Strings are a natural representation of biological data such as DNA, RNA and protein sequences. The problem of finding a string that summarizes a set of sequences has direct application in relative compression algorithms for genome and proteome analysis, where reference sequences need to be chosen. Median strings have been used as representatives of a set of strings in different domains. However, several formulations of those problems are NP-Complete. Alternatively, heuristic approaches that iteratively refine an initial coarse solution by applying edit operations have been proposed. Recently, we investigated the selection of the optimal edit operations to speed up convergence without spoiling the quality of the approximated median string. We propose a novel algorithm that outperforms state of the art heuristic approximations to the median string in terms of convergence speed by estimating the effect of a perturbation in the minimization of the expressions that define the median strings. We present corpus of comparative experiments to validate these results.

The Median String Problem is W[1]-Hard under the Levenshtein distance, thus, approximation heuristics are used. Perturbation-based heuristics have been proved to be very competitive as regards the ratio approximation accuracy/convergence speed. However, the computational burden increase with the size of the set. In this paper, we explore the idea of reducing the size of the problem by selecting a subset of representative elements, i.e. pivots, that are used to compute the approximate median instead of the whole set. We aim to reduce the computation time through a reduction of the problem size while achieving similar approximation accuracy. We explain how we find those pivots and how to compute the median string from them. Results on commonly used test data suggest that our approach can reduce the computational requirements (measured in computed edit distances) by 8% with approximation accuracy as good as the state of the art heuristic.

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The pandemic context forced most of the Higher Education Institutions, and therefore their faculties, to face a semester of teaching in an online mode. The universities defined general guidelines and strategies to try to guarantee a minimum quality of the process. However, the literature reports that, depending on its particularities, preparing a semester-long online course requires a design of several months. Therefore, each lecturer worked according to her experience, guidelines, and support she received: "What could be done, was done." This article reports the teaching experience of the 1st semester 2020 of the Computer Science undergraduate program at the Universidad Católica de Temuco. Some lecturers of this undergraduate program had previous experience in online training; hence they designed strategies that differed in teaching, evaluation, and interaction methodologies while using several technologies available to them. A sample of 115 of our students answered a questionnaire to assess the preferences and problems during this semester. The results show a heterogeneous set of clusters. However, they point to a constructivist asynchronous vision in a group of our students that prefer short videos uploaded by their professors, and the resources that, by their means, they find on the Internet, point towards a constructivist asynchronous vision in a group of our students. The results also allowed us to rescue good practices that help in proposing an emergency online teaching model that will guide the design of the second-semester courses.