Generating Component-based Supervised Learning Programs From Crowdsourced Examples

We present CrowdLearn, a new system that processes an existing corpus of crowdsourced machine learning programs to learn how to generate effective pipelines for solving supervised machine learning problems. CrowdLearn uses a probabilistic model of program likelihood, conditioned on the current sequence of pipeline components and on the characteristics of the input data to the next component in the pipeline, to predict candidate pipelines. Our results highlight the effectiveness of this technique in leveraging existing crowdsourced programs to generate pipelines that work well on a range of supervised learning problems

Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example

Data management and analysis tasks are often carried out using spreadsheet software. A popular feature in most spreadsheet platforms is the ability to define data-dependent formatting rules. These rules can express actions such as "color red all entries in a column that are negative" or "bold all rows not containing error or failure." Unfortunately, users who want to exercise this functionality need to manually write these conditional formatting (CF) rules. We introduce CORNET, a system that automatically learns such conditional formatting rules from user examples. CORNET takes inspiration from inductive program synthesis and combines symbolic rule enumeration, based on semi-supervised clustering and iterative decision tree learning, with a neural ranker to produce accurate conditional formatting rules. In this demonstration, we show CORNET in action as a simple add-in to Microsoft Excel. After the user provides one or two formatted cells as examples, CORNET generates formatting rule suggestions for the user to apply to the spreadsheet.Comment: 4 Pages, VLDB 2023 Demonstration Trac

Tratamiento térmico de espumación de precursores de aluminio-silicio en horno solar de lecho fluidificado

La fabricación de espumas de aluminio de poro cerrado mediante técnicas pulvimetalúrgias, conlleva una etapa de calentamiento del material (precursor) hasta su fusión y la formación de la espuma. En el presente trabajo, como continuación de los trabajos con energía solar concentrada aplicada directamente a la espumación de precursores Al10Si+0.8%TiH2, se han sometido al tratamiento de espumación en un horno solar de lecho fluidificado con el objetivo de obtener un tratamiento en volumen más homogéneo y uniforme, de manera que se puedan determinar la viabilidad del tratamiento, el procedimiento y los tiempos de residencia necesarios para llevar a cabo este proceso de manera discontinua o preindustrial. Así se han obtenido espumas 0,68 g/cm3 de densidad sin colapso de poros o decantación de aluminio, si bien como consecuencia del calentamiento preferencial por la base del molde, las espumas obtenidas presentan un tamaño de poro ligeramente más grande en la base de la espuma

RECLAMO: virtual and collaborative honeynets based on trust management and autonomous systems applied to intrusion management

Security intrusions in large systems is a problem due to its lack of scalability with the current IDS-based approaches. This paper describes the RECLAMO project, where an architecture for an Automated Intrusion Response System (AIRS) is being proposed. This system will infer the most appropriate response for a given attack, taking into account the attack type, context information, and the trust and reputation of the reporting IDSs. RECLAMO is proposing a novel approach: diverting the attack to a specific honeynet that has been dynamically built based on the attack information. Among all components forming the RECLAMO's architecture, this paper is mainly focused on defining a trust and reputation management model, essential to recognize if IDSs are exposing an honest behavior in order to accept their alerts as true. Experimental results confirm that our model helps to encourage or discourage the launch of the automatic reaction process

Developing speaking competences in technical English for Spanish civil engineering students

[EN] Traditionally, Spanish schools of civil engineering provide their students a class on “Technical English” in order to develop their language skills. However, this class does not cover all the skills that the student would need in the labor market and mainly focuses in the reading and writing skills, and in a lower degree in the speaking and listening ones. This paper proposes a series of innovative and informal training activities (cine-forum on technical civil engineering topics and role playing on real professional situations) that allow Spanish civil engineering students to develop English skills that can rarely be worked in the classroom (i.e. speaking, negotiating and conversing), encouraging debate, participation, and fostering their self-confidence to speak about technical-English topics in public. Although the students’ level of English is much lower than expected, they all agree on the importance of technical English for their future career. The results also show the students’ lack in skills that are difficult to train in regular classes (speaking and talking). Consequently, this situation would require to provide complementary activities like the ones suggested in this project in order to develop these skills and increase the students’ demand for engineering classes taught in English.Romero De Ávila Serrano, V.; Diaz García, S.; Asensio Sánchez, L.; Lozano Galant, JA.; Moyano Enríquez De Salamanca, A.; Porras Soriano, R.; Poveda Bautista, E.... (2017). Developing speaking competences in technical English for Spanish civil engineering students. En Proceedings of the 3rd International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 1228-1236. https://doi.org/10.4995/HEAD17.2017.55641228123

AL: autogenerating supervised learning programs

We present AL, a novel automated machine learning system that learns to generate new supervised learning pipelines from an existing corpus of supervised learning programs. In contrast to existing automated machine learning tools, which typically implement a search over manually selected machine learning functions and classes, AL learns to identify the relevant classes in an API by analyzing dynamic program traces that use the target machine learning library. AL constructs a conditional probability model from these traces to estimate the likelihood of the generated supervised learning pipelines and uses this model to guide the search to generate pipelines for new datasets. Our evaluation shows that AL can produce successful pipelines for datasets that previous systems fail to process and produces pipelines with comparable predictive performance for datasets that previous systems process successfully.United States. Defense Advanced Research Projects Agency (Grants A8650-15-C-7564 and FA8750-14-2-0242

Las voces del Derecho: estrategia didáctica innovadora en la mediación virtual del aprendizaje

Este artículo consiste en la sistematización académica de una experiencia didáctica implementada en la Facultad de Derecho de la Universidad de Costa Rica, mediante la colaboración docente multidisciplinar y la confluencia de esfuerzos en el campo de la docencia universitaria y la tecnología educativa. Entre sus principales aportes, se destacan la innovación educativa como eje para potenciar la interacción del aprendizaje y la consolidación de colaboraciones desde el plano docente para el mejoramiento de la metodología académica universitaria.his article consists of the academic systematization of a didactic experience implemented in the Law School at University of Costa Rica, through multidisciplinary teaching collaboration and the confluence of efforts in the field of university teaching and educational technology. Among its main contributions, educational innovation stands out as the axis to enhance learning interaction and the consolidation of collaborations from the teaching level for the improvement of university academic methodology.UCR::Vicerrectoría de Docencia::Ciencias Sociales::Facultad de Ciencias Sociales::Escuela de Ciencias PolíticasUCR::Vicerrectoría de Docencia::Ciencias Sociales::Facultad de DerechoUCR::Vicerrectoría de Docencia::Ciencias Sociales::Facultad de Ciencias Sociales::Escuela de HistoriaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Sociales::Instituto de Investigación en Educación (INIE