Examples of the present disclosure relate to an apparatus, device, method, and computer program for monitoring a processing device from a trusted domain. The apparatus comprises interface circuitry, machine-readable instructions, and processing circuitry to execute the machine-readable instructions to receive a request for monitoring the processing device from the trusted domain; authenticate the request; obtain information on a failure report related to a component of the processing device, with a possible failure having occurred at runtime of the processing device; and provide the information on the failure report in the trusted domain.

To effectively utilize work information acquired by maintenance of an instrument in a plant, an apparatus is provided, which includes an acquisition unit that acquires work information about at least one of a calibration or an adjustment performed on the instrument in a plant; an extraction unit that extracts a plurality of data elements to be included in output information having a predetermined output format from the work information; and a generation unit that generates the output information from the plurality of data elements.

System and method for providing fault tolerance in virtualized computer systems use a first guest and a second guest running on virtualization software to produce outputs, which are produced when a workload is executed on the first and second guests. An output of the second guest is compared with an output of the first guest to determine if there is an output match. If there is no output match, the first guest is paused and a resynchronization of the second guest is executed to restore a checkpointed state of the first guest on the second guest. After the resynchronization of the second guest, the paused first guest is caused to resume operation.

The disclosure herein describes training and using an uncorrectable error (UE) state prediction model based on telemetry error data. Sets of UE state labels and non-UE state labels are generated from a first set of collected telemetry data, wherein the UE state labels each reference a UE and telemetry data of an interval prior to the referenced UE. Statistical features are extracted from telemetry data of the sets of UE state labels and non-UE state labels, and the extracted statistical features are used to train a UE state prediction model. A second set of collected telemetry data is obtained, and a UE event is predicted based on the second set of collected telemetry data using the trained UE state prediction model. A preventative operation is performed on a memory page of the system based on the predicted UE event, whereby the predicted UE event is prevented from occurring.

Described are techniques including a computer-implemented method of determining, by a service processor, that a first set of callouts of a first error log matches a previous set of callouts of a previous error log. The method further comprises combining the first error log with the previous error log in a first group in a service processor log of the service processor. The method further comprises transmitting information related to the first group to a management console communicatively coupled to the service processor.

In one embodiment, a method for providing a verbalized diagnostic message to a user of an information handling system includes: determining, by an embedded controller, an error associated with the information handling system; accessing, by the embedded controller, a memory device of the information handling system, the memory device storing a plurality of diagnostic files; selecting, by the embedded controller, a diagnostic file from the plurality of diagnostic files, the diagnostic file indicating the error associated with the information handling system; generating, by the embedded controller, the verbalized diagnostic message; sending, by the embedded controller, the verbalized diagnostic message to an audio device of the information handling system in an embedded controller audio stream; and presenting, by the audio device, the verbalized diagnostic message to the user via one or more speakers of the information handling system.

Embodiments of the present disclosure provide a system for continuous and real-time management and monitoring of robotic process automation bots. In particular, the architecture of the system may comprise a centralized hub which provides various features and functions for bot management and monitoring, such as real-time health status updates, granular logging and notification functions, failure detection and reporting for debugging, bot inventory systems, or the like. Through the use of the components and/or features as described herein, the system may provide an efficient way to manage and monitor robotic process automation bots within a computing environment.

A Peripheral Component Interconnect Express (PCIe) interface device coupled to an external device through a link including a plurality of lanes according to the present disclosure includes an EQ controller controlling the PCIe interface device to perform an equalization operation for determining a transmitter or receiver setting of each of the plurality of lanes, and an EQ information storage storing log information indicating a number of equalization operation attempts with respect to each of a plurality of EQ coefficients and storing error information about an error occurring in an LO state with respect to each of the plurality of EQ coefficients, which includes a transmitter coefficient or a receiver coefficient, wherein the EQ controller determines a final EQ coefficient using the log information and the error information.

A computer system provides performs defect tracking and remediation. Video data is received, from a client computing device, corresponding to a recording of a graphical user interface of an application of the client computing device, wherein the video data is obtained in response to the application encountering an error. Feedback is obtained from a user of the client computing device by engaging the user via a natural language processing model. The video data and the feedback are analyzed to determine one or more operations to reproduce the error. One or more corrective actions, based on the determined one or more operations, are provided to remediate the error in the application. Embodiments of the present invention further include a method and program product for performing defect tracking and remediation in substantially the same manner described above.

A method for employing a differentiable ranking based graph sparsification (DRGS) network to use supervision signals from downstream tasks to guide graph sparsification is presented. The method includes, in a training phase, generating node representations by neighborhood aggregation operators, generating sparsified subgraphs by top-k neighbor sampling from a learned neighborhood ranking distribution, feeding the sparsified subgraphs to a task, generating a prediction, and collecting a prediction error to update parameters in the generating and feeding steps to minimize an error, and, in a testing phase, generating node representations by neighborhood aggregation operators related to testing data, generating sparsified subgraphs by top-k neighbor sampling from a learned neighborhood ranking distribution related to the testing data, feeding the sparsified subgraphs related to the testing data to a task, and outputting prediction results to a visualization device.