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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.

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.

Concourse is a distributed database warehouse for transactions, search, and analytics across time. Developers use Concourse to quickly build mission critical software while also benefiting from real time insight into their most important data. With Concourse, end-to-end data management requires no extra infrastructure, no prior configuration and no additional coding–all of which greatly reduce costs and allow developers to focus on core business problems.

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.

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.

HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. With a nominal target, one might want to look for segments of the data where there is a high probability of a minority value occuring (given the constraint of a minimum support). For a numeric target, one might be interested in finding segments where this is higher on average than in the whole data set. For example, in a health insurance scenario, find which health insurance groups are at the highest risk (have the highest claim ratio), or, which groups have the highest average insurance payout.

<html>IJ Plugins Toolkit is a set of ImageJ plugins grouped into:<ul> <li>3D IO - import and export of data in 3D formats.</li> <li>3D Toolkit - operations on stacks interpreted as 3D images, including morphological operations.</li> <li>Color - color space conversion, color edge detection (color and multi-band images).</li> <li>Filters - fast median filters and various anisotropic diffusion filters.</li> <li>Graphics - Texture Synthesis - A plugin to perform texture synthesis using the image quilting algorithm of Efros and Freeman.</li> <li>Segmentation - image segmentation through clustering, thresholding, and region growing.</li></ul></html>