What is behind a summary-evaluation decision?

Research in psychology has reported that, among the variety of possibilities for assessment methodologies, summary evaluation offers a particularly adequate context for inferring text comprehension and topic understanding. However, grades obtained in this methodology are hard to quantify objectively. Therefore, we carried out an empirical study to analyze the decisions underlying human summary-grading behavior. The task consisted of expert evaluation of summaries produced in critically relevant contexts of summarization development, and the resulting data were modeled by means of Bayesian networks using an application called Elvira, which allows for graphically observing the predictive power (if any) of the resultant variables. Thus, in this article, we analyzed summary-evaluation decision making in a computational framework

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.

PUE attack detection in CWSNs using anomaly detection techniques

Cognitive wireless sensor network (CWSN) is a new paradigm, integrating cognitive features in traditional wireless sensor networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in cognitive wireless sensor networks is an important problem since these kinds of networks manage critical applications and data. The specific constraints of WSN make the problem even more critical, and effective solutions have not yet been implemented. Primary user emulation (PUE) attack is the most studied specific attack deriving from new cognitive features. This work discusses a new approach, based on anomaly behavior detection and collaboration, to detect the primary user emulation attack in CWSN scenarios. Two non-parametric algorithms, suitable for low-resource networks like CWSNs, have been used in this work: the cumulative sum and data clustering algorithms. The comparison is based on some characteristics such as detection delay, learning time, scalability, resources, and scenario dependency. The algorithms have been tested using a cognitive simulator that provides important results in this area. Both algorithms have shown to be valid in order to detect PUE attacks, reaching a detection rate of 99% and less than 1% of false positives using collaboration.

Effects of coaches' timeouts on basketball teams' offensive and defensive performances according to momentary differences in score and game period.

The aim of the present study was to assess the effects of game timeouts on basketball teams? offensive and defensive performances according to momentary differences in score and game period. The sample consisted of 144 timeouts registered during 18 basketball games randomly selected from the 2007 European Basketball Championship (Spain). For each timeout, five ball possessions were registered before (n?493) and after the timeout (n?475). The offensive and defensive efficiencies were registered across the first 35 min and last 5 min of games. A k-means cluster analysis classified the timeouts according to momentary score status as follows: losing ( ?10 to ?3 points), balanced ( ?2 to 3 points), and winning (4 to 10 points). Repeated-measures analysis of variance identified statistically significant main effects between pre and post timeout offensive and defensive values. Chi-square analysis of game period identified a higher percentage of timeouts called during the last 5 min of a game compared with the first 35 min (64.999.1% vs. 35.1910.3%; x ?5.4, PB0.05). Results showed higher post timeout offensive and defensive performances. No other effect or interaction was found for defensive performances. Offensive performances were better in the last 5 min of games, with the least differences when in balanced situations and greater differences when in winning situations. Results also showed one interaction between timeouts and momentary differences in score, with increased values when in losing and balanced situations but decreased values when in winning situations. Overall, the results suggest that coaches should examine offensive and defensive performances according to game period and differences in score when considering whether to call a timeout.

Cognitive strategies for security in wireless sensor networks

Las redes de sensores inalámbricas son uno de los sectores con más crecimiento dentro de las redes inalámbricas. La rápida adopción de estas redes como solución para muchas nuevas aplicaciones ha llevado a un creciente tráfico en el espectro radioeléctrico. Debido a que las redes inalámbricas de sensores operan en las bandas libres Industrial, Scientific and Medical (ISM) se ha producido una saturación del espectro que en pocos años no permitirá un buen funcionamiento. Con el objetivo de solucionar este tipo de problemas ha aparecido el paradigma de Radio Cognitiva (CR). La introducción de las capacidades cognitivas en las redes inalámbricas de sensores permite utilizar estas redes para aplicaciones con unos requisitos más estrictos respecto a fiabilidad, cobertura o calidad de servicio. Estas redes que aúnan todas estas características son llamadas redes de sensores inalámbricas cognitivas (CWSNs). La mejora en prestaciones de las CWSNs permite su utilización en aplicaciones críticas donde antes no podían ser utilizadas como monitorización de estructuras, de servicios médicos, en entornos militares o de vigilancia. Sin embargo, estas aplicaciones también requieren de otras características que la radio cognitiva no nos ofrece directamente como, por ejemplo, la seguridad. La seguridad en CWSNs es un aspecto poco desarrollado al ser una característica no esencial para su funcionamiento, como pueden serlo el sensado del espectro o la colaboración. Sin embargo, su estudio y mejora es esencial de cara al crecimiento de las CWSNs. Por tanto, esta tesis tiene como objetivo implementar contramedidas usando las nuevas capacidades cognitivas, especialmente en la capa física, teniendo en cuenta las limitaciones con las que cuentan las WSNs. En el ciclo de trabajo de esta tesis se han desarrollado dos estrategias de seguridad contra ataques de especial importancia en redes cognitivas: el ataque de simulación de usuario primario (PUE) y el ataque contra la privacidad eavesdropping. Para mitigar el ataque PUE se ha desarrollado una contramedida basada en la detección de anomalías. Se han implementado dos algoritmos diferentes para detectar este ataque: el algoritmo de Cumulative Sum y el algoritmo de Data Clustering. Una vez comprobado su validez se han comparado entre sí y se han investigado los efectos que pueden afectar al funcionamiento de los mismos. Para combatir el ataque de eavesdropping se ha desarrollado una contramedida basada en la inyección de ruido artificial de manera que el atacante no distinga las señales con información del ruido sin verse afectada la comunicación que nos interesa. También se ha estudiado el impacto que tiene esta contramedida en los recursos de la red. Como resultado paralelo se ha desarrollado un marco de pruebas para CWSNs que consta de un simulador y de una red de nodos cognitivos reales. Estas herramientas han sido esenciales para la implementación y extracción de resultados de la tesis. ABSTRACT Wireless Sensor Networks (WSNs) are one of the fastest growing sectors in wireless networks. The fast introduction of these networks as a solution in many new applications has increased the traffic in the radio spectrum. Due to the operation of WSNs in the free industrial, scientific, and medical (ISM) bands, saturation has ocurred in these frequencies that will make the same operation methods impossible in the future. Cognitive radio (CR) has appeared as a solution for this problem. The networks that join all the mentioned features together are called cognitive wireless sensor networks (CWSNs). The adoption of cognitive features in WSNs allows the use of these networks in applications with higher reliability, coverage, or quality of service requirements. The improvement of the performance of CWSNs allows their use in critical applications where they could not be used before such as structural monitoring, medical care, military scenarios, or security monitoring systems. Nevertheless, these applications also need other features that cognitive radio does not add directly, such as security. The security in CWSNs has not yet been explored fully because it is not necessary field for the main performance of these networks. Instead, other fields like spectrum sensing or collaboration have been explored deeply. However, the study of security in CWSNs is essential for their growth. Therefore, the main objective of this thesis is to study the impact of some cognitive radio attacks in CWSNs and to implement countermeasures using new cognitive capabilities, especially in the physical layer and considering the limitations of WSNs. Inside the work cycle of this thesis, security strategies against two important kinds of attacks in cognitive networks have been developed. These attacks are the primary user emulator (PUE) attack and the eavesdropping attack. A countermeasure against the PUE attack based on anomaly detection has been developed. Two different algorithms have been implemented: the cumulative sum algorithm and the data clustering algorithm. After the verification of these solutions, they have been compared and the side effects that can disturb their performance have been analyzed. The developed approach against the eavesdropping attack is based on the generation of artificial noise to conceal information messages. The impact of this countermeasure on network resources has also been studied. As a parallel result, a new framework for CWSNs has been developed. This includes a simulator and a real network with cognitive nodes. This framework has been crucial for the implementation and extraction of the results presented in this thesis.

Análisis de algoritmos para posicionamiento de aeronaves mediante respuestas SSR utilizando recepción omnidireccional

El transporte aéreo constituye un sector estratégico para el crecimiento económico de cualquier país. El sistema de gestión de tráfico aéreo ATM tiene como objetivo el movimiento seguro y eficiente de las aeronaves dentro del espacio aéreo y de los aeropuertos, siendo la seguridad, en la fase táctica, gestionada por el servicio de control de la circulación aérea. Mediante los procesos de control el tráfico aéreo es vigilado a través de sensores, regulado y guiado de forma organizada y segura. Es precisamente sobre la vigilancia donde se enfoca el contenido de la tesis, en el desarrollo de nuevos conceptos que proporcionen información de vigilancia de ‘bajo coste’ basados en las señales existentes proporcionadas por la infraestructura actual de radar secundario y por los sistemas de posicionamiento basados en satélite que utiliza la ADS-B. El conocimiento y acceso en tiempo real a las trayectorias de las aeronaves es un elemento de valor añadido no sólo para la provisión de los servicios de control de tránsito aéreo, sino para todos los actores del transporte aéreo o de la investigación, siendo uno de los elementos clave en el concepto operacional de los dos grandes proyectos tecnológicos, SESAR en Europa y NextGen en EE.UU.. En las últimas décadas el control de la circulación aérea en espacios aéreos de media y alta densidad de tráfico se ha basado en tecnologías complejas que requieren importantes infraestructuras como son el radar primario de vigilancia (PSR) y el radar secundario de vigilancia (SSR). La filosofía de los programas SESAR y NextGen siguiendo las directrices de la OACI es la de alejarse de las tecnologías basadas en tierra para evolucionar hacia nuevas tecnologías más dinámicas basadas en satélite como la ADS-B. Pero hasta que la implementación y operación de la ADS-B sea completa, existirá un período de transición que implica la coexistencia de aeronaves equipadas o no con ADS-B. El objetivo de la presente Tesis es determinar las metodologías y algoritmos más adecuados para poder hibridar las dos tecnologías descritas anteriormente, utilizando para ello un receptor de bajo coste con antena estática omnidireccional, que analice todas las señales presentes en el canal que comparten el SSR y ADS-B. Mediante esta hibridación se podrá obtener la posición de cualquier aeronave que transmita respuestas a interrogaciones SSR, en cualquiera de sus modos de trabajo, o directamente mensajes de posición ADS-B. Para desarrollar los algoritmos propuestos, además del hardware correspondiente, se han utilizado las aplicaciones LabVIEW para funciones de adquisición de datos reales, y el software MATLAB® para el desarrollo de algoritmos y análisis de datos. La validación de resultados se ha realizado mediante los propios mensajes de posición ADS-B y a través de las trazas radar proporcionadas por la entidad pública empresarial ENAIRE. La técnica desarrollada es autónoma, y no ha requerido de ninguna otra entrada que no sea la recepción omnidireccional de las señales. Sin embargo para la validación de resultados se ha utilizado información pública de las ubicaciones de la red de estaciones SSR desplegadas sobre territorio español y portugués y trazas radar. Los resultados obtenidos demuestran, que con técnicas basadas en superficies de situación definidas por los tiempos de llegada de las respuestas, es posible determinar con una precisión aceptable la posición de las estaciones SSR y la posición de cualquier aeronave que responda mediante el Modo A a éstas. ABSTRACT Air transport is a strategic sector for the economic growth of any country. The air traffic management system (ATM) aims at the safe and efficient movement of aircraft while operating within the airspace and airports, where safety, in the tactical phase, is managed by the air traffic control services. Through the air traffic control processes, aircraft are monitored by sensors, regulated and guided in an organized and safe manner. It is precisely on surveillance where this thesis is focused, developing new concepts that provide a 'low cost' surveillance information based on existing signals provided by currently secondary radar infrastructure and satellite-based positioning systems used by ADS-B. Having a deeper knowledge and a real-time access to the trajectories of the aircraft, is an element of added value not only for the provision of air traffic control services, but also for all air transport or research actors. This is one of the key elements in the operational concept proposed by the two large scale existing technological projects, SESAR in Europe and NextGen in the US. In recent decades, air traffic control in medium and high traffic density areas has been based on complex technologies requiring major infrastructures, such as the primary surveillance radar (PSR) and secondary surveillance radar (SSR). The philosophy of SESAR and NextGen programs, both following the guidelines of ICAO, is to move away from land-based technologies and evolving into some new and more dynamic satellite-based technologies such as ADS-B. Nevertheless, until the ADS-B implementation and operation is fully achieved, there will be a transitional period where aircraft with and without ADS-B equipment will have to coexist. The main objective of this thesis is to determine those methodologies and algorithms which are considered more appropriate to hybridize those two technologies, by using a low cost omnidirectional receiver, which analyzes all signals on the SSR and ADS-B shared channel. Through this hybridization, it is possible to obtain the position of any aircraft answering the SSR interrogations, in any of its modes of operation, or through the emission of ADS-B messages. To develop the proposed algorithms, LabVIEW application has been used for real-time data acquisition, as well as MATLAB software for algorithm development and data analysis, together with the corresponding hardware. The validation of results was performed using the ADS-B position messages and radar tracks provided by the Public Corporate Entity ENAIRE The developed technique is autonomous, and it does not require any other input other than the omnidirectional signal reception. However, for the validation of results, not only radar records have been used, but also public information regarding the position of SSR stations spread throughout the Spanish and Portuguese territory. The results show that using techniques based in the definition of positioning surfaces defined by the responses’ times of arrival, it is possible to determine with an acceptable level of accuracy both the position of the SSR stations as well as the position of any aircraft which transmits Mode A responses.

Classification of Satellite Images by means of Fuzzy Rules generated by a Genetic Algorithm

The data acquired by Remote Sensing systems allow obtaining thematic maps of the earth's surface, by means of the registered image classification. This implies the identification and categorization of all pixels into land cover classes. Traditionally, methods based on statistical parameters have been widely used, although they show some disadvantages. Nevertheless, some authors indicate that those methods based on artificial intelligence, may be a good alternative. Thus, fuzzy classifiers, which are based on Fuzzy Logic, include additional information in the classification process through based-rule systems. In this work, we propose the use of a genetic algorithm (GA) to select the optimal and minimum set of fuzzy rules to classify remotely sensed images. Input information of GA has been obtained through the training space determined by two uncorrelated spectral bands (2D scatter diagrams), which has been irregularly divided by five linguistic terms defined in each band. The proposed methodology has been applied to Landsat-TM images and it has showed that this set of rules provides a higher accuracy level in the classification process

Leveraging heterogeneity for energy minimization in data centers

Energy consumption in data centers is nowadays a critical objective because of its dramatic environmental and economic impact. Over the last years, several approaches have been proposed to tackle the energy/cost optimization problem, but most of them have failed on providing an analytical model to target both the static and dynamic optimization domains for complex heterogeneous data centers. This paper proposes and solves an optimization problem for the energy-driven configuration of a heterogeneous data center. It also advances in the proposition of a new mechanism for task allocation and distribution of workload. The combination of both approaches outperforms previous published results in the field of energy minimization in heterogeneous data centers and scopes a promising area of research.

Leakage and temperature aware server control for improving energy efficiency in data centers

Reducing the energy consumption for computation and cooling in servers is a major challenge considering the data center energy costs today. To ensure energy-efficient operation of servers in data centers, the relationship among computa- tional power, temperature, leakage, and cooling power needs to be analyzed. By means of an innovative setup that enables monitoring and controlling the computing and cooling power consumption separately on a commercial enterprise server, this paper studies temperature-leakage-energy tradeoffs, obtaining an empirical model for the leakage component. Using this model, we design a controller that continuously seeks and settles at the optimal fan speed to minimize the energy consumption for a given workload. We run a customized dynamic load-synthesis tool to stress the system. Our proposed cooling controller achieves up to 9% energy savings and 30W reduction in peak power in comparison to the default cooling control scheme.