Abstract: A computerized method is disclosed for automated handling of data ingestion anomalies. The method features training a data model based on a first volume of data associated with a first time period. Thereafter, using the data model, a predictive analysis is conducted on a second volume of data associated with a second time period subsequent to the first time period to produce a predicted data ingestion volume. After, a correlative analysis between the predicted data ingestion volume and an actual data ingestion volume during the second time period is conducted to produce a prediction error. A notification is generated based on the prediction error.

Abstract: Techniques are described for error log anomaly detection. In an implementation, error logs from an application are processed to generate training data. The error logs, for instance, are processed to remove personal information and other data such as numerical strings. The processed error logs are converted into embeddings to generate the training data. The training data is utilized to train an anomaly detection model. For instance, as part of training the anomaly detection model, an anomaly threshold is defined based on a loss value determined from output of the anomaly detection model. Further error logs from the application are then processed by the trained anomaly detection model to determine which of the further error logs are error anomalies, such as based on comparing loss values for the further error logs to the anomaly threshold.

Abstract: Techniques disclosed herein relate generally to notification generation in a medical device system. In some embodiments, the techniques involve generating a first notification via a medical application executing on a first device, determining that the first notification was not acknowledged within a predetermined time frame, and causing generation of a second notification at a medical device carried by a user to notify the user that the first notification has not been acknowledged.

Abstract: Provided is an apparatus and method for predicting the remaining lifetime of an environment data collection sensor in a livestock house. The method for predicting the remaining lifetime of an environment data collection sensor in a livestock house includes (a) collecting livestock house environment information, (b) detecting an error of a sensor from the collected livestock house environment information, (c) generating a dataset for learning, (d) generating a sensor lifetime prediction model to performing learning, and (e) predicting a lifetime of the sensor using a result of the learning.

Abstract: Failover is performed appropriately when a failure occurs in a physical server. In a plurality of physical servers of a storage system, one or more protocol VMs and one or more file system VMs are created. The protocol VMs perform processing related to a protocol for a file storage with a client via a front-end network. The file system VMs perform processing related to management of files in the file storage. A first physical server causes, when a failure occurs in a second physical server, a physical server other than the second physical server to operate a file system VM to be operated in place of the file system VM of the second physical server, and controls the protocol VM of the physical server other than the second physical server to perform processing to be performed by the protocol VM of the second physical server.

Abstract: Execution status of managed time series processing tasks may be tracked. Status of a time series processing task that operations on different portions of a time series may be respectively captured. A request for the status of one of the portions of the time series with respect to the time series processing task may be received. The status may be identified and returned. For failed tasks, a failure reason may be generated by the time series processing system and included in a response with a failure status.

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: 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 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: 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: 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: 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 automated test equipment for testing one or more devices under test, comprises at least one port processing unit, comprising a high-speed-input-output interface, HSIO, for connecting with at least one of the devices under test, a memory for storing data received by the port processing unit from one or more connected devices under test, and a streaming error detection block, configured to detect a command error in the received data, wherein the port processing unit is configured to, in response to detection of the command error, limit the storing in the memory of data following, in the received data, after the command which is detected to be erroneous. A method and computer program for automated testing of one or more devices under test are also described.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for failure handling. This failure handling method includes determining a sector set failure type associated with at least one failed sector set of a disk; if the sector set failure type indicates that the number of failed sector sets in the at least one failed sector set is greater than a first threshold number, generating an instruction for replacing the disk; and otherwise performing at least one of the following: migrating data from a failed sector set in which the number of failed sectors is greater than a second threshold number to a spare sector set, and performing a failure recovery for a failed sector set in which the number of failed sectors is less than or equal to the second threshold number.

Abstract: A method of managing a virtual machine environment is described. According to the method, a cloud application that is used for management of a plurality of virtual machines may receive, from a first virtual machine, an indication of one or more configuration parameters associated with the first virtual machine. The cloud application may generate an executable package based on metadata associated with the first virtual machine. The executable package may be configured to be executable by a set of default drivers on a second virtual machine upon bootup of the second virtual machine to configure the second virtual machine in accordance with the one or more configuration parameters. The cloud application may transmit, to the second virtual machine, the executable package for configuring the second virtual machine in accordance with the one or more configuration parameters.

Abstract: Embodiments of this application provide a recovery method for a terminal device startup failure and a terminal device. The method includes: determining that a failure indication event occurs in a startup process, where the failure indication event is used to indicate a startup failure; determining at least one recovery policy based on a type of the failure indication event and/or a cause of the failure indication event; and performing startup recovery based on the at least one recovery policy.

Abstract: When a restart event is detected within a technology landscape, restart-impacted performance metrics and non-restart-impacted performance metrics may be identified. The non-restart-impacted performance metrics may continue to be included within a performance characterization of the technology landscape. The restart-impacted performance metrics may be monitored, while being excluded from the performance characterization. The restart-impacted performance metric of the restart-impacted performance metrics may be transitioned to a non-restart-impacted performance metric, based on a monitored value of the restart-impacted performance metric following the restart event.

Abstract: A method for handling a trusted execution environment operating system crash is provided. The method includes: when it is detected, in a running process of a security service, that a TEE OS crashes, an electronic device stores a hardware status parameter of a TEE and a security context of an REE that are obtained when the TEE OS crashes, and suspends the security service; the electronic device restarts the TEE OS; the electronic device sets, based on the stored hardware status parameter of the TEE, a hardware status parameter of the TEE obtained after the TEE OS is restarted; the electronic device sets, based on the stored security context of the REE, a security context of the REE obtained after the TEE OS is restarted and a context of the TEE obtained after the TEE OS is restarted; and the electronic device restores the security service.

Abstract: In an example embodiment, a workflow engine is introduced within a cloud landscape. Runbooks re then implemented as workflow templates within the workflow engine. The workflow engine allows for creation of workflows from the workflow templates as well as composing workflows from individual steps. The workflow engine provides a mechanism to describe workflow templates and workflow sets as code.