Adaptable internet-of-things (IoT) computing systems and methods are disclosed for improved and flexible declarative control of streaming data, such as Big Data, in compute intense environments. A declarative scripting engine determines an input data stream based on a first declarative statement defining input data stream variable(s) of a declarative scripting language in declarative scripting module(s). The input data stream is bound to a stream controller and is ingested into computer memory. The declarative scripting engine generates a snapshot data stream based on a second declarative statement in the declarative scripting module(s), and is derived from the input data stream. A stream model is defined, where a listener entity comprising an event is triggered based on the input data stream or the snapshot data stream as ingested into the stream model.

A system for determining a dependency task tree includes an interface and a processor. The interface is configured to receive a task list. The task list is associated with compiling, testing, packaging, and/or deploying a program. The processor is configured to determine a dependency task tree. The dependency task tree includes all tasks in the task list and all prerequisite tasks for each task in the task list and provides the dependency task tree. The interface is configured to receive the dependency task tree. The processor is configured to determine a set of tasks such that a task of the set of tasks does not depend on any other task; add the set of tasks to a task queue; in response to determining that all dependencies of the dependent task are completed; and continue executing tasks from the task queue until all tasks in the dependency task tree are completed.

A code completion system for a CLI utilizes neural transformer models with attention to generate candidates to complete a line of CLI code. The code completion system uses a first deep learning model to predict at most k candidate command names to follow n immediately preceding lines of CLI code which are presented to a developer. Upon the developer accepting one of the candidate command names, the code completion system uses a second deep learning model to predict at most k parameter strings to complete the line of CLI code.

An example system includes a plurality of scriptable devices, each of which exposes an interface. The system may also comprise a medium storing data indicating, for each of a mapped plurality of device operations, one or more inputs to provide to a corresponding device from the plurality of scriptable devices and a conversion between that device operation and a corresponding application command. In such a system, the medium may also store a plurality of scripts, each of which may have a corresponding scriptable device from the plurality of scriptable devices and may comprise a set of device operations. The system may also comprise a computer to repeatedly preform an operation cycle comprising determining whether an application command should be executed and, when it should, generating a globally unique identifier corresponding to that command and adding that globally unique identifier and a device operation corresponding to that command to a log.

Application generation and integration application generation methods are disclosed. An application generation function includes automatically providing a set of software artifacts to an application developer that may be used as an aid to providing an application with augmented capabilities for an end-user. The augmented capabilities configured to adhere to certain rules regarding certification criteria and requirements of a multi-tenant cloud infrastructure. Generated applications and plug-in capabilities may further be required to meet certification criteria applicable to an application store hosted within a cloud infrastructure.

An information processing apparatus switches alteration detection processing depending on timing of execution of alteration detection to perform alteration detection processing for each file to be accessed to detect an alteration in an extended application, and switches alteration detection execution determination processing depending on a type of access to an extended application package.

Graphical elements in a user interface (UI) may be detected in robotic process automation (RPA) using convolutional neural networks (CNNs). Such processes may be particularly well-suited for detecting graphical elements that are too small to be detected using conventional techniques. The accuracy of detecting graphical elements (e.g., control objects) may be enhanced by providing neural network-based processing that is robust to changes in various UI factors, such as different resolutions, different operating system (OS) scaling factors, different dots-per-inch (DPI) settings, and changes due to UI customization of applications and websites, for example.

A computer-implemented method and system for reducing the amount of memory space required to store applications written in dynamic scripting languages loads a program module into memory and removes a category of program code, such as debug information or function definitions, from the program module. The method and system also receives a request for debug information, or a function call or query, and determines whether or not the corresponding program code is in memory. If not, then the location in storage is identified where the program module is stored, and another copy containing the corresponding program code is loaded into memory. The corresponding program code is located and copied into the program module in memory, and a response is provided to the request.

Techniques are described for performing browser-driven application capture of application installations. When the browser on the client machine detects a request to begin an application capture session, it downloads an orchestrator binary from an origin server. The orchestrator is a self-extracting executable that decompresses components responsible for preparing the client machine for the application capture session. Preparing the client machine includes starting a local web server, executing a registry script to create the necessary registry state, mounting a virtual disk, and deploying an agent that will record state changes on the client machine. Once the client machine has been prepared, the application installation can begin. During the installation process, the agent intercepts state changes occurring on the client machine and redirects them to the virtual disk. Once finished, the application capture session is completed by adding identity and metadata information to the virtual disk to generate the application package.

Systems and methods for interfacing with a third-party website. In one embodiment, a computer system is configured to directly interface with a website via a webpage to change certain numerical values through the use of digital codes. The digital codes are applied to a data entry interface on the webpage, and the responses are monitored and transmitted back to a server system.