Methods and systems for allowing software components that operate at a specific exception level (e.g., EL-3 to EL-1, etc.) to repeatedly or continuously observe or evaluate the integrity of software components operating at a lower exception level (e.g., EL-2 to EL-0) to ensure that the software components have not been corrupted or compromised (e.g., subjected to malware, cyberattacks, etc.) include a computing device that identifies, by a component operating at a higher exception level (“HEL component”), at least one of a current vector base address (VBA), an exception raising instruction (ERI) address, or a control and system register value associated with a component operating at a lower exception level (“LEL component”). The computing device may perform a responsive action in response to determining that the current VBA, the ERT address, or control and system register value do not match the corresponding reference data.

Methods, systems, and computer programs are presented for scheduling user notifications to maximize short-term and long-term benefits from sending the notifications. One method includes an operation for identifying features of a state used for reinforcement learning. The state is associated with an action to decide if a notification to a user is to be sent and a reward for sending the notification to the user. Further, the method includes capturing user responses to notifications sent to users to obtain training data and training a machine-learning (ML) algorithm with reinforcement learning based on the features and the training data to obtain an ML model. Additionally, the method includes receiving a request to send a notification to the user, and deciding, by the ML model, whether to send the notification based on a current state. The notification is sent to the user based on the decision.

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 client device is configured to communicate with an access point over a wireless network, exchanging data with the access point over a selected communication channel. The client device stores an identifier of the selected communication channel. After the wireless connection to the access point has ended, the client device initiates a process to reconnect to the access point over the selected communication channel using the stored identifier.

Disclosed herein are systems and method for gradually updating software object instances on a plurality of computer nodes. In an exemplary aspect, in response to receiving a notification from a software object instance, a system may register the software object instance at an update server. The system may store and deploy a plurality of links, wherein each deployed link uniquely corresponds to a registered software object instance. The system may then associate two or more subsets of the plurality of links with two or more update locations, in accordance with an update policy. The system may place an update to the software object instance at the two or more update locations in accordance with an update policy. In response to receiving an update request via a link from a computing node, the system may further redirect the update request to an update location associated with the link.

Systems and methods are disclosed for event-based application control. A system extension is configured to leverage an endpoint security API for monitoring event activity within operating system kernel processes. The system extension registers with the endpoint security API particular event types for which the system extension would like to receive notifications. In response to receiving notifications regarding detected events corresponding to the registered event types, the system extension determines if the event, and its corresponding process, are safe and allowable to execute. In various embodiments, the system leverages whitelists, blacklists, and rules policies for making a safeness determination regarding the event notification. The system extension transmits this determination to the operating system via the endpoint security API.

Embodiments of the present invention provide systems and methods for evaluating and weighting resource usage activity data. The system may establish a communicable link to a user device via a user application to receive resource activity data and historical data from one or more users or systems via multiple communication channels. The system may evaluate the historical data and determine evaluation criteria based on perceived chance of loss associated with particular metadata characteristics, and use the evaluation criteria as weighted metrics for determining an overall evaluation score for the user based on indication from resource activity data that the user has conducted resource transfers with entities or channels identified in the historical data.

A system enabling a hypervisor to assign processor resources for specific tasks to be performed by a virtual machine. An example method may comprise: receiving, by a hypervisor running on a host computer system, a virtual processor (“vCPU”) assignment request from a virtual device driver running on a virtual machine managed by the hypervisor, assigning a vCPU for executing a task associated with the assignment request, and causing the virtual device driver to execute the task using the vCPU.

A method, apparatus and product for chat-based application interface for automation. Using a natural language interface, receiving user input. Based on the user input, determining an automation process of a computer program having a user interface (UI), to be executed. The automation process is executed by utilizing the UI to input data thereto or execute functionality thereof. Additionally or alternatively, a conversation to be implemented by a natural language interface may be defined. The conversation is configured to obtain from the user one or more values corresponding to one or more parameters. The conversation is associated with a parameterized automation process depending on the one or more parameters. The parameterized automation process is invoked automatically by a natural language interface and using one or more values provided by the user to the natural language interface for the one or more parameters.

Methods, devices, and systems for point value change notification are described herein. One system (100) includes a message broker (108) to receive data from a data acquisition (DAQ) system, a first building management system (BMS) instance (104) connected to the message broker (108) to process a first portion of the DAQ data, a second BMS instance (104) connected to the message broker (108) to process a second portion of the DAQ data, and a web application (118) connected to the message broker (108) to generate a notification of a change in point value of a portion of the first portion or the second portion of the DAQ data, where the first BMS instance (104) and the second BMS instance (104) are provisioned with a plurality of computing resources deployed in a computing environment (102, 502) and are ultimately executed on hardware.