Nuxeo is a content management platform for visual identity, content and customer experience management. Nuxeo is an easy to use and quick to deploy application, an ideal solution for organizations seeking a reliable and accessible software solution for their video and multimedia needs.
Nuxeo is a platform for managing content and customer experience, based on a triple store (application, media, version). Its web user interface (UI) is accessible through any device. This UI is user-friendly and ready for search, tasks and workflows to create and execute applications on a modular scale. Nuxeo uses a powerful set of APIs, the API Gateway, the REST API, to be used by any programming language.
Nuxeo can be easily installed in any LAMP stack and has its own Docker image, so it is possible to deploy it in just a few minutes. Nuxeo is designed for developers and architects and requires no programming skills to deploy and run. Nuxeo is easy to deploy and maintain and scales from tens of users to thousands.
Nuxeo is based on the following technologies: NodeJS, ExpressJS, AngularJS, jQuery, Bootstrap, Google Charts, TypeScript, MongoDB, Redis, New Relic.
KEYMODULE Description:
This module offers Nuxeo commerce product templates and storefronts, allowing to configure one or many storefronts and create new storefronts.
KEYAUTHOR Descripiton:
Marco Dubreuil
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Discovery is an open source business intelligence (BI) solution used to query databases, model relationships, and extract intelligence.
DESCRIPTION:
Discovery is an open source business intelligence (BI) solution used to query databases, model relationships, and extract intelligence. It is designed to be a self-contained environment in which to perform ad-hoc queries and report generation. It is based on SQL and can connect to a large number of SQL databases, including Oracle, SQL Server, PostgreSQL, and MySQL.
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Extracts intelligence from any source that exposes a SQL database and allows the definition of queries in a visually based language.
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Boundary diffusion is a prominent model-free approach to generate high-quality semantic segmentation results. However, the low segmentation accuracy makes it hard to extract boundary-related information with strong boundary accuracy. Here, we propose to incorporate this information into the semantic segmentation framework for robust boundary encoding. We name our model as robust boundary encoding with boundary diffusion (ROBIN). It extends the boundary diffusion framework with a boundary layer, which guides the existing pixels to the boundary to further encode boundary-related information. The boundary layer is a new concept for boundary diffusion. This layer is embedded in the gradient of the boundary-evoked signals to realize boundary enhancement. In this way, the boundary layer not only regularizes the existing pixels to the boundary but also encodes boundary-related information. Experiments show that the boundary layer of our proposed model outperforms the boundary layer in the existing boundary diffusion model. The boundary layer results in more accurate boundaries and encodes boundary-related information more robustly, thus it is helpful to improve the semantic segmentation accuracy. Extensive comparisons demonstrate that our proposed model achieves the best performance.Q:
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