Model Driven Solutions (United States)

El empleo de modelos matemáticos para la explicación de fenómenos probabilísticos ha sido imprescindible en la investigación científica. No obstante, en el ámbito educativo es frecuente trabajar con variables que no cumplen las características requeridas por el Modelo Lineal (ML), utilizado durante mucho tiempo como única opción para representar datos de dependencia; por el contrario, elModelo Lineal Generalizado (MLG) responde muy adecuadamente a los problemas generados por la métrica de las variables. En este trabajo se comentan los aspectos particulares del MLG en relación al ML dentro del entorno en el que cobran sentido ambos: el modelado estadístico.Así mismo se anima al uso del software estadístico R, poco conocido en el ámbito de los estudios educativos, pero especialmente sensible a las particularidades matemáticas del Modelo Lineal Generalizado y al modo de trabajar con el modelado estadístico.Descriptores: modelado estadístico R

Over the last few decades, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of data and services, which has shifted the access control paradigm from a fixed desktop environment to dynamic cloud environments. Fog computing is associated with a new access control paradigm to reduce the overhead costs by moving the execution of application logic from the centre of the cloud data sources to the periphery of the IoT-oriented sensor networks. Indeed, accessing information and data resources from a variety of IoT sources has been plagued with inherent problems such as data heterogeneity, privacy, security and computational overheads. This paper presents an extensive survey of security, privacy and access control research, while highlighting several specific concerns in a wide range of contextual conditions (e.g., spatial, temporal and environmental contexts) which are gaining a lot of momentum in the area of industrial sensor and cloud networks. We present different taxonomies, such as contextual conditions and authorization models, based on the key issues in this area and discuss the existing context-sensitive access control approaches to tackle the aforementioned issues. With the aim of reducing administrative and computational overheads in the IoT sensor networks, we propose a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework, combining the benefits of the cloud, IoT and context-aware computing; and ensuring proper access control and security at the edge of the end-devices. Our goal is not only to control context-sensitive access to data resources in the cloud, but also to move the execution of an application logic from the cloud-level to an intermediary-level where necessary, through adding computational nodes at the edge of the IoT sensor network. A discussion of some open research issues pertaining to context-sensitive access control to data resources is provided, including several real-world case studies. We conclude the paper with an in-depth analysis of the research challenges that have not been adequately addressed in the literature and highlight directions for future work that has not been well aligned with currently available research.

The aim of this study was to compare national estimates of self-reported and measured height and weight, BMI, and obesity prevalence among adults from US surveys. Self-reported height and weight data came from the National Health and Nutrition Examination Survey (NHANES), the National Health Interview Survey, and the Behavioral Risk Factor Surveillance System for the years 1999 to 2016. Measured height and weight data were available from NHANES. BMI was calculated from height and weight; obesity was defined as BMI ≥ 30. In all three surveys, mean self-reported height was higher than mean measured heightin NHANES for both men and women. Mean BMI from self-reported data was lower than mean BMI from measured data across all surveys. For women, mean self-reported weight, BMI, and obesity prevalence in theNational Health Interview Survey and Behavioral Risk Factor Surveillance System were lower than self-report in NHANES. The distribution of BMI was narrower for self-reported than for measured data, leading to lower estimates of obesity prevalence. Self-reported height, weight, BMI, and obesity prevalence were not identical across the three surveys, particularly for women. Patterns of misreporting of height and weight and their effects on BMI and obesity prevalence are complex.

<div class="section abstract"><div class="htmlview paragraph">This work presents current methods to analyze and improve the architecture of Simulink models. The methods follow the “principles for architectural design” of part 6 on software development of the ISO 26262 functional safety standard for road vehicles, the dominating standard in the automotive industry. The methods presented describe how the abstract architectural principles of the ISO 26262 can be implemented in the context of model-based development using Simulink. Therefore we demonstrate how different metrics can be used to improve or enforce the compliance with the principles. In contrast to previous publications we will not primarily focus on the metrics itself, but emphasize the architectural principles themselves and expose the architectural implications of applying the metrics. As the architectural principles of the ISO 26262 are targeted at reducing the overall complexity, we will also focus on metrics and methods that help to reduce the models complexity.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Software systems, and automotive software in particular, are becoming increasingly configurable to fulfill customer needs. New methods such as product line engineering facilitate the development and enhance the efficiency of such systems. In modern, versatile systems, the number of theoretically possible variants easily exceeds the number of actually built products. This produces two challenges for quality assurance and especially testing. First, the costs of conventional test methods increase substantially with every tested variant. And secondly, it is no longer feasible to build every possible variant for the purpose of testing. Hence, efficient criteria for selecting variants for testing are necessary.</div><div class="htmlview paragraph">In this contribution, we propose a new test design method that enables systematic sampling of variants from test cases. We present six optimization criteria to enable control of test effort and test quality by sampling variants with different characteristics. This approach is inherently different to conventional design methods, where firstly variants are selected for testing and then test cases are designed for each variant. Finally, we demonstrate and discuss the feasibility of our approach and compare the results to established test methods for multivariable systems.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Huge Simulink controller models often consists of (almost) identical subsystems, very often resulting from copy-and-paste operations and only slight adaptation of the subsystems by the model engineer. Although this “copy-and-paste” approach might help to achieve initial results very fast, in the long-run such subsystem clones can create considerable problems. Like code clones, model clones increase the effort for testing and maintenance. Model clones also tend to influence the code efficiency and code quality in a negative way in case the Simulink model is used as a basis for code generation. JUST SIMPLIFY is an approach for detecting model clones in a Simulink model automatically based on model metrics calculations. This approach has been implemented in our model metrics and complexity measurement tool M-XRAY. JUST SIMPLIFY allows reducing the effort for model refactoring by avoiding time consuming manual search for model clones. As a result, the effort for testing and for maintaining models can be reduced and the code quality can be improved significantly.</div></div>