Machine Learning Models and Applications for Early Detection.

From the various perspectives of machine learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the early detection (ED) of anomalies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLMs) operate with a focus on ED in a multidisciplinary manner and, specifically, how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVMs), decision trees (DTs), Random Forests (RFs), naive Bayesian classifier (NB), K-Nearest Neighbors (KNNs), artificial neural networks (ANNs), and Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBMs) and Stacking Ensemble Models (SEMs). It was identified that MLMs for ED in multiple areas under SBMs' and SEMs' implementation achieved accuracies greater than 80% and 90%, respectively. In fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.

Digital Transformation in Higher Education Institutions: A Systematic Literature Review.

Higher education institutions (HEIs) have been permeated by the technological advancement that the Industrial Revolution 4.0 brings with it, and forces institutions to deal with a digital transformation in all dimensions. Applying the approaches of digital transformation to the HEI domain is an emerging field that has aroused interest during the recent past, as they allow us to describe the complex relationships between actors in a technologically supported education domain. The objective of this paper is to summarize the distinctive characteristics of the digital transformation (DT) implementation process that have taken place in HEIs. The Kitchenham protocol was conducted by authors to answer the research questions and selection criteria to retrieve the eligible papers. Nineteen papers (1980-2019) were identified in the literature as relevant and consequently analyzed in detail. The main findings show that it is indeed an emerging field, none of the found DT in HEI proposals have been developed in a holistic dimension. This situation calls for further research efforts on how HEIs can understand DT and face the current requirements that the fourth industrial revolution forced.

Apropiación social de la ciencia y la tecnología a través de una iniciativa de intervención e inclusión educativa de niños y adolescentes de territorios vulnerables de la minería usando la robótica, como una alternativa para la construcción de la paz / Social appropriation of science and technology through an initiative of intervention and educational inclusion of children and adolescents in mining vulnerable territories, by using robotics as an alternative to building peace

Resumen En este artículo se presenta una iniciativa de intervención e inclusión educativa de niños y adolescentes de territorios vulnerables de influencia de la minería. Para lograr lo anterior se diseñaron un conjunto de robots educativos y material didáctico complementario que fueron utilizados en las diferentes sesiones de la iniciativa. La población beneficiada fueron niños y adolescentes que trabajan día a día en tareas propias de la minería y no han tenido la oportunidad de ingresar a la escuela o han desertado de ella buscando recursos económicos para sostener sus familias. Dentro de la población beneficiada, también se cuenta con los maestros de las escuelas asentadas en los territorios mineros, con el propósito de enseñarles nuevas metodologías de enseñanza y aprendizaje para atender a la población desescolarizada. 2500 niños y adolescentes fueron beneficiados con la iniciativa, así como 250 maestros.

Abstract This article presents an intervention initiative and an educational inclusion of children and adolescents from vulnerable territories of mining influence. In order to achieve this, a set of educational robots and complementary teaching materials was designed and used in the different sessions of the initiative. The beneficiaries were children and adolescents who work day by day in mining tasks and have not had the opportunity to enter the school system or to have defected from it, by seeking financial resources in order to support their families. Within the beneficiaries, there are also the teachers of the schools settled in the mining territories, in order to teach them new teaching and learning methodologies in order to serve the deschooled population. Two thousand five hundred children and adolescents benefited from the initiative and two hundred and fifty teachers did, too.