Extracting knowledge through friendsourcing: application to new financial services

Personalization has become a key factor for the success of new ICT services. However, the personal information required is not always available in a single site, but scattered in heterogeneous sources, and extracting knowledge from raw information is not an easy job. As a result, many organizations struggle to obtain knowledge on their users useful enough for their business purposes. This paper introduces a comprehensive personal data framework that opens the knowledge extraction process up to collaboration by the involvement of new actors, while enabling users to monitor and control it. The contributions have been validated in a financial services scenario where socioeconomic knowledge on some users is generated by tapping into their social network and used to assists them in raising money from their friends.

Data normalization and integration in robotic systems using web services technologies

The robotics is one of the most active areas. We also need to join a large number of disciplines to create robots. With these premises, one problem is the management of information from multiple heterogeneous sources. Each component, hardware or software, produces data with different nature: temporal frequencies, processing needs, size, type, etc. Nowadays, technologies and software engineering paradigms such as service-oriented architectures are applied to solve this problem in other areas. This paper proposes the use of these technologies to implement a robotic control system based on services. This type of system will allow integration and collaborative work of different elements that make up a robotic system.

Noise-tolerance feasibility for restricted-domain Information Retrieval systems

Information Retrieval systems normally have to work with rather heterogeneous sources, such as Web sites or documents from Optical Character Recognition tools. The correct conversion of these sources into flat text files is not a trivial task since noise may easily be introduced as a result of spelling or typeset errors. Interestingly, this is not a great drawback when the size of the corpus is sufficiently large, since redundancy helps to overcome noise problems. However, noise becomes a serious problem in restricted-domain Information Retrieval specially when the corpus is small and has little or no redundancy. This paper devises an approach which adds noise-tolerance to Information Retrieval systems. A set of experiments carried out in the agricultural domain proves the effectiveness of the approach presented.

Knowledge mediation and overlapping in interfirm networks

Purpose – The literature on interfirm networks devotes scant attention to the ways collaborating firms combine and integrate the knowledge they share and to the subsequent learning outcomes. This study aims to investigate how motorsport companies use network ties to share and recombine knowledge and the learning that occurs both at the organizational and dyadic network levels. Design/methodology/approach – The paper adopts a qualitative and inductive approach with the aim of developing theory from an in-depth examination of the dyadic ties between motorsport companies and the way they share and recombine knowledge. Findings – The research shows that motorsport companies having substantial competences at managing knowledge flows do so by getting advantage of bridging ties. While bridging ties allow motorsport companies to reach distant and diverse sources of knowledge, their strengthening and the formation of relational capital facilitate the mediation and overlapping of that knowledge. Research limitations/implications – The analysis rests on a qualitative account in a single industry and does not take into account different types of inter-firm networks (e.g. alliances; constellations; consortia etc.) and governance structures. Cross-industry analyses may provide a more fine-grained picture of the practices used to recombine knowledge and the ideal composition of inter-firm ties. Practical implications – This study provides some interesting implications for scholars and managers concerned with the management of innovation activities at the interfirm level. From a managerial point of view, the recognition of the different roles played by network spanning connections is particularly salient and raises issues concerning the effective design and management of interfirm ties. Originality/value – Although much of the literature emphasizes the role of bridging ties in connecting to diverse pools of knowledge, this paper goes one step further and investigates in more depth how firms gather and combine distant and heterogeneous sources of knowledge through the use of strengthened bridging ties and a micro-context conducive to high quality relationships.

Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.

Diseño e implementación de un sistema de soporte de decisiones para el Centro de Documentación Regional “Juan Bautista Vázquez”

El volumen de datos en bibliotecas ha aumentado enormemente en los últimos años, así como también la complejidad de sus fuentes y formatos de información, dificultando su gestión y acceso, especialmente como apoyo en la toma de decisiones. Sabiendo que una buena gestión de bibliotecas involucra la integración de indicadores estratégicos, la implementación de un Data Warehouse (DW), que gestione adecuadamente tal cantidad de información, así como su compleja mezcla de fuentes de datos, se convierte en una alternativa interesante a considerar. El artículo describe el diseño e implementación de un sistema de soporte de decisiones (DSS) basado en técnicas de DW para la biblioteca de la Universidad de Cuenca. Para esto, el estudio utiliza una metodología holística, propuesto por Siguenza-Guzman et al. (2014) para la evaluación integral de bibliotecas. Dicha metodología evalúa la colección y los servicios, incorporando importantes elementos para la gestión de bibliotecas, tales como: el desempeño de los servicios, el control de calidad, el uso de la colección y la interacción con el usuario. A partir de este análisis, se propone una arquitectura de DW que integra, procesa y almacena los datos. Finalmente, estos datos almacenados son analizados y visualizados a través de herramientas de procesamiento analítico en línea (OLAP). Las pruebas iniciales de implementación confirman la viabilidad y eficacia del enfoque propuesto, al integrar con éxito múltiples y heterogéneas fuentes y formatos de datos, facilitando que los directores de bibliotecas generen informes personalizados, e incluso permitiendo madurar los procesos transaccionales que diariamente se llevan a cabo.

Event Monitoring Based On Web Services for Heterogeneous Event Sources

This article discusses event monitoring options for heterogeneous event sources as they are given in nowadays heterogeneous distributed information systems. It follows the central assumption, that a fully generic event monitoring solution cannot provide complete support for event monitoring; instead, event source specific semantics such as certain event types or support for certain event monitoring techniques have to be taken into account. Following from this, the core result of the work presented here is the extension of a configurable event monitoring (Web) service for a variety of event sources. A service approach allows us to trade genericity for the exploitation of source specific characteristics. It thus delivers results for the areas of SOA, Web services, CEP and EDA.

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.

Identification of micro-scale anthropogenic CO2, heat and moisture sources – processing eddy covariance fluxes for a dense urban environment

Anthropogenic emissions of heat and exhaust gases play an important role in the atmospheric boundary layer, altering air quality, greenhouse gas concentrations and the transport of heat and moisture at various scales. This is particularly evident in urban areas where emission sources are integrated in the highly heterogeneous urban canopy layer and directly linked to human activities which exhibit significant temporal variability. It is common practice to use eddy covariance observations to estimate turbulent surface fluxes of latent heat, sensible heat and carbon dioxide, which can be attributed to a local scale source area. This study provides a method to assess the influence of micro-scale anthropogenic emissions on heat, moisture and carbon dioxide exchange in a highly urbanized environment for two sites in central London, UK. A new algorithm for the Identification of Micro-scale Anthropogenic Sources (IMAS) is presented, with two aims. Firstly, IMAS filters out the influence of micro-scale emissions and allows for the analysis of the turbulent fluxes representative of the local scale source area. Secondly, it is used to give a first order estimate of anthropogenic heat flux and carbon dioxide flux representative of the building scale. The algorithm is evaluated using directional and temporal analysis. The algorithm is then used at a second site which was not incorporated in its development. The spatial and temporal local scale patterns, as well as micro-scale fluxes, appear physically reasonable and can be incorporated in the analysis of long-term eddy covariance measurements at the sites in central London. In addition to the new IMAS-technique, further steps in quality control and quality assurance used for the flux processing are presented. The methods and results have implications for urban flux measurements in dense urbanised settings with significant sources of heat and greenhouse gases.

Human Multipotent Mesenchymal Stromal Cells from Distinct Sources Show Different In Vivo Potential to Differentiate into Muscle Cells When Injected in Dystrophic Mice

Limb-girdle muscular dystrophies are a heterogeneous group of disorders characterized by progressive degeneration of skeletal muscle caused by the absence or deficiency of muscle proteins. The murine model of Limb-Girdle Muscular Dystrophy 2B, the SJL mice, carries a deletion in the dysferlin gene. Functionally, this mouse model shows discrete muscle weakness, starting at the age of 4-6 weeks. The possibility to restore the expression of the defective protein and improve muscular performance by cell therapy is a promising approach for the future treatment of progressive muscular dystrophies (PMD). We and others have recently shown that human adipose multipotent mesenchymal stromal cells (hASCs) can differentiate into skeletal muscle when in contact with dystrophic muscle cells in vitro and in vivo. Umbilical cord tissue and adipose tissue are known rich sources of multipotent mesenchymal stromal cells (MSCs), widely used for cell-based therapy studies. The main objective of the present study is to evaluate if MSCs from these two different sources have the same potential to reach and differentiate in muscle cells in vivo or if this capability is influenced by the niche from where they were obtained. In order to address this question we injected human derived umbilical cord tissue MSCs (hUCT MSCs) into the caudal vein of SJL mice with the same protocol previously used for hASCs; we evaluated the ability of these cells to engraft into recipient dystrophic muscle after systemic delivery, to express human muscle proteins in the dystrophic host and their effect in functional performance. These results are of great interest for future therapeutic application.

Influence of Sources and Meteorology on Surface Concentrations of Gases and Aerosols in a Coastal Industrial Complex

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Use of different carbon sources in cultivation of baker's yeast for production of glycerol-3-phosphate dehydrogenase

The physiological state of yeast cells changes during culture growth as a consequence of environmental changes (nutrient limitations, pH and metabolic products). Cultures that grow exponentially are heterogeneous cell populations made up of cells regulated by different metabolic and/or genetic control systems. The strain of baker's yeast selected by plating commercial compressed yeast was used for the production of glycerol-3- phosphate dehydrogenase. Glycerol-3-phosphate dehydrogenase (GPD) has been widely used in the enzyme assays with diverse compounds of industrial interest, such as glycerol or glycerol phosphate, as well as a number of important bioanalytical applications. Each cell state determines the level of key enzymes (genetic control), fluxes through metabolic pathways (metabolic control), cell morphology and size. The present study was carried out to determine the effects of environmental conditions and carbon source on GPD production from baker's yeast. Glucose, glycerol, galactose and ethanol were used as carbon sources. Glycerol and ethanol assimilations required agitation, which was dependent on the medium volume in the fermentation flask for the greatest accumulation of intracellular GPD. Enzyme synthesis was also affected by the initial pH of the medium and inoculum size. The fermentation time required for a high level of enzyme formation decreased with the inoculum size. The greatest amount of enzyme (0.45 U/ml) was obtained with an initial pH of 4.5 in the medium containing ethanol or glycerol. The final pH was maintained in YP-ethanol, but in the YP-glycerol the final pH increased to 6.9 during growth.