Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios

Computing the Importance of Schema Elements Taking Into Account the Whole SCHEMA

Conceptual Schemas are one of the most important artifacts in the development cycle of information systems. To understand the conceptual schema is essential to get involved in the information system that is described within it. As the information system increases its size and complexity, the relative conceptual schema will grow in the same proportion making di cult to understand the main concepts of that schema/information system. The thesis comprises the investigation of the in uence of the whole schema in computing the relevance of schema elements. It will include research and implementation of algorithms for scoring elements in the literature, an study of the di erent results obtained once applied to a few example conceptual schemas, an extension of those algorithms including new components in the computation process like derivation rules, constraints and the behavioural subschema speci cation, and an in-depth comparison among the initial algorithms and the extended ones studying the results in order to choose those algorithms that give the most valuable output

Computing the Importance of Schema Elements Taking Into Account the Whole SCHEMA

Conceptual Schemas are one of the most important artifacts in the development cycle of information systems. To understand the conceptual schema is essential to get involved in the information system that is described within it. As the information system increases its size and complexity, the relative conceptual schema will grow in the same proportion making di cult to understand the main concepts of that schema/information system. The thesis comprises the investigation of the in uence of the whole schema in computing the relevance of schema elements. It will include research and implementation of algorithms for scoring elements in the literature, an study of the di erent results obtained once applied to a few example conceptual schemas, an extension of those algorithms including new components in the computation process like derivation rules, constraints and the behavioural subschema speci cation, and an in-depth comparison among the initial algorithms and the extended ones studying the results in order to choose those algorithms that give the most valuable output

Bank transaction text label mining algorithms

The banking transaction monitoring system implements decision support mechanisms for online payment control procedures for legal entities considering the dynamic risk profile of the client. The system includes a set of algorithms for the intellectual analysis of transaction parameters, including a text label for the purpose of payment, and decision support for an employee of the financial monitoring unit. The development of algorithms for analyzing textual labels for the purpose of payments allows us to clarify the dynamic payment profile of the user and increase the validity of the recommendations of the monitoring system. A block diagram of a system for identifying high-risk banking transactions based on data mining algorithms has been developed. Algorithms for data mining of textual labels of the payment purpose have been developed and the effectiveness of the proposed solution on field data has been evaluated. An algorithm is proposed for the phased analysis of the text label of the payment destination, including the stages of preprocessing, filtering, normalizing and constructing a classifier based on a set of regular expressions and intelligent analysis technologies. The difference between the algorithm is the use of adaptive category dictionaries and the multi-pass application of heterogeneous classifiers, which makes it possible to increase the validity of the decision on whether the transaction belongs to one of the selected classes

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms

ASPECT-BASED OPINION MINING OF PRODUCT REVIEWS IN MICROBLOGS USING MOST RELEVANT FREQUENT CLUSTERS OF TERMS

Aspect-based Opinion Mining (ABOM) systems take as input a corpus about a product and aim to mine the aspects (the features or parts) of the product and obtain the opinions of each aspect (how positive or negative the appraisal or emotions towards the aspect is). A few systems like Twitter Aspect Classifier and Twitter Summarization Framework have been proposed to perform ABOM on microblogs. However, the accuracy of these techniques are easily affected by spam posts and buzzwords. In this thesis we address this problem of removing noisy aspects in ABOM by proposing an algorithm called Microblog Aspect Miner (MAM). MAM classifies the microblog posts into subjective and objective posts, represents the frequent nouns in the subjective posts as vectors, and then clusters them to obtain relevant aspects of the product. MAM achieves a 50% improvement in accuracy in obtaining relevant aspects of products compared to previous systems

Analizador semántico : Mujer Olímpica 2012

Como parte de las conclusiones del Seminario Permanente “Mujer y Deporte”, celebrado en la Facultad de Ciencias de la Actividad Física y del Deporte de la Universidad Politécnica de Madrid, en las jornadas sobre “El deporte femenino en los medios de comunicación”, celebrado en la semana del 5 al 11 de noviembre de 2009, se establece que en nuestro país “el éxito de las mujeres en algunos deportes es igual o superior al de los hombres y, a pesar de ello, las mujeres siguen sin tener apenas cabida en los medios de comunicación”. Las razones por las que este fenómeno existe son múltiples y complejas aunque se puede señalar como factor principal la diferencia en el poder de financiación y apoyo económico que se da entre las categorías femenina y masculina dentro de un mismo deporte. El presente proyecto se plantea como trabajo de investigación que pretende mostrar cómo, a través de plataformas tecnológicas como son la Web 2.0., se puede dar impulso al deporte femenino. Para la consecución de este objetivo se ha desarrollado un algoritmo de búsqueda de noticias que traten sobre los logros de las deportistas femeninas españolas en disciplinas olímpicas y paraolímpicas, para la posterior visualización en una plataforma web. La plataforma web diseñada se encuentra totalmente automatizada no necesitando de ningún web master al uso, reduciendo así los costes de mantenimiento y explotación. La aplicación podría ser configurada para el seguimiento de eventos especiales como pueden ser competiciones europeas o las olimpiadas del 2012. Este buscador de noticias será el primer buscador semántico en dicha materia. En resumen, este proyecto pretende ser una puerta que acerque el deporte femenino a los usuarios de Internet, demostrando así que los logros del deporte español no son sólo masculinos sino que el deporte femenino logra los mismos o incluso mejores éxitos. ______________________________________________________________________________________________________________________The statement "the success of women in some sports is equal or superior to men, and despite this, women still have no place in the media”, is part of the conclusions of the Permanent Seminar on "Women and Sport" held in the Faculty of Physical Education and Sport, of the Polytechnic University of Madrid, at the conference on "Women's sports in the media, 2009”. The reasons for this phenomenon are many and complex but one of the main factors highlights the difference in funding possibilities and financial support that exists between male and female categories in the same sport. This project aims to show how through technology platforms, such as Web 2.0, you can give stimulus to women's sports. To achieve this goal we have developed a search algorithm that retrieves news of female athletes’ achievements in Spanish Olympic and Paralympics disciplines. The results will be displayed in a web platform which will serve as a proof of concept. The web platform is fully automated not needing of any webmaster, thus reducing maintenance and operating costs. The application could be configured to monitor special events, as well as national, European or international competitions such as the Olympic Games in 2012. The news search engine is the first semantic search engine in that area. In summary, this project will be a door that brings female sport to Internet users, showing that Spanish sport achievers are not only men but also women who achieve the same or better successes.Ingeniería Técnica en Informática de Gestió

Linked Data Supported Information Retrieval

Um Inhalte im World Wide Web ausfindig zu machen, sind Suchmaschienen nicht mehr wegzudenken. Semantic Web und Linked Data Technologien ermöglichen ein detaillierteres und eindeutiges Strukturieren der Inhalte und erlauben vollkommen neue Herangehensweisen an die Lösung von Information Retrieval Problemen. Diese Arbeit befasst sich mit den Möglichkeiten, wie Information Retrieval Anwendungen von der Einbeziehung von Linked Data profitieren können. Neue Methoden der computer-gestützten semantischen Textanalyse, semantischen Suche, Informationspriorisierung und -visualisierung werden vorgestellt und umfassend evaluiert. Dabei werden Linked Data Ressourcen und ihre Beziehungen in die Verfahren integriert, um eine Steigerung der Effektivität der Verfahren bzw. ihrer Benutzerfreundlichkeit zu erzielen. Zunächst wird eine Einführung in die Grundlagen des Information Retrieval und Linked Data gegeben. Anschließend werden neue manuelle und automatisierte Verfahren zum semantischen Annotieren von Dokumenten durch deren Verknüpfung mit Linked Data Ressourcen vorgestellt (Entity Linking). Eine umfassende Evaluation der Verfahren wird durchgeführt und das zu Grunde liegende Evaluationssystem umfangreich verbessert. Aufbauend auf den Annotationsverfahren werden zwei neue Retrievalmodelle zur semantischen Suche vorgestellt und evaluiert. Die Verfahren basieren auf dem generalisierten Vektorraummodell und beziehen die semantische Ähnlichkeit anhand von taxonomie-basierten Beziehungen der Linked Data Ressourcen in Dokumenten und Suchanfragen in die Berechnung der Suchergebnisrangfolge ein. Mit dem Ziel die Berechnung von semantischer Ähnlichkeit weiter zu verfeinern, wird ein Verfahren zur Priorisierung von Linked Data Ressourcen vorgestellt und evaluiert. Darauf aufbauend werden Visualisierungstechniken aufgezeigt mit dem Ziel, die Explorierbarkeit und Navigierbarkeit innerhalb eines semantisch annotierten Dokumentenkorpus zu verbessern. Hierfür werden zwei Anwendungen präsentiert. Zum einen eine Linked Data basierte explorative Erweiterung als Ergänzung zu einer traditionellen schlüsselwort-basierten Suchmaschine, zum anderen ein Linked Data basiertes Empfehlungssystem

Six papers on computational methods for the analysis of structured and unstructured data in the economic domain

This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events.This work investigates the application of computational methods for structured and unstructured data. The domains of application are two closely connected fields with the common goal of promoting the stability of the financial system: systemic risk and bank supervision. The work explores different families of models and applies them to different tasks: graphical Gaussian network models to address bank interconnectivity, topic models to monitor bank news and deep learning for text classification. New applications and variants of these models are investigated posing a particular attention on the combined use of textual and structured data. In the penultimate chapter is introduced a sentiment polarity classification tool in Italian, based on deep learning, to simplify future researches relying on sentiment analysis. The different models have proven useful for leveraging numerical (structured) and textual (unstructured) data. Graphical Gaussian Models and Topic models have been adopted for inspection and descriptive tasks while deep learning has been applied more for predictive (classification) problems. Overall, the integration of textual (unstructured) and numerical (structured) information has proven useful for systemic risk and bank supervision related analysis. The integration of textual data with numerical data in fact, has brought either to higher predictive performances or enhanced capability of explaining phenomena and correlating them to other events

The structure and dynamics of multilayer networks

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.Comment: In Press, Accepted Manuscript, Physics Reports 201