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: A method includes decoding, by at least one hardware processor, a notification of a changed database code of a database. A query is executed responsive to the notification. The query indicates a data processing command and a data object in the database. A regression in the changed database code is detected based on multiple regression testing operations applied to the data processing command and the data object. Analysis of the regression is performed to detect a rollout parameter of a plurality of rollout parameters as a root cause of the regression. The plurality of rollout parameters are associated with the changed database code. A determination is made on whether to perform a mitigation action for the regression based on the rollout parameter.

Abstract: The disclosure describes a method of monitoring the dynamic power consumption of ReRAM crossbars and determines the occurrence of faults when a changepoint is detected in the monitored power-consumption time series. Statistical features are computed before and after the changepoint and train a predictive model using machine-learning techniques. In this way, the computationally expensive fault localization and error-recovery steps are carried out only when a high fault rate is estimated. With the proposed fault-detection method and the predictive model, the test time is significantly reduced while high classification accuracy for well-known AI/ML datasets using a ReRAM-based computing system (RCS) can still be ensured.

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: Systems and methods for determining the priority of a set of reports available to a user. The method comprising determining, by a reporting application, a set of reports available to a user, each report including one or more metrics. The method further comprising obtaining metric data associated with each of the set of reports available to the user, accessing one or more priority calculation parameters for each report in the set of reports, calculating a priority value of each report of the set of reports based on the metric data and the priority calculation parameters for each report, and determining one or more high priority reports based on the priority value of each report of the set of reports.

Abstract: A method of error logging includes: receiving from a user interface a first input from a user indicating that they have seen a visible error in a displayed graphical output of a computer application, tracking the gaze of the user to estimate the position, on at least a first display showing at least part of the displayed graphical output, at which the user's gaze is directed, receiving from the user interface a second input from the user indicating that they have identified the visible error with their gaze, capturing data relating to the displayed graphical output of the computer application, the data including a capture of at least part of the displayed graphical output including the identified visible error, and the estimated positon of the user's gaze with respect to the displayed graphical output, and generating an error report including the captured data.

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: 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: 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: A bus system for a process system, having a first bus subscriber which transmits bus messages and having at least one first bus subscriber which receives bus messages, wherein the transmitting first bus subscriber and the receiving first bus subscriber are connected to one another via a first data bus, wherein the transmitting first bus subscriber is designed such that it transmits control commands to the receiving first bus subscriber, wherein the receiving first bus subscriber is designed such that it executes the control commands of the transmitting first bus subscriber and achieves the object of providing a bus system that is designed to be fail-safe in a special way.

Abstract: A test and measurement system has a test and measurement instrument, a test automation platform, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive a waveform created by operation of a device under test, generate one or more tensor arrays, apply machine learning to a first tensor array of the one or more tensor arrays to produce equalizer tap values, apply machine learning to a second tensor array of the one of the one or more tensor arrays to produce predicted tuning parameters for the device under test, use the equalizer tap values to produce a Transmitter and Dispersion Eye Closure Quaternary (TDECQ) value, and provide the TDECQ value and the predicted tuning parameters to the test automation platform.

Abstract: A safety module having a plurality of microcontrollers receives an analog input and determines a value of the analog input. The microcontrollers each determine a respective ternary state of the device by identifying, from three candidate ranges of values, a range of values in which the value falls, wherein at least two of the plurality of microcontrollers uses different candidate ranges of values, determining, based on the identified range, a ternary state corresponding to the range, and assigning the determined ternary state as the respective ternary state. The safety module determines whether the ternary states from the two microcontrollers map to a fault state, and, where they do, cause a command a command to be output to the device to enter a safe state.

Abstract: The state transition condition can be adjusted flexibly and easily while taking advantage of the handshake-type technique. A state transition setting screen 11 of an entire state transition flow including a name 12 of each state based on a communication standard and a state transition condition 13 to be executed between states is displayed on a display screen 6a, and input setting for the state transition condition is possible on the state transition setting screen 11.

Abstract: An API adapter test support system includes an API adapter test support apparatus, a user terminal simulation apparatus and a user server simulation apparatus. The user terminal simulation apparatus and the user server simulation apparatus are connected to a wholesale service apparatus to which an API adapter. The API adapter test support apparatus includes a test scenario creation unit configured to create a test scenario that ensures comprehensiveness of a test case of an API adapter test, a control signal test scenario execution unit configured to execute a control signal API test for the API adapter by using the test scenario, and a data signal test scenario execution unit configured to execute a data signal API test for the user terminal simulation apparatus and the user server simulation apparatus by using the test scenario.

Abstract: Embodiments of systems and methods for adapting a log severity level that is applied to log files associated with a target application based on user interaction. In an illustrative, non-limiting embodiment, an adaptive log level control system includes computer-executable instructions for monitoring user interaction with a target application executed on the IHS, identifying a usage pattern of the user interaction, and determining that the usage pattern is indicative of a problem with the target application. The instructions may then adjust a log severity level of log files associated with the target application according to the detected problem.

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: A VIN verification system allow an accurate detection of incorrectly entered VIN in an electronic file followed by correction of the incorrectly entered VIN. A server of the VIN verification system verify characters and letters of the VIN in the electronic file to detect data entry and/or transmission errors. The server upon determining that the characters and entered in the VIN are incorrect will then replace the characters and letters with alternate or substitute characters and letters and generate a new VIN in the electronic file. The server will reexamine the new VIN to validate the new VIN, and then transmit the electronic file to another web application for further processing.

Abstract: An information handling system may include at least one processor and a memory. The information handling system may be configured to: execute an application comprising a plurality of microservices on the at least one processor, wherein the application is configured to service external requests by executing a plurality of application programming interface (API) calls among the plurality of microservices; and for each API call: determine a span ID associated with such API call; determine a trace ID associated with a particular external request that is associated with such API call; and log the span ID and the trace ID.

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.