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fireblocks-sdk from group com.fireblocks.sdk (version 3.1.0)

The Fireblocks Official SDK is a comprehensive software development kit that enables seamless integration and interaction with the Fireblocks platform. Fireblocks is a cutting-edge blockchain infrastructure platform that provides secure and scalable solutions for managing digital assets and transactions. This SDK empowers developers to build robust applications that can interact with the Fireblocks platform's features, including creating and managing vault accounts, initiating secure transactions, managing assets, and more. It abstracts complex interactions with the Fireblocks API, making it easier for developers to leverage the platform's capabilities while adhering to best practices in security and efficiency. Whether you're working on a cryptocurrency exchange, wallet application, or any other financial platform involving digital assets, the Fireblocks Official SDK simplifies the integration process and allows you to harness the power of Fireblocks' secure infrastructure. Stay ahead in the world of digital assets with this powerful SDK at your fingertips.</description>

Group: com.fireblocks.sdk Artifact: fireblocks-sdk
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Artifact fireblocks-sdk
Group com.fireblocks.sdk
Version 3.1.0
Last update 25. July 2024
Organization not specified
URL https://github.com/fireblocks/java-sdk
License MIT License
Dependencies amount 7
Dependencies jackson-core, jackson-annotations, jackson-databind, jackson-datatype-jsr310, jackson-databind-nullable, jsr305, java-jwt,
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luzzu-webapp from group io.github.luzzu (version 4.0.0)

Group: io.github.luzzu Artifact: luzzu-webapp
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Artifact luzzu-webapp
Group io.github.luzzu
Version 4.0.0
Last update 26. October 2018
Organization not specified
URL Not specified
License not specified
Dependencies amount 6
Dependencies luzzu-semantics, commons-codec, luzzu-highlevel-operations, guava, jackson-mapper-asl, jackson-core-asl,
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luzzu-annotations from group io.github.luzzu (version 4.0.0)

Group: io.github.luzzu Artifact: luzzu-annotations
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Artifact luzzu-annotations
Group io.github.luzzu
Version 4.0.0
Last update 26. October 2018
Organization not specified
URL Not specified
License not specified
Dependencies amount 3
Dependencies luzzu-semantics, luzzu-assessment, luzzu-lowlevel-operations,
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luzzu-assessment from group io.github.luzzu (version 4.0.0)

Group: io.github.luzzu Artifact: luzzu-assessment
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Artifact luzzu-assessment
Group io.github.luzzu
Version 4.0.0
Last update 26. October 2018
Organization not specified
URL Not specified
License not specified
Dependencies amount 3
Dependencies luzzu-semantics, luzzu-lowlevel-operations, luzzu-ld-qualitymetrics-commons,
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luzzu from group io.github.luzzu (version 4.0.0)

Luzzu is a Quality Assessment Framework for Linked Open Datasets. It is a generic framework based on the Dataset Quality Ontology (daQ), allowing users to define their own quality metrics. Luzzu is an integrated platform that: - assesses Linked Data quality using a library of generic and user-provided domain specific quality metrics in a scalable manner; - provides queryable quality metadata on the assessed datasets; - assembles detailed quality reports on assessed datasets. Furthermore, the infrastructure: - scales for the assessment of big datasets; - can be easily extended by the users by creating their custom and domain-specific pluggable metrics, either by employing a novel declarative quality metric specification language or conventional imperative plugins; - employs a comprehensive ontology framework for representing and exchanging all quality related information in the assessment workflow; - implements quality-driven dataset ranking algorithms facilitating use-case driven discovery and retrieval.

Group: io.github.luzzu Artifact: luzzu
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Artifact luzzu
Group io.github.luzzu
Version 4.0.0
Last update 26. October 2018
Organization ADAPT Centre, Trinity College Dublin, Ireland
URL https://luzzu.github.io/Framework/
License GNU GENERAL PUBLIC LICENSE, Version 2.0
Dependencies amount 15
Dependencies hamcrest-all, apache-jena-libs, jena-core, log4j, slf4j-api, jcl-over-slf4j, slf4j-log4j12, slf4j-jdk14, commons-collections4, jackson-core, httpclient, httpcore, httpcore-nio, httpasyncclient, httpasyncclient-cache,
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luzzu-webapp from group de.unibonn.iai.eis (version 2.0.1)

Group: de.unibonn.iai.eis Artifact: luzzu-webapp
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Artifact luzzu-webapp
Group de.unibonn.iai.eis
Version 2.0.1
Last update 09. June 2017
Organization not specified
URL Not specified
License not specified
Dependencies amount 6
Dependencies luzzu-semantics, commons-codec, luzzu-operations, guava, jackson-mapper-asl, jackson-core-asl,
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luzzu-annotations from group de.unibonn.iai.eis (version 2.0.1)

Group: de.unibonn.iai.eis Artifact: luzzu-annotations
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Artifact luzzu-annotations
Group de.unibonn.iai.eis
Version 2.0.1
Last update 09. June 2017
Organization not specified
URL Not specified
License not specified
Dependencies amount 3
Dependencies luzzu-semantics, luzzu-assessment, luzzu-operations,
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luzzu-assessment from group de.unibonn.iai.eis (version 2.0.1)

Group: de.unibonn.iai.eis Artifact: luzzu-assessment
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Artifact luzzu-assessment
Group de.unibonn.iai.eis
Version 2.0.1
Last update 09. June 2017
Organization not specified
URL Not specified
License not specified
Dependencies amount 1
Dependencies luzzu-semantics,
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luzzu from group de.unibonn.iai.eis (version 2.0.1)

Luzzu is a Quality Assessment Framework for Linked Open Datasets. It is a generic framework based on the Dataset Quality Ontology (daQ), allowing users to define their own quality metrics. Luzzu is an integrated platform that: - assesses Linked Data quality using a library of generic and user-provided domain specific quality metrics in a scalable manner; - provides queryable quality metadata on the assessed datasets; - assembles detailed quality reports on assessed datasets. Furthermore, the infrastructure: - scales for the assessment of big datasets; - can be easily extended by the users by creating their custom and domain-specific pluggable metrics, either by employing a novel declarative quality metric specification language or conventional imperative plugins; - employs a comprehensive ontology framework for representing and exchanging all quality related information in the assessment workflow; - implements quality-driven dataset ranking algorithms facilitating use-case driven discovery and retrieval.

Group: de.unibonn.iai.eis Artifact: luzzu
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Artifact luzzu
Group de.unibonn.iai.eis
Version 2.0.1
Last update 09. June 2017
Organization Enterprise Information Systems - University of Bonn
URL http://eis-bonn.github.io/Luzzu/
License GNU GENERAL PUBLIC LICENSE, Version 2.0
Dependencies amount 10
Dependencies mockito-core, hamcrest-all, apache-jena-libs, log4j, slf4j-api, jcl-over-slf4j, slf4j-log4j12, slf4j-jdk14, commons-collections4, jackson-core-asl,
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openimaj from group org.openimaj (version 1.3.10)

OpenIMAJ (Open Intelligent Multimedia in Java) is a collection of libraries and tools for multimedia analysis written in the Java programming language. OpenIMAJ intends to be the first truly complete multimedia analysis library and contains modules for analysing images, videos, text, audio and even webpages. The OpenIMAJ image and video analysis and feature extraction modules contain methods for processing visual content and extracting state-of-the-art features, including SIFT. The OpenIMAJ clustering and nearest-neighbour libraries contain efficient, multi-threaded implementations of clustering algorithms including Hierarchical K-Means and Approximate K-Means. The clustering library makes it possible to easily create visual-bag-of-words representations for images and video with very large vocabularies. The text-analysis modules contain implementations of a statistical language classifier and low-level processing pipeline. A number of modules deal with content creation, including interactive slideshows and animations. The hardware integration modules allow cross-platform integration with devices including webcams, the Microsoft Kinect, and even devices such as GPS's. OpenIMAJ also incorporates a number of tools to enable extremely-large-scale multimedia analysis using a distributed computing approach based on Apache Hadoop.

Group: org.openimaj Artifact: openimaj
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Artifact openimaj
Group org.openimaj
Version 1.3.10
Last update 09. February 2020
Organization The University of Southampton
URL http://www.openimaj.org
License New BSD
Dependencies amount 0
Dependencies No dependencies
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