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Cloudinary is a cloud service that offers a solution to a web application's entire image management pipeline. Upload images to the cloud. Automatically perform smart image resizing, cropping and conversion without installing any complex software. Integrate Facebook or Twitter profile image extraction in a snap, in any dimension and style to match your website’s graphics requirements. Images are seamlessly delivered through a fast CDN, and much much more. This Java library allows to easily integrate with Cloudinary in Java applications.

Cloudinary is a cloud service that offers a solution to a web application's entire image management pipeline. Upload images to the cloud. Automatically perform smart image resizing, cropping and conversion without installing any complex software. Integrate Facebook or Twitter profile image extraction in a snap, in any dimension and style to match your website’s graphics requirements. Images are seamlessly delivered through a fast CDN, and much much more. This Java library allows to easily integrate with Cloudinary in Java applications.

Azure Video Analyzer provides a platform to build intelligent video applications that span the edge and the cloud. The platform offers the capability to capture, record, and analyze live videos along with publishing the results, video, and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open-source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline. Use the client library for Video Analyzer Edge to simplify interactions with the Microsoft Azure IoT SDKs (https://github.com/azure/azure-iot-sdks) and programmatically construct pipeline topologies and live pipelines.

The chart-fx charting library is an extension in the spirit of Oracle's XYChart and performance/time-proven JDataViewer charting functionalities. Emphasis was put on plotting performance for both large number of data points and real-time displays, as well as scientific accuracies leading to error bar/surface plots, and other scientific plotting features (parameter measurements, fitting, multiple axes, zoom, ...). The library also contains a small set of math routines that can operate directly on the DataSet primitive for fitting, computing spectra, linear algebra, FIR/IIR filtering, and other functionalities common to signal processing.

A Java component which displays bytes in Hex- and ASCII-format. The component is designed as a standalone lib. A complete interface which provides the function of an HexEditor based on this lib exisits under the Fork of this project. \r\n This library is simply taken directly from sourceforge at https://sourceforge.net/projects/hexlib/ and made it available in maven central repository. No other changes have been made (including keeping the same license).

This module contains library classes for accessing the ThreadFix REST API. The ThreadFix IDE plugins use this library to retrieve application and vulnerability marker information, and in the ThreadFix scanner plugins to get endpoint information and upload scans to ThreadFix applications. The easiest way to start using the library is with the PluginClient or ThreadFixRestClient classes. Both have constructors that take a PropertiesManager instance, which holds the ThreadFix API key and url information. The default PropertiesManager implementation stores these properties in threadfix.properties, but the ThreadFix plugins extend the default PropertiesManager class to override this behavior for the target platform.

This library is the the backend for the Fitzhì solution. Fitzhì aims to gather all technical data regarding your application portfolio into ONE single portal. Fitzhì provides also analysis and assessments at the project scope, but its main target is the portfolio. The main ambition is to provide a global, and dynamic vision of the technical risk for your IT. The dream functionality for Fitzhì should be the ONE single dashboard, one Single Point Point Of Quality, available for the CTO, or CIO, and ready to resume in one second, one view, the current situation and the historic trend.

With distributed systems being developed as a part of the cloud age. With focus on performance associated with an application dependent on one DB instance, there is a need to bring down the dependency to a database to generate unique numbers for creating a private key when lots of data needs to be stored in the database tables. This library is intended to provide a way of generating unique numbers on the fly in a JAVA based distributed system. This is not a new concept but this library helps in keeping you focused on the business logic that you need to develop for your applications rather than spending time on how this is done. Typically applications powered by RDBMS can leverage the benefit as instant scalability might not be available for these setups when the load to the system spikes.

This is client side library for forker, and the one you most likely want to use in your own applications. It provides the ProcessBuilder replacement, ForkerBuilder, as well the helpers in OSCommand. ForkerBuilder is capable of launching processes using non-blocking I/O or attached to a PTY. It has many additional features not available in the standard process builder including permission elevation, daemonize, processor affinity and more. Parts of the non-blocking API are based on NuProcess (https://github.com/brettwooldridge/NuProcess)

Rule Learner is an open source Java library for learning business rules from historical data. It supports supervised Machine Learning by incorporating well-known algorithms such as RIPPER and C4.5 provided by open source tools such as Weka. Rule Learner is implemented as a Machine Learning component of OpenRules Decision Manager. It allows business analysts to place their historical data in simple Excel tables and to automatically discover business rules that find certain pattern in this data. The generated rules are human readable and presented in Excel format executable by OpenRules Decision Manager.