Disclosed implementations provide executable models, such as artificial intelligence models that can be owned, traded, and used in various execution environments. By coupling a model with a strictly defined interface definition, the model can be executed in various execution environments that support the interface. Coupling the model with a non-fungible cryptographic token allows the model and other components to be owned and traded as a unit. The tradeable composite units have utility across multiple supported execution environments, such as video game environments, chat bot environments and financial trading environments. Additionally, the interface allows for the creation of pipelines and systems from multiple complementary composite units.

Disclosed implementations provide executable models, such as artificial intelligence models that can be owned, traded, and used in various execution environments. By coupling a model with a strictly defined interface definition, the model can be executed in various execution environments that support the interface. Coupling the model with a non-fungible cryptographic token allows the model and other components to be owned and traded as a unit. The tradeable composite units have utility across multiple supported execution environments, such as video game environments, chat bot environments and financial trading environments. Additionally, the interface allows for the creation of pipelines and systems from multiple complementary composite units.

The present disclosure discloses a model loading method and apparatus for a head-mounted display device and a head-mounted display device. The method includes: obtaining a type of a target handheld device, in which the target handheld device is a handheld device connected to a current application service; determining whether the type of the target handheld device is an existing type in the head-mounted display device; obtaining, in response to the type of the target handheld device being not the existing type in the head-mounted display device, corresponding model resource data based on the type of the target handheld device; and generating, based on the model resource data, a handheld model corresponding to the target handheld device, and loading the handheld model.

The present disclosure discloses a model loading method and apparatus for a head-mounted display device and a head-mounted display device. The method includes: obtaining a type of a target handheld device, in which the target handheld device is a handheld device connected to a current application service; determining whether the type of the target handheld device is an existing type in the head-mounted display device; obtaining, in response to the type of the target handheld device being not the existing type in the head-mounted display device, corresponding model resource data based on the type of the target handheld device; and generating, based on the model resource data, a handheld model corresponding to the target handheld device, and loading the handheld model.

A method and system of generating a documentation includes monitoring user actions regarding a model development, wherein the user actions are captured in a source code development environment. Semantic meaning is provided for each user action captured in the source code development environment. A degree of impact of each user action is determined in connection with the model. Actions having a degree of impact in the development of the model that is above a predetermined threshold are identified as impactful actions. An interactive knowledge graph is identified based on the user actions, semantic meaning of each action, and the determined degree of impact of each user action. The interactive knowledge graph is provided to be displayed in a navigable way.

A method and system of generating a documentation includes monitoring user actions regarding a model development, wherein the user actions are captured in a source code development environment. Semantic meaning is provided for each user action captured in the source code development environment. A degree of impact of each user action is determined in connection with the model. Actions having a degree of impact in the development of the model that is above a predetermined threshold are identified as impactful actions. An interactive knowledge graph is identified based on the user actions, semantic meaning of each action, and the determined degree of impact of each user action. The interactive knowledge graph is provided to be displayed in a navigable way.

A method and system provides for asynchronous two-way binding between a user interface and a data model which are implemented on different frameworks. The system includes data, data model, and bridge controllers. The data controller provides a front-end binding framework that interacts with a user interface of a user device to manage a bindable property or method for a view on the user device. The data model controller provides a back-end binding framework that manages a data model, the front-end binding framework and the back-end binding framework being different types of frameworks. The bridge controller implements asynchronous two-way binding for the bindable property or method between the front-end binding framework and the back-end binding framework to update the bindable property or method in the data model when data changes at the user interface and to update the view on the user device when data changes at the data model.

A method and system provides for asynchronous two-way binding between a user interface and a data model which are implemented on different frameworks. The system includes data, data model, and bridge controllers. The data controller provides a front-end binding framework that interacts with a user interface of a user device to manage a bindable property or method for a view on the user device. The data model controller provides a back-end binding framework that manages a data model, the front-end binding framework and the back-end binding framework being different types of frameworks. The bridge controller implements asynchronous two-way binding for the bindable property or method between the front-end binding framework and the back-end binding framework to update the bindable property or method in the data model when data changes at the user interface and to update the view on the user device when data changes at the data model.

Methods, systems, and computer-readable media for automated code generation using analysis of design diagrams are disclosed. A diagram-to-code system determines one or more security properties of a plurality of components associated with a software product. Relationships between the components are indicated in a software design diagram. At least some of the security properties are determined using input to a user interface. The diagram-to-code system generates one or more secure code packages based (at least in part) on the software design diagram and the one or more security properties. The secure code package(s) implement one or more security controls associated with the software product. The secure code package(s) are provided to a developer. The secure code package(s) and additional program code from the developer are compiled into a compiled software product. Execution of the compiled software product mitigates security vulnerabilities using the one or more security controls.

A cloud platform includes several web services that facilitate the automated tuning and deployment of pre-trained deep learning models configured for software engineering tasks. The automated tuning and deployment allow a developer to fine-tune a pre-existing model without having access to the parameters of the pre-existing and the fine-tuned model in a manner that does not require user management input. The cloud platform provides a set of files for each pre-trained models used to automatically build a fine-tuning infrastructure to fine-tune a model and a deployment infrastructure that deploys the fine-tuned model without requiring user input.