This standard is to improve the user experience of mobile gaming by solving the issues between the high demands from game applications and the resource limitation of a mobile device. Message exchange is critical to coordinate the resource allocation and to optimize the performance of gaming that ensure fluent user experiences in games. Additionally, this standard specifies evaluation methods and criteria that enables the control and optimization.
A framework of blockchain-based Internet of Things (IoT ) data management is defined in this standard. It identifies the common building blocks of the framework that blockchain enabled during IoT data lifecycle including data acquisition, processing, storage, analyzing, usage/exchange and obsoletion, and the interactions among these building blocks.
A set of processes by which organizations seek to make their services age appropriate is established in this standard. The growing desire of organizations to design digital products and services with children in mind and reflects their existing rights under the United Nations Convention on the Rights of the Child (the Convention) is supported by this standard. While different jurisdictions may have different laws and regulations in place, the best practice for designing digital services that impact directly or indirectly on children is offered by this standard. It sets out processes through the life cycle of development, delivery and distribution,…
Data has become one of the most important assets in ICT area. Secure multi-party computation plays a very important role in balancing data usage and data protection. It could build trust and security in data collaboration and big data analysis related areas. A technical framework for secure multi-party computation is provided in this standard, including specifying the following: an overview of secure multi-party computation; a technical framework of secure multi-party computation; security levels of secure multi-party computation; and use cases based on secure multi-party computation.
Self-discipline and professional ethics of cryptocurrency exchange platforms, as well as relevance between them and to cryptocurrency wallets are covered in this standard. Exchange business logic, operational procedures, user authentication programs are also covered in this standard. In addition, a small but necessary technical category of requirements, including terminologies, basic architectural framework, key indicators, end-user interface specifications, in order to achieve the previously mentioned goals is covered in this standard.
Provided in this recommended practice is a comprehensive methodology for technology domain exploration, development of strategy, technology evaluation, implementation, management, operations, program optimization, and successful enterprise scaling for IPA programs while utilizing terminology as established in IEEE Std 2755™-2017 and technology taxonomy as established in IEEE Std 2755.1™-2019. This recommended practice is a compilation of best practices from industry leaders on the proven methods from the initial discovery and exploration of the transformative capabilities of IPA technology through to developing and running an enterprise-wide program.
This standard can be applied to internet-based business scenarios, and can also be served serve as a practical guide to achieve help assess business security risk control through the big data technology. This standard can be applied in other types of organization, including public or privately-owned or state-owned enterprises, associations, or organizations, or by individuals, to improve assessment of their protection capability against business security risks based on big data technology.
Federated machine learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across repositories owned by different organizations or devices. A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide. It defines the architectural framework and application guidelines for federated machine learning, including description and definition of federated machine learning; the categories federated machine learning and the application scenarios to which each category applies; performance evaluation of federated machine learning; and associated regulatory requirements.
A framework for support of drone applications is established in this standard. Typical drone application classes, application scenarios, and required application execution environments are specified. The general facility requirements of drone applications are listed, including flight platform, flight control system, ground control station, payload, control link and data link, takeoff and landing system, etc. The drone safety and management requirements include airworthiness, airspace and air traffic requirements, qualification of operators, qualification of personnel, insurance, confidentiality, and others. The general operation process is detailed. The operation results stipulate the operation record and operation report, including data classification, data collection and processing,…