Search Results for "data capture framework"
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Showing 22 open source projects for "data capture framework"
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FATE
An industrial grade federated learning framework
FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. ... -
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CryptoSwift
Collection of standard and secure cryptographic algorithms
...Performance characteristic is different in Release build. XCFrameworks require Xcode 11 or later and they can be integrated similarly to how we’re used to integrating the .framework format. Embedded frameworks require a minimum deployment target of iOS 9 or macOS Sierra (10.12). CryptoSwift uses array of bytes aka Array<UInt8> as a base type for all operations. Every data may be converted to a stream of bytes. You will find convenience functions that accept String or Data, and it will be internally converted to the array of bytes. -
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Python Outlier Detection
A Python toolbox for scalable outlier detection
...PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. In total, 17 benchmark datasets are used for comparison, which can be downloaded at ODDS. -
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MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning. -
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CloudI is an open-source private cloud computing framework for efficient, secure, and internal data processing. CloudI provides scaling for previously unscalable source code with efficient fault-tolerant execution of ATS, C/C++, Erlang/Elixir, Go, Haskell, Java, JavaScript/node.js, OCaml, Perl, PHP, Python, Ruby, or Rust services. The bare essentials for efficient fault-tolerant processing on a cloud!
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Active Learning
Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. ... -
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Code Catalog in Python
Algorithms and data structures for review for coding interview
code-catalog-python serves as a grab-bag of small, readable Python examples that illustrate common algorithms, data structures, and utility patterns. Each snippet aims to be self-contained and easy to study, with clear inputs, outputs, and the essential logic on display. The catalog format lets you scan for an example, copy it, and adapt it to your use case without wading through a large framework. It favors clarity over micro-optimizations so learners can grasp the idea before worrying about edge performance. ... -
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CognitionMaster
object-based image analysis framework
Wienert S, Heim D, Kotani M, Lindequist B, Stenzinger A, Ishii M, Hufnagl P, Beil M, Dietel M, Denkert C, Klauschen F. CognitionMaster: an object-based image analysis framework. Diagn Pathol 2013, 8:34 -
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CoRoPa stands for Computational Rough Paths. The aim of CoRoPa is to provide a software framework for various ideas related to Rough Path Theory, including rough differential equations and the digital description of serial data streams.
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Your monitoring isn't a stack. It's a pile. Fix that.Replace Datadog, New Relic, and Sentry without adding three more dashboards.
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The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded.
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owl reasoning over big biomedical data
A OWL reasoning framework for the analysis of big biomedical data
A general OWL reasoning framework for the analysis of big biomedical data and implement a MapReduce-based property chain reasoning prototype system. OWL reasoning method is ideally suitable for problems involved complex semantic associations because it is able to infer logical consequences based on a set of asserted rules or axioms. MapReduce framework isused to solve the problem of scalability. -
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S-Match
S-Match is a semantic matching framework.
S-Match is a semantic matching framework. S-Match takes any two tree like structures (such as database schemas, classifications, lightweight ontologies) and returns a set of correspondences between those tree nodes which semantically correspond to one another. S-Match contains implementations of the semantic matching, minimal semantic matching and structure preserving semantic matching algorithms. S-Match applies as a solution in many fields, including: information integration,... -
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CADI is an implementation of the JPIP standard (JPEG2000 Interactive Protocol - Part 9). CADI includes a JPIP server, a JPIP proxy, and a JPIP Client. CADI is designed and programmed with the aim to provide a flexible framework to test and develop ne
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A simple rendering, image processing engine. Includes a framework width (multiplatform) event handling, plugin management, a templated based math library and some exotic features like general linear camera, gpu based (OpenGL) image process...
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HDRFlow is a framework to process high-dynamic range (HDR) and RAW images. It's written in C++, and is both cross-platform and hardware accelerated on modern GPUs.
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Public description: The 'cbear' framework is a set of libraries and common utilities for different programming languages and platforms.
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Very useful framework library, contains many frequent used code snippets, algorithms, data structures, WinForms & APS.NET controls for all-day work.
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The below is a list of all files released for the Crossbow's MNAV100CA with SPB400. This framework may provide a complete solution for AHRS, Navigation, WiFi Comm, and Data Logging, for various robotic applications.
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This is an image coder with fixed sampling, at a rate of up to 24 images per second (on XC2V1000-4 @ 40 MHz with res. up to 352x288).
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Bookinex is an application designed to index your collection of books, it supports scanning of Barcodes to recognize ISB Numbers and querying web services to collect data.
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GraphSmart is a collection of graph data structures and algorithms utilizing generics and the Microsoft .NET 2.0 Framework. Included items are commonly used in software engineering but not included in the .NET 2.0 Framework class library.
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EpFamvir
C++ Software framework that supports data-intensive distributed apps.
* EpFamvirproject in this site is not maintained anymore. * The project has been moved to https://github.com/juhgiyo/EpFamvir EpFamvir is a Visual C++ Software framework that supports data-intensive distributed applications.. The features includes: - Famvir Distributed File System - Job Tracker and Task Tracker: the MapReduce Engine For detailed documentation, please download this full document (EPFamvir Documentation.chm) from the "Downloads" Section. * If the documentation file is not visible after finishing download. ...
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