On automation and certification of a homological method to process biomedical digital images

In this paper a methodology to extract and compute homological information from biomedical images is proposed; automating some processes, up to now, manually done. The main features of our approach are the usage of several programming languages (Java, Common Lisp and Haskell) and the application of formal methods (namely theorem provers) to verify the correctness of some of the automated process. As case study to test the suitability of our approach, we have applied it to measure the number of synapses of a neuron

Application of multi-regression machine learning algorithms to solve ocean water mass mixing in the Atlantic Ocean

The distribution of any non-conservative variable in the deep open ocean results from the circulation and mixing of water masses (WMs) of contrasting origin and from the initial preformed composition, modified during ongoing simultaneous biological and/or geochemical processes. Estimating the contribution of the WMs composing a sample is useful to trace the distribution of each water mass and to quantitatively separate the physical (mixing) and biogeochemical components of the variability of any, non- conservative variable (e.g., dissolved organic carbon, prokaryote biomass) in the ocean. Other than potential temperature and salinity, additional semi-conservative and non-conservative variables have been used to solve the mixing of more than three water masses using Optimum Multi-Parameter (OMP) approaches. Successful application of an OMP analysis requires knowledge of the characteristics of the water masses in their source regions as well as their circulation and mixing patterns. Here, we propose the application of multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). Our model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Therefore, basic hydrographic data collected during typical research cruises or autonomous systems can be used as input variables and provide results in real time. The model can be fed with new solutions from compatible OMP analyses as well as with new water masses not previously considered in it. Our tool will provide knowledge on water mass composition and distribution to a broader community of marine scientists not specialized in OMP analysis and/or in the oceanography of the studied area. This will allow a quantitative analysis of the effect of water mass mixing on the variables or processes under study

Creación de un servidor de integración continua para gestión y corrección de entregas de prácticas

En este trabajo se presenta un servidor web público (http://iscima.unirioja.es/) desarrollado para permitir, tanto a estudiantes como a profesores, comprobar que los envíos de prácticas de programación cumplen una serie de requisitos mínimos (en cuanto a estructura de directorios, compilación y comprobación de tests). Para completar el trabajo se muestra un caso de uso de aplicación de la herramienta en una asignatura y las conclusiones de su uso. Además del servidor, que automatiza y reduce el trabajo de los profesores, también se ha aprovechado su utilización para inculcar a los estudiantes conceptos y metodologías propias de Ingeniería del Software como la entrega continua y el uso de tests. En este trabajo se presenta dicha herramienta informática, sus soluciones tecnológicas, y una valoración de los cambios detectados en el aprendizaje de los estudiantes por el uso de la misma.In this work, we present a web application (http://iscima.unirioja.es/) devoted to allow both students and teachers to check that lab submissions satisfy a set of minimal requirements (related to structure, compilation and satisfiability of tests). Moreover, we present a case study where the tool is applied to a programming module and the conclusions obtained from its use. The web application is not only used to automatize and reduce the teachers’ work, but it also serves to introduce concepts about continuous integration and testing to the students. In this paper, we introduce the web application, its technological solutions, and an evaluation of the tool based on the students’ improvements.Trabajo parcialmente financiado por el Vicerrectorado de Profesorado de la Universidad de La Rioja a través de un proyecto de innovación docente, y por el Plan de financiación de los grupos de investigación de la Universidad de La Rioja [REGI2018/52]

Perplexity as a tool for the allocation of proficiency levels to utterances written by foreign language learners

La asignación de niveles de competencia a escritos producidos por aprendices de una lengua es una tarea altamente subjetiva. Es por esto que el desarrollo de métodos que evalúen escritos de manera automática puede ayudar tanto al profesorado como al alumnado. En este trabajo, hemos explorado dos vías mediante el uso del corpus CAES. Dicho corpus está formado por escritos de aprendices de español y etiquetado con niveles CEFR (hasta el C1). La primera aproximación es un modelo de aprendizaje profundo llamado Deep-ELE que asigna niveles de competencia a las frases. La segunda aproximación llevada a cabo ha consistido en estudiar la perplejidad de las frases de los estudiantes de distintos niveles, para luego clasificarlos en niveles. Ambas aproximaciones han sido evaluadas, y se ha comprobado que pueden usarse de manera exitosa para clasificar frases por niveles. En concreto, el modelo Deep-ELE obtiene una accuracy de 81,3% y un QWK de 0,83. Como conclusión, este trabajo es un paso para entender cómo las herramientas del procesado de lenguaje natural pueden ayudar a las personas que aprenden un segundo idioma.The allocation of proficiency levels to utterances written by foreign language learners is a subjective task. Therefore, the development of methods to automatically evaluate written sentences can help both students and teachers. In this work, we have explored two different approaches to tackle this task by using the corpus CAES, which contains written utterances of learners of Spanish labelled with CEFR levels (up to C1). The first approach is a deep learning model called Deep-ELE which assigns proficiency levels to sentences. The second approach consists in studying the perplexity of sentences written by students of different levels, to later allocate levels to those sentences based on such an analysis. Both approaches have been evaluated, and results confirm that they can be used to successfully classify written sentences into proficiency levels. In particular, the Deep-ELE model reaches an accuracy of 81.3% and a weighted Cohen Kappa of 0.83. As a conclusion, this work is a step towards better understanding how natural language processing methods can help learners of a second language.Esta investigación ha sido parcialmente financiada por los proyectos AFIANZA 2022/02, PID2020-115225RB-I00 de MCIN/AEI/ 10.13039/501100011033 y PID2020-116641GB-I00 de MCIN/AEI/10.13039/501100011033

Towards a certified computation of homology groups for digital images

International audienceIn this paper we report on a project to obtain a verified computation of homology groups of digital images. The methodology is based on program- ming and executing inside the COQ proof assistant. Though more research is needed to integrate and make efficient more processing tools, we present some examples partially computed in COQ from real biomedical images

La perplejidad como herramienta para estimar la asignación de nivel de competencia en escritos de una lengua extranjera

The allocation of proficiency levels to utterances written by foreign language learners is a subjective task. Therefore, the development of methods to automatically evaluate written sentences can help both students and teachers. In this work, we have explored two different approaches to tackle this task by using the corpus CAES, which contains written utterances of learners of Spanish labelled with CEFR levels (up to C1). The first approach is a deep learning model called Deep-ELE which assigns proficiency levels to sentences. The second approach consists in studying the perplexity of sentences written by students of different levels, to later allocate levels to those sentences based on such an analysis. Both approaches have been evaluated, and results confirm that they can be used to successfully classify written sentences into proficiency levels. In particular, the Deep-ELE model reaches an accuracy of 81.3% and a weighted Cohen Kappa of 0.83. As a conclusion, this work is a step towards better understanding how natural language processing methods can help learners of a second language.La asignación de niveles de competencia a escritos producidos por aprendices de una lengua es una tarea altamente subjetiva. Es por esto que el desarrollo de métodos que evalúen escritos de manera automática puede ayudar tanto al profesorado como al alumnado. En este trabajo, hemos explorado dos vías mediante el uso del corpus CAES. Dicho corpus está formado por escritos de aprendices de español y etiquetado con niveles CEFR (hasta el C1). La primera aproximación es un modelo de aprendizaje profundo llamado Deep-ELE que asigna niveles de competencia a las frases. La segunda aproximación llevada a cabo ha consistido en estudiar la perplejidad de las frases de los estudiantes de distintos niveles, para luego clasificarlos en niveles. Ambas aproximaciones han sido evaluadas, y se ha comprobado que pueden usarse de manera exitosa para clasificar frases por niveles. En concreto, el modelo Deep-ELE obtiene una accuracy de 81,3% y un QWK de 0,83. Como conclusión, este trabajo es un paso para entender cómo las herramientas del procesado de lenguaje natural pueden ayudar a las personas que aprenden un segundo idioma