Ranking web services using centralities and social indicators

Nowadays, developers of web application mashups face a sheer overwhelming variety and pluralism of web services. Therefore, choosing appropriate web services to achieve specific goals requires a certain amount of knowledge as well as expertise. In order to support users in choosing appropriate web services it is not only important to match their search criteria to a dataset of possible choices but also to rank the results according to their relevance, thus minimizing the time it takes for taking such a choice. Therefore, we investigated six ranking approaches in an empirical manner and compared them to each other. Moreover, we have had a look on how one can combine those ranking algorithms linearly in order to maximize the quality of their outputs.

The relevance of seed size in modulating leaf physiology and early plant performance in two tree species

he size of seeds and the microsite of seed dispersal may affect the early establishment of seedlings through different physiological processes. Here, we examined the effects of seed size and light availability on seedling growth and survival, and whether such effects were mediated by water use efficiency. Acorns of Quercus petraea and the more drought-tolerant Quercus pyrenaica were sowed within and around a tree canopy gap in a sub-Mediterranean forest stand. We monitored seedling emergence and measured predawn leaf water potential (Ψpd), leaf nitrogen per unit area (Na), leaf mass per area, leaf carbon isotope composition (δ13C) and plant growth at the end of the first summer. Survival was measured on the next year. Path analysis revealed a consistent pattern in both species of higher δ13C as Ψpd decreased and higher δ13C as seedlings emerged later in the season, indicating an increase in 13C as the growing season is shorter and drier. There was a direct positive effect of seed size on δ13C in Q. petraea that was absent in Q. pyrenaica. Leaf δ13C had no effect on growth but the probability of surviving until the second year was higher for those seedlings of Q. pyrenaica that had lower δ13C on the first year. In conclusion, leaf δ13C is affected by seed size, seedling emergence time and the availability of light and water, however, leaf δ13C is irrelevant for first year growth, which is directly dependent on the amount of seed reserves.

Evaluation of a User-Adapted Spoken Language Dialogue System: Measuring the Relevance of the Contextual Information Sources

We present an evaluation of a spoken language dialogue system with a module for the management of userrelated information, stored as user preferences and privileges. The flexibility of our dialogue management approach, based on Bayesian Networks (BN), together with a contextual information module, which performs different strategies for handling such information, allows us to include user information as a new level into the Context Manager hierarchy. We propose a set of objective and subjective metrics to measure the relevance of the different contextual information sources. The analysis of our evaluation scenarios shows that the relevance of the short-term information (i.e. the system status) remains pretty stable throughout the dialogue, whereas the dialogue history and the user profile (i.e. the middle-term and the long-term information, respectively) play a complementary role, evolving their usefulness as the dialogue evolves.

Relevance of motion-related assessment metrics in laparoscopic surgery

INTRODUCTION: Motion metrics have become an important source of information when addressing the assessment of surgical expertise. However, their direct relationship with the different surgical skills has not been fully explored. The purpose of this study is to investigate the relevance of motion-related metrics in the evaluation processes of basic psychomotor laparoscopic skills, as well as their correlation with the different abilities sought to measure. METHODS: A framework for task definition and metric analysis is proposed. An explorative survey was first conducted with a board of experts to identify metrics to assess basic psychomotor skills. Based on the output of that survey, three novel tasks for surgical assessment were designed. Face and construct validation study was performed, with focus on motion-related metrics. Tasks were performed by 42 participants (16 novices, 22 residents and 4 experts). Movements of the laparoscopic instruments were registered with the TrEndo tracking system and analyzed. RESULTS: Time, path length and depth showed construct validity for all three tasks. Motion smoothness and idle time also showed validity for tasks involving bi-manual coordination and tasks requiring a more tactical approach respectively. Additionally, motion smoothness and average speed showed a high internal consistency, proving them to be the most task-independent of all the metrics analyzed. CONCLUSION: Motion metrics are complementary and valid for assessing basic psychomotor skills, and their relevance depends on the skill being evaluated. A larger clinical implementation, combined with quality performance information, will give more insight on the relevance of the results shown in this study.

Collective Organization of Complex Networks: Emergent Scales, Vulnerability, and Targeting of Desired Dynamics

Cuando una colectividad de sistemas dinámicos acoplados mediante una estructura irregular de interacciones evoluciona, se observan dinámicas de gran complejidad y fenómenos emergentes imposibles de predecir a partir de las propiedades de los sistemas individuales. El objetivo principal de esta tesis es precisamente avanzar en nuestra comprensión de la relación existente entre la topología de interacciones y las dinámicas colectivas que una red compleja es capaz de mantener. Siendo este un tema amplio que se puede abordar desde distintos puntos de vista, en esta tesis se han estudiado tres problemas importantes dentro del mismo que están relacionados entre sí. Por un lado, en numerosos sistemas naturales y artificiales que se pueden describir mediante una red compleja la topología no es estática, sino que depende de la dinámica que se desarrolla en la red: un ejemplo son las redes de neuronas del cerebro. En estas redes adaptativas la propia topología emerge como consecuencia de una autoorganización del sistema. Para conocer mejor cómo pueden emerger espontáneamente las propiedades comúnmente observadas en redes reales, hemos estudiado el comportamiento de sistemas que evolucionan según reglas adaptativas locales con base empírica. Nuestros resultados numéricos y analíticos muestran que la autoorganización del sistema da lugar a dos de las propiedades más universales de las redes complejas: a escala mesoscópica, la aparición de una estructura de comunidades, y, a escala macroscópica, la existencia de una ley de potencias en la distribución de las interacciones en la red. El hecho de que estas propiedades aparecen en dos modelos con leyes de evolución cuantitativamente distintas que siguen unos mismos principios adaptativos sugiere que estamos ante un fenómeno que puede ser muy general, y estar en el origen de estas propiedades en sistemas reales. En segundo lugar, proponemos una medida que permite clasificar los elementos de una red compleja en función de su relevancia para el mantenimiento de dinámicas colectivas. En concreto, estudiamos la vulnerabilidad de los distintos elementos de una red frente a perturbaciones o grandes fluctuaciones, entendida como una medida del impacto que estos acontecimientos externos tienen en la interrupción de una dinámica colectiva. Los resultados que se obtienen indican que la vulnerabilidad dinámica es sobre todo dependiente de propiedades locales, por tanto nuestras conclusiones abarcan diferentes topologías, y muestran la existencia de una dependencia no trivial entre la vulnerabilidad y la conectividad de los elementos de una red. Finalmente, proponemos una estrategia de imposición de una dinámica objetivo genérica en una red dada e investigamos su validez en redes con diversas topologías que mantienen regímenes dinámicos turbulentos. Se obtiene como resultado que las redes heterogéneas (y la amplia mayora de las redes reales estudiadas lo son) son las más adecuadas para nuestra estrategia de targeting de dinámicas deseadas, siendo la estrategia muy efectiva incluso en caso de disponer de un conocimiento muy imperfecto de la topología de la red. Aparte de la relevancia teórica para la comprensión de fenómenos colectivos en sistemas complejos, los métodos y resultados propuestos podrán dar lugar a aplicaciones en sistemas experimentales y tecnológicos, como por ejemplo los sistemas neuronales in vitro, el sistema nervioso central (en el estudio de actividades síncronas de carácter patológico), las redes eléctricas o los sistemas de comunicaciones. ABSTRACT The time evolution of an ensemble of dynamical systems coupled through an irregular interaction scheme gives rise to dynamics of great of complexity and emergent phenomena that cannot be predicted from the properties of the individual systems. The main objective of this thesis is precisely to increase our understanding of the interplay between the interaction topology and the collective dynamics that a complex network can support. This is a very broad subject, so in this thesis we will limit ourselves to the study of three relevant problems that have strong connections among them. First, it is a well-known fact that in many natural and manmade systems that can be represented as complex networks the topology is not static; rather, it depends on the dynamics taking place on the network (as it happens, for instance, in the neuronal networks in the brain). In these adaptive networks the topology itself emerges from the self-organization in the system. To better understand how the properties that are commonly observed in real networks spontaneously emerge, we have studied the behavior of systems that evolve according to local adaptive rules that are empirically motivated. Our numerical and analytical results show that self-organization brings about two of the most universally found properties in complex networks: at the mesoscopic scale, the appearance of a community structure, and, at the macroscopic scale, the existence of a power law in the weight distribution of the network interactions. The fact that these properties show up in two models with quantitatively different mechanisms that follow the same general adaptive principles suggests that our results may be generalized to other systems as well, and they may be behind the origin of these properties in some real systems. We also propose a new measure that provides a ranking of the elements in a network in terms of their relevance for the maintenance of collective dynamics. Specifically, we study the vulnerability of the elements under perturbations or large fluctuations, interpreted as a measure of the impact these external events have on the disruption of collective motion. Our results suggest that the dynamic vulnerability measure depends largely on local properties (our conclusions thus being valid for different topologies) and they show a non-trivial dependence of the vulnerability on the connectivity of the network elements. Finally, we propose a strategy for the imposition of generic goal dynamics on a given network, and we explore its performance in networks with different topologies that support turbulent dynamical regimes. It turns out that heterogeneous networks (and most real networks that have been studied belong in this category) are the most suitable for our strategy for the targeting of desired dynamics, the strategy being very effective even when the knowledge on the network topology is far from accurate. Aside from their theoretical relevance for the understanding of collective phenomena in complex systems, the methods and results here discussed might lead to applications in experimental and technological systems, such as in vitro neuronal systems, the central nervous system (where pathological synchronous activity sometimes occurs), communication systems or power grids.

Dominance intensity methods for ranking of alternatives in mcdm with imprecise information

Los decisores cada vez se enfrentan a problemas más complejos en los que tomar una decisión implica tener que considerar simultáneamente muchos criterios que normalmente son conflictivos entre sí. En la mayoría de los problemas de decisión es necesario considerar criterios económicos, sociales y medioambientales. La Teoría de la Decisión proporciona el marco adecuado para poder ayudar a los decisores a resolver estos problemas de decisión complejos, al permitir considerar conjuntamente la incertidumbre existente sobre las consecuencias de cada alternativa en los diferentes atributos y la imprecisión sobre las preferencias de los decisores. En esta tesis doctoral nos centramos en la imprecisión de las preferencias de los decisores cuando éstas pueden ser representadas mediante una función de utilidad multiatributo aditiva. Por lo tanto, consideramos imprecisión tanto en los pesos como en las funciones de utilidad componentes de cada atributo. Se ha considerado el caso en que la imprecisión puede ser representada por intervalos de valores o bien mediante información ordinal, en lugar de proporcionar valores concretos. En este sentido, hemos propuesto métodos que permiten ordenar las diferentes alternativas basados en los conceptos de intensidad de dominación o intensidad de preferencia, los cuales intentan medir la fuerza con la que cada alternativa es preferida al resto. Para todos los métodos propuestos se ha analizado su comportamiento y se ha comparado con los más relevantes existentes en la literatura científica que pueden ser aplicados para resolver este tipo de problemas. Para ello, se ha realizado un estudio de simulación en el que se han usado dos medidas de eficiencia (hit ratio y coeficiente de correlación de Kendall) para comparar los diferentes métodos. ABSTRACT Decision makers increasingly face complex decision-making problems where they have to simultaneously consider many often conflicting criteria. In most decision-making problems it is necessary to consider economic, social and environmental criteria. Decision making theory provides an adequate framework for helping decision makers to make complex decisions where they can jointly consider the uncertainty about the performance of each alternative for each attribute, and the imprecision of the decision maker's preferences. In this PhD thesis we focus on the imprecision of the decision maker's preferences represented by an additive multiattribute utility function. Therefore, we consider the imprecision of weights, as well as of component utility functions for each attribute. We consider the case in which the imprecision is represented by ranges of values or by ordinal information rather than precise values. In this respect, we propose methods for ranking alternatives based on notions of dominance intensity, also known as preference intensity, which attempt to measure how much more preferred each alternative is to the others. The performance of the propose methods has been analyzed and compared against the leading existing methods that are applicable to this type of problem. For this purpose, we conducted a simulation study using two efficiency measures (hit ratio and Kendall correlation coefficient) to compare the different methods.

Relevance of the glottal pulse and the vocal tract in gender detection

Gender detection is a very important objective to improve efficiency in tasks as speech or speaker recognition, among others. Traditionally gender detection has been focused on fundamental frequency (f0) and cepstral features derived from voiced segments of speech. The methodology presented here consists in obtaining uncorrelated glottal and vocal tract components which are parameterized as mel-frequency coefficients. K-fold and cross-validation using QDA and GMM classifiers showed that better detection rates are reached when glottal source and vocal tract parameters are used in a gender-balanced database of running speech from 340 speakers.

Ranking factors affecting emissions of GHG from incubated agricultural soils

Agriculture significantly contributes to global greenhouse gas (GHG) missions and there is a need to develop effective mitigation strategies. The efficacy of methods to reduce GHG fluxes from agricultural soils can be affected by a range of interacting management and environmental factors. Uniquely, we used the Taguchi experimental design methodology to rank the relative importance of six factors known to affect the emission of GHG from soil: nitrate (NO3?) addition, carbon quality (labile and non-labile C), soil temperature, water-filled pore space (WFPS) and extent of soil compaction. Grassland soil was incubated in jars where selected factors, considered at two or three amounts within the experimental range, were combined in an orthogonal array to determine the importance and interactions between factors with a L16 design, comprising 16 experimental units. Within this L16 design, 216 combinations of the full factorial experimental design were represented. Headspace nitrous oxide (N2O), methane (CH4) and carbon dioxide (CO2) concentrations were measured and used to calculate fluxes. Results found for the relative influence of factors (WFPS and NO3? addition were the main factors affecting N2O fluxes, whilst glucose, NO3? and soil temperature were the main factors affecting CO2 and CH4 fluxes) were consistent with those already well documented. Interactions between factors were also studied and results showed that factors with Little individual influence became more influential in combination. The proposed methodology offers new possibilities for GHG researchers to study interactions between influential factors and address the optimized sets of conditions to reduce GHG emissions in agro-ecosystems, while reducing the number of experimental units required compared with conventional experimental procedures that adjust one variable at a time.

Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification

Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.

Interval-based ranking in noisy evolutionary multiobjective optimization

As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present ?-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standardmulti-objective evolutionary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experimental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization.

Non uniform embedding based on relevance analysis with reduced computational complexity: application to the detection of pathologies from biosignal recordings

Nonlinear analysis tools for studying and characterizing the dynamics of physiological signals have gained popularity, mainly because tracking sudden alterations of the inherent complexity of biological processes might be an indicator of altered physiological states. Typically, in order to perform an analysis with such tools, the physiological variables that describe the biological process under study are used to reconstruct the underlying dynamics of the biological processes. For that goal, a procedure called time-delay or uniform embedding is usually employed. Nonetheless, there is evidence of its inability for dealing with non-stationary signals, as those recorded from many physiological processes. To handle with such a drawback, this paper evaluates the utility of non-conventional time series reconstruction procedures based on non uniform embedding, applying them to automatic pattern recognition tasks. The paper compares a state of the art non uniform approach with a novel scheme which fuses embedding and feature selection at once, searching for better reconstructions of the dynamics of the system. Moreover, results are also compared with two classic uniform embedding techniques. Thus, the goal is comparing uniform and non uniform reconstruction techniques, including the one proposed in this work, for pattern recognition in biomedical signal processing tasks. Once the state space is reconstructed, the scheme followed characterizes with three classic nonlinear dynamic features (Largest Lyapunov Exponent, Correlation Dimension and Recurrence Period Density Entropy), while classification is carried out by means of a simple k-nn classifier. In order to test its generalization capabilities, the approach was tested with three different physiological databases (Speech Pathologies, Epilepsy and Heart Murmurs). In terms of the accuracy obtained to automatically detect the presence of pathologies, and for the three types of biosignals analyzed, the non uniform techniques used in this work lightly outperformed the results obtained using the uniform methods, suggesting their usefulness to characterize non-stationary biomedical signals in pattern recognition applications. On the other hand, in view of the results obtained and its low computational load, the proposed technique suggests its applicability for the applications under study.

Recuperación cruzada de información pública clínico-genómica a partir de consultas sobre repositorios clínicos privados

Parte de la investigación biomédica actual se encuentra centrada en el análisis de datos heterogéneos. Estos datos pueden tener distinto origen, estructura, y semántica. Gran cantidad de datos de interés para los investigadores se encuentran en bases de datos públicas, que recogen información de distintas fuentes y la ponen a disposición de la comunidad de forma gratuita. Para homogeneizar estas fuentes de datos públicas con otras de origen privado, existen diversas herramientas y técnicas que permiten automatizar los procesos de homogeneización de datos heterogéneos. El Grupo de Informática Biomédica (GIB) [1] de la Universidad Politécnica de Madrid colabora en el proyecto europeo P-medicine [2], cuya finalidad reside en el desarrollo de una infraestructura que facilite la evolución de los procedimientos médicos actuales hacia la medicina personalizada. Una de las tareas enmarcadas en el proyecto P-medicine que tiene asignado el grupo consiste en elaborar herramientas que ayuden a usuarios en el proceso de integración de datos contenidos en fuentes de información heterogéneas. Algunas de estas fuentes de información son bases de datos públicas de ámbito biomédico contenidas en la plataforma NCBI [3] (National Center for Biotechnology Information). Una de las herramientas que el grupo desarrolla para integrar fuentes de datos es Ontology Annotator. En una de sus fases, la labor del usuario consiste en recuperar información de una base de datos pública y seleccionar de forma manual los resultados relevantes. Para automatizar el proceso de búsqueda y selección de resultados relevantes, por un lado existe un gran interés en conseguir generar consultas que guíen hacia resultados lo más precisos y exactos como sea posible, por otro lado, existe un gran interés en extraer información relevante de elevadas cantidades de documentos, lo cual requiere de sistemas que analicen y ponderen los datos que caracterizan a los mismos. En el campo informático de la inteligencia artificial, dentro de la rama de la recuperación de la información, existen diversos estudios acerca de la expansión de consultas a partir de retroalimentación relevante que podrían ser de gran utilidad para dar solución a la cuestión. Estos estudios se centran en técnicas para reformular o expandir la consulta inicial utilizando como realimentación los resultados que en una primera instancia fueron relevantes para el usuario, de forma que el nuevo conjunto de resultados tenga mayor proximidad con los que el usuario realmente desea. El objetivo de este trabajo de fin de grado consiste en el estudio, implementación y experimentación de métodos que automaticen el proceso de extracción de información trascendente de documentos, utilizándola para expandir o reformular consultas. De esta forma se pretende mejorar la precisión y el ranking de los resultados asociados. Dichos métodos serán integrados en la herramienta Ontology Annotator y enfocados a la fuente de datos de PubMed [4].---ABSTRACT---Part of the current biomedical research is focused on the analysis of heterogeneous data. These data may have different origin, structure and semantics. A big quantity of interesting data is contained in public databases which gather information from different sources and make it open and free to be used by the community. In order to homogenize thise sources of public data with others which origin is private, there are some tools and techniques that allow automating the processes of integration heterogeneous data. The biomedical informatics group of the Universidad Politécnica de Madrid cooperates with the European project P-medicine which main purpose is to create an infrastructure and models to facilitate the transition from current medical practice to personalized medicine. One of the tasks of the project that the group is in charge of consists on the development of tools that will help users in the process of integrating data from diverse sources. Some of the sources are biomedical public data bases from the NCBI platform (National Center for Biotechnology Information). One of the tools in which the group is currently working on for the integration of data sources is called the Ontology Annotator. In this tool there is a phase in which the user has to retrieve information from a public data base and select the relevant data contained in it manually. For automating the process of searching and selecting data on the one hand, there is an interest in automatically generating queries that guide towards the more precise results as possible. On the other hand, there is an interest on retrieve relevant information from large quantities of documents. The solution requires systems that analyze and weigh the data allowing the localization of the relevant items. In the computer science field of the artificial intelligence, in the branch of information retrieval there are diverse studies about the query expansion from relevance feedback that could be used to solve the problem. The main purpose of this studies is to obtain a set of results that is the closer as possible to the information that the user really wants to retrieve. In order to reach this purpose different techniques are used to reformulate or expand the initial query using a feedback the results that where relevant for the user, with this method, the new set of results will have more proximity with the ones that the user really desires. The goal of this final dissertation project consists on the study, implementation and experimentation of methods that automate the process of extraction of relevant information from documents using this information to expand queries. This way, the precision and the ranking of the results associated will be improved. These methods will be integrated in the Ontology Annotator tool and will focus on the PubMed data source.

Local relevance evaluation in the accreditation bodies for engineering masters programs

Accreditation models in the international context mainly consider the evaluation of learning outcomes and the ability of programs (or higher education institutions) to achieve the educational objectives stated in their mission. However, it is not clear if these objectives and therefore their outcomes satisfy real national and regional needs, a critical point in engineering master's programs, especially in developing countries. The aim of this paper is to study the importance of the local relevancy evaluation of these programs and to analyze the main models of quality assurance and accreditation bodies of USA, Europe and Latin America, in order to ascertain whether the relevancy is evaluated or not. After a literature review, we found that in a free-market economic context and international education, the accreditation of master's programs follows an international accreditation model, and doesńt take in account in most cases criteria and indicators for local relevancy. It concludes that it is necessary both, international accreditation to ensure the effectiveness of the program (achievement of learning outcomes) and the national accreditation through which it could ensure local relevancy of programs, for which we are giving some indicators.

Relevance evaluation of engineering masters program in Peru

In a context of mass higher education, it is necessary to ensure not only quality but also the relevance of engineering master's programs, namely the appropriateness of the objectives and outcomes to the needs and interests of the program beneficiaries. After a literature review we analyzed the evaluation models of three organizations in Peru: the Board of Evaluation, Accreditation and Certification of the University Education Quality CONEAU, the Institute of Quality and Accreditation of Computing, Engineering and Technology - ICACIT and the Pontificia Universidad Catolica del Peru. The result of this study is a model for relevance evaluation for an engineering master¿s program in Peru.

Relevance of Higher Education. How to measure it? The case of masters of engineering in Peru

Existing evaluation models for higher education have, mainly, accreditation purposes, and evaluate the efficiency of training programs, that is to say, the degree of suitability between the educational results and the objectives of the program. However, it is not guaranteed that those objectives adequate to the needs and real interests of students and stakeholders, that is to say, they do not assess the relevance of the programs, a very important aspect in developing countries. From the review of experiences, this paper proposes a model for evaluating the relevance of engineering masters program, and applies it to the case of a master?s degree at the University of Piura, Peru. We conclude that the proposed model is applicable to other masters program, offers an objective way for determining is a training program keep being relevant, and identifies improvement opportunities