Search Results for "execute"

JavaCV uses wrappers from the JavaCPP Presets of commonly used libraries by researchers in the field of computer vision (OpenCV, FFmpeg, libdc1394, FlyCapture, Spinnaker, OpenKinect, librealsense, CL PS3 Eye Driver, videoInput, ARToolKitPlus, flandmark, Leptonica, and Tesseract) and provides utility classes to make their functionality easier to use on the Java platform, including Android. JavaCV also comes with hardware accelerated full-screen image display (CanvasFrame and GLCanvasFrame), easy-to-use methods to execute code in parallel on multiple cores (Parallel), user-friendly geometric and color calibration of cameras and projectors (GeometricCalibrator, ProCamGeometricCalibrator, ProCamColorCalibrator), detection and matching of feature points (ObjectFinder), a set of classes that implement direct image alignment of projector-camera systems (mainly GNImageAligner, ProjectiveTransformer, ProjectiveColorTransformer, ProCamTransformer, and ReflectanceInitializer), and more.

AgentScope Java is an agent-oriented programming framework that enables Java developers to build intelligent, LLM-powered applications using a dynamic reasoning-acting (ReAct) paradigm. It provides a comprehensive toolkit for creating autonomous agents that can plan, execute, and adjust complex workflows, making decisions about which tools to invoke and how to solve multi-step problems. The framework includes runtime controls such as safe interruption and graceful cancellation to manage agent execution robustly in production environments. It also supports human-in-the-loop intervention, allowing developers or users to inject guidance at any point during reasoning while preserving state and tool context. ...

...Astron Agent enables organizations to design complex agent-driven processes that coordinate models, automation tools, and enterprise systems. It also integrates robotic process automation capabilities so agents can execute tasks across digital systems instead of only generating responses. Astron Agent supports scalable and high-availability deployments, allowing teams to run reliable AI agent infrastructure in distributed environments. It includes collaboration features that allow teams to develop, manage, and operate AI applications together. With its extensible architecture and enterprise-focused design, it aims to help organizations build production-ready intelligent agent solutions.

...It was designed to support the demanding internal infrastructure of Netflix, where thousands of workflows must process massive volumes of data reliably and efficiently every day. The platform enables engineers and data scientists to define workflows using structured configuration files and execute tasks across diverse compute environments, including scripts, containers, and notebook environments. Maestro provides built-in mechanisms for retry logic, task scheduling, dependency management, and error handling, which are essential when orchestrating production-scale pipelines.

An IDE for people interested in Machine Vision/Image Processing. Written in Java, using JAI. It allows users to view image data and also provides a drag and drop environment that users can create/execute graphs of JAI operators.

This project develops a Java API and tools to specify and execute organisations for multiagent systems based on Moise model. This model is built upon concepts like groups, roles, global goals and plans, and obligations.

...A number of algorithms are provided out-of-the-box, including NSGA-II, NSGA-III, ε-MOEA, GDE3 and MOEA/D. In addition, the MOEA Framework provides the tools necessary to rapidly design, develop, execute and statistically test optimization algorithms.

...Core Ellipsis components are implemented as MBeans which provides for basic ser-vices to execute agents and control their life-cycle. One of the core components is a special MBean (AgentModul) that makes it possible to load an agent powered by agent engines. Agent engines can execute one type of agent. Based on this mechanism, agents can be added dynamically to a running system, can be stopped (for example in case of an error) and restarted later.

The OntoMap Architecture is about using Natural Language to execute users requests, implemented as software components. Natural language requests are converted to an interlingua. This allows the use of different human languages to describe the requests.