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Enric Marti; Jaume Rocarias; Ricardo Toledo; Aura Hernandez-Sabate |
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Title |
Caronte: plataforma Moodle con gestion flexible de grupos. Primeras experiencias en asignaturas de Ingenieria Informatica |
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Miscellaneous |
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2009 |
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15th Jornadas de Enseñanza Universitaria de la Informatica |
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461–468 |
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En este artículo se presenta Caronte, entorno LMS (Learning Management System) basado en Moodle. Una característica importante del entorno es la gestión flexible de grupos en una asignatura. Entendemos por grupo un conjunto de alumnos que realizan un trabajo y uno de ellos entrega la actividad propuesta (práctica, encuesta, etc.) en representación del grupo. Hemos trabajado en la confección de estos grupos, implementando un sistema de inscripción por contraseña.
Caronte ofrece un conjunto de actividades basadas en este concepto de grupo: encuestas, tareas (entrega de trabajos o prácticas), encuestas de autoevaluación y cuestionarios, entre otras.
Basada en nuestra actividad de encuesta, hemos definido una actividad de Control, que permite un cierto feedback electrónico del profesor sobre la actividad de los alumnos.
Finalmente, se presenta un resumen de las experiencias de uso de Caronte sobre asignaturas de Ingeniería Informática en el curso 2007-08. |
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Barcelona, Spain |
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978-84-692-2758-9 |
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JENUI |
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IAM;RV;ADAS |
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IAM @ iam @ MRT2009 |
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1202 |
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Author |
Enric Marti; Jordi Rocarias; Ricardo Toledo |
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Title |
Caront: gestió flexible de grups d’alumnes en una asignatura i activitats sobre grups. Nova activitat de control |
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2008 |
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V Jornades d’Innovació Docent |
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IAM;RV;CIC;ADAS |
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IAM @ iam @ MRT2008a |
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1617 |
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Enric Marti; Petia Radeva; Ricardo Toledo; Jordi Vitria |
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Title |
Experiencia de aplicación de la metodología de aprendizaje por proyectos en asignaturas de Ingeniería Informática para una mejor adaptación a los créditos ECTS i al Espacio Europeo de Educación Superior |
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2005 |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR) |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR) |
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IAM;RV;OR;MILAB;ADAS;MV |
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IAM @ iam @ MRT2005 |
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1608 |
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Enric Marti; S.Fernandez; G.Estape |
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BlueStar: implementació d’una aplicació per a la visualització 3D de funcions reals, com a eina d’aprenentatge |
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2007 |
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IV Jornades d’Innovació Docent UAB |
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Bellaterra (Spain) |
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IAM; |
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IAM @ iam @ MFE2007 |
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1591 |
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Enric Marti; Xavier Binefa; G.EstapeRV |
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Title |
Caronte, plataforma para la gestión de la actividad docente de una asignatura. Análisis de su impacto en ingenierías, para su adaptación al EEES |
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2008 |
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, Ministerio de Ciencia e Innovacion, DGU |
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Direccion General de Universidades |
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IAM;OR;RV |
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IAM @ iam @ MBE2008 |
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1589 |
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Author |
Fernando Vilariño; Enric Marti |
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New didactic techniques in the EHES applying mobile technologies |
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2008 |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestió d’Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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Agencia de Gestio d´Ajuts Universitaris I de Recerca (AGAUR), Generalitat de Catalunya |
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MILAB;IAM;MV;SIAI |
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IAM @ iam @ VIM2008 |
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1664 |
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G.Estape; Enric Marti |
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Title |
L’ús d’aplicacions de visualització 3D com a eina d’aprenenetatge en activitats formatives dirigides i autònomes: el cas del programa Bluestar |
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2008 |
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V Jornades d’Innovació Docent UAB |
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IAM |
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IAM @ iam @ ESM2008 |
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1495 |
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J.L.Bruguera; R.Casado; M.Martinez; I.Corral; Enric Marti; L.A.Branda |
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El apoyo institucional como elemento favorecedor de la coordinación docente: experiencias en diferentes universidades |
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2009 |
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Revista Red-U |
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IAM |
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IAM @ iam @ BCM2009 |
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1490 |
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Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
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A flexible outlier detector based on a topology given by graph communities |
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2020 |
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Arxiv |
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Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings. |
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IAM; DAG; 600.139; 600.145; 600.140; 600.121 |
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Admin @ si @ RBG2020 |
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3475 |
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Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen |
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Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images |
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2023 |
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Arxiv |
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107241 |
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This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori. |
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Admin @ si @ CCG2023 |
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3855 |
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