Recibido: 31 de enero 2017Aceptado: 10 de marzo 2017 _____________________________________________________________________
ResumenMedir y monitorear la resiliencia de los sistemas agrosilvopastoriles de pequeños productores es cada vez más urgente para ayudarles a enfrentar de mejor manera los efectos del clima cambiante. Aunque se reconocen otras formas de medir la resiliencia climática, la novedosa herramienta SHARP desarrollada por la FAO permite su identificación, medición y la priorización de acciones para mejorarse de una manera participativa, tomando en cuenta los intereses de los productores. En coordinación con el equipo desarrollador de la aplicación de la FAO en Roma, se tradujeron y utilizaron el cuestionario y la matriz para la evaluación de la resiliencia en 16 fincas agroecológicas situadas en los alrededores del Valle Central de Costa Rica. Una vez colectados e ingresados los datos en SHARP, se llevaron a cabo tres análisis: 1) comparación del perfil de las fincas estudiadas con indicadores a nivel nacional; 2) análisis de brechas para identificar las oportunidades de mejora en términos de resiliencia climática; 3) un análisis para adaptar la herramienta al contexto nacional. A pesar de que las fincas seleccionadas para el estudio son reconocidas como agroecológicas, su nivel medio de resiliencia medido (x̄=15,25; s=1,16) sugiere la necesidad de implementar prácticas de manejo que contribuyan con la construcción de agroecosistemas más resilientes.
Palabras claveAdaptación, agroecología, agroecosistemas, cambio climático, desarrollo rural.
We detect and arrange events in private photo archives by putting these photos into context. The problem is seen as a fully automated mining in one's personal life and behavior. To this end, we build a contextual meaningful hierarchy of events based on personal photos. With the analysis of very simple cues of time, space and perceptual visual appearance we are refining and validating the event borders and their relation in an iterative way. Beginning with discriminating between routine and unusual events, we are able to robustly recognize the basic nature of an event. Further combination of the given cues efficiently gives a hierarchy of events that coincides with the given ground-truth at an F-measure of 0.83 for event detection and 0.70 for its hierarchical representation. We process the given task in a fully unsupervised and computationally inexpensive manner. Using standard clustering and machine learning techniques, sparse events in the collection would tend to be neglected by automated approaches. Opposed to these methods, the proposed approach is invariant to the distribution of the photo collection regarding the sparsity and denseness in time, space and visual appearance. This is improved by introducing a momentum of attraction measure for a meaningful representation of personal events.
Company data, ranging from basic company information such as company name(s) and incorporation date to complex balance sheets and personal data about directors and shareholders, are the foundation that many data value chains depend upon in various sectors (e.g., business information, marketing and sales, etc.). Company data becomes a valuable asset when data is collected and integrated from a variety of sources, both authoritative (e.g., national business registers) and non-authoritative (e.g., company websites). Company data integration is however a difficult task primarily due to the heterogeneity and complexity of company data, and the lack of generally agreed upon semantic descriptions of the concepts in this domain. In this article, we introduce the euBusinessGraph ontology as a lightweight mechanism for harmonising company data for the purpose of aggregating, linking, provisioning and analysing basic company data. The article provides an overview of the related work, ontology scope, ontology development process, explanations of core concepts and relationships, and the implementation of the ontology. Furthermore, we present scenarios where the ontology was used, among others, for publishing company data (business knowledge graph) and for comparing data from various company data providers. The euBusinessGraph ontology serves as an asset not only for enabling various tasks related to company data but also on which various extensions can be built upon.
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