Abstract: A method, system, and computer-usable medium are disclosed for acquiring and using audio detected during operation of a hard disk drive to determine the status of the hard disk drive. One general aspect of the disclosure is directed to a computer-implemented method that includes receiving analog audio detected by an audio transducer in proximity to moving mechanical components of a hard disk drive, converting the received analog audio to digital audio, and assigning a health classification status to the hard disk drive based on a classification of the digital audio.

Abstract: An approach is provided in which the approach builds a combination model that includes a normal status model and an abnormal status model. The normal status model is built from a set of time-sequenced normal status records and the abnormal status model is built from a set of time-sequenced abnormal status records. The approach computes a set of time-sequenced coefficient combination values of the normal status model and the abnormal status model based on applying a set of fitting coefficient characteristics to the normal status model and the abnormal status model. The approach performs goal seek analysis on a system using the combination model and the set of time-sequenced coefficient combination values.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an operation guard system executed within a cloud platform, session information representative of a session of a user within the cloud platform, the session information including user information and operation information, determining, by the operation guard system, that the user is signed into a technical group for execution of an operation represented in the operation information, and in response, providing, by the operation guard system, a risk score associated with the operation, and determining, by the operation guard system and at least partially based on the risk score, that the operation is a risk-oriented operation based on the risk score, and in response, preventing execution of the operation and transmitting an alert.

Abstract: A system for root cause analysis based on process optimization data is provided. The system receives log data associated with a first trace between a first activity and a second activity of a process. The system further determines a state of inefficiency between the first activity and the second activity based on the received log data. The system further applies a first machine learning (ML) model on the received log data. The system further determines a first label and a first value to be associated with the first trace of the process based on the application of the first ML model. The system further generates presentation data associated with the determined state of inefficiency of the first trace based on the determination of the first label and the first value and further transmits the generated presentation data on a user device.

Abstract: An analysis system includes a control module generates data gathering parameters and data analysis parameters based on one or more inputs regarding an evaluation of a system aspect under test of a system, a data input module receives system gathered data regarding the system aspect under test to produce gathered data, and a data analysis module configured to generate the evaluation of the system aspect under test based on the data analysis parameters and the gathered data One or more databases store one or more of the gathered data, the data analysis parameters, and the evaluation of the system aspect under test and one or more data extraction modules interact with the system aspect under test to extract data from the system aspect under test in accordance with a respective portion of the data gathering parameters to produce the system gathered data and provide the system gathered data.

Abstract: An analysis system includes a control module generates data gathering parameters and data analysis parameters based on one or more inputs regarding an evaluation of a system aspect under test of a system, a data input module receives system gathered data regarding the system aspect under test to produce gathered data, and a data analysis module configured to generate the evaluation of the system aspect under test based on the data analysis parameters and the gathered data One or more databases store one or more of the gathered data, the data analysis parameters, and the evaluation of the system aspect under test and one or more data extraction modules interact with the system aspect under test to extract data from the system aspect under test in accordance with a respective portion of the data gathering parameters to produce the system gathered data and provide the system gathered data.

Abstract: Embodiments of systems and methods for enhanced drift remediation with causal methods and online model modification are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: detect drift in an Artificial Intelligence (AI) or Machine Learning (ML) model configured to make a prediction or a causal reasoning graphical or structural inference based upon input data, identify a root cause of the drift, and tag the input data with an indication of the root cause.

Abstract: Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.

Abstract: Methods, systems, and devices for predictive media management for read disturb are described. A read disturbance manager can monitor a bit error rate for a block of a memory die. The read disturbance manager can detect that a degradation of the bit error rate satisfies a degradation threshold specific to the memory die. In some cases, the read disturbance manager can perform a write operation to write data from the block of the memory die to a second block of the memory die based on detecting that the degradation of the bit error rate satisfies the degradation threshold.

Abstract: A failure handling apparatus (100) is provided with: an acceptance unit (15) that accepts specification of condition information in an execution condition; a code generation unit (16) that generates a program code of a conditional expression based on the specified condition information; a template generation unit (17) that generates an input template of a plurality of failure handling rules, including an input field of a determination criterion value for determining extracted information, based on the program code and an input field of a handling content; and a list generation unit (18) that sets input values, for the input template, in the input fields and stores the input values in a storage unit as a list.

Abstract: Device initialization by an access-restricted virtual machine, including: restricting access by a first operating system to a device during one or more device initialization operations, wherein the first operating system is executing in a first virtual machine supported by a hypervisor; determining, by a second operating system executing in a second virtual machine supported by the hypervisor, one or more modifications attempted by the first operating system to the device; and performing, by the second operating system, the one or more modifications to the device.

Abstract: A specification of an information technology change is received via an information technology service management system. The specification of the information technology change is analyzed to determine features of the information technology change. Machine-generated data is analyzed to identify a phenomena detected in the machine-generated data. To a machine learning model, the features of the information technology change and features of the detected phenomena in the machine-generated data are provided to determine a correlation between the information technology change and the detected phenomena in the machine-generated data.

Abstract: Aspects of the disclosure relate to automated error processing. A computing platform may receive historical error/solution information. The computing platform may train, using the historical error/solution information, an artificial intelligence engine to automatically identify solutions for current errors for a plurality of users. The computing platform may identify current errors for a user of the plurality of users. The computing platform may notify the user of the current errors. The computing platform may receive a request to correct an error of the one or more current errors. The computing platform may identify, using the artificial intelligence engine, a solution to the error. The computing platform may automatically perform actions to achieve the solution. The computing platform may send, after performing the actions, commands directing an event processing system to process an event with which the error was associated, which may cause the event processing system to process the event.

Abstract: A method for operating a processing unit. The processing unit addresses virtual memory areas in order to access a RAM memory unit and these individual virtual memory areas respectively being mapped onto a physical memory area of the RAM memory unit. A check of the RAM memory unit for errors is performed. If, in the course of this check, a physical memory area of the RAM memory unit is determined to be faulty, this faulty physical memory area is designated as faulty. A check is performed to determine whether a free physical memory area exists in RAM memory unit onto which no virtual memory area is mapped and which is not designated as faulty. If such a free physical memory area exists, the virtual memory area that is currently mapped onto the physical memory area recognized as faulty is henceforth mapped onto this free physical memory area.

Abstract: A computer-implemented method for cloud-based testing of a payment network may include receiving a test configuration for testing a payment processing network, configuring a simulated worker generator for generating a plurality of simulated workers according to the received test configuration, reading commands to be executed by each simulated worker among the plurality of simulated workers from a command bank according to the received test configuration, configuring the plurality of simulated workers according to the commands and the received test configuration, starting a swarm test of the payment processing network by the plurality of simulated workers, reading results of the swarm test from the plurality of simulated workers, and saving the results to storage.

Abstract: Techniques reconstruct a storage system. A first extent access load and a second extent access load of a first malfunctioning extent and a second malfunctioning extent in a malfunctioning storage device in the storage system are acquired, respectively. The first malfunctioning extent is selected as a source extent in response to determining that the first extent access load is lower than the second extent access load. Among multiple idle extents in the storage system, a set of destination extents that can be used as a reconstruction destination of the source extent are generated. A destination extent is selected from the set of destination extents as the reconstruction destination of the source extent. It is possible to preferentially select a reconstruction destination for a malfunctioning extent with a low access load, thereby increasing the reconstruction speed of the storage system.

Abstract: An apparatus comprises a processing device configured to receive a service request associated with a given asset, to obtain a log file associated with the given asset, to split the log file into log segments, to generate sets of log pattern identifiers for the log segments, and to determine risk scores for the log segments utilizing a machine learning model that takes as input the sets of log pattern identifiers and provides as output information characterizing risk of the log segments. The processing device is also configured to identify critical areas of the log file based at least in part on the determined risk scores, a given critical area comprising a sequence of log segments having determined risk scores above a designated risk score threshold. The processing device is further configured to analyze the identified critical areas to determine remedial actions to be applied for resolving the service request.

Abstract: A redundant processing fabric in an autonomous vehicle may include: processing, by a first processing unit of a plurality of processing units, sensor data from a first sensor of a plurality of sensors, where the plurality of processing units are coupled to the plurality of sensors via a switched fabric, wherein the plurality of processing units and plurality of sensors are included in the autonomous vehicle, wherein the sensor data corresponds to an environment external to the autonomous vehicle; determining a failure in processing the sensor data by the first processing unit; and severing, in the switched fabric, a first communications path between the first sensor and the first processing unit; and establishing, in the switched fabric, a second communications path between the first sensor and a redundant processing unit.

Abstract: Apparatuses, methods, systems, and program products are disclosed for analytics-based anomaly detection. An apparatus includes a processor and a memory that stores code executable by the processor. The code is executable by the processor to receive measurement data for a component of a device, determine a compression rate of the received measurement data by applying a compression algorithm to the measurement data, and generate an alert indicating a possible anomaly in an operation of the component represented by the measurement data in response to the determined compression rate satisfying a threshold compression rate for the at least one condition of the component.

Abstract: A method and system for health rank based virtual machine restoration using a conformal framework. With respect to enterprise data protection, solutions need to address two primary responsibilities: at the onset of failure or disaster, restore any running applications, as well as any lost or damaged data; and minimize (if not eliminate) the future occurrence of such failures or disasters. In tackling the latter responsibility, the disclosed method and system leverage health-score assessments to ensure the restoration of virtual machines onto healthy infrastructure. The aforementioned health-score assessments employ clustering to identify, and a conformal framework to rank, healthy hosts onto which virtual machines may be restored.