Explorando las Bases para el Estudio de la Identidad de los Profesores Indígenas de Inglés en Colombia

This article addresses the invisibilization of the existence of indigenous teachers in the Colombian ELT (English language teaching) field. Their existence, which is admittedly a phenomenon that lacks quantitative saliency, offers opportunities to reflect on the epistemological asymmetries that traditionally have linked the Colombian ELT field to an instrumental mainstream bilingualism, often ignoring the conditions of linguistic and cultural diversity in the country. Besides, there is an exploration of how the study of indigenous teachers’ identities might contribute to the re-signification of pedagogy; this paper elaborates on the idea that scholars in the Colombian ELT have already built some horizons of understanding between the ELT and the diversities and epistemic privileges of Colombian indigeneity. The article is part of an ongoing research on the identities of indigenous teachers in the Colombian ELT being carried out within the Interinstitutional Ph.D. in Education at Universidad Distrital Francisco José de Caldas, BogotáEste artículo aborda la invisibilizacion de la existencia de los profesores indígenas en el campo de la enseñanza de inglés. Su existencia, el cual es un fenómeno que carece de prominencia cuantitativa, ofrece oportunidades de reflexionar sobre las asimetrías epistemológicas que tradicionalmente han conectado el campo de la enseñanza de inglés con un bilingüismo instrumental dominante, en el que usualmente se ignorarn las condiciones de diversidad lingüística y cultural en el pais. Además, hay una exploración de cómo el estudio de las identidades de los profesores indigenas pueden contribuir a la resignificación de la pedagogía. Este documento elabora la idea de que los académicos en la enseñanza de inglés en Colombia han construido algunos horizontes de comprensión entre la enseñanza de inglés y las diversidades y los privilegios epistemólogicos de la idigeneidad colombiana. El artículo es parte de la investigación continua de las indigeneidades de los profesores indigenas que se desarrolla en el Doctorado Interinstitucional en la Universidad Distrital Francisco José de Caldas en Bogotá

Too-connected-to-fail Institutions and Payments System’s Stability: Assessing Challenges for Financial Authorities

The most recent episode of market turmoil exposed the limitations resulting from the traditional focus on too-big-to-fail institutions within an increasingly systemic-crisis-prone financial system, and encouraged the appearance of the too-connected-to-fail (TCTF) concept. The TCTF concept conveniently broadens the base of potential destabilizing institutions beyond the traditional banking-focused approach to systemic risk, but requires methodologies capable of coping with complex, cross-dependent, context-dependent and non-linear systems. After comprehensively introducing the rise of the TCTF concept, this paper presents a robust, parsimonious and powerful approach to identifying and assessing systemic risk within payments systems, and proposes some analytical routes for assessing financial authorities’ challenges. Banco de la Republica’s approach is based on a convenient mixture of network topology basics for identifying central institutions, and payments systems simulation techniques for quantifying the potential consequences of central institutions failing within Colombian large-value payments systems. Unlike econometrics or network topology alone, results consist of a rich set of quantitative outcomes that capture the complexity, cross-dependency, context-dependency and non-linearity of payments systems, but conveniently disaggregated and dollar-denominated. These outcomes and the proposed analysis provide practical information for enhanced policy and decision-making, where the ability to measure each institution’s contribution to systemic risk may assist financial authorities in their task to achieve payments system’s stability.Payments systems, too-connected-to-fail, too-big-to-fail, systemic risk, network topology, simulation, central bank liquidity. Classification JEL:E58, E44, C63, G21, D85.

Feature Selection and Improving Classification Performance for Malware Detection

The ubiquitous advance of technology has been conducive to the proliferation of cyber threats, resulting in attacks that have grown exponentially. Consequently, researchers have developed models based on machine learning algorithms for detecting malware. However, these methods require significant amount of extracted features for correct malware classification, making that feature extraction, training, and testing take significant time; even more, it has been unexplored which are the most important features for accomplish the correct classification. In this Thesis, it is created and analyzed a dataset of malware and clean files (goodware) from the static and dynamic features provided by the online framework VirusTotal. The purpose was to select the smallest number of features that keep the classification accuracy as high as the state of the art researches. Selecting the most representative features for malware detection relies on the possibility reducing the training time, given that it increases in O(n2) with respect to the number of features, and creating an embedded program that monitors processes executed by the OS. Thus, feature selection was made taking the most important features. In addition, classification algorithms such as Random Forest, Support Vector Machine and Neural Networks were used in a novel combination that not only showed an increase in accuracy, but also in the training speed from hours to just minutes. Next, the model was tested on one additional dataset of unseen malware files. Results showed that “9” features were enough to distinguish malware from goodware files within an accuracy of 99.60%

Location, Location, Location: Contrasting Roles of Synaptic and Extrasynaptic NMDA Receptors in Huntington's Disease

Abnormally enhanced N-methyl-D-aspartate (NMDA) receptor function is implicated in Huntington's disease (HD). In this issue of Neuron and a recent issue of Nature Medicine, an abnormal balance between the activity of NMDA receptors at synaptic (prosurvival) and extrasynaptic (proapoptotic) sites has been uncovered in a cellular and a mouse model of HD

Deep learning and 5G and beyond for child drowning prevention in swimming pools

Drowning is a major health issue worldwide. The World Health Organization’s global report on drowning states that the highest rates of drowning deaths occur among children aged 1–4 years, followed by children aged 5–9 years. Young children can drown silently in as little as 25 s, even in the shallow end or in a baby pool. The report also identifies that the main risk factor for children drowning is the lack of or inadequate supervision. Therefore, in this paper, we propose a novel 5G and beyond child drowning prevention system based on deep learning that detects and classifies distractions of inattentive parents or caregivers and alerts them to focus on active child supervision in swimming pools. In this proposal, we have generated our own dataset, which consists of images of parents/caregivers watching the children or being distracted. The proposed model can successfully perform a seven-class classification with very high accuracies (98%, 94%, and 90% for each model, respectively). ResNet-50, compared with the other models, performs better classifications for most classes.Peer ReviewedPostprint (published version

Deep learning and Internet of Things for tourist attraction recommendations in smart cities

The version of record is available online at: http://dx.doi.org/10.1007/s00521-021-06872-0We propose a tourist attraction IoT-enabled deep learning-based recommendation system to enhance tourist experience in a smart city. Travelers will enter details about their travels (traveling alone or with a companion, type of companion such as partner or family with kids, traveling for business or leisure, etc.) as well as user side information (age of the traveler/s, hobbies, etc.) into the smart city app/website. Our proposed deep learning-based recommendation system will process this personal set of input features to recommend the tourist activities/attractions that best fit his/her profile. Furthermore, when the tourists are in the smart city, content-based information (already visited attractions) and context-related information (location, weather, time of day, etc.) are obtained in real time using IoT devices; this information will allow our proposed deep learning-based tourist attraction recommendation system to suggest additional activities and/or attractions in real time. Our proposed multi-label deep learning classifier outperforms other models (decision tree, extra tree, k-nearest neighbor and random forest) and can successfully recommend tourist attractions for the first case [(a) searching for and planning activities before traveling] with the loss, accuracy, precision, recall and F1-score of 0.5%, 99.7%, 99.9%, 99.9% and 99.8%, respectively. It can also successfully recommend tourist attractions for the second case [(b) looking for activities within the smart city] with the loss, accuracy, precision, recall and F1-score of 3.7%, 99.5%, 99.8%, 99.7% and 99.8%, respectively.This work has been supported by the Agencia Estatal de Investigación of Spain under project PID2019-108713RB-C51/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

La casa tipo extremeña en la arquitectura popular de la comarca de La Serena

En número dedicado a: La provincia de Badajo

The Evolution of World Trade from 1995 to 2014:A Network Approach

This paper employs network analysis to study world trade from 1995 to 2014. We focus on the main connective features of the world trade network (WTN) and their dynamics. Results suggest that countries’ efforts to attain the benefits of trade have resulted in an intertwined network that is increasingly dense, reciprocal, and clustered. Trade linkages are distributed homogeneously among countries, but their intensity (i.e. their value) is highly concentrated in a small set of countries. The main connective features of the WTN were not affected by the 2007-2008 international financial crisis. However, we find that the crisis marks a turning point in the evolution of the WTN from a two-group (led by the US and Germany) to a three-group (led by the US, Germany, and China) hierarchical structure; gravity models of international trade may explain this evolution. Furthermore, we find that WTN’s connective features do not conform to a linear aggregation of sectorial trade networks

Arquitectura de adobe en la Ribera del Duero

En número dedicado a: La provincia de Burgo