidas con método normatizado podría evidenciar disminución de la parasitemia o probable curación en 71,9% de los pacientes tratados, lo que habría que confirmar con el seguimiento serológico.Fil: Lacunza, Carlos Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; ArgentinaFil: Sánchez Negrette, Olga. Universidad Nacional de Salta; ArgentinaFil: Mora, Maria Celia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; ArgentinaFil: Garcia Bustos, Maria Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; ArgentinaFil: Basombrío, Miguel Ángel Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; Argentin

Modeling Bacterial Species: Using Sequence Similarity with Clustering Techniques

Existing studies have challenged the current definition of named bacterial species, especially in the case of highly recombinogenic bacteria. This has led to considering the use of computational procedures to examine potential bacterial clusters that are not identified by species naming. This paper describes the use of sequence data obtained from MLST databases as input for a k-means algorithm extended to deal with housekeeping gene sequences as a metric of similarity for the clustering process. An implementation of the k-means algorithm has been developed based on an existing source code implementation, and it has been evaluated against MLST data. Results point out to potential bacterial clusters that are close to more than one different named species and thus may become candidates for alternative classifications accounting for genotypic information. The use of hierarchical clustering with sequence comparison as similarity metric has the potential to find clusters different from named species by using a more informed cluster formation strategy than a conventional nominal variant of the algorithm