Resultados de investigación

Database for article on social norms, benefits, and perceived behavioral control, This work was supported by the Ministerio de Economía e Industria under Grant (R&D projects PID2019-105726RB-I00)

Archivo de datos (Access), Dataset containing vibration and current data of a 4kW induction motor with 6306-2Z/C3 bearings that rotates at 1500rpm (25Hz) with a supply frequency of 50Hz. We provide a description of the dataset and the dataset itself in MAT and JSON formats.

Code and data of the article: Target inductive methods for zero-shot regression https://doi.org/10.1016/j.ins.2022.03.075

Data from the article "F. Ortin, O. Rodriguez-Prieto, N. Pascual, M. Garcia. Heterogeneous tree structure classification to label Java programmers according to their expertise level. Future Generation Computer Systems (105), pp. 380-394, 2020. https://doi.org/10.1016/j.future.2019.12.016", Open-source code repositories are a valuable asset to creating different kinds of tools and services, utilizing machine learning and probabilistic reasoning. Syntactic models process Abstract Syntax Trees (AST) of source code to build systems capable of predicting different software properties. The main difficulty of building such models comes from the heterogeneous and compound structures of ASTs, and that traditional machine learning algorithms require instances to be represented as n-dimensional vectors rather than trees. In this article, we propose a new approach to classify ASTs using traditional supervised-learning algorithms, where a feature learning process selects the most representative syntax patterns for the child subtrees of different syntax constructs. Those syntax patterns are used to enrich the context information of each AST, allowing the classification of compound heterogeneous tree structures. The proposed approach is applied to the problem of labeling the expertise level of Java programmers. The system is able to label expert and novice programs with an average accuracy of 99.6%. Moreover, other code fragments such as types, fields, methods, statements and expressions could also be classified, with average accuracies of 99.5%, 91.4%, 95.2%, 88.3% and 78.1%, respectively., This work has been partially funded by the Spanish Department of Science, Innovation and Universities: project RTI2018-099235-B-I00. The authors have also received funds from the University of Oviedo through its support of official research groups (GR-2011-0040).

Esta investigación de carácter naturalista, obtiene datos del impacto de un proyecto basado en el empleo de juegos serios para la mejora de la competencia en comunicación lingüística del alumnado en situación de desventaja socieducativa

Data from the article "F. Ortin, M. Garcia, S. McSweeney. Rule-based program specialization to optimize gradually typed code. Knowledge-Based Systems (179), pp. 145-173, 2019. https://doi.org/10.1016/j.knosys.2019.05.013", Both static and dynamic typing provide different benefits to the programmer. Statically typed languages support earlier type error detection and more opportunities for compiler optimizations. Dynamically typed languages facilitate the development of runtime adaptable applications and rapid prototyping. Since both approaches provide benefits, gradually typed languages support both typing approaches in the very same programming language. Gradual typing has been an active research field in the last years, turning out to be a strong influence on commercial languages. However, one important drawback of gradual typing is the runtime performance cost of the additional type checks performed at runtime. In this article, we propose a rule-based program specialization mechanism to provide significant performance optimizations of gradually typed code. Our system gathers dynamic type information of the application by simulating its execution. That type information is used to optimize the generated code, reducing the number of type checks performed at runtime. Moreover, program specialization allows the early detection of compile-time type errors, providing static type safety. To ensure the correctness of the proposed approach, we prove its soundness and efficiency properties. The specialization system has been implemented as part of a full-fledged programming language, measuring the runtime performance gain. The generated code performs significantly better than the state-of-theart techniques to optimize dynamically typed code. Unlike the existing approaches, our system does not consume additional memory resources at runtime, because program specialization is performed statically. Program specialization involves an average compilation time increase from 2% to 11.75%., This work has been partially funded by the Spanish Department of Science, Innovation and Universities: project RTI2018-099235-B-I00. The authors have also received funds from the Banco Santander, Spain through its support of the Campus of International Excellence