Download JAR files tagged by about with all dependencies

The matching web service client library for the server in this example project to connect to the server. This library is ideally provided by the service provider of the server, so that users can go ahead and hack their userland code against this client library, without having to worry about the transport. Whether the client library uses REST or SOAP is up to the implementor, both are possible. A user of the client library usually does not care what technology is used underneath (as long as things are working correctly).

The Attribution Maven Plugin is a tool for producing information about a project's dependencies, in a way that can be consumed by external reporting tools. It currently leverages the Maven Project Info Reports Plugin (https://maven.apache.org/components/plugins/maven-project-info-reports-plugin/) to obtain all of a project's data, and then output the information to an XML file, such as: target/attribution.xml. This plugin came from the need to get some of the same information produced by the Maven Project Info Reports Plugin, and have it ready to be consumed by tools which may require input in a certain format, such as a CSV. By producing an attribution XML file, scripts can be created to translate the data into these other consumable formats.

COBYLA2 is an implementation of Powell's nonlinear derivative free constrained optimization that uses a linear approximation approach. The algorithm is a sequential trust region algorithm that employs linear approximations to the objective and constraint functions, where the approximations are formed by linear interpolation at n + 1 points in the space of the variables and tries to maintain a regular shaped simplex over iterations. It solves nonsmooth NLP with a moderate number of variables (about 100). Inequality constraints only. The initial point X is taken as one vertex of the initial simplex with zero being another, so, X should not be entered as the zero vector.

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.