Actualmente en Honduras existe una deficiencia en la detección de líneas telefónicas utilizadas para tráfico gris en redes GSM, debido a la variabilidad de los factores principales considerados y la rigidez de los métodos tradicionales de... more
Actualmente en Honduras existe una deficiencia en la detección de líneas telefónicas utilizadas para tráfico gris en redes GSM, debido a la variabilidad de los factores principales considerados y la rigidez de los métodos tradicionales de detección. El presente estudio tiene como propósito conocer factores característicos de líneas telefónicas utilizadas para tráfico gris en Honduras, con la finalidad de utilizar el conocimiento de dicho perfil para la detección de casos de fraude mediante una red neuronal con aprendizaje supervisado. Los resultados del estudio se presentan de forma separada para el enfoque cualitativo y cuantitativo, de forma que se comprendan los factores más significativos encontrados en cada análisis. Con la caracterización tanto del perfil de línea telefónica de tráfico gris como de línea telefónica legal, se procedió a construir un modelo de red neuronal con un 99% de efectividad de detección, y para efectos prácticos de aplicabilidad se describen las bases para la posterior construcción de un modelo de detección de tráfico gris.
Deep learning technology has enabled the development of increasingly complex safety-related autonomous systems using high-performance computers, such as GPU, which provide the required high computing performance for the execution of... more
Deep learning technology has enabled the development of increasingly complex safety-related autonomous systems using high-performance computers, such as GPU, which provide the required high computing performance for the execution of parallel computing algorithms, such as matrix–matrix multiplications (a central computing element of deep learning software libraries). However, the safety certification of parallel computing software algorithms and GPU-based safety-related systems is a challenge to be addressed. For example, achieving the required fault-tolerance and diagnostic coverage for random hardware errors. This paper contributes with a safe matrix–matrix multiplication software implementation for GPUs with random hardware error-detection capabilities (permanent, transient) that can be used with different architectural patterns for fault-tolerance, and which serves as a foundation for the implementation of safe deep learning libraries for GPUs. The proposed contribution is comple...
Graphics Processing Unit (GPU) devices and their associated software programming languages and frameworks can deliver the computing performance required to facilitate the development of next-generation high-performance safety-critical... more
Graphics Processing Unit (GPU) devices and their associated software programming languages and frameworks can deliver the computing performance required to facilitate the development of next-generation high-performance safety-critical systems such as autonomous driving systems. However, the integration of complex, parallel, and computationally demanding software functions with different safety-criticality levels on GPU devices with shared hardware resources contributes to several safety certification challenges. This survey categorizes and provides an overview of research contributions that address GPU devices’ random hardware failures, systematic failures, and independence of execution.
Critical real-time systems require strict resource provisioning in terms of memory and timing. The constant need for higher performance in these systems has led industry to recently include GPUs. However, GPU software ecosystems are by... more
Critical real-time systems require strict resource provisioning in terms of memory and timing. The constant need for higher performance in these systems has led industry to recently include GPUs. However, GPU software ecosystems are by their nature closed source, forcing system engineers to consider them as black boxes, complicating resource provisioning. In this work, we reverse engineer the internal operations of the GPU system software to increase the understanding of their observed behaviour and how resources are internally managed. We present our methodology that is incorporated in GMAI (GPU Memory Allocation Inspector), a tool that allows system engineers to accurately determine the exact amount of resources required by their critical systems, avoiding underprovisioning. We first apply our methodology on a wide range of GPU hardware from different vendors showing its generality in obtaining the properties of the GPU memory allocators. Next, we demonstrate the benefits of such ...
Este documento contiene aspectos técnicos sobre el lenguaje de programación R, cuenta con conceptos fundamentales y algunos ejemplos recopilados de fuentes externas.
This document analyzes the changes resulting from a leap in the educational modality in the professional skills required for systems engineers as a result of the global coronavirus pandemic, taking as a fundamental basis a comparison... more
This document analyzes the changes resulting from a leap in the educational modality in the professional skills required for systems engineers as a result of the global coronavirus pandemic, taking as a fundamental basis a comparison between face-to-face education and modern virtual education. Where it was found that virtual education considerably improves self-management, initiative, research and analysis skills at the same time that interpersonal communication and teamwork worsen, it was also determined that virtual education offers a considerable approach to telework since they share many similarities. Keywords: Face-to-face education, Virtual education, Professional skills, Telework, Self-management.
