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Search Results: 1 - 10 of 54341 matches for " Carlos Fernandez-Llatas "
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When the Social Meets the Semantic: Social Semantic Web or Web 2.5
Salvatore F. Pileggi,Carlos Fernandez-Llatas,Vicente Traver
Future Internet , 2012, DOI: 10.3390/fi4030852
Abstract: The social trend is progressively becoming the key feature of current Web understanding (Web 2.0). This trend appears irrepressible as millions of users, directly or indirectly connected through social networks, are able to share and exchange any kind of content, information, feeling or experience. Social interactions radically changed the user approach. Furthermore, the socialization of content around social objects provides new unexplored commercial marketplaces and business opportunities. On the other hand, the progressive evolution of the web towards the Semantic Web (or Web 3.0) provides a formal representation of knowledge based on the meaning of data. When the social meets semantics, the social intelligence can be formed in the context of a semantic environment in which user and community profiles as well as any kind of interaction is semantically represented (Semantic Social Web). This paper first provides a conceptual analysis of the second and third version of the Web model. That discussion is aimed at the definition of a middle concept (Web 2.5) resulting in the convergence and integration of key features from the current and next generation Web. The Semantic Social Web (Web 2.5) has a clear theoretical meaning, understood as the bridge between the overused Web 2.0 and the not yet mature Semantic Web (Web 3.0).
A Semantic Layer for Embedded Sensor Networks
Salvatore F. Pileggi,Carlos Fernandez-Llatas,Vicente Traver
ARPN Journal of Systems and Software , 2011,
Abstract: Sensor Networks progressively assumed the critical role of bridges between the real world and information systems, through always more consolidated and efficient sensor technologies that enable advanced heterogeneous sensor grids. Sensor data is commonly used by advanced systems and intelligent applications in order to archive complex goals. Processes that build high-level knowledge from sensor data are commonly considered as the key core concept. This paper proposes a semantic layer that would optimally support the knowledge building in sensor systems as well as it enables semantic interaction model at different levels (module, subsystem, system). The semantic layer proposed in the paper is currently used by several architectures and applications in the context of different domains.
Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation
Carlos Fernández-Llatas,Teresa Meneu,Vicente Traver,José-Miguel Benedi
International Journal of Environmental Research and Public Health , 2013, DOI: 10.3390/ijerph10115671
Abstract: Born in the early nineteen nineties, evidence-based medicine (EBM) is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice. This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientific progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized.
Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales
Carlos Gershenson,Nelson Fernandez
Physics , 2012, DOI: 10.1002/cplx.21424
Abstract: Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this paper we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focusing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. Homeostasis can be seen as a measure of the stability of the system. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales.
Measuring Complexity in an Aquatic Ecosystem
Nelson Fernandez,Carlos Gershenson
Physics , 2013,
Abstract: We apply formal measures of emergence, self-organization, homeostasis, autopoiesis and complexity to an aquatic ecosystem; in particular to the physiochemical component of an Arctic lake. These measures are based on information theory. Variables with an homogeneous distribution have higher values of emergence, while variables with a more heterogeneous distribution have a higher self-organization. Variables with a high complexity reflect a balance between change (emergence) and regularity/order (self-organization). In addition, homeostasis values coincide with the variation of the winter and summer seasons. Autopoiesis values show a higher degree of independence of biological components over their environment. Our approach shows how the ecological dynamics can be described in terms of information.
Support detection in super-resolution
Carlos Fernandez-Granda
Mathematics , 2013,
Abstract: We study the problem of super-resolving a superposition of point sources from noisy low-pass data with a cut-off frequency f. Solving a tractable convex program is shown to locate the elements of the support with high precision as long as they are separated by 2/f and the noise level is small with respect to the amplitude of the signal.
Super-Resolution of Point Sources via Convex Programming
Carlos Fernandez-Granda
Mathematics , 2015,
Abstract: We consider the problem of recovering a signal consisting of a superposition of point sources from low-resolution data with a cut-off frequency f. If the distance between the sources is under 1/f, this problem is not well posed in the sense that the low-pass data corresponding to two different signals may be practically the same. We show that minimizing a continuous version of the l1 norm achieves exact recovery as long as the sources are separated by at least 1.26/f. The proof is based on the construction of a dual certificate for the optimization problem, which can be used to establish that the procedure is stable to noise. Finally, we illustrate the flexibility of our optimization-based framework by describing extensions to the demixing of sines and spikes and to the estimation of point sources that share a common support.
Towards a new metamodel for the Task Flow Model of the Discovery Method
Carlos Alberto Fernandez-y-Fernandez
Computer Science , 2012,
Abstract: This paper presents our proposal for the evolution of the metamodel for the Task Algebra in the Task Flow model for the Discovery Method. The original Task Algebra is based on simple and compound tasks structured using operators such as sequence, selection, and parallel composition. Recursion and encapsulation were also considered. We propose additional characteristics to improve the capabilities of the metamodel to represent accurately the Task Flow Model.
Integrating formal methods into traditional practices for software development: an overview
Carlos Alberto Fernandez-y-Fernandez
Computer Science , 2014,
Abstract: This paper shows an overview of a research project for integrating formal methods in popular practices for software development in Mexico. The article shows only the main results from the survey about methods and practices and an overview of the initial proposal of practices applying lightweight formal methods to requirements specification and software modelling.
Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes
Carlos Fernández-Llatas,José-Miguel Benedi,Juan M. García-Gómez,Vicente Traver
Sensors , 2013, DOI: 10.3390/s131115434
Abstract: The analysis of human behavior patterns is increasingly used for several research fields. The individualized modeling of behavior using classical techniques requires too much time and resources to be effective. A possible solution would be the use of pattern recognition techniques to automatically infer models to allow experts to understand individual behavior. However, traditional pattern recognition algorithms infer models that are not readily understood by human experts. This limits the capacity to benefit from the inferred models. Process mining technologies can infer models as workflows, specifically designed to be understood by experts, enabling them to detect specific behavior patterns in users. In this paper, the eMotiva process mining algorithms are presented. These algorithms filter, infer and visualize workflows. The workflows are inferred from the samples produced by an indoor location system that stores the location of a resident in a nursing home. The visualization tool is able to compare and highlight behavior patterns in order to facilitate expert understanding of human behavior. This tool was tested with nine real users that were monitored for a 25-week period. The results achieved suggest that the behavior of users is continuously evolving and changing and that this change can be measured, allowing for behavioral change detection.
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