A mixed distribution to fix the threshold for Peak-Over-Threshold wave height estimation

Modelling extreme values distributions, such as wave height time series where the higher waves are much less frequent than the lower ones, has been tackled from the point of view of the Peak-OverThreshold (POT) methodologies, where modelling is based on those values higher than a threshold. This threshold is usually predefned by the user, while the rest of values are ignored. In this paper, we propose a new method to estimate the distribution of the complete time series, including both extreme and regular values. This methodology assumes that extreme values time series can be modelled by a normal distribution in a combination of a uniform one. The resulting theoretical distribution is then used to fx the threshold for the POT methodology. The methodology is tested in nine real-world time series collected in the Gulf of Alaska, Puerto Rico and Gibraltar (Spain), which are provided by the National Data Buoy Center (USA) and Puertos del Estado (Spain). By using the Kolmogorov-Smirnov statistical test, the results confrm that the time series can be modelled with this type of mixed distribution. Based on this, the return values and the confdence intervals for wave height in diferent periods of time are also calculated

TG2M: Trajectory Generator and Guidance Module for the Aerial Vehicle Control Language AVCL

This paper presents a novel framework for high-level complex mission planning – AVCL and its built-in trajectory generator module – TG 2 M. For the mission planning we present a language capable of describing the missions and capabilities of a heterogeneous group of vehicles, which includes its interpreter, a definition of a base-vehicle, and a Mission Planner (MP) that uses GIS as the data-model for the world. This MP is not tied to a particular set of vehicles, sensors or commands, which means that at any given time new functionality can be loaded and displayed to the human operator as new options and commands, allowing to control and display N mission at the same time. In addition for low-level mission guidance, the TG 2 M addresses the feature of generating complex trajectories within mission-specific constraints, improving the typical civil system which use basic trajectory-generation algorithms, capable only of linear waypoint navigation, with little or non-existent control over the trajectory. Final experiments will test the TG 2 M mathematical framework for trajectory generation showing the AVCL capabilities for the mission planning and control of the UAV

La formación docente en la sociedad del conocimiento: ISFODOSU

El sistema educativo dominicano afronta profundos cambios en su orientación y en su administración, como consecuencia de las grandes transformaciones sociales, económicas, culturales y tecnológicas, que exigen progresivos niveles de calidad de los servicios educativos. La tesis comienza con un análisis del contexto social, económico y cultural que afecta en la actualidad a la República Dominicana: exposición de la convergencia y naturaleza de los cambios que afectan a la sociedad global y sus consecuencias para la educación. Asimismo se exploraron las dimensiones de la sociedad del conocimiento, circunstancia condicionante de las políticas educativas y sistemas de educación vigentes, que han de orientar los cambios a introducir en la educación dominicana para alcanzar un sistema educativo de calidad para la situación actual. El objetivo general de la investigación se centró en el conocimiento de la situación educativa y formativa que se lleva a cabo en el Instituto Superior de Formación Docente Salomé Ureña, ISFODOSU: avatares históricos y presentes, estructura organizativa y funcional, limitaciones y carencias y la necesidad de cambio que la sociedad demanda al Instituto. En la investigación se evaluó el desempeño de los docentes y la gestión directiva y administrativa del ISFODOSU, recabando la opinión de los profesores y de los estudiantes, mediante la técnica de encuesta por cuestionario diferenciado para profesores y alumnos. De forma complementaria se recopiló información mediante la observación y la entrevista grupal. Entre otros hallazgos se mencionan envejecimiento del profesorado, baja valoración de la gestión directiva, falta de disponibilidad para el trabajo en equipo, escasa apertura a los cambios institucionales, carencias en la coordinación en asignaturas, prácticas y pasantías, débil comunicación con los alumnos y ausencia de canales de comunicación de los alumnos con los gestores directivos. Son obvias algunas deficiencias en edificios y no existen líneas de investigación y extensión definidas

Spiking row-by-row FPGA Multi-kernel and Multi-layer Convolution Processor.

Spiking convolutional neural networks have become a novel approach for machine vision tasks, due to the latency to process an input stimulus from a scene, and the low power consumption of these kind of solutions. Event-based systems only perform sum operations instead of sum of products of framebased systems. In this work an upgrade of a neuromorphic event-based convolution accelerator for SCNN, which is able to perform multiple layers with different kernel sizes, is presented. The system has a latency per layer from 1.44 μs to 9.98μs for kernel sizes from 1x1 to 7x7

Error-Correcting Output Codes in the Framework of Deep Ordinal Classification

Automatic classification tasks on structured data have been revolutionized by Convolutional Neural Networks (CNNs), but the focus has been on binary and nominal classification tasks. Only recently, ordinal classification (where class labels present a natural ordering) has been tackled through the framework of CNNs. Also, ordinal classification datasets commonly present a high imbalance in the number of samples of each class, making it an even harder problem. Focus should be shifted from classic classification metrics towards per-class metrics (like AUC or Sensitivity) and rank agreement metrics (like Cohen’s Kappa or Spearman’s rank correlation coefficient). We present a new CNN architecture based on the Ordinal Binary Decomposition (OBD) technique using Error-Correcting Output Codes (ECOC). We aim to show experimentally, using four different CNN architectures and two ordinal classification datasets, that the OBD+ECOC methodology significantly improves the mean results on the relevant ordinal and class-balancing metrics. The proposed method is able to outperform a nominal approach as well as already existing ordinal approaches, achieving a mean performance of RMSE=1.0797 for the Retinopathy dataset and RMSE=1.1237 for the Adience dataset averaged over 4 different architectures

Hybridization of neural network models for the prediction of Extreme Significant Wave Height segments

This work proposes a hybrid methodology for the detection and prediction of Extreme Significant Wave Height (ESWH) periods in oceans. In a first step, wave height time series is approximated by a labeled sequence of segments, which is obtained using a genetic algorithm in combination with a likelihood-based segmentation (GA+LS). Then, an artificial neural network classifier with hybrid basis functions is trained with a multiobjetive evolutionary algorithm (MOEA) in order to predict the occurrence of future ESWH segments based on past values. The methodology is applied to a buoy in the Gulf of Alaska and another one in Puerto Rico. The results show that the GA+LS is able to segment and group the ESWH values, and the neural network models, obtained by the MOEA, make good predictions maintaining a balance between global accuracy and minimum sensitivity for the detection of ESWH events. Moreover, hybrid neural networks are shown to lead to better results than pure models

Sistema concurrente de detección de intrusiones con correlación de alertas en entornos distribuidos

Los escenarios típicos de un NIDS suelen ser redes de tamaño muy variado, desde domésticas hasta de grandes empresas. Pero también hay propuestas para adaptarlos a la computación en la nube. Al ser este tipo de computación un paradigma bastante reciente presenta riesgos de seguridad que creemos que pueden ser reducidos con un NIDS. El sistema de detección de intrusos propuesto en el presente documento propone una serie de medidas para adaptar un NIDS a un entorno de computación en la nube y, motivados por dos carencias que podría presentar esta propuesta, se proponen dos mejoras, la primera de ellas será la mejora de velocidad de análisis mediante el uso de paralelismo tanto a nivel GPU como CPU y la segunda será añadirle un sistema de correlación de alertas. Como método para conseguir estos objetivos se han evaluado diferentes vías que se desarrollan a lo largo de este documento. OpenStack permitirá desplegar un sistema de computación en la nube sobre uno o varios nodos físicos, CUDA y OpenMP hacer uso de paralelismo a nivel de GPU y CPU, y la logica difusa etiquetar las alertas en cada uno de los tipos de ataque. Como líneas de investigación futuras quedaría el desarrollo de un algoritmo de ordenación que explote el paralelismo a nivel de CPU y optimizar la correlación de alertas. [ABSTRACT] Typical scenarios of a NIDS usually are varied sized networks, from domestic to large companies. But there are also proposals to adapt it to the cloud computing. Since this kind of computing paradigm presents fairly recent security risks, we believe may be reduced with NIDS. The intrusion detection system proposed in this document proposes a series of measures to adapt a NIDS to an environment of cloud computing and motivated by two shortcomings that could present, this proposal proposes two improvements, the first of which will improve analysis speed by using parallelism at CPU and GPU and the second generate a system alert correlation. As a method for achieving these goals are assessed different ways that develop throughout this document. OpenStack will help us to deploy a cloud computing on one or more physical nodes, CUDA and OpenMP will help us to use parallelism at GPU and CPU level, and fuzzy logic will help us to label each attack. As future research lines would be the development of a sorting algorithm that exploits parallelism and optimize CPU level alert correlation