A survey of feature selection in Internet traffic characterization

In the last decade, the research community has focused on new classification methods that rely on statistical characteristics of Internet traffic, instead of pre-viously popular port-number-based or payload-based methods, which are under even bigger constrictions. Some research works based on statistical characteristics generated large fea-ture sets of Internet traffic; however, nowadays it?s impossible to handle hun-dreds of features in big data scenarios, only leading to unacceptable processing time and misleading classification results due to redundant and correlative data. As a consequence, a feature selection procedure is essential in the process of Internet traffic characterization. In this paper a survey of feature selection methods is presented: feature selection frameworks are introduced, and differ-ent categories of methods are briefly explained and compared; several proposals on feature selection in Internet traffic characterization are shown; finally, future application of feature selection to a concrete project is proposed

Diseño e implementación de un sistema de medición y monitorización de la calidad de servicio y experiencia de redes IPTV basadas en el códec H.264

La nueva generación de difusión y distribución de la televisión a través de la red IP (IPTV, Internet Protocol Television) provoca la necesidad de nuevas herramientas de control y monitorización de la calidad percibida por los usuarios de dicho servicio. Estas herramientas de monitorización deben ser capaces de proporcionar resultados efectivos sobre la calidad de experiencia (QoE) y cumplir los requisitos de respuesta en tiempo real, de tal manera que sea posible una monitorización online del servicio ofrecido al cliente. Este Trabajo Fin de Máster tiene como objetivo la implementación de una herramienta de monitorización de vídeo en alta definición (1920x1080) codificado en H.264, capaz de estimar la calidad percibida mediante la medida exclusiva de parámetros de la red (calidad de servicio, tasa de pérdidas de paquetes), siendo capaz de funcionar en tiempo real. Se ha desarrollado una herramienta basada en un método indirecto de estimación de calidad. La herramienta precisa de información de calidad de servicio, QoS, para relacionarla con la calidad de experiencia. Para ello, es necesario la generación de un modelado matemático, mediante regresión multivariante, obteniendo un modelo de correlación entre la tasa de pérdidas y la calidad percibida. Para la generación del modelado matemático es necesario disponer de una base de datos con un número significativo de secuencias de vídeo diferentes. Se han desarrollado dos variantes de la herramienta, en función del tipo de encapsulación empleado en la transmisión del vídeo H.264 por la red IP: RTP y MPEG-TS. Los resultados obtenidos en la herramienta cumplen con los objetivos propuestos, consiguiendo una estimación de MOS adecuada en casi todos los casos, salvo en ciertas situaciones de alto movimiento, para ambas aproximaciones de IPTV (MPEG-TS y RTP).The new generation of broadcasting and distribution of television through IP networks (IPTV, Internet Protocol Television) creates the need for new tools to control and monito the quality perceived by users of such service. These monitoring tools must be able to provide effective results on the Quality of Experience (QoE) and meet the requirements of real-time response, so that an online monitoring service can be offered to the customer. This master thesis aims at implementing an HD video (1920x1080) monitoring tool encoded in H.264, capable of estimating the perceived video quality by only measuring network parameters (Quality of Service, packet loss rate), being able to operate in real time. A tool has been developed based on an indirect method to estimate the perceived quality. The tool requires information about the service quality, QoS, in order to link it with the quality of experience. For this purpose, it is necessary to develop a mathematical model, using multivariate regression model to obtain a correlation between the packet loss rate and the perceived quality. For the development of this mathematical model it is necessary to have a database with a significant number of different video sequences. Two variants of the tool have been developed, depending on the type of encapsulation used in H.264 video transmission over the IP network: RTP and MPEG-TS. Results obtained in the tool meet the objectives, achieving a suitable MOS estimation in almost all cases, except in the case of high degree of motion, for both IPTV approaches (Native RTP and MPEG-TS)

Modelado y detección de elementos de interés en secuencias de vídeo de carreteras mediante técnicas de visión artificial

Los sistemas avanzados de ayuda a la conducción (ADAS, Advanced Driver Assistance System) están teniendo un importante crecimiento y desarrollo en los últimos años, aumentando en gran medida la seguridad en la conducción. Entre estos sistemas destacan los basados en vídeo, cuyo funcionamiento reside en el análisis de imágenes de la escena. Dichos sistemas necesitan información fiable y robusta del plano de la carretera, destacando la detección de líneas divisorias de carretera, para facilitar y mejorar la tarea de análisis posterior. Este trabajo de final de grado tiene como objetivo la implementación de métodos que consigan obtener esa información fiable, eliminando la distorsión de perspectiva que presentan las imágenes capturas por la cámara. Se trata de obtener una imagen rectificada del plano de carretera, obteniendo una vista cenital. Se han desarrollado dos implementaciones diferentes para la consecución del objetivo, que difieren en la cantidad y tipo de información previa requerida sobre la escena. Se van a comparar ambas alternativas, así como el efecto de considerar el cálculo del punto de fuga de forma robusta para conseguir una estabilización y mejora en la imagen rectificada.Advanced Driver Assistance System (ADAS) are having a significant growth and development in recent years, greatly increasing driving safety. Amongst these, we must highlight the video based systems whose operation lies in the analysis of images of the scene. Such systems need a reliable and robust road plane, emphasizing the detection of dividing lines of the road to facilitate and enhance further analysis tasks. This final degree work aims the implementation of methods to succeed in obtaining such reliable information, eliminating the perspective distortion shown by the images captured by the camera. It is about getting a rectified image of the road plane, getting an bird-view. We have developed two different implementations to achieve the objective, which differ in the amount and type of prior information required on the scene. Both alternatives are compared, as well as the effect of computing robustly the vanishing point to achieve stabilized and improved rectified images

A novel P2P and cloud computing hybrid architecture for multimedia streaming QoS cost functions

Since its appearance, peer-to-peer technology has given raise to various multimedia streaming applications. Today, cloud computing offers different service models as a base for successful end user applications. In this paper we propose joining peer-to-peer and cloud computing into new architectural realization of a distributed cloud computing network for multimedia streaming, in a both centralized and peer-to-peer distributed manner. This architecture merges private and public clouds and it is intended for a commercial use, but in the same time scalable to offer the possibility of non-profitable use. In order to take advantage of the cloud paradigm and make multimedia streaming more efficient, we introduce APIs in the cloud, containing build-in functions for automatic QoS calculation, which permits negotiating QoS parameters such as bandwidth, jitter and latency, among a cloud service provider and its potential clients

SLBN: A Scalable Max-min Fair Algorithm for Rate-Based Explicit Congestion Control

The growth of the Internet has increased the need for scalable congestion control mechanisms in high speed networks. In this context, we propose a rate-based explicit congestion control mechanism with which the sources are provided with the rate at which they can transmit. These rates are computed with a distributed max-min fair algorithm, SLBN. The novelty of SLBN is that it combines two interesting features not simultaneously present in existing proposals: scalability and fast convergence to the max-min fair rates, even under high session churn. SLBN is scalable because routers only maintain a constant amount of state information (only three integer variables per link) and only incur a constant amount of computation per protocol packet, independently of the number of sessions that cross the router. Additionally, SLBN does not require processing any data packet, and it converges independently of sessions' RTT. Finally, by design, the protocol is conservative when assigning rates, even in the presence of high churn, which helps preventing link overshoots in transient periods. We claim that, with all these features, our mechanism is a good candidate to be used in real deployments

Dynamics of fourier modes in torus generative adversarial networks

Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution. Despite their success, the training process of a GAN is highly unstable, and typically, it is necessary to implement several accessory heuristics to the networks to reach acceptable convergence of the model. In this paper, we introduce a novel method to analyze the convergence and stability in the training of generative adversarial networks. For this purpose, we propose to decompose the objective function of the adversary min–max game defining a periodic GAN into its Fourier series. By studying the dynamics of the truncated Fourier series for the continuous alternating gradient descend algorithm, we are able to approximate the real flow and to identify the main features of the convergence of GAN. This approach is confirmed empirically by studying the training flow in a 2-parametric GAN, aiming to generate an unknown exponential distribution. As a by-product, we show that convergent orbits in GANs are small perturbations of periodic orbits so the Nash equillibria are spiral attractors. This theoretically justifies the slow and unstable training observed in GAN

Brief Announcement: Node Sampling Using Centrifugal Random Walks.

We propose distributed algorithms for sampling networks based on a new class of random walks that we call Centrifugal Random Walks (CRW). A CRW is a random walk that starts at a source and always moves away from it. We propose CRW algorithms for connected networks with arbitrary probability distributions, and for grids and networks with regular concentric connectivity with distance based distributions. All CRW sampling algorithms select a node with the exact probability distribution, do not need warm-up, and end in a number of hops bounded by the network diameter

Data Augmentation techniques in time series domain: A survey and taxonomy

With the latest advances in Deep Learning-based generative models, it has not taken long to take advantage of their remarkable performance in the area of time series. Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. These features are not usually abundant in the real world, where they are usually limited and often have constraints that must be guaranteed. Therefore, an effective way to increase the amount of data is by using Data Augmentation techniques, either by adding noise or permutations and by generating new synthetic data. This work systematically reviews the current state-of-the-art in the area to provide an overview of all available algorithms and proposes a taxonomy of the most relevant research. The efficiency of the different variants will be evaluated as a central part of the process, as well as the different metrics to evaluate the performance and the main problems concerning each model will be analysed. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.Comment: 33 pages, 9 figure