Download JAR files tagged by models with all dependencies

This open source software is a reference implementation, consisting of software and related files, for the OMG specification called the Semantics of a Foundational Subset for Executable UML Models (fUML). The reference implementation is intended to implement the execution semantics of UML activity models, accepting an XMI file from a conformant UML model as its input and providing an execution trace of the selected activity model(s) as its output. The core execution engine, which is directly generated from the normative syntactic and semantic models for fUML, may also be used as a library implementation of fUML in other software.

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 Systems Biology Simulation Core Library provides an efficient and exhaustive Java™ implementation of methods to interpret the content of models encoded in the Systems Biology Markup Language (SBML) and its numerical solution. This library is based on the JSBML project. It can be used on every operating system for which a Java Virtual Machine is available. Version 2.0 and beyond support simulation in three frameworks: constraint-based analysis, stochastic simulation, and ordinary differential equation systems. SBSCL supports SED-ML and COMBINE archives in OMEX format.

This is a handy tool many developers already use to create better interaction models for their custom Alexa skills. The tool introduces an easy to read grammar for generating hundreds and thousands of variant sample utterances and slot values with just a few written lines. The resulting JSON file can be used to upload the model to your Alexa skill right away either via your web browser into Alexa skill builder interface or with help of SMAPI.

SEWOL provides support for the handling of workflow traces. Specifically it allows to specify the shape and content of process traces in terms of entries representing the execution of a specific workflow activity. SEWOL also allows to write these traces on disk as a log file with the help of a special file writer for process logs. Currently it supports plain text, Petrify, MXML and XES log file types. In order to specify security-related context information, SEWOL provides access control models such as access control lists (ACL) and role-based access control models (RBAC). All types of models can be conveniently edited with the help of appropriate dialogs.

Rserve is a TCP/IP server which allows other programs to use facilities of R (see www.r-project.org) from various languages without the need to initialize R or link against R library. Every connection has a separate workspace and working directory. Client-side implementations are available for popular languages such as C/C++, PHP and Java. Rserve supports remote connection, authentication and file transfer. Typical use is to integrate R backend for computation of statstical models, plots etc. in other applications.

This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier. Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data. For more information, see: Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.