Embodiments validate representational state transfer (“REST”) application program interfaces (“API”). Embodiments receive a REST API specification that provides information for a plurality of REST APIs and parse the REST API specification to extract, for each REST API, a corresponding Uniform Resource Locator (“URL”), and corresponding parameter names response codes and payloads. Embodiments convert the parsed REST API specification into a converted text file, the converting including parameter constraints and parameter default values. Embodiments then generate all possible combinations of test data for each REST API from the converted text file and perform one or more test operations on each of the combinations of test data.

A system and method are disclosed for secure multi-party computations. The system performs operations including establishing an API for coordinating joint operations between a first access point and a second access point related to performing a secure prediction task in which the first access point and the second access point will perform private computation of first data and second data without the parties having access to each other's data. The operations include storing a list of assets representing metadata about the first data and the second data, receiving a selection of the second data for use with the first data, managing an authentication and authorization of communications between the first access point and the second access point and performing the secure prediction task using the second data operating on the first data.

Representational state transfer (REST) based geo-aware content transfer includes a REST server configured for receiving an application programming interface (API) request from a client device. The REST server obtains a upload universal resource locator (URL) targeting a caching server that is geographically closest to the client device, constructs an upload link containing the upload URL and a completion callback, and sends the upload link to the client device. The client device uses the upload URL to upload content to the caching server. The caching server interprets the completion callback to obtain a completion URL and, upon completion of content uploading, makes a REST API call using the completion URL. Responsive to the REST API call, the REST server executes an inbound operation to complete the uploading to a content management system and returns the content URL to the caching server which sends the content URL to the client device.

A social media platform provides a map-based graphical user interface (GUI) with multiple geographically anchored icons that are selectable to trigger playback of respective galleries of ephemeral messages. Each of the plurality of location-based ephemeral galleries comprises media content contributed by multiple different users. The ephemeral galleries are compiled based on geotag data associated with respective ephemeral messages submitted by multiple users to be publicly viewable via the map-based GUI. Two or more different types of galleries are represented in the map-based GUI by different, visually distinct types of gallery icon.

A cloud-based API metadata management method and system. The cloud-based API metadata management method includes the step of installing an API tool by loading the API tool stored in a database by an API tool installation unit of a cloud server, the step of defining a standard API by the API tool installation unit of the cloud server, and creating an API for each hospital server by an API creation unit, and the step of testing the created API for each hospital server by a test unit of the cloud server, correcting data if there is error data, and storing and distributing the data and API for each hospital server if there is no error data.

The present disclosure provides a method in a first network entity for service Application Programming Interface, API, publishing. The method includes: receiving, from a second network entity, an API publish request for publishing a service API, the API publish request containing a list of identifiers of network entities that have published the service API; and transmitting, when an identifier of the first network entity is included in the list, an API publish response indicating failure of publishing of the service API to the second network entity, without creating a new resource for the service API at the first network entity or further publishing the service API to any network entity.

Various embodiments of the present application set forth a computer-implemented method comprising receiving, at an endpoint device, a user input associated with a first remote application running on a workstation instance associated with the user, determining, based on a context associated with the user input, a first asset associated with the user input, and causing the workstation instance to modify an asset file in a local file system of the workstation instance, wherein the asset file corresponds to at least a portion of the first asset.

A wireless communication network to serve a User Equipment (UE) over Network Exposure Functions (NEFs) that have Application Programming Interfaces (APIs). In the wireless communication network, a NEF Interface Function (NIF) receives a NEF request from a network function. The NIF correlates the NEF request with one of the APIs. The NIF selects one of the NEFs based on the one of the APIs. The NIF translates the NEF request into an API call based on the one of the APIs. The NIF transfers the API call to the one of the NEFs. The one of the NEFs receive the API call and responsively performs a network task for the UE based on the API call.

In a computer system, an orchestration platform includes extensible components that interact with external systems and technology. The platform scales by way of a plurality of application servers using a clustering architecture.

Various embodiments of the present disclosure provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for determining optimized solutions to input problems in a containerized, cloud-based (e.g., serverless) manner. In one embodiment, an example method is provided. The method comprises: receiving a problem type of an input problem originating from a client computing entity; mapping the problem type to one or more selected solver types; generating one or more container instances of one or more compute containers, each compute container corresponding to a selected solver type; generating a problem output using the one or more container instances; and providing the problem output comprising a solution to the input problem to the client computing entity. In various embodiments, optimized solutions for input problems are determined using a cloud-based multi-domain solver system configured to dynamically allocate computing and processing resources between different solution-determining tasks.