Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

A method may include receiving, by a first computing system, a first message indicative of a rate at which a second computing system is requesting to make application programming interface (API) calls. The method may further include based at least in part on the first message, configuring the first computing system to enable the second computing system to use an access credential to make API calls at the rate. The method may also include sending, from the first computing system to the second computing system, the access credential.

Methods, apparatus, and processor-readable storage media for providing an automated services exchange are described herein. An example computer-implemented method includes obtaining provider requests from one or more service providers, wherein each of the provider requests comprises an indication of at least one type of service provided by the corresponding service provider and attributes associated with the at least one type of the service; processing the provider requests, wherein the processing for a respective one of the provider requests comprises generating a corresponding set of metrics associated with the at least one type of service and the attributes of the respective provider request; and matching a given one of the provider requests to at least one consumer request based at least in part on: the processing and constraints identified in the at least one consumer request with respect to at least a portion of the attributes of the given provider request.

In an approach, a processor receives a request to access an electronic resource from a device. A processor causes the device to generate a Completely Automated Public Turing test (CAPTCHA), where the CAPTCHA comprises: a virtual keyboard; an ordered string of characters required to be input; and presentation of a highlighted key of the virtual keyboard on the device, wherein (i) the highlighted key is a first key visually distinct from other keys of the virtual keyboard and (ii) the first key corresponds to a character of the ordered string of characters. A processor receives a result of the CAPTCHA from the device. A processor performs an action based on the result.

In one embodiment, a method comprises: initiating, by an executable agent within a secure executable container executed by a network device, a monitoring of a network-based service between the network device and a second network device having a two-way trusted relationship with the network device within a secure peer-to-peer data network, the network-based service based on a securely-stored secure data structure or a securely-transmitted secure data structure in the secure peer-to-peer data network; executing, by the executable agent, a secure machine learning operation based on one or more user actions associated with the network-based service, wherein the secure executable container prevents any access of any unencrypted data structure, or accessing the secure peer-to-peer data network, without authorized access via a prescribed Application Programming Interface (API); and autonomically executing, by the executable agent, an improved operation for the network-based service based on the machine learning.

Computer-implemented methods and systems for integrating computer applications are disclosed. One method includes querying a primary computer application for current state of a newly created object; receiving the current state of the object, and generating object data for a secondary computer application based on the current state of the object. The method further includes communicating an object creation request to a secondary computer application, the object creation request including the generated object data, receiving a secondary computer application object identifier from the secondary computer application upon creation of the object at the secondary computer application, and communicating the secondary computer application object identifier to the primary computer application for storing in a record of the object created at the primary computer application.

Techniques for implementing a dynamic API bot for robotic process automation are disclosed. In some embodiments, a computer system performs operations comprising: providing a data file having a predefined template comprising dedicated fields for an identification of an API, a type of call method, metadata identifying one or more objects, and data of the object(s); providing a low-code no-code (LCNC) development platform configured to enable a user to develop a bot by dragging and dropping application components of the bot; receiving, via the LCNC development platform, a configuration of the bot comprising a configuration of the application components of the bot and an identification of the data file; and running the bot, the bot being configured to generate a request using the data file, converting the data of the object(s) into a payload in a format required by the API based on the data file.

Disclosed are systems and methods for providing a hybrid mobile application. The method may include: receiving specification of at least one feature of a mobile application. The mobile application may be specific to a first programming platform. The method may further include generating the at least one feature based on the specification. The at least one feature may be written in a programming language specific to a second programming platform, and the second programming platform may be different from the first programming platform. The method may also include compiling a programming wrapper based on the at least one feature of the mobile application, combining the at least one feature and the programming wrapper into a programming component, packaging the programming component into the mobile application, and distributing the mobile application in a repository of the first programming platform.

Described herein are systems and methods for abstracted analysis system design for a dynamic API scanning service. The disclosure provides a simplified API scanning service by abstracting underlining security scanning techniques and configurations. This presents a normalized view to users of the service. Both input parameters and scan output data is abstracted from users, and is driven based on logic in the service. By providing this simplified view, users can quickly, and without prior security scanning knowledge, use this service to measure their security exposure and mitigate as needed.

To dynamically allow chaining of logical processing units comprising endpoints, at least a type of an endpoint, and address information whereto connect the endpoint is configured, wherein the type of the endpoint is either a host port type or a logical processing unit type. During offloading from a central processing unit one or more functions to be performed by at least one further processing unit, the central processing unit is interacting with the one or more logical processing units via endpoints of the host port type and logical processing units are interacting via endpoints of the logical processing unit port type, the interaction using the address information.