IEEE

A set of operator interfaces frequently used in artificial intelligence (AI) applications is defined in this standard, where the AI operators refer to the standard building blocks and primitives for performing basic AI operations. The functionality and the specific input and output operands of an AI operator are discussed, as well as both generality and efficiency. Various types of operators, such as those related to basic mathematics, neural network, and machine learning, are highlighted.

Efficient coding tool sets for compression, decompression, and reconstructing of the immersive visual content data is provided. The target applications and services include, but are not limited to, virtual reality (VR), such as unmanned aerial vehicle-based VR, augmented reality, panorama video, free-view TV, panoramic stereo video, and other video-/audio-enabled services and applications, such as immersive video streaming, broadcasting, storage, and communication.

This standard provides a general shared function model for cloud computing, in order to normalize how functions are shared between cloud service providers (CSPs) and cloud service customers (CSCs). The standard specifies functions ownership from seven aspects for three main cloud service delivery models, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). The seven aspects of function are as follows:1) The physical infrastructure function.2) The virtualization infrastructure function.3) The operating system function.4) The network control function.5) The application function.6) The data function.7) The identity and access management function. Each of the seven aspects considers many factors including security, management, etc. In this standard, levels 1 to 4, which are clearly the service provider's function for IaaS, PaaS, and SaaS, are briefly mentioned for the integrity of the model. This standard focuses on levels 5, 6, and 7.

A functional model for federation based on the NIST Cloud Federation Reference Architecture is defined in this standard. This model allows a range of deployment topologies and governance. As a general federation model, it can be applied to many application domains using different implementation approaches. As such, it includes cloud-to-cloud federation and interoperability.

Advice is given for cloud computing ecosystem participants (cloud vendors, service providers, and users) of standards-based choices in areas such as application interfaces, portability interfaces, management interfaces, interoperability interfaces, file formats, and operation conventions. These choices are grouped into multiple logical profiles, which are organized to address different cloud roles.

The requirements for a systems/software engineering process for privacy-oriented considerations regarding products, services, and systems utilizing employee, customer, or other external user's personal data are defined by this standard. Organizations and projects that are developing and deploying products, systems, processes, and applications that involve personal information are candidate users of the IEEE 7002 standard. Specific procedures, diagrams, and checklists are provided for users of the IEEE 7002 standard to perform conformity assessments on their specific privacy practices. Privacy impact assessments (PIAs) are described as a tool for both identifying where privacy controls and measures are needed and for confirming they are in place.

A baseline architectural framework will be defined in this standard. In addition, the general process for digital asset management on blockchain will be outlined.

Artificial intelligence (AI) computing differs from generic computing in terms of device formation, operators, and usage. AI server systems, including AI server, cluster, and high-performance computing (HPC) infrastructures are designed specifically for this purpose. The performance of these infrastructures is important to users not only on generic models but also on the ones for specific domains. Formal methods for the performance benchmarking for AI server systems are provided in this standard, including approaches for test, metrics, and measure. In addition, the technical requirements for benchmarking tools are discussed.

This standard defines a model for ethical considerations and practices in the design, creation and use of empathic technology, incorporating systems that have the capacity to identify, quantify, respond to, or simulate affective states, such as emotions and cognitive states. This includes coverage of 'affective computing', 'emotion Artificial Intelligence' and related fields.

The standard establishes requirements for blockchain based digital identity systems. The standard addresses the following attributes of the system, including but not limited to, digital identity definition, distributed digital identity creation, distributed digital identity authentication, distributed digital identity note (refers to identity credentials such as identity card, work card, member card), data or asset circulation protocols.

This guide provides a technological framework that facilitates the increase of trustworthiness of AI (Artificial Intelligence) systems, by using explainable artificial intelligence (XAI) technologies and methods including the following aspects:1) the requirements of providing XAI systems in different application scenarios;2) the categorization of a series of XAI tools that offer human-understandable explanations; and3) a set of measurable solutions to evaluate XAI systems in terms of performances concerning the accuracy, privacy, and security.

This standard describes a Hyperspace Transaction Protocol (HSTP) that enables interoperable, semantically compatible connections between connected hardware (e.g. autonomous drones, sensors, smart devices, robots) and software (e.g. services, platforms, applications, artificial intelligence systems) and includes specifications for: 1) a spatial range query format and response language for requesting data about objects within a dimensional range (spatial, temperature, pressure, motion) and their content. 2) a semantic data ontology schema for describing objects, relations, and actions in a standardized way 3) a verifiable credentialing and certification method for permissioning create, retrieve, update, and delete (CRUD) access to devices, locations, users, and data; and4) a human and machine-readable contracting language that enables the expression and automated execution of legal, financial and physical activities.