Search Results for "e learning system using java"
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Showing 15 open source projects for "e learning system using java"
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Netflix Maestro
Netflix’s Workflow Orchestrator
Maestro is a large-scale workflow orchestration platform originally developed by Netflix to coordinate complex data processing and machine learning workflows across distributed systems. The system acts as a general-purpose workflow orchestrator that manages the execution, scheduling, monitoring, and recovery of large pipelines used for analytics and AI operations. It was designed to support the demanding internal infrastructure of Netflix, where thousands of workflows must process massive... -
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tika-python
Python binding to the Apache Tika™ REST services
A Python port of the Apache Tika library that makes Tika available using the Tika REST Server. This makes Apache Tika available as a Python library, installable via Setuptools, Pip and easy to install. To use this library, you need to have Java 7+ installed on your system as tika-python starts up the Tika REST server in the background. To get this working in a disconnected environment, download a tika server file (both tika-server.jar and tika-server.jar.md5, which can be found here) and set... -
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Text-to-image Playground
A playground to generate images from any text prompt using SD
dalle-playground is an open-source web application that allows users to generate images from natural language text prompts using modern text-to-image generative models. Originally built around DALL-E Mini, the project later transitioned to using Stable Diffusion, enabling more detailed and higher-quality image synthesis. The system combines a backend machine learning service with a browser-based frontend interface that lets users experiment interactively with prompt engineering and generative AI. ... -
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goodsKill
Microservices-based flash sale system for high-concurrency testing
goodsKill is a simulated flash sale microservices project built with Spring Cloud 2023.x, Spring Boot 3.x, and Dubbo 3.x. It provides a unified API for testing high-concurrency seckill scenarios while demonstrating modern backend architecture patterns. goodsKill integrates key components such as Nacos for service discovery and configuration, Redis and MongoDB for data handling, and Kafka or RabbitMQ for messaging. It supports distributed transactions, database sharding, and multiple... -
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VALL-E
PyTorch implementation of VALL-E (Zero-Shot Text-To-Speech)
...VALL-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as an acoustic prompt. Experiment results show that VALL-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. -
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ASRT Speech Recognition
A Deep-Learning-Based Chinese Speech Recognition System
ASRT is an end-to-end deep-learning Chinese ASR system built with TensorFlow/Keras, using convolution + CTC and a Max-Entropy HMM language model. It provides a REST/gRPC server backend and client SDKs in multiple languages (Python, Java, Go, Windows). Notably lightweight, it performs well without needing GPU acceleration and runs across platforms, targeting developers and researchers building Chinese voice interfaces. -
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Oryx
Lambda architecture on Apache Spark, Apache Kafka for real-time
Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large-scale machine learning. It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering. The application is written in Java, using Apache Spark, Hadoop, Tomcat, Kafka, Zookeeper and more. Configuration uses a single Typesafe Config config file, wherein applications configure an entire deployment of the system. ... -
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Easy Machine Learning
Easy Machine Learning is a general-purpose dataflow-based system
Machine learning algorithms have become the key components in many big data applications. However, the full potential of machine learning is still far from being realized because using machine learning algorithms is hard, especially on distributed platforms such as Hadoop and Spark. The key barriers come from not only the implementation of the algorithms themselves but also the processing for applying them to real applications which often involve multiple steps and different algorithms. Our... -
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H2O-3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning
H2O-3 is an open-source machine learning platform designed to build scalable and distributed machine learning models across large datasets. The system operates as an in-memory computing platform that allows data scientists to train models quickly using distributed resources. It supports many machine learning algorithms including generalized linear models, gradient boosting machines, deep learning networks, and ensemble techniques. -
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Phrasal
Statistical phrase-based machine translation system
Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java. At its core, it provides much the same functionality as the core of Moses. Distinctive features include: providing an easy to use API for implementing new decoding model features, the ability to translating using phrases that include gaps (Galley et al. 2010), and conditional extraction of phrase-tables and lexical reordering models. Developed by The Natural Language Processing Group... -
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This site contains four packages of Mass and mass-based density estimation. 1. The first package is about the basic mass estimation (including one-dimensional mass estimation and Half-Space Tree based multi-dimensional mass estimation). This packages contains the necessary codes to run on MATLAB. 2. The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system. 3. The third package DEMassBayes includes the source and object files of a Bayesian...
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Agent.GUI
The Project moved to github https://github.com/EnFlexIT/AgentWorkbench
The project has moved to github https://github.com/EnFlexIT/AgentWorkbench Agent.GUI is a simulation framework and toolkit based on the JADE framework. It provides functionalities for time aspects, agent-environment interaction, visualization and load balancing, Furthermore, the included application focuses the usability for end users. -
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G-Asks is a question generation system, developed by LATTE(Learning and Affect Technologies Engineering) research group at The University of Sydney. It uses Natural Language Processing techniques and Machine learning algorithms to generate specific trigger questions. If you use this software in a publication, please cite the paper 2. 1.Ming Liu and Rafael A. Calvo (2012) “Using Information Extraction to Generate Trigger Question for Academic Writing Support”, 11th International Conference...
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Using agent technology JAMAS (Java Awareness Multi-Agent System) tries to assist distributed programmers in coordinating parallel development of Java code. E-JAMAS implements JAMAS and it’s features as a plug-in for the Eclipse platform.
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