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The selection of software technologies is an important but complex task. We consider developers of JavaScript (JS) applications, for whom the assessment of JS libraries has become difficult and time-consuming due to the growing number of technology options available. A common strategy is to browse software repositories via search engines (e.g., NPM, or Google), although it brings some problems. First, given a technology need, the engines might return a long list of results, which often causes information overload issues. Second, the results should be ranked according to criteria of interest for the developer. However, deciding how to weight these criteria to make a decision is not straightforward. In this work, we propose a two-phase approach for assisting developers to retrieve and rank JS technologies in a semi-automated fashion. The first-phase (ST-Retrieval) uses a meta-search technique for collecting JS technologies that meet the developer's needs. The second-phase (called ST-Rank), relies on a machine learning technique to infer, based on criteria used by other projects in the Web, a ranking of the output of ST-Retrieval. We evaluated our approach with NPM and obtained satisfactory results in terms of the accuracy of the technologies retrieved and the order in which they were ranked.

The Misspecification-Resistant Information Criterion (MRIC) proposed in [H.-L. Hsu, C.-K. Ing, H. Tong: On model selection from a finite family of possibly misspecified time series models. The Annals of Statistics. 47 (2), 1061--1087 (2019)] is a model selection criterion for univariate parametric time series that enjoys both the property of consistency and asymptotic efficiency. In this article we extend the MRIC to the case where the response is a multivariate time series and the predictor is univariate. The extension requires novel derivations based upon random matrix theory. We obtain an asymptotic expression for the mean squared prediction error matrix, the vectorial MRIC and prove the consistency of its method-of-moments estimator. Moreover, we prove its asymptotic efficiency. Finally, we show with an example that, in presence of misspecification, the vectorial MRIC identifies the best predictive model whereas traditional information criteria like AIC or BIC fail to achieve the task.

Engineered living materials (ELMs) are the most relevant contemporary revolution in materials science and engineering. These ELMs aim to outperform current examples of "smart", active or multifunctional materials, enabling countless industrial and societal applications. The "living" materials facilitate unique properties, including autonomy, intelligent responses, self-repair, and even self-replication. Within this dawning field, most reviews and documents have divided ELMs into biological ELMs, which are solely made of cells, and hybrid living materials, which consist of abiotic chassis and living cells. Considering that the most relevant feature of living material is that they are made of (or include) living cell colonies and microorganisms, we consider that ELMs should be classified and presented differently, more related to life taxonomies than materials science disciplines. Towards solving the current need for the classification of ELMs, this study presents the first complete proposal of taxonomy for these ELMs. Here, life taxonomies and materials classifications are hybridized hierarchically. Once the proposed taxonomy is explained, its applicability is illustrated by classifying several examples of biological ELMs and hybrid living materials, and its utility for guiding research in this field is analyzed. Finally, possible modifications and improvements are discussed, and a call for collaboration is launched for progressing in this complex and multidisciplinary field.

Context: software projects are common resources in Software Engineering experiments, although these are often selected without following a specific strategy, which reduces the representativeness and replication of the results. An option is the use of preserved collections of software projects, but these must be current, with explicit guidelines that guarantee their updating over a long period of time. Goal: to carry out a systematic secondary study about the strategies to select software projects in empirical studies to discover the guidelines taken into account, the degree of use of project collections, the meta-data extracted and the subsequent statistical analysis conducted. Method: A systematic mapping study to identify studies published from January 2013 to December 2020. Results: 122 studies were identified, of which the 72% used their own guidelines for project selection and the 27% used existent project collections. Likewise, there was no evidence of a standardized framework for the project selection process, nor the application of statistical methods that relates with the sample collection strategy., Comment: 31 pages, in Spanish, 15 tables, 6 figures

Nowadays, technological advances have influenced all human activities, creating new dynamics and ways of communication. In this context, some artists have incorporated these advances in their creative process, giving rise to unique aesthetic expressions referred to in the literature as Generative Art, which is characterized by assigning part of the creative process to a system that acts with certain autonomy (Galanter, 2003). This research work introduces a computational system for creating generative art using a Brain-Computer Interface (BCI) which portrays the user's brain activity in a digital artwork. In this way, the user takes an active role in the creative process. In aims of showing that the proposed system materializes in an artistic piece the user's mental states by means of a visual and sound representation, several tests are carried out to ensure the reliability of the BCI device sent data. The generated artwork uses brain signals and concepts of geometry, color and spatial location to give complexity to the autonomous construction. As an added value, the visual and auditory production is accompanied by an olfactory and kinesthetic component which complements the art pieces providing a multimodal communication character.