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This standard defines the framework of knowledge graphs (KGs). The framework describes the input requirement of KG; construction process of KG, that is, extraction, storage, fusion, and understanding; performance metrics; applications of KG; verticals; KG-related artificial intelligence (AI) technologies; and other required digital infrastructure.

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This standard defines an architecture framework description for the Internet of Things (IoT). The architecture ontology and methodology of the framework architecture conforms to the international standard ISO/IEC/IEEE 42010:2011. The architecture framework description is motivated by concerns commonly shared by IoT system stakeholders across multiple domains (transportation, healthcare, Smart Grid, etc.). This standard provides a conceptual basis for the notion of things in the IoT and then… read more elaborates the shared concerns as a collection of architecture viewpoints that form the body of the framework description. read less

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This standard is intended to provide a standard framework for evaluating the quality of digital humans that look and act like actual humans. The quality of digital humans is related to the human factor for immersive content service that defines metrics for evaluating the realism of digital humans. The evaluation needs to define a framework that handles the digital human content as test data, define test methods and test cases, and provide a evaluation report of the test results. Therefore, the… read more framework for evaluating the quality of digital humans includes the following: - A set of cognitive-psychological factors that define how users feel the realism of digital humans. - Definitions on methods and metadata that describe the tests for the quality of digital humans. - A procedure that allows the quality evaluation of digital humans. read less

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This document provides a reference framework for trustworthy federated machine learning, including the principles of trustworthy federated machine learning, requirements for different roles and principles of trustworthy federated machine learning, and several technologies to realize trustworthy federated machine learning. It also lists some scenarios where trustworthy federated machine learning can be applied.

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This standard provides the framework for performing and reporting reliability predictions. It applies to hardware products including electronic, electrical, and mechanical devices and assemblies.

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