Systems and methods are provided for user interface deployment that include a server with a cloud application and a client device with a client application. The cloud application is a fully functional application, such as a headless application, and transmits scene graph data including presentation data and behavior data for a first set of user interface elements viewable at the client device in a first operational state. The client application locally renders the first set of user interface elements on a client device display to locally render the portion of the scene graph related to the first operational state. In response to a user input, the client application transmits event or state data to the cloud application and may perform an action associated with the user input while awaiting receipt of updated scene graph data, possibly related to a subsequent operational state, reducing or eliminating the perception of latency.

A non-transitory computer-readable medium stores computer-readable instructions that are executable by a processor of a first terminal device compatible with a first platform, the instructions being configured to, when executed by the processor, cause the first terminal device to accept selection of an image processing apparatus connected with the first terminal device, accept selection of a function to be set up on the first terminal device from among one or more functions executable by the image processing apparatus, install, into the first terminal device, a first program compatible with the image processing apparatus and the first platform, and output an access information image. The access information image represents access information based on the selected function and an address of a web page representing a site of a supply source for a second program compatible with the image processing apparatus and a second platform.

The present application is directed towards systems and methods for identifying and grouping code objects into functional areas with boundaries crossed by entry points. An analysis agent may select a first functional area of a source installation of an application to be transformed to a target installation of the application from a plurality of functional areas of the source installation, each functional area comprising a plurality of associated code objects; and identify a first subset of the plurality of associated code objects of the first functional area having associations only to other code objects of the first functional area, and a second subset of the plurality of associated code objects of the first functional area having associations to code objects in additional functional areas, the second subset comprising entry points of the first functional area.

A mobile communication device. The mobile communication device comprises a radio frequency transceiver, a processor, a memory comprising a system partition and a non-system partition, wherein the system partition comprises a hidden application, and an installer application stored in the system partition of the memory. When executed by the processor, the installer application receives a first message comprising an identity of an enterprise and a communication address of an application download service, sends a second message to the communication address received in the first message, wherein the second message comprises the identity of the enterprise received in the first message, receives a third message that comprises an enterprise application associated with the identity of the enterprise, installs the enterprise application in the system partition of memory in the place of the hidden application, and sets the status of the enterprise application to visible status.

The disclosed technology is generally directed to communications in an IoT environment. In one example of the technology, a virtual IoT device is maintained and controlled to act as a representation of a connected device. Communications are received from an IoT support service. The received communications from the IoT support service are acted in response to, including sending communications to the IoT support service in response to the received communications, and changing the virtual IoT device as if the virtual IoT device were the connected device. The connected device is communicated with based on changes in the virtual IoT device.

Disclosed are various embodiments for implementing automatic firmware rollbacks after a configured number of attempts to execute the latest firmware update associated with an electronic computing unit have occurred. In a computing device, such as an electronic computing unit, a watchdog automatically generates a system reset in response to a software fault. A reset counter is configured to count a number of system resets generated by the watchdog. In response to the occurrence of the watchdog, a configurable threshold value associated with a threshold data record is compared with the value associated with the reset counter. In response to a determination that the threshold value associated with the threshold data record is greater than or equal to the value associated with the reset counter, a rollback application is executed such that the rollback application performs a swap of a first version of a firmware with a second version of the firmware.

The disclosed technology is generally directed to communications in an IoT environment. In one example of the technology, a plurality of module twins that respectively correspond to a plurality of modules of edge applications on a plurality of edge devices are stored. The plurality of module twins individually include metadata associated with the corresponding module of the plurality of modules. At least one module of the plurality of modules to be modified by adding a declarative rule is determined, such that the declarative rule causes determining whether an event has been triggered based on a particular event having been determined to have occurred, and, in response to the triggering of the event, communicating the triggering of the event to at least one declarative target. The determined at least one module of the plurality of modules is caused to be modified by adding the declarative rule.

A method includes receiving an API call including a request to resolve an API set contract to an associated host binary file, where the API set contract defines a set of APIs. The method further includes evaluating a runtime context of the API call to determine an application identifier for an application that initiated the request to resolve the API set contract to the associated host binary file. The method further includes executing elements of a first host binary instead of a second host binary responsive to determining that the API call was placed by a first application and executing elements of the second different host binary instead of the first host binary responsive to determining that the API call was placed by a second application.

An Internet of Things (IoT) gateway integrated into a real-time monitoring system for skid-mounted natural gas compression systems. The IoT gateway enables remote monitoring, troubleshooting, and diagnosing of natural gas compression systems by providing access to cellular and satellite communication networks for communicating operational data to one or more remote servers. The IoT gateway can be configured to select a communication network based on an order of priority and other various criteria. The order of priority and the selection criteria may be updated over the air. The IoT gateway can be further configured to receive and relay software and other updates to one or more components of the natural gas compression system. The IoT gateway is configured to meet various regulatory compliance standards and is explosion proof.

Methods and systems are presented for consuming different data sources, and deploying artificial intelligence and machine learning programs on different target devices or infrastructures. Many data types can be transformed into machine learning data shards (MLDS) while many machine learning programs written in various programming languages or frameworks are transformed to common operator representations. Operator representations are transformed into execution graphs (EG) for a chosen target device or infrastructure. The MLDS and EG are input to the targeted devices and infrastructures, which then execute the machine learning programs (now transformed to EGs) on the MLDS to produce trained models or predictions with trained models.