Embodiments of the present disclosure provide a method, device, and computer program product for error evaluation. A method for error evaluation comprises in accordance with a determination that an error occurs in a data protection system, obtaining context information related to an operation of the data protection system; determining, based on the context information and using a trained deep learning model, a type of the error in the data protection system from a plurality of predetermined types, the deep learning model being trained based on training context information and a label on a ground-truth type of an error associated with the training context information; and providing the determined type of the error in the data protection system. In this way, it is possible to achieve automatic classification of errors in the data protection system, thereby improving the efficiency in error classification and saving the operation costs. Therefore, more rapid and more accurate measures can be taken to handle the errors.

This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for monitoring interception of an image display pipeline and indicating a determined interception to a user. A display processor may detect that at least one application is activated based on one or more CRC values that correspond to the at least one application. The display processor may generate a UI indication for the at least one application based on detecting that the at least one application is activated and transmit, based on generating the UI indication, at least one UI layer. The at least one UI layer may correspond to the UI indication for the at least one application.

Methods, systems, and apparatuses to improve the handling of exceptions during the retrieval and processing of health records from various data sources are provided. During the retrieval and processing of health records, exceptions to typical behavior are recorded with context at the data extraction protocol level, at the health record level and at the level of elements with the document. Accordingly, insights may be developed and configurations, rules, or coding changes, based on the detected exceptions may be proposed. In some instances, an operator may be notified about the exceptions such that the operator may act on the insight. In some instances, the processing of extracted records (documents, messages) may be deferred until the operator has made appropriate changes to configuration, rules, or code. In some instances, the system may supplement and/or replace the operator with machine learning engines that act on the developed insights.

Methods, systems, and apparatuses to improve the handling of exceptions during the retrieval and processing of health records from various data sources are provided. During the retrieval and processing of health records, exceptions to typical behavior are recorded with context at the data extraction protocol level, at the health record level and at the level of elements with the document. Accordingly, insights may be developed and configurations, rules, or coding changes, based on the detected exceptions may be proposed. In some instances, an operator may be notified about the exceptions such that the operator may act on the insight. In some instances, the processing of extracted records (documents, messages) may be deferred until the operator has made appropriate changes to configuration, rules, or code. In some instances, the system may supplement and/or replace the operator with machine learning engines that act on the developed insights.

In a dispersed storage network where slices of secure user data are stored on geographically separated storage units (44), a managing unit (18) connected to the network (20) may seek to broadcast and update secure access control list information across the network (20). Upon a target device (e.g., devices 12, 14, 16, 18, or 44) receiving the broadcast the target device creates and sends an access control list change notification message to all other system devices that should have received the same broadcast if the broadcast is a valid request to update access control list information. The target device waits for responses from the other system devices to validate that the broadcast has been properly sent to a threshold number of other system devices before taking action to operationally change local data in accordance with the broadcast.

A memory device having an error notification function includes an error correction code (ECC) engine detecting and correcting an error bit by performing an ECC operation on data of the plurality of memory cells, and an error notifying circuit configured to output an error signal according to the ECC operation. The ECC engine outputs error information corresponding to the error bit corresponding to a particular address corrected by the ECC operation. The error notifying circuit may output the error signal when the particular address is not the same as any one of existing one or more failed addresses.

Exemplary embodiments provide a tiered error correction code (ECC) Chipkill system, comprising: a device ECC incorporated into at least a portion of a plurality of memory devices that corrects n-bit memory device-level failures in the respective memory device, and transmits a memory device failure signal when any memory device-level failure is greater than n-bits and beyond correction capability of the device ECC device; and a system-level ECC device external to the plurality of memory devices is responsive to receiving the memory device failure signal to correct the memory device failure based on a system ECC parity.

Described is a method which comprises performing a first read from a portion of a non-volatile memory, the first read to provide a first codeword; decoding the first codeword; determining whether the decoding operation failed; performing a second read from the portion of the non-volatile memory when it is determined that the decoding operation failed, the second read to provide a second codeword; and decoding the second codeword with an errors-and-erasures decoding process.

A memory system includes a nonvolatile memory including memory cells, and a memory controller. The memory controller is configured to read first data through application of a first read voltage to each of the memory cells, perform a first decoding process with respect to the first data, when the first decoding process fails, perform a tracking process. The tracking process includes reading second data indicating a threshold voltage level of each of the memory cells through application of a plurality of second read voltages to each of the memory cells, and obtaining, with respect to each of the memory cells, likelihood information using the second data. The second read voltages are shifted by a predetermined amount. The memory controller is further configured to perform a second decoding process with respect to the second data using the likelihood information.

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data error detection, according to embodiments of the present invention. In one embodiment, a target data set having a plurality of values for which to identify incompatible data is obtained. A pattern for each of the plurality of values is generated using at least one generalization language. A pair of patterns that represent a pair of values is utilized to identify a compatibility indicator that corresponds with a pair of training patterns in a compatibility index that match the pair of patterns. The compatibility indicator indicates the pair of patterns are incompatible with one another based on a statistical analysis performed in association with a corpus of data external to the target data set. An indication that the values are incompatible with one another is provided.