46 research outputs found

    El Proyecto fin de carrera como medio conductor para la iniciación a la investigación

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    Los proyectos fin de carrera son una herramienta útil para la atracción de estudiantes hacia las líneas de investigación de los distintos profesores. En base a la experiencia de los autores como profesores este artículo pretende presentar algunos de los principales errores que se comenten cuando se pretende utilizar los proyectos fin de carrera como mecanismo para introducir a los alumnos en el mundo de la investigación. De la misma manera se presentan algunas pautas para evitar caer en dichos errores.Peer Reviewe

    CUESTOR: Una nueva aproximación integral a la evaluación automática de prácticas de programación

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    A pesar de que existen diversas aproximaciones para la evaluación automática de prácticas de programación, su aplicación fuera de los entornos en que fueron diseñados no siempre es posible. En este trabajo se presenta una nueva plataforma abierta que proporciona los mecanismos necesarios para realizar una evaluación completa de un ejercicio de programación realizado en C o en Java. Este proceso de evaluación incluye la verificación del cumplimiento de los requisitos especificados, el método de resolución, la calidad del código fuente y la comprobación del plagio. El funcionamiento de cada uno de los componentes de evaluación ha sido verificado de forma exhaustiva mediante la utilización de las entregas realizadas por los alumnos en años anteriores.Peer Reviewe

    GoogleWave: Una herramienta para la evaluación de trabajos realizados fuera del aula

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    La evaluación de los trabajos en grupo es siempre difícil para el profesorado porque éste desconoce la cantidad de esfuerzo que ha dedicado cada alumno al trabajo asignado. Este artículo pretende presentar la herramienta Google Wave1 como una herramienta capaz de aportar una serie de funcionalidades no aportadas anteriormente por ninguna otra herramienta que facilitan al profesorado la evaluación del esfuerzo de cada alumno durante la realización de un trabajo en grupo.Peer Reviewe

    La Sobre-evaluación

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    Este trabajo resume una experiencia negativa vivida durante la impartición de una asignatura encuadrada dentro del Espacio Europeo de Educación Superior (EEES) debido a una mala planificación de la evaluación que desemboca en lo que denominaremos sobre-evaluación. Por sobre-evaluación se entiende el excesivo número de pruebas a las que se somete al alumno y que le obligan a pasar más tiempo preparándolas que adquiriendo o asentando conocimientos. Y como consecuencia de esa experiencia negativa se presentan los medios que se han puesto para evitar repetir dicha experiencia en el curso siguiente en la misma asignatura así como las algunas de las conclusiones alcanzadas por los profesores de dicha asignatura.SUMMARY: This work summarizes a negative experience happened during the classes of a subject within the European Space for Higher Education, due to a wrong planning which provoked the so-called phenomenon: over-assessment. Over-assessment means the excessive number of tasks that a student must do. As a result of this fact, each student spends more time preparing his tasks than learning new knowledge. From this negative experiment, the following course the same teachers applied several modifications with respect to that subject in order to avoid past errors. These modifications allowed improving the results obtained by the students, especially, due to the better planning of the tasks proposed to assess students.Peer Reviewe

    The current role of machine learning and explainability in actuarial science

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    Actuarial science seeks to evaluate, predict and manage the impact of future events. Nowadays, the actuary faces the challenge of predicting and managing risks efficiently, with a universe of information growing exponentially in real-time and with a business dynamic that demands constant competitiveness and innovation. The techniques associated with data engineering and data science open a window of tools that seek, through technology, to improve the processes of product design, pricing, reserves and establishment of market niches practically and realistically, considering the pros and cons that brings the availability and constant updating of information, as well as the computational times that this implies. Therefore, this article aims to review the application of Explainable Machine Learning techniques as an alternative to the development of more efficient and practical actuarial models.Facultad de Informátic

    A T1OWA Fuzzy Linguistic Aggregation Methodology for Searching Feature-based Opinions.

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Online services such as Amazon, Tripadvisor, Ebay, etc., allow users to express sentiments about different products or services. Not only that, in some cases it is also possible to express sentiments about the different features characterizing those products or services. Most users express sentiments about individual features by using numerical values, which sometimes do not allow users to reflect properly what they are meaning and therefore they are misleading. To overcome this key issue and make users’ opinions in online services more comprehensive, a new methodology for representing sentiments using linguistic term sets instead of numerical values is presented. In addition, this methodology will allow to implement importance degrees on the different features characterizing users’ opinions. From both sentiments and importance of the features, the most important opinions for each user is derived via an aggregation step based on the Type-1 Ordered Weighted Averaging (T1OWA) operator, which is able to aggregate the corresponding fuzzy set representations of linguistic terms. Furthermore, the final output of the T1OWA based-search process can easily be interpreted by users because it is always of the same type (fuzzy) and defined in the same domain of the original fuzzy linguistic labels. A case study is presented where the T1OWA operator methodology is used to assess different opinions according to different user profiles

    Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

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    Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis

    Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

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    BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years
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