Abstract: A graphics processing system includes a plurality of processing units, wherein the graphics processing system is configured to process a task first and second times at the plurality of processing units. Data identifying which processing unit of the plurality of processing units the task has been allocated to is consulted on allocating the task to a processing unit for processing for a second time, and, in response, the task is allocated for processing for the second time to any processing unit of the plurality of processing units other than the processing unit to which the task was allocated for processing for a first time.

Abstract: A flow logging utility tracer (“FLUTE”) system can present a FLUTE user interface through which the FLUTE system can receive a trace request. The FLUTE system can interface, via a FLUTE controller, with a FLUTE adapter corresponding to a cloud environment associated with the trace request. The FLUTE adaptor can extract event log data from a log repository corresponding to the cloud environment. The FLUTE adapter can filter the event log data according to a logging specification. The FLUTE adapter can transform the filtered event log data for compatibility with a FLUTE stitcher. The FLUTE stitcher can generate tracing data based upon the event log data. The FLUTE stitcher can correlate the tracing data based upon a common identifier enforced by the logging specification, prepare the tracing data for publication to a distributed tracing system, and publish the tracing data to the distributed tracing system for storage.

Abstract: A set of devices may be monitored and a subset of the set of devices which is experiencing a problem may be determined. A cardinality of the subset of the set of devices may be divided by a cardinality of the set of devices to obtain a ratio. An expected distribution over a set of attribute values may be determined by multiplying the ratio with a count of devices corresponding to each attribute value. An observed distribution over the set of attribute values may be determined by counting devices which are experiencing the problem corresponding to each attribute value. A probability value may be determined which represents a probability that the expected distribution matches the observed distribution. A report may be generated which includes attributes and the associated probability values, where an attribute is included in the report if the corresponding probability is less than a threshold.

Abstract: A method for performing a backup includes: obtaining a file system backup metadata associated with a target file system of a target production host; processing the file system backup metadata to determine protected data and unprotected data in the target file system and protection failure details associated with the unprotected data; generating, using the protected data, the unprotected data, and the protection failure details associated with the unprotected data, a graphical user interface (GUI) specifying a protection status of the target file system; and displaying, on a display of a computing device and to a user, the GUI.

Abstract: A machine learning module is trained by receiving inputs comprising attributes of a computing environment, where the attributes affect a likelihood of failure in the computing environment. In response to an event occurring in the computing environment, a risk score that indicates a predicted likelihood of failure in the computing environment is generated via forward propagation through a plurality of layers of the machine learning module. A margin of error is calculated based on comparing the generated risk score to an expected risk score, where the expected risk score indicates an expected likelihood of failure in the computing environment corresponding to the event. An adjustment is made of weights of links that interconnect nodes of the plurality of layers via back propagation to reduce the margin of error, to improve the predicted likelihood of failure in the computing environment.

Abstract: Alert correlation helps reduce the number of alerts that IT staff have to act upon. Methods include a computer program product that applies a machine driven deep learning model to effectively correlate alerts caused by a common root cause. The methods of correlation provide the user the context of the root cause for the alerts. Therefore, it helps the user to quickly identify, understand and resolve the problem thereby reducing the mean time to identification and resolution. Alerts caused by the same root cause therefor come together.

Abstract: Disclosed herein are a high-speed synchronization apparatus and method based on a blockchain unit file. The high-speed synchronization apparatus includes memory configured to store at least one program, a database including unit files having a uniform size in which blocks are to be sequentially stored, and a processor configured to execute the program, wherein the program is configured to perform comparing a size of a block owned by a relevant peer node with a size of a block in use in a blockchain framework, wherein the size of the block owned by the relevant peer node is represented by a peer block size and the size of the block in use is represented by a channel block size, and when the peer block size is less than the channel block size, acquiring blocks from a peer node that owns a block having the channel block size and synchronizing the blocks.

Abstract: A computer based system for social network assistance is disclosed. Via use of the system and related methods, a high-volume social media account (for example, an airline Twitter account) may be managed to enable prompt and thorough responses to incoming social media messages. In this manner, customer issues and feedback may be more effectively addressed, resulting in improved customer satisfaction and goodwill associated with the owner of the high-volume social media account.

Abstract: A method includes receiving a plurality of configurations comprising a first configuration for provisioning a first set of network services at a first resource of an edge device and a second configuration for provisioning a second set of network services at the first resource, a first configuration group identifier identifying a configuration group for the first configuration, and a first network performance parameter for the configuration group. The method further includes determining a performance factor for the first resource providing the first set of network services to one or more client devices. The method further includes, in response to determining that the performance factor does not satisfy the first network performance parameter for the configuration group and that the first configuration group identifier identifies the configuration group for the first configuration, moving the first configuration from the first resource to a second resource of the edge device.

Abstract: A method for managing a production environment includes obtaining telemetry information for a set of logical devices in the production environment, in response to the obtaining, perform a preprocessing of the telemetry information to obtain pre-processed data, perform a condition analysis on the pre-processed data to obtain a set of condition indicators, generating a downtime prediction model using the set of condition indicators and the telemetry information, and based on the downtime prediction model, initiating generation of a replacement logical device.

Abstract: This disclosure describes implementations for categorizing and displaying a Gantt chart of categorized errors, as well as performing an odd pattern analysis to identify anomalous errors including obtaining a log file that includes a number of log entries where each log entry includes an error message. Converting each error message into an error vector using a predictive model. Determining a category for each error vector using a K-nearest neighbor algorithm, and generating a Gantt chart representing error messages arranged hierarchically by category as a function of time for display at a user interface.

Abstract: A communication system including: a cloud facility and a capture server, wherein the capture serve receives session identification information from the cloud facility that receives a session generation request from the communication facility. If a capture function is enabled for the SIM associated with the session identification information, the capture server starts a capture process for capturing a packet whose session identification information included in a header matches session identification information associated with the SIM whose capture function has been enabled, among packets received by mirroring of data transmitted toward the cloud facility, and the cloud facility transmits a response to the session generation request to the communication facility connected to the cloud facility, after the capture process is started.

Abstract: Systems and methods for detecting and managing incidents are disclosed. In one embodiment, a method for detecting an incident includes receiving issue data created for an issue tracking system; analyzing the received issue data over a predetermined interval; determining whether a potential incident has occurred based on the analysis; upon determining that a potential incident has occurred, creating an incident management assistant program; identifying one or more relevant users to communicate an alert to; and communicating the alert to the identified relevant users, the alert including a pointer to the incident management program.

Abstract: Systems and methods for ransomware protection in collaboration systems. One embodiment operates in conjunction with a collaboration system that manages one or more user devices that store device-local copies of content objects in storage areas of the one or more user devices. The collaboration system is accessed by the one or more user devices to facilitate collaboration activity over the content objects. Patterns of collaboration activity are classified, and in some cases, such patterns are deemed to be indicative of ransomware. If ransomware is indicated to be running on a user device, the collaboration system is notified that a ransomware process has been detected on the user device. The collaboration system delivers a prevention policy to other user devices that interface with the collaboration system. The prevention policy is in turn enforced by the other user devices to prevent synchronization of content object changes made by the ransomware.

Abstract: A USB slave device and its power quality testing method, where the USB slave device is preset to enable only minimum functions required for the power quality testing method. When the USB slave device is connected to the USB power supply device, the power quality testing is activated. If the USB slave device is not rebooted after a period of time, the power quality testing is determined to be passed and fully functionality of the USB salve device is activated.

Abstract: A method including: generating a conversation flow signature based on a set of communication transcripts, each of the communication transcripts being associated with a support request for a product, each of the communication transcripts being a text transcript of a communication between a respective customer and a respective customer support agent; classifying the conversation flow signature into one of a plurality of categories, the conversation flow signature being classified by using a machine learning classifier that is trained based on customer support records, each of the plurality of categories corresponding to a respective set of steps for configuring or repairing the product; and outputting an indication of the respective set of steps that is associated with the category in which the conversation flow signature is classified.

Abstract: Systems and methods involving provision of machine-learning-based prediction of future failure, anomaly, etc. in execution of batch processes are disclosed. In one illustrative implementation, an exemplary method may comprise obtaining historical data from prior execution of one or more batch processes, training a machine learning model to predict one or more future failure(s) and/or future flag(s) in execution of a future batch process, generating and/or collecting descriptive analytics pertinent to execution of the batch processes, and predicting a future failure and/or future flag in execution of the batch processes using the trained machine learning model and/or the descriptive analytics.

Abstract: The present embodiments relate to data center outage detection and alert generation. An outage detection service as described herein can process near real-time data from various sources in a datacenter and process the data using a model to determine one or more projected sources of a detected outage. The model as described herein can include one or more machine learning models incorporating a series of rules to process near-real time data and offline data and determine one or more projected sources of an outage. An alert message can be generated to provide the projected sources of the outage and other data relevant to the outage.

Abstract: According to one embodiment, a memory system is connectable to a host. The memory system includes a nonvolatile memory and a controller. The nonvolatile memory includes a plurality of blocks. The controller is electrically coupled to the nonvolatile memory. The controller controls the nonvolatile memory. When receiving, from the host, a first command for changing a state of an allocated block to a reallocatable state in a case where a second command that is yet to be executed or being executed involving read of data from the allocated block has been received from the host, the controller changes the state of the allocated block to the reallocatable state after the second command is finished.

Abstract: A memory device includes a command interface that when operating receives a write command, an input output interface that when in operation receives data signals in conjunction with the write command, and error detection circuitry coupled to the input output interface. The error detection circuitry is configured to generate a first signal indicative of a first period of time during which a first determination is made regarding a first portion of the data signals utilizing a first data strobe signal as a first clock signal, generate a second signal indicative of a second period of time during which a second determination is made regarding a second portion of the data signals utilizing a second data strobe signal as a second clock signal, and generate a control signal based upon the first signal, the second signal, and a slower of the first data strobe signal and the second data strobe signal.

Abstract: One or more non-transitory computer readable media contain program instructions that, when executed by one or more processors, cause the one or more processors to perform operations including: creating and managing a plurality of smart entities, each of the smart entities including a plurality of attributes; receiving inbound data from one or more enterprise applications; translating the inbound data into values for one or more of the plurality of attributes; writing the plurality of attributes to the smart entities; reading the plurality of attributes from the smart entities; translating the plurality of attributes into outbound data; and providing the outbound data to the one or more enterprise applications.

Abstract: A system and method for crowdsourcing monitoring RF spectrum in a global distributed and decentralized radio-miner system architecture is provided. A system and method comprising a cloud network comprising a plurality of clients and a plurality of radio-miners. An analog consensus mechanism that is rooted in analog capability and performance. The plurality of radio-miners demonstrating one or more technological requirements associated with one or more monitoring tasks provided by the plurality of clients. The consensus mechanism dependent on real-time analysis of over-the-air (OTA) RF signals performed by the plurality of radio-miners and the plurality of radio-miners adding to a blockchain once one or more radio parameters from the plurality of radio-miners are validated by geographically spread radio miners.

Abstract: Disclosed is a solution for diagnosing problems from logs used in an application development environment. A random sample of log statements is collected. The log statements can be completely unstructured and/or do not conform to any natural language. The log statements are tagged with predefined classifications. A natural language processing (NLP) classifier model is trained utilizing the log statements tagged with the predefined classification. New log statements can be classified into the plurality of predefined classifications utilizing the trained NLP classifier model. From the log statements thus classified, statements having a problem classification can be identified and presented through a dashboard running in a browser. Outputs from the trained NLP classifier model can be provided as input to another trained model for automatically and quickly identifying a type of problem associated with the statements, eliminating a need to manually sift through tens or hundreds of thousands of lines of logs.

Abstract: A display system provided in production equipment having driving means and monitoring means with controllable features includes a control unit, a display unit, and a storage unit. The control unit obtains a feature output over time from at least one among the driving means and the monitoring means, and causes the storage unit to store the feature; constructs, in each prescribed time, a causal relationship model that indicates a causal factor selected from among the driving means and the monitoring means for at least one abnormality that can occur in the production equipment and that also indicates the relationship between causal factors on the basis of the obtained feature; and displays, on the display unit, a plurality of causal relationship models constructed in a plurality of times.

Abstract: A system for diagnosing an additive manufacturing device is provided. The system includes one or more processors, one or more non-transitory memory modules communicatively coupled to the one or more processors and storing machine-readable instructions. The machine-readable instructions, when executed, cause the one or more processors to: determine parameters associated with at least one subsystem of the additive manufacturing device, the parameters being related to a build generated by the additive manufacturing device; compare the parameters with threshold values; and determine a failure mode, among a plurality of failure modes, associated with a subsystem of the at least one subsystem of the additive manufacturing device based on the comparison of the parameters with the threshold values.

Abstract: According to certain aspects of the present disclosure, a computer-implemented method is provided. The method includes receiving, at a mobile device management server from a threat feed server, at least one security statement. The method includes parsing the at least one security statement into parsed information. The method includes creating a custom threat feed of common vulnerabilities and exposures with at least the parsed information. The method includes selectively creating an alert associated with one common vulnerability and exposure of the common vulnerabilities and exposures, wherein the alert comprises a remediation action associated with the one common vulnerability and exposure. The method includes determining at least one managed device, managed by the mobile device management server, and associated with the remediation action of the alert. Systems and machine-readable media are also provided.

Abstract: An information handling system may include a processor, a display device communicatively coupled to the processor, and a management controller communicatively coupled to the processor and the display device and configured to, in response to a failure of the information handling system, determine a component of the information handling system as a source of failure, generate a unique failure code associated with the failure, encrypt the unique failure code to generate an encrypted unique failure code, and display the encrypted unique failure code to the display device.

Abstract: Implementations described herein relate to methods, systems, and computer-readable media to monitor a distributed computing system. In some implementations, a method may include obtaining a first plurality of monitoring metrics of a respective application of one or more applications executing over the distributed computing system, obtaining time-series data of the first plurality of monitoring metrics, programmatically analyzing the time-series data of the first plurality of monitoring metrics to determine a second plurality of monitoring metrics, wherein the second plurality of monitoring metrics is determined to be a predictive set of the first plurality of monitoring metrics, and monitoring the distributed computing system by monitoring the second plurality of monitoring metrics.

Abstract: An anomaly factor estimation apparatus includes a memory and a processor configured to execute training an anomaly detection model for performing anomaly detection based on an error of output data with respect to input data represented by a multidimensional numerical vector; inputting test data into the anomaly detection model, to determine whether the test data corresponds to an anomaly based on output data output from the anomaly detection model and the test data; and calculating a degree of contribution to the anomaly for each dimension of the test data based on a likelihood of said each dimension, in response to determining that the test data corresponds to the anomaly.

Abstract: An acquirer acquires abnormality information indicating that an abnormality has occurred in equipment provided in a conference room. The equipment is, for example, a display device such as a display or a projector. A conference information generator generates conference information indicating a progress status of a conference which is carried out in the conference room by processing information output from a device provided in the conference room. An information transmitter determines a first timing which is a timing at which abnormality-relevant information which is information to be transmitted due to occurrence of the abnormality is transmitted using the conference information and transmits the abnormality-relevant information to the equipment or another device at the first timing.

Abstract: A method is provided for diagnosing probable root causes of anomalies in computing environments. The method is performed by an anomaly analytics server in networked communication with hosts. The method includes (a) receiving a topology map; (b) receiving event data associated with each host; (c) receiving an anomaly baseline defining thresholds for identifying an anomalous status in each host application; (d) analyzing the event data and the anomaly baseline to determine a host anomaly status for each host; (e) analyzing the host anomaly status and the topology map to determine a tier anomaly status for each of the host tiers; (f) identifying at least one host tier as a root cause host tier by processing the tier anomaly statuses and the set of tier dependencies; and (g) generating an alert identifying each root cause host tier as a probable root cause of an anomaly.

Abstract: One or more non-transitory computer readable media contain program instructions that, when executed by one or more processors, cause the one or more processors to perform operations including: creating and managing a plurality of smart entities, each of the smart entities including a plurality of attributes; receiving inbound data from one or more enterprise applications; translating the inbound data into values for one or more of the plurality of attributes; writing the plurality of attributes to the smart entities; reading the plurality of attributes from the smart entities; translating the plurality of attributes into outbound data; and providing the outbound data to the one or more enterprise applications.

Abstract: In an embodiment, an electronic circuit includes: a plurality of signal channels; a signal collection circuit configured to determine an action of the electronic circuit based on channel signals from the plurality of signal channels; and a first signal management circuit coupled between the plurality of signal channels and the signal collection circuit, the first signal management circuit including: a set of internal registers, a set of user registers, and a decoder configured to program the set of internal registers based on a content of the set of user registers, where the first signal management circuit is configured to receive the channel signals via the plurality of signal channels, generate first aggregated signals based on the received channel signals and a content of the set of internal registers, and transmitting the first aggregated signals to the signal collection circuit.

Abstract: A system and method of deployment of malware detection traps by at least one processor may include performing a first interrogation of a first Network Asset (NA) of a specific NA family; determining, based on the interrogation, a value of one or more first NA property data elements of the first NA; obtaining one or more second NA property data elements corresponding to the specific NA family; integrating the one or more first NA property data elements and the one or more second NA property data elements to generate a template data element, corresponding to the specific NA family; producing, from the template data element, a malware detection trap module; and deploying, on one or more computing devices of a computer network, one or more instantiations of the malware detection trap module as decoys of the first NA.

Abstract: Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.

Abstract: A TCU includes: a wide-area communication section that performs wireless communication with a base station; a short-range communication section that performs wireless communication with an access point; a vehicle-to-vehicle communication section that performs wireless communication with a relay vehicle; and a communication control section that transmits upload data by using any one of the wide-area communication section, the short-range communication section, and the vehicle-to-vehicle communication section, wherein the communication control section transmits the upload data more preferentially by using the short-range communication section or the vehicle-to-vehicle communication section than by using the wide-area communication section when an upload deferment condition is met, and transmits the upload data more preferentially by using the wide-area communication section than by using the short-range communication section and the vehicle-to-vehicle communication section when the upload deferment condition is

Abstract: Embodiments of the invention relate to an application monitoring and unplanned event remediation system, which analyzes event data to generate dynamic testing strategies and then apply said testing strategies in real-time to predict future events. The invention utilizes past event data to prioritize testing of particular application conditions, allowing for continuous monitoring without creating unnecessary strain on available computing resources. Because a variety of application operating states can result in unplanned events, the present invention provides the functional benefit of adapting testing strategies in real-time as an application's operating state changes, increasing the likelihood of correctly predicting a future event. The present invention also provides a system of alerting a managing entity system to a future event, as well as providing instructions for preventative or remedial actions which may serve to lessen the potential impact of an unplanned event on a managing entity.

Abstract: A CCI (I3C SDR) processing section determines status of an index when requested to be accessed by an I3C master for a read operation. An error handling section then controls an I3C slave 13 to detect occurrence of an error based on the status of the index and to neglect all communication until communication is restarted or stopped by the I3C master, the I3C slave 13 being further controlled to send a NACK response when performing acknowledge processing on a signal sent from the I3C master. This technology can be applied to the I3C bus, for example.

Abstract: Methods, systems, graphical user interfaces (GUIs), and computer-readable media for presenting GUI elements generated based on information associated with an event are generally described. An event information presentation system may be configured to present GUI elements generated based on substantially real-time event information associated with a live event, such as a sporting event. Illustrative event information may include object movement and location information for objects such as event participants (for instance, players) and articles (for instance, a football for a football game event). The event information may be interpreted based on activity categories to automatically differentiate, organize, highlight, or the like the event information in order to generate relevant and meaningful GUI elements.

Abstract: A computerized method of diagnosing a mislabeling of a source type of a received event. The method comprising operations of receiving an event by a source type analysis logic with a data index and query system, wherein the event includes a portion of raw machine data and is associated with a specific point in time, obtaining an original source type assigned to the event and one or more predicted source types. The one or more predicted source types are determined by analysis of a data representation of the event in view of training data and the training data includes a plurality of data representations corresponding to known source types. Additionally, the computerized method also includes an operation of, determining whether the event has been mislabeled and in response to determining the event has been mislabeled, diagnosing a source of the mislabeling.

Abstract: Disclosed are embodiments for improving remote diagnostics of a computer system. Some embodiments obtain operational parameter values and log data from a plurality of network devices, and provide the operational parameter values and log data to a machine learning model. The model is trained to identify a root cause of a degradation of the computer system based on the operational parameter values and log data, and to provide recommendations of log data level settings for the network devices. If the model identifies a root cause of the degradation with sufficient confidence, a remedial action is identified and applied to the computer system. If the confidence level is insufficient, log data level settings of the network devices are modified based on the recommendations of the model. This process is performed iteratively, for example, such that the model receives log data based on its recommended log data levels, until a root cause is identified with sufficient confidence.

Abstract: Plan of action and milestones (POA&M) automated generation engine (PAGE) systems are provided along with related methods. A number of distributed tamper protected configuration scanning systems configured to scan computer files in selected target systems across a network and generate target system configuration scan results files. The PAGE system also includes a number of POA&M configuration selection user interfaces and visualization systems enabling users to select, inform, and customize POA&M outputs based on POA&M configuration files that include POA&M library files that are associated with different types of plans or actions (e.g., cyber security configuration for networked computers). An expert system is also provided for receiving a plurality of configuration file inputs (e.g., configuration scan results file and various baseline files, e.g.

Abstract: Techniques for providing lifecycle handling of faults associated with a storage appliance. The techniques can include recording information or details of one or more detected faults associated with a storage appliance in a fault database, analyzing the recorded fault information/details by an analytic tool, and determining whether to perform immediate recovery or defer recovery of the detected fault(s) based at least on a recommendation of the analytic tool. In this way, taking the storage appliance offline upon occurrence of a single fault can, for the most part, be avoided. In addition, because the detection of faults is not performed within a fault domain for a filesystem handling files for a single or limited number of volume families, a deduplication domain for the files of the filesystem is not restricted to the single or limited number of volume families.

Abstract: A semiconductor memory apparatus including a data memory array, a parity memory array, a data read/write and correction part, a parity read/write part and a syndrome generating and decoding part is provided. The data read/write and correction part reads the data memory array and outputs a first application reading data. The parity read/write part reads the parity memory array and outputs a parity reading data. During a read cycle of an application data, the syndrome generating and decoding part generates a syndrome writing data according to the first application reading data, compares and decodes the syndrome writing data with the parity reading data to generate a verifying comparison data. In the same read cycle, the data read/write and correction part corrects the application data according to the verifying comparison data, and writes the corrected application data back to the data memory array and outputs a corresponding output data.

Abstract: A storage system and method for crash analysis are provided. In one embodiment, a storage system comprises a memory and a controller. The controller is configured to store a crash log in a buffer in the controller; determine whether the storage system has crashed; and in response to determining that the storage system has crashed, send the crash log from the buffer in the controller to a host in communication with the storage system. Other embodiments are provided.

Abstract: A method includes detecting an error that has occurred in one or more assets of an enterprise system and generating a visual error signature of the detected error, the visual error signature comprising at least a portion of a graph-based visualization of operation of the assets. The method also includes providing the generated visual error signature for the detected error as input to a machine learning model and utilizing the machine learning model to classify the visual error signature for the detected error as belonging to at least a given one of a plurality of error classes, the machine learning model being trained using historical visual error signatures for previously-detected errors. The method further includes identifying at least one action taken to remediate each of one or more previously-detected errors of the given error class and remediating the detected error utilizing one or more of the identified actions.

Abstract: A method includes obtaining information regarding a given asset to be repaired, providing the information regarding the given asset to an encoder of a deep learning model, and receiving, from a decoder of the deep learning model, a recommendation for a troubleshooting action to be performed on the given asset. The method also includes performing the recommended troubleshooting action on the given asset, determining whether the recommended troubleshooting action results in a successful repair of the given asset and, responsive to determining that the recommended troubleshooting action does not result in a successful repair of the given asset, augmenting the information regarding the given asset based at least in part on an output vocabulary of the decoder corresponding to the recommended troubleshooting action. The method further includes repeating the providing, receiving, performing and determining steps utilizing the augmented information regarding the given asset.

Abstract: A method and an apparatus for implementing a buffer cache for a persistent file system in a non-volatile memory is provided. A set of data is maintained in one or more extents in a non-volatile random-access memory (NVRAM) of a computing device. At least one buffer header is allocated in a dynamic random-access memory (DRAM) of the computing device. In response to a read request by a first process executing on the computing device to access one or more first data blocks in a first extent of the one or more extents, the first process is granted direct read access of the first extent in the NVRAM. A reference to the first extent in the NVRAM is stored in a first buffer header. The first buffer header is associated with the first process. The first process uses the first buffer header to directly access the one or more first data blocks in the NVRAM.

Abstract: An apparatus includes a memory and a hardware processor. The memory stores a plurality of previous software errors and a plurality of solutions to the plurality of previous software errors. The processor receives a video of a user describing an error in a software application and tracks a facial feature of the user to determine an emotional intensity. The processor converts an audio portion of the video to a text file and analyzes the text file to determine the error. In response to the emotional intensity exceeding a threshold, the processor determines, based on the plurality of previous software errors, a series of steps that, when performed, replicate the error, determines, based on the series of steps and the plurality of solutions, a change to the source code of the application that fixes the error, and implements the change to the software code.

Abstract: A system controller performs collective setting processing which collectively performs setting with respect to one or more controlled devices including a medical device. The system controller judges a degree of error relating to a state of the controlled device which occurred when the collective setting processing is performed. The system controller displays an error removing screen for removing an occurred error based on a judgement result of the degree of error. A setting instruction operation is an operation which selects a desired scene from a plurality of scenes which are set in advance corresponding to a progress of a surgery. The degree of error is associated with the plurality of scenes, and the system controller judges the degree of the occurred error based on the scene which is underway of the surgery.