Towards Accessibility and Inclusion of Native Mobile Applications Available for Ecuador in Google Play Store

This article aims to evaluate the level of compliance with the accessibility requirements of the most popular native Android mobile applications, for which a sample of 50 Google Play Store applications available in Ecuador was taken. A five-phase method using the Accessibility Scanner tool was used to evaluate the apps. The results revealed that 47.5% are related to problems with tactile orientation, followed by the labeling of elements with 28.2%, and text contrast with 9.2%. The highest number of barriers found in the evaluation of mobile applications corresponds to the principle of operability with 53.9%. This study reveals that, although social networks are widely used, they have 28.7% of accessibility problems. Basing accessibility analysis exclusively on an automatic tool is very limited since it neither detects all errors nor are the errors they detect accurate. However, we suggest complementing the automatic review evaluations with a manual method based on heuristics to ensure an adequate level of accessibility in mobile apps. In addition, we recommend using this study as a starting point to create a software tool using WCAG 2.1 based on artificial intelligence algorithms to help developers evaluate accessibility in mobile apps.This research was funded by Universidad de Las Américas-Ecuador, an internal research project INI.PAV.20.01

Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study

The present work proposes the application of machine learning techniques to predict the final grades (FGs) of students based on their historical performance of grades. The proposal was applied to the historical academic information available for students enrolled in the computer engineering degree at an Ecuadorian university. One of the aims of the university’s strategic plan is the development of a quality education that is intimately linked with sustainable development goals (SDGs). The application of technology in teaching–learning processes (Technology-enhanced learning) must become a key element to achieve the objective of academic quality and, as a consequence, enhance or benefit the common good. Today, both virtual and face-to-face educational models promote the application of information and communication technologies (ICT) in both teaching–learning processes and academic management processes. This implementation has generated an overload of data that needs to be processed properly in order to transform it into valuable information useful for all those involved in the field of education. Predicting a student’s performance from their historical grades is one of the most popular applications of educational data mining and, therefore, it has become a valuable source of information that has been used for different purposes. Nevertheless, several studies related to the prediction of academic grades have been developed exclusively for the benefit of teachers and educational administrators. Little or nothing has been done to show the results of the prediction of the grades to the students. Consequently, there is very little research related to solutions that help students make decisions based on their own historical grades. This paper proposes a methodology in which the process of data collection and pre-processing is initially carried out, and then in a second stage, the grouping of students with similar patterns of academic performance was carried out. In the next phase, based on the identified patterns, the most appropriate supervised learning algorithm was selected, and then the experimental process was carried out. Finally, the results were presented and analyzed. The results showed the effectiveness of machine learning techniques to predict the performance of students.This work was supported in part by the Spanish Ministry of Science, Innovation and Universities through the ProjectECLIPSE-UA under Grant RTI2018-094283-B-C32

Digital marketing, elements of the public sector competition value chain in Barranquilla, (Colombia)

La organización en la actualidad están obligadas a generar mayores beneficios a sus consumidores para lograr mayor posicionamiento en el mercado, eso depende del manejo de factores de competitividad internos y externos que predominan en las organizaciones medianas en el sector de la publicidad digital en Barranquilla. El objetivo de esta investigación fue describir el marketing digital del sector publicitario. La investigación es descriptiva con diseño no experimental y transversal. La muestra estuvo conformada por 15 empresas, cumpliendo los criterios: Empresa mediana, con departamento de Marketing digital, domiciliada en Barranquilla. Los resultados fueron descripción el marketing digital del sector publicitario, de acuerdo a los factores internos y externos en estas empresas presentan donde existe una consistencia moderada en la dinámica de respuesta de la empresa ante factores externos y viceversa. Se concluyó que las empresas de este sector requieren de estrategias que promuevan el desarrollo de los indicadores internos de competitividad que respondan a los factores cambiantes externo.The organization is currently forced to generate greater benefits to its consumers to achieve greater market positioning, that depends on the management of internal and external competitiveness factors that predominates in medium-sized organizations in the digital advertising sector in Barranquilla. The objective of this research was to describe the digital marketing of the advertising sector. The research is descriptive with non-experimental and transversal design. The sample was composed by 15 companies, fulfilling the criteria: Medium company, with department of Digital Marketing, placed in Barranquilla. The results were a description digital marketing of the advertising sector, according of the internal and external factors in these companies present where there is a moderate consistency in the dynamics of the company’s response to external factors and vice versa. It was concluded that companies in this sector have difficulties in strategies that promote the development of internal competitiveness indicators that respond to changing external factors

Introduction to CRM 4.0 an approach to the tourism sector

El CRM 4.0 surge de la necesidad de retener a los clientes, brindando una prestación de servicio más personalizado por medio de las tecnologías de la información, este artículo establece una introducción que permite la integración de la filosofía CRM al sector turismo permitiendo la automatización de procesos importantes de fidelización de los turistas, de recolección de datos para analizar, retener y generar una mejor experiencia en clientes actuales y potenciales del sector por medio de una arquitectura modelo de industria 4.0, se presentan diferentes argumentos y conceptos que nos permiten comprender la relevancia de la implementación del CRM en el turismo, Se enfatiza en las tecnologías que pertenecen a la industria 4.0 como un aspecto que facilita la explicación del nivel de expectativas , percepciones de los clientes y mejora la experiencia turística.The CRM 4.0 arises from the need to retain customers, providing a more personalized service through information technology, this article establishes an introduction that allows the integration of the CRM philosophy to the tourism sector allowing the automation of important processes of tourist loyalty, data collection to analyze, retain and generate a better experience in current and potential customers in the sector through an architecture, different arguments and concepts are presented that allow us to understand the relevance of CRM implementation in the tourism, Emphasizes on the technologies that belong to industry 4.0 as an aspect that facilitates the explanatio

Extracción y análisis de características para identificación, agrupamiento y modificación de la fuente de imágenes generadas por dispositivos móviles

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 02/10/2017.Nowadays, digital images play an important role in our society. The presence of mobile devices with integrated cameras is growing at an unrelenting pace, resulting in the majority of digital images coming from this kind of device. Technological development not only facilitates the generation of these images, but also the malicious manipulation of them. Therefore, it is of interest to have tools that allow the device that has generated a certain digital image to be identified. The digital image source can be identified through the features that the generating device permeates it with during the creation process. In recent years most research on techniques for identifying the source has focused solely on traditional cameras. The forensic analysis techniques of digital images generated by mobile devices are therefore of particular importance since they have specific characteristics which allow for better results, and forensic techniques for digital images generated by another kind of device are often not valid. This thesis provides various contributions in two of the main research lines of forensic analysis, the field of identification techniques and the counter-forensics or attacks on these techniques. In the field of digital image source acquisition identification techniques, both closed and open scenarios are addressed. In closed scenarios, the images whose acquisition source are to be determined belong to a group of devices known a priori. Meanwhile, an open scenario is one in which the images under analysis belong to a set of devices that is not known a priori by the fo rensic analyst. In this case, the objective is not t he concrete image acquisition source identification, but their classification into groups whose images all belong to the same mobile device. The image clustering t echniques are of particular interest in real situations since in many cases the forensic analyst does not know a priori which devices have generated certain images. Firstly, techniques for identifying the device type (computer, scanner or digital camera of the mobile device) or class (make and model) of the image acquisition source in mobile devices are proposed, which are two relevant branches of forensic analysis of mobile device images. An approach based on different types of image features and Support Vector Machine as a classifier is presented. Secondly, a technique for the ident ification in open scenarios that consists of grouping digital images of mobile devices according to the acquisition source is developed, that is to say, a class-grouping of all input images is performed. The proposal is based on the combination of hierarchical grouping and flat grouping using the Sensor Pattern Noise. Lastly, in the area of att acks on forensic t echniques, topics related to the robustness of the image source identificat ion forensic techniques are addressed. For this, two new algorithms based on the sensor noise and the wavelet transform are designed, one for the destruction of t he image identity and another for its fo rgery. Results obtained by the two algorithms were compared with other tools designed for the same purpose. It is worth mentioning that the solution presented in this work requires less amount and complexity of input data than the tools to which it was compared. Finally, these identification t echniques have been included in a tool for the forensic analysis of digital images of mobile devices called Theia. Among the different branches of forensic analysis, Theia focuses mainly on the trustworthy identification of make and model of the mobile camera that generated a given image. All proposed algorithms have been implemented and integrated in Theia thus strengthening its functionality.Actualmente las imágenes digitales desempeñan un papel importante en nuestra sociedad. La presencia de dispositivos móviles con cámaras fotográficas integradas crece a un ritmo imparable, provocando que la mayoría de las imágenes digitales procedan de este tipo de dispositivos. El desarrollo tecnológico no sólo facilita la generación de estas imágenes, sino también la manipulación malintencionada de éstas. Es de interés, por tanto, contar con herramientas que permitan identificar al dispositivo que ha generado una cierta imagen digital. La fuente de una imagen digital se puede identificar a través de los rasgos que el dispositivo que la genera impregna en ella durante su proceso de creación. La mayoría de las investigaciones realizadas en los últimos años sobre técnicas de identificación de la fuente se han enfocado únicamente en las cámaras tradicionales. Las técnicas de análisis forense de imágenes generadas por dispositivos móviles cobran, pues, especial importancia, ya que éstos presentan características específicas que permiten obtener mejores resultados, no siendo válidas muchas veces además las técnicas forenses para imágenes digitales generadas por otros tipos de dispositivos. La presente Tesis aporta diversas contribuciones en dos de las principales líneas del análisis forense: el campo de las t écnicas de identificación de la fuente de adquisición de imágenes digitales y las contramedidas o at aques a est as técnicas. En el primer campo se abordan tanto los escenarios cerrados como los abiertos. En el escenario denominado cerrado las imágenes cuya fuente de adquisición hay que determinar pertenecen a un grupo de dispositivos conocidos a priori. Por su parte, un escenario abierto es aquel en el que las imágenes pertenecen a un conjunto de dispositivos que no es conocido a priori por el analista forense. En este caso el obj etivo no es la identificación concreta de la fuente de adquisición de las imágenes, sino su clasificación en grupos cuyas imágenes pertenecen todas al mismo dispositivo móvil. Las técnicas de agrupamiento de imágenes son de gran interés en situaciones reales, ya que en muchos casos el analist a forense desconoce a priori cuáles son los dispositivos que generaron las imágenes. En primer lugar se presenta una técnica para la identificación en escenarios cerrados del tipo de dispositivo (computador, escáner o cámara digital de dispositivo móvil) o la marca y modelo de la fuente en dispositivos móviles, que son dos problemáticas relevantes del análisis forense de imágenes digitales. La propuesta muestra un enfoque basado en distintos tipos de características de la imagen y en una clasificación mediante máquinas de soporte vectorial. En segundo lugar se diseña una técnica para la identificación en escenarios abiertos que consiste en el agrupamiento de imágenes digitales de dispositivos móviles según la fuente de adquisición, es decir, se realiza un agrupamiento en clases de todas las imágenes de ent rada. La propuesta combina agrupamiento jerárquico y agrupamiento plano con el uso del patrón de ruido del sensor. Por último, en el área de los ataques a las técnicas fo renses se tratan temas relacionados con la robustez de las técnicas forenses de identificación de la fuente de adquisición de imágenes. Se especifican dos algoritmos basados en el ruido del sensor y en la transformada wavelet ; el primero destruye la identidad de una imagen y el segundo falsifica la misma. Los resultados obtenidos por estos dos algoritmos se comparan con otras herramientas diseñadas para el mismo fin, observándose que la solución aquí presentada requiere de menor cantidad y complejidad de datos de entrada. Finalmente, estas técnicas de identificación han sido incluidas en una herramienta para el análisis forense de imágenes digitales de dispositivos móviles llamada Theia. Entre las diferentes ramas del análisis forense, Theia se centra principalmente en la identificación confiable de la marca y el modelo de la cámara móvil que generó una imagen dada. Todos los algoritmos desarrollados han sido implementados e integrados en Theia, reforzando así su funcionalidad.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

A model for the automated detection of fraudulent healthcare claims using data mining methods

Abstract : The menace of fraud today cannot be underestimated. The healthcare system put in place to facilitate rendering medical services as well as improving access to medical services has not been an exception to fraudulent activities. Traditional healthcare claims fraud detection methods no longer suffice due to the increased complexity in the medical billing process. Machine learning has become a very important technique in the computing world today. The abundance of computing power has aided the adoption of machine learning by different problem domains including healthcare claims fraud detection. The study explores the application of different machine learning methods in the process of detecting possible fraudulent healthcare claims fraud. We propose a data mining model that incorporates several knowledge discovery processes in the pipeline. The model makes use of the data from the Medicare payment data from the Centre for Medicare and Medicaid Services as well as data from the List of Excluded Individual or Entities (LEIE) database. The data was then passed through the data pre-processing and transformation stages to get the data to a desirable state. Once the data is in the desired state, we apply several machine learning methods to derive knowledge as well as classify the data into fraudulent and non-fraudulent claims. The results derived from the comprehensive benchmark used on the implemented version of the model, have shown that machine learning methods can be used to detect possible fraudulent healthcare claims. The models based on the Gradient Boosted Tree Classifier and Artificial Neural Network performed best while the Naïve Bayes model couldn’t classify the data. By applying the correct pre-processing method as well as data transformation methods to the Medicare data, along with the appropriate machine learning methods, the healthcare fraud detection system yields nominal results for identification of possible fraudulent claims in the medical billing process.M.Sc. (Computer Science