Examples associated with keyboard emulation are described. One example system includes an input/output controller. The system also includes a remote signal receiver. A control module receives a first signal via the remote signal receiver. Based on the first signal, the control module communicates with the input/output controller to emulate a keyboard input.

Arrangements for autonomously re-initializing one or more applications after a detected change in device state are provided. In some examples, a configuration file may be received from one or more computing devices, such as a server, hosting one or more client-facing applications. In some examples, the configuration file may be modified. For instance, one or more properties or attributes may be modified or added to identify applications that have an always running status and identifying a custom class having automatic start enabled. A modified configuration file may be generated and transmitted to the one or more devices. Accordingly, upon detecting a change of device state (e.g., reboot, refresh, or the like) the modified configuration file may reboot and cause the identified applications to automatically or autonomously re-load, re-initialize and recompile prior to receiving a first request for access from a customer or user device.

Disclosed herein are embodiments of methods, devices and systems for device fingerprinting and automatic and dynamic software deployment to one or more endpoints on a computer network. The device fingerprinting systems and devices herein are configured to operate with limited data without sitting between network devices and the internet, without monitoring all network traffic, and without limited or no active scanning. The embodiments herein may passively collect information as distributed peers and may perform very limited active scans. In some embodiments, the information is used as an input to a custom hierarchical learning model to fingerprint devices on a network by identifying attributes of the devices such as the operating system family, operating system version, and device role. In some embodiments, a dynamic deployer selection process may be utilized to simply and efficiently deploy software. Some embodiments herein involve end-to-end encryption of credentials in a deployment process.

A method including generating, by a state engine from data describing behaviors of users in an environment external to the state engine, an executable process. An agent executes the executable process by determining, from the data describing the behaviors of the users, a problem of at least some of the users, and selects, based on the problem, a chosen action to alter the problem. At a first time, a first electronic communication describing the chosen action to the at least some of the users is transmitted. Ongoing data describing ongoing behaviors of the users is monitored. A reward is generated based on the ongoing data to change a parameter of the agent. The parameter of the agent is changed to generate a modified agent. The modified agent executes the executable process to select a modified action. At a second time, a second electronic communication describing the modified action is transmitted.

A smart forms solution that enables transactions institutions to provide configuration parameters in a streamlined manner so that developers can construct end-to-end solutions in an automated manner includes performing, by a processor, operations including: receiving data from a form in a webpage; validating the received data; saving the validated data; determining a location to send the saved data; sending the saved data to the determined location; invoking an API at the determined location with the saved data; and using the saved data to perform a manual operation.

One embodiment provides a method, including: receiving a dataset and a model corresponding to a bias checker, wherein the bias checker detects bias within both the dataset and the model, based upon a bias checking algorithm and a bias checking policy, wherein the dataset comprises a plurality of attributes; testing the bias checking algorithm of the bias checker by (i) generating test cases that modify the dataset by introducing bias therein and (ii) running the bias checker against the modified dataset; testing the bias checking policy of the bias checker by generating a plurality of test cases and running the bias checker against the plurality of test cases; and providing a notification to a user regarding whether the bias checker failed to indicate bias for one or more of the plurality of attributes.

A method may include receiving a first definition of an object type from a first software component and a second definition of the object type from a second software component. The object type may be labeled by an ID. The method may further include storing, in a dynamic graph, a node labeled by the ID, and storing, in a type definition repository external to the dynamic graph, the first definition of the object type and the second definition of the object type. The method may further include receiving, from the first software component, a modified first definition of the object type. The method may further include replacing, in the type definition repository and using the ID, the first definition of the object type with the modified first definition, and transmitting, to the second software component, a message indicating a need to lookup, by the ID, the modified first definition.

Processing stacked data structures is provided. A system receives an input audio signal detected by a sensor of a local computing device, identifies an acoustic signature, and identifies an account corresponding to the signature. The system establishes a session and a profile stack data structure including a first profile layer having policies configured by a third-party device. The system pushes, to the profile stack data structure, a second profile layer retrieved from the account. The system parses the input audio signal to identify a request and a trigger keyword. The system generates, based on the trigger keyword and the second profile layer, a first action data structure compatible with the first profile layer. The system provides the first action data structure for execution. The system disassembles the profile stack data structure to remove the first profile layer or the second profile layer from the profile stack data structure.

Processing stacked data structures is provided. A system receives an input audio signal detected by a sensor of a local computing device, identifies an acoustic signature, and identifies an account corresponding to the signature. The system establishes a session and a profile stack data structure including a first profile layer having policies configured by a third-party device. The system pushes, to the profile stack data structure, a second profile layer retrieved from the account. The system parses the input audio signal to identify a request and a trigger keyword. The system generates, based on the trigger keyword and the second profile layer, a first action data structure compatible with the first profile layer. The system provides the first action data structure for execution. The system disassembles the profile stack data structure to remove the first profile layer or the second profile layer from the profile stack data structure.

This disclosure describes, in part, techniques for utilizing global models to generate local models for electronic devices in an environment, and techniques for utilizing the global models and/or the local models to provide notifications that are based on anomalies detected within the environment. For instance, a remote system may receive an identifier associated with an electronic device and identify a global model using the identifier. The remote system may then receive data indicating state changes of the electronic device and use the data and the global model to generate a local model associated with the electronic device. Using the global model and/or local model, the remote system can identify anomalies associated with the electronic device and, in response to identifying an anomaly, notify the user. The remote system can further cause the electronic device to change states after receiving a request from the user.