The aim of this project is the design and implementation of a system for analyzing the explainability of similarity between short texts (< 200 words), in Spanish language, with a special focus on the academic domain. For the system implementation, different models based on the BERT architecture will be used. A concise analysis of the explainability of the proposed system will be conducted, aiming to understand the intrinsic functioning of the method and to provide feedback to stakeholders, such as the author of the evaluated text or the professional deciding to use the system. Furthermore, based on the obtained results, an estimation of the system's goodness will be carried out through statistical analysis. This will enable both a comparison with other possible implementations and the proposal of future improvements that could have a positive impact on a more realistic assessment of texts.

Student plagiarism is a major problem in universities worldwide. In this paper, we focus on plagiarism in answers to computer programming assignments, where students mix and/or modify one or more original solutions to obtain counterfeits. Although several software tools have been developed to help the tedious and time consuming task of detecting plagiarism, little has been done to assess their quality, because determining the real authorship of the whole submission corpus is practically impossible for markers. In this paper, we present a grammar evolution technique which generates benchmarks for testing plagiarism detection tools. Given a programming language, our technique generates a set of original solutions to an assignment, together with a set of plagiarisms of the former set which mimic the basic plagiarism techniques performed by students. The authorship of the submission corpus is predefined by the user, providing a base for the assessment and further comparison of copy-catching tools. We give empirical evidence of the suitability of our approach by studying the behavior of one advanced plagiarism detection tool (AC) on four benchmarks coded in APL2, generated with our technique.

In the last years, the computers have increased their capacityof calculus and networks - for the interconnectionof these machines - have been improved until obtaining theactual high rates of data transferring. The programs thatnowadays try to take advantage of these new technologies,cannot be written using the traditional techniques of programming,since most of the algorithms were designed forbeing executed in only one processor, in a nonconcurrentform, instead of being executed concurrently in a set of processors,working and communicating through a network.This work aims to present the ongoing development of a newmethod to simulate the Ferromagnetic Potts model, takinginto account these new technologies.

Recent large scale experiments have shown that the normalized information distance, an algorithmic information measure, is among the best similarity metrics for melody classification. This paper proposes the use of this distance as a fitness function which may be used by genetic algorithms to automatically generate music in a given pre-defined style. The minimization of this distance of the generated music to a set of musical guides makes it possible to obtain computer-generated music which recalls the style of a certain human author. The recombination operator plays an important role in this problem and thus several variations are tested to fine tune the genetic algorithm for this application. The superiority of the relative pitch envelope over other music parameters, such as the lengths of the notes, brought us to develop a simplified algorithm that nevertheless obtains interesting results.

Student plagiarism is a mayor problem in universities worldwide. In this paper,we focus on plagiarism in answers to computer programming assignments,where student mix and/or modify one or more original solutions to obtain counterfeits. Although several software tools have been implemented to help the tedious and time consuming task of detecting plagiarism, little has been done to assess their quality, because, in fact, determining the original subset of the whole solutionset is practically impossible for graders. In this article we present a Grammatical Evolution technique which generates benchmarks. Given a programming language, our technique generates a set of original solutions to an assignment, together with a set of plagiarisms of the former set which mimic the way in which students act. The phylogeny of the coded solutions is predefined, providing a base for the evaluationof the performance of copy-catching tools. We give empirical evidence of the suitability of our approach by studying the behavior of one state-of-the-art detection tool (AC) on four benchmarks coded in APL2, generated with this technique.

This paper describes Christiansen grammar evolution (CGE), a new evolutionary automatic programming algorithm that extends standard grammar evolution (GE) by replacing context-free grammars by Christiansen grammars. GE only takes into account syntactic restrictions to generate valid individuals. CGE adds semantics to ensure that both semantically and syntactically valid individuals are generated. It is empirically shown that our approach improves GE performance and even allows the solution of some problems are difficult to tackle by GE

This correspondence studies the influence of noise on the normalized compression distance (NCD), a measure based on the use of compressors to compute the degree of similarity of two files. This influence is approximated by a first order differential equation which gives rise to a complex effect, which explains the fact that the NCD may give values greater than 1, observed by other authors. The model is tested experimentally with good adjustment. Finally, the influence of noise on the clustering of files of different types is explored, finding that the NCD performs well even in the presence of quite high noise levels