Abstract: Described herein is a system and method for determining whether a detected issue in a computing system is a bug introduced by a developer or an intermittent issue. When an issue is detected, information about the issue is received. A determination is then made as to whether the issue is a new issue or whether it has been previously detected. When it is determined that the issue is a new issue, information about the issue is stored in a storage device. A feature vector is generated for the issue and is analyzed to determine a probability that the issue is a bug. When it is determined that the issue is a bug, the occurrence of the issue is reported to a user of the system that can correct the bug. Once the bug is corrected, the correction is provided back to the system and is used to train the system.

Abstract: A storage device, and a method for operating a storage device. In some embodiments, the storage device includes storage media, and the method includes: determining, by the storage device, that the storage device is in a first fault state from which recovery is possible by power cycling the storage device or by formatting the storage media; determining, by the storage device, that the storage device is in a second fault state from which partial recovery is possible by operating the storage device with reduced performance, with reduced capacity, or in a read-only mode; and operating the storage device with reduced performance, with reduced capacity, or in the read-only mode.

Abstract: A system to implement debugging for a multi-threaded processor is provided. The system includes a hardware thread scheduler configured to schedule processing of data, and a plurality of schedulers, each configured to schedule a given pipeline for processing instructions. The system further includes a debug control configured to control at least one of the plurality of schedulers to halt, step, or resume the given pipeline of the at least one of the plurality of schedulers for the data to enable debugging thereof. The system further includes a plurality of hardware accelerators configured to implement a series of tasks in accordance with a schedule provided by a respective scheduler in accordance with a command from the debug control. Each of the plurality of hardware accelerators is coupled to at least one of the plurality of schedulers to execute the instructions for the given pipeline and to a shared memory.

Abstract: The present disclosure discloses a client device having a digital signature. The client device includes processing circuitry configured to send a message to be stored in nodes after the nodes reach a consensus on the message. The message includes the digital signature of the client device. The processing circuitry obtains results from a subset of the nodes that receive the message. The results have respective digital signatures of the subset of the nodes. The nodes are in a first consensus mode for reaching the consensus on the message. After verifying the digital signatures, the processing circuitry determines whether one or more of the nodes has malfunctioned based on the results. Based on a determination that the one or more of the nodes has malfunctioned, the processing circuitry triggers the nodes to switch from the first consensus mode to a second consensus mode for reaching the consensus on the message.

Abstract: The system determines data which indicate memory usage by a process and information related to a number of instances of the process. The system determines for the process: a first average amount of memory; a first standard deviation; a first value indicating a difference between a current amount of memory used by the process and the first average amount; a second average number of process instances; a second standard deviation; and a second value indicating a difference between a current number of process instances and the second average number. The system performs a corrective action to address a memory spike associated with the process responsive to comparing at least one of: the current amount of memory used by the process with a watermark; the first value with the first standard deviation scaled; the current number of instances with a watermark; and the second value with the second standard deviation scaled.

Abstract: Facilitating detection of anomalies of a target entity is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise training a model on a first set of variables that are constrained by a second set of variables. The second set of variables can characterize elements of a defined entity. The first set of variables can define a normality of the defined entity. The operations also can comprise employing the model to identify expected parameters and unexpected parameters associated with the defined entity to at least a defined level of confidence.

Abstract: Described herein is a system and method for determining whether a detected issue in a computing system is a bug introduced by a developer or an intermittent issue. When an issue is detected, information about the issue is received. A determination is then made as to whether the issue is a new issue or whether it has been previously detected. When it is determined that the issue is a new issue, information about the issue is stored in a storage device. A feature vector is generated for the issue and is analyzed to determine a probability that the issue is a bug. When it is determined that the issue is a bug, the occurrence of the issue is reported to a user of the system that can correct the bug. Once the bug is corrected, the correction is provided back to the system and is used to train the system.

Abstract: The present disclosure is for systems and methods for connecting offline machine learning training systems with online near-real time machine learning scoring systems. It is not trivial to connect an offline training environment with an online scoring environment. For example, offline training environments are usually static and contain large amounts of historical data that is needed for the initial training of models. Once trained, the model algorithms are then migrated into an online scoring environment for transactional or event based scoring. This migration effectively breaks the connection between the data in the offline environment and the model now running in the online environment. When new or shifting data occurs in the online environment, the static model running in the online environment goes unaltered to the changing inputs. The present disclosure solves the issues that are caused by the break in the offline and online environments.

Abstract: Systems and methods for updating software in a hazard detection system are described herein. Software updates may be received by, stored within, and executed by a hazard detection system, without disturbing the system's ability to monitor for alarm events and sound an alarm in response to a monitored hazard event. The software updates may be received as part of a periodic over-the-air communication with a remote server or as part of a physical connection with a data source such as a computer. The software updates may include several portions of code designed to operate with different processors and/or devices within the hazard detection system. The software updates may also include language specific audio files that can be accessed by the hazard detection system to play back language specific media files via a speaker.

Abstract: A computer system determines stack usage. An intercept function is executed to store a stack marker in a stack, wherein the intercept function is invoked when a program enters or exits each function of a plurality of functions of the program. A plurality of stack markers are identified in the stack and a memory address is determined for each stack marker during execution of the program to obtain a plurality of memory addresses. The plurality of memory addresses are analyzed to identify a particular memory address associated with a greatest stack depth. A stack usage of the program is determined based on the greatest stack depth. Embodiments of the present invention further include a method and program product for determining stack usage in substantially the same manner described above.

Abstract: In accordance with an embodiment, described herein are systems and methods for supporting a declarative non code self-learning advisory framework in an orchestration based application integration. The systems and methods can provide an advisory framework as a component of an integration platform which can allow declaratively defined recommendations, guidance, warnings etc. to be shown to the consumer of the platform on occurrence of certain events. The advisory framework can provide benefits such as: 1) allowing any entity to declaratively define/modify the rules and advices which will immediately get reflected across the customer fleet without dependency on product's release cadence; 2) where such updates to declaratively defined rules and advices does not involve any code changes to do the product; 3) comprises a structure which is generic and not component specific; and 4) can have self-learning capabilities from the generated product metrics.

Abstract: Systems and methods are described for optimizing logging of decision outcomes in distributed transaction protocols. An example method may comprise: executing, by a processing device, a transaction manager to coordinate a distributed transaction for a plurality of participants; transmitting, by the processing device via the transaction manager, prepare messages to the plurality of participants; serializing a transaction log record of the distributed transaction in parallel with the transmitting of the prepare messages; speculatively writing the serialized transaction log record in persistent memory while the transaction manager performs the serializing of the transaction log record; and updating a speculative indicator associated with the distributed transaction to indicate validity of the transaction log record.

Abstract: In an embodiment, a system includes a plurality of memory components and a processing device that is operatively coupled with the plurality of memory components. The processing device includes a host interface, an access management component, a media management component (MMC), and an MMC-restart manager that is configured to perform operations including detecting a triggering event for restarting the MMC, and responsively performing MMC-restart operations that include suspending operation of the access management component; determining whether the MMC is operating, and if so then suspending operation of the MMC; resetting the MMC; resuming operation of the MMC; and resuming operation of the access management component.

Abstract: A system and method may cause a computing device to operate according to a selected operational mode. A diagnostic application may selectively execute one or more tests on the computing device; record a result of executing a test; and perform an action based on the result. An operational mode selected may be a safe mode that includes executing only a portion of an operating system and the diagnostic application. A test may include executing an application and selectively validating performance of one or more resources of a computing device while the application is executing. A test may include executing an application selected based on a rule related to a category of applications.

Abstract: A storage device includes a memory storage region and a controller having a processor. The processor retrieves user data from the memory storage region using a physical block address corresponding to a logical block address (LBA), in response to a read command. The retrieved user data includes a first hash received through a host interface in a prior host data transmission. The processor further performs error correction on the user data to generate error-corrected user data. The processor further causes a cryptographic engine to produce a second hash of the error-corrected user data. The first hash is compared to the second hash associated with the error-corrected user data to determine a match result. A notification is generated in response to the match result.

Abstract: Detecting non-callable external component APIs is provided. It is determined whether a first function call stack of a worker thread in a multi-threaded application of the computer matches a second function call stack of the worker thread. In response to determining that the first function call stack matches the second function call stack of the worker thread, an external component application programming interface (API) corresponding to the worker thread is identified from a function call stack of the worker thread. The external component API corresponding to the worker thread is marked as non-callable in an API state map. The worker thread is marked as being in a hang state. The worker thread in the hang state is terminated as a remediation action step to maintain performance.

Abstract: Automatic definition of windows for trace analysis. For each process step, the trace data are aligned to both the start of the process step and the end of the process step, and statistics including rate of change are calculated from both the start of the process step and the end of the process step. Windows are generated based on analysis of the calculated statistics.

Abstract: A data accessing method using dynamic speed adjustment with aid of a thermal control unit, and associated apparatus such as memory device, memory controller, etc. are provided. The data accessing method includes: utilizing a thermal control unit to start monitoring temperature at a predetermined intra-controller location of the memory controller; in response to at least one accessing request from a host device, controlling a transmission interface circuit to perform data transmission between the host device and the memory controller at an original communications speed, for accessing data in the NV memory; in response to the temperature being greater than a first temperature threshold, detecting an increment of the temperature between a first start time point and a first end time point; based on at least one first predetermined rule, determining a first communications speed according to the increment; and switching from the original communications speed to the first communications speed.

Abstract: An illustrative resolution is generated using a computer for a reported operational issue while using a computer system. A learning model is developed using the computer to facilitate resolving an operational error of a computer system. The learning model can include generating an error report, based on tracking user inputs into a computer system and a plurality of state transitions of the computer system, respectively, when the operational error is not resolved by the user inputs. An error screenshot for each operational step associated with the user inputs is generated. A state transition is cross referenced to a corresponding operational step, and to a best practice in a user's manual, respectively. A deviation of an operational step from a best practice in the user's manual, respectively, is generated. An error screenshot is marked illustratively to show the deviation from the best practice in comparison to the operational step.

Abstract: Methods, apparatus, systems and articles of manufacture for an example event processor are disclosed to retrieve an input event and an input event timestamp corresponding to the input event, generate an output event based on the input event and the input event timestamp, in response to determination that an input event threshold is exceeded within a threshold of time, and an anomaly detector to retrieve the output event, determine whether the output event indicates threat to functional safety of a system on a chip, and in response to determining the output event indicates threat to functional safety of the system on a chip, adapt a process for the system on a chip to preserve functional safety.