Abstract: This disclosure pertains to systems and methods that allow users to specifically detect and correct errors within workflows quickly, efficiently, and with minimal repercussions. Once the error is corrected, users have the option to continue the workflow from the point of failure or from a point prior to the point of failure, while also having the option to bypass completed actions and/or other actions in a workflow. The improved communication technology comprises functionality that pauses relevant workflows or processes as soon as the error is detected and while it is corrected. The disclosure also improves communication technology between the networks and servers of separate parties relevant and/or dependent on successful execution of other workflows or processes; allowing for effective understanding, implementation, and engagement of business processes across organizational boundaries.

Abstract: Some embodiments provide a novel method for collecting and analyzing attributes of data flows associated with machines executing on a plurality of host computers to detect anomalous behavior. In some embodiments, an anomalous behavior is detected for at least one particular flow associated with at least one machine executing on the host computer. In some embodiments, anomaly detection is based on the context data from the guest introspection agent and deep packet inspection. An identifier of the detected anomalous behavior is stored, in some embodiments. The stored attributes are provided, in some embodiments, to a server for further analysis.

Abstract: Systems, methods and apparatus of predictive management of failing portions of data storage media in a data storage device. For example, the data storage device can include: one or more storage media components; a controller configured to store data into, and retrieve data from, a portion of the one or more storage media components; and an artificial neural network configured to receive, as input from the controller, parameters relevant to health of the portion and generate an anomaly classification based on the input. The controller can be configured to adjust a data storage usage of the portion in response to the anomaly classification. For example, the controller can improve data reliability operations to reduce the likelihood of data loss and/or catastrophic failure.

Abstract: A method is provided, comprising: retrieving telemetry data from a first storage device; generating a failure risk score for the first storage device, the failure risk score being generated by using a failure inference engine, the failure inference engine being arranged to execute a predictive failure model for calculating the failure risk score; and outputting the failure risk score for use in servicing the first storage device.

Abstract: A device and method for traffic monitoring and a non-transitory tangible machine-readable medium for use in the device are disclosed. The device stores a probability model. The device records a packet quantity transmitted by a device under test in a monitoring time period. The device determines that the device under test is in an abnormal state when a probability of occurrence corresponded to by the packet quantity and the monitoring time period is lower than a probability threshold, wherein the probability of occurrence is determined by the probability model.

Abstract: Techniques for determining causality are described. An exemplary method includes receiving a request to determine a cause of an unhealthy system; receiving one or more of anomaly information and event information associated with the unhealthy system; evaluating the received one or more of the anomaly information and event information associated with the unhealthy system to determine there is a known causality between anomalies or events leading to the unhealthy system; and providing a causality indication for the known causality, the causality indication including an identification of a causality source and a causality target.

Abstract: One embodiment provides a system which facilitates data management. The system allocates a superblock of a storage device, wherein the superblock is in an open state. The system writes data to the superblock. The system monitors, by a controller of the storage device, an amount of time that the superblock remains in the open state. Responsive to detecting a failure associated with a flash translation layer (FTL) module, the system determines that the monitored amount of time exceeds a predetermined threshold, and seals, by the controller, the superblock by writing directly to a respective free page in the superblock while bypassing one or more data-processing modules.

Abstract: A method is provided for detecting copying of a machine learning model. A plurality of inputs is provided to a first machine learning model. The first machine learning model provides a plurality of output values. A sequence of bits of a master input is divided into a plurality of subsets of bits. The master input may be an image. Each subset of the plurality of subsets of bits corresponds to one of the plurality of output values. An ordered sequence of the inputs is generated based on the plurality of subsets of bits. The ordered sequence of the inputs is inputted to a second machine learning model. It is then determined if output values from the second machine learning model reproduces the predetermined master input. If the predetermined master input is reproduced, the second machine learning model is a copy of the first machine learning model.

Abstract: Systems and methods for predictive technology incident reduction are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for predictive technology incident reduction may include: (1) receiving a change record for a proposed change to a computer application or a computer network infrastructure; (2) analyzing the potential change for an adverse potential cross impact with another computer application or a computer system; (3) predicting a probability of failure and an impact of the proposed change using a model; (4) in response to a low predicted probability of failure, or a high predicted probability of failure with a low predicted impact: approving the proposed change; and implementing the proposed change; and (5) in response to a high predicted probability of failure and a high predicted impact, rejecting the proposed change.

Abstract: In an approach for generating hardware failure labels, a processor receives sensor data from a plurality of sensors associated with a hardware system. A processor calculates an adaptive stress factor, wherein the adaptive stress factor is a dynamic selection model. A processor calculates an adaptive stress time window, wherein the adaptive stress time window is a spatial distribution of the adaptive stress factor. A processor calculates a relative duty cycle, wherein the relative duty cycle is a first function of an internal state of the hardware system, a type of input to the hardware system, the adaptive stress factor, and the adaptive stress time window. A processor generates a failure label, wherein the failure label is calculated as a second function of the relative duty cycle and a design duty cycle.

Abstract: A computer-implemented method for identifying a stopping point of an active learning process includes calculating an error rate for a set of documents, determining whether minimum coding exists, checking an error rate, detecting that an uncertainty rate decreases, and displaying an indication that the stopping point has been reached. A computing system for determining a stopping point of an active learning process includes a processors and a memory storing instructions that, when executed, cause the computing system to calculate an error rate, determine that minimum coding exists, check an error rate, detect decreasing uncertainty rate, and display a stopping point indication. A non-transitory computer readable medium storing program instructions that when executed, cause a computer system to calculate an error rate, determine that minimum coding exists, check an error rate, detect decreasing uncertainty rate, and display a stopping point indication.

Abstract: A POS terminal has: a device driver; a POS application that communicates with a peripheral through the device driver; a filter driver that acquires communication information related to communication performed between the POS application and the peripheral through the device driver, the communication information including time information; and a failure detection application that acquires communication information from the filter driver over a predetermined period, defines a decision reference according to the communication information acquired over the predetermined period, and makes a decision about an abnormality related to the peripheral through a comparison between the defined decision reference and communication information acquired from the filter driver after the predetermined period.

Abstract: An apparatus for determining probability of error in identifying evidence includes a computer. The apparatus includes a non-transitory memory in communication with the computer in which is stored a software program, and prior and posterior probability distributions from a plurality of independent tests conducted on an item of evidence. For each test, the computer forms a factor distribution from the test's probability distributions using the software program stored in the non-transitory memory of the computer. The computer convolves the independent factor distributions to form a joint factor distribution using the software program. The computer calculates a tail probability from the joint factor distribution using the software program to determine a probability of error in identifying the evidence. The computer stores the probability of error in the non-transitory memory. A method. A computer program.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for resynchronizing data in a storage system. One of the methods includes receiving, by a first storage subsystem, a plurality of write requests corresponding to respective meta data blocks, wherein the first storage subsystem comprises a meta object; storing, by the first storage subsystem and for each write request, in each disk of the meta object, a version of the corresponding meta data block; determining that a particular disk of the meta object has failed; determining whether one or more valid versions of the meta data block are stored in respective other disks of the meta object; and in response to determining that one or more valid versions of the meta data block are stored in respective other disks of the meta object, resynchronizing the meta data block in the particular disk.

Abstract: A method for managing data includes identifying, by a compute acceleration device (CAD), a first chunk to be health-checked using storage metadata, generating a first chunk identifier using the first chunk, making a determination that the first chunk identifier does not match a second chunk identifier stored in the storage metadata, and in response to the determination: obtaining a plurality of chunks associated with the first chunk, regenerating the first chunk using the plurality of chunks to generate a new first chunk, storing the new first chunk in a data node, wherein the CAD is executing in the data node, updating the storage metadata based on storage of the new first chunk to obtain updated storage metadata, and sending a copy of the updated storage metadata to at least one other CAD in a second data node.

Abstract: Embodiments for a system and method of monitoring performance metrics of a computer network, by defining key performance indicators for the performance metrics of the computer network, collecting performance data for each of the key performance indicators, and providing one or more anomaly detection policies to define anomalous performance of the computer network using defined threshold values. An anomaly detection policy is applied to the collected performance data to detect abnormal performance and a notification is sent to a user upon each instance of the detected abnormal performance. The anomaly detection policy includes an algorithm applied to the assets, and one or more notification rules that dictate how the notification message is sent to the user.

Abstract: An accelerator manager monitors and logs performance of multiple accelerators, analyzes the logged performance, determines from the logged performance of a selected accelerator a desired programmable device for the selected accelerator, and specifies the desired programmable device to one or more accelerator developers. The accelerator manager can further analyze the logged performance of the accelerators, and generate from the analyzed logged performance an ordered list of test cases, ordered from fastest to slowest. A test case is selected, and when the estimated simulation time for the selected test case is less than the estimated synthesis time for the test case, the test case is simulated and run. When the estimated simulation time for the selected test case is greater than the estimated synthesis time for the text case, the selected test case is synthesized and run.

Abstract: One embodiment of the present invention can provide a system for identifying a root cause of an anomaly event in operation of one or more machines is provided. During operation, the system can obtain sensor data from a set of sensors associated with the one or more machines, convert the sensor data into a set of sensor states, build an optimal DAG based on the set of sensor states to model causal dependency; determining, by using the DAG, a probability of an anomaly state of a target sensor given a state of a direct neighbor sensor, and determining a root cause of the anomaly event associated with the target sensor by back-tracking the anomaly state in the DAG.

Abstract: A computer provides a secure, virtual personalized network (SVPN) for a first user with master privileges and at least a second user with guest privileges in the SVPN. Notably, the computer may execute a virtual machine that provides a container for the SVPN of the first user, and the first electronic device associated with the first user and a second electronic device associated with the second user may execute instances of an application that facilitates secure communication in the SVPN. Moreover, the first electronic device may store a set of first encryption keys and the second electronic device may store a set of second encryption keys, which allow the first electronic device and the second electronic device to securely communicate with each other via the SVPN. Note that the computer may not be able to access the set of first encryption keys or the set of second encryption keys.

Abstract: At least some aspects of the disclosure provide for a system. In at least some examples, the system includes a microcontroller, an energy storage element, a monitoring circuit, and a communication bridge. The monitoring circuit is coupled to the energy storage element and configured to determine whether a fault associated with the energy storage element is present, generate an indication of the fault when the fault with the energy storage element is present, and transmit the indication of the fault. The communication bridge is configured to receive the indication of the fault and assert a fault signal and transmit the fault signal to the microcontroller.