Abstract: Methods, systems, and devices for analyzing vibration data to determine and mitigate an occurrence of an anomaly at an industrial asset. A device may obtain and preprocess vibration data, which may include grouping the vibration data according to respective time intervals, and the device may filter the grouped data to obtain the most recent measurement readings. The device may use an anomaly detection algorithm to detect outliers in the datasets for each group of vibration data and compare an amplitude of the outliers to a threshold for the respective group. The device may determine that the vibration data includes an anomaly based on quantities of outliers in first and second subsets of the vibration data that are within a detection window. The device may generate a report including an indication of the anomaly and transmit the report to an operator or engineer for the facility.

Abstract: A vehicle includes a data communication network, a serial data bus, and a plurality of electronic nodes in signal communication with the serial data bus. The vehicle further includes a node identification system configured to store a several different diagnostic tests, along with expected operating data corresponding to a given diagnostic test. The node identification system sorts the plurality of nodes into individual node groups in response to performing one or more diagnostic tests among the different available diagnostic tests.

Abstract: Aspects of the subject disclosure may include, for example, a method in which a processing system collects information associated with trouble tickets each including a problem abstract and a log text. The method includes analyzing the log text to obtain a problem resolution for that ticket; defining ticket clusters according to the problem abstracts, and labeling the clusters. The processing system creates a library of the labeled clusters, each entry including a cluster label, a problem abstract for that cluster, and a resolution summary for that problem abstract, indicating a mapping of the problem abstract to the resolution summary for that cluster. The method includes training, based on the mapping, machine-learning applications for a predicted resolution summary for each cluster and for classifying a new ticket. The method includes assigning the new ticket to a cluster according to the classifying. Other embodiments are disclosed.

Abstract: An image of an object is received into an augmented reality (AR) device. Information is received for identifying the object or a component associated with the object. A database is accessed to retrieve data relating to the object or the component associated with the object, and a heat map is created as a function of the data relating to the object or the component associated with the object. The heat map is displayed on a computer display device.

Abstract: Disclosed here is a system to automatically predict and reduce hardware related outages. The system can obtain a performance indicator associated with a wireless telecommunication network including a system performance indicator or an application log, along with a machine learning model trained to predict and resolve a hardware error based on the performance indicator. The machine learning model can detect an anomaly associated with the performance indicator by detecting an infrequent occurrence in the performance indicator. The machine learning model can determine whether the anomaly is similar to a prior anomaly indicating a prior hardware error. Upon determining that the anomaly is similar to the prior hardware error, the machine learning model can predict an occurrence of the hardware error.

Abstract: Upon receiving a corrupted message in 5G or 6G, a receiver generally rejects the message or ignores it entirely, because determining which message elements are faulted is difficult and complex. AI-based procedures are provided for localizing faults in specific message elements, and for determining the corrected values when possible. AI inputs may include the amplitude or phase modulation quality of each message element, the measured SNR of each message element, the modulation quality of a preceding demodulation reference, and current backgrounds, among other factors. After training (adjusting according to measured network data), the AI model may then determine the most likely faulted message elements, and may also direct the search for the most likely corrected values. By recovering the original corrected message without an unnecessary retransmission, the system can save time, reduce transmission energy, and avoid generating backgrounds. Many additional aspects are disclosed.

Abstract: The example embodiments are directed to a system and method that applies knowledge developed by a subject matter expert with respect to a physical asset. In one example, the method includes receiving knowledge and issue resolution information developed of subject matter experts in association with historical issues for an asset, generating a plurality of data clusters for the asset based on the knowledge, wherein each historical issue of the asset is mapped to a cluster and includes a plurality of resolutions for the issue, receiving a new issue and new issue information, and processing the new issue by extracting keywords from the new issue information and assigning the new issue to a data cluster from among the plurality of data clusters based on the extracted keywords, and outputting, to a display, a cause of the new issue and potential solutions for the new issue.

Abstract: Some embodiments of the invention provide a method for defining code-based policies. The method generates a policy-builder first view of a policy for display in a graphical user interface (GUI) by processing a syntax tree that is generated from a code second view of the policy. The method receives, through the policy-builder first view, a modification to a portion of the policy. To reflect the modification, the method updates a portion of the syntax tree that corresponds to the portion of the policy that is affected by the modification. Based on the updating of the syntax tree, the method updates the code second view by modifying a portion of the code second view that corresponds to the updated portion of the syntax tree.

Abstract: Techniques described herein relate to a method for performing testing operations for information handling systems.

Abstract: Techniques perform storage management. In accordance with such techniques, in response to determining that there is an abnormal process in a process group of an application, and a first size of storage space occupied by the abnormal process exceeds a first threshold, an abnormal process is terminated. A second size of storage space occupied by other processes than the abnormal process in the process group after the abnormal process is terminated is determined. In response to the second size exceeding a second threshold, at least one process in the process group to reduce the second size is terminated, where the at least one process is different from the abnormal process.

Abstract: An automated method for determining a complexity of a task. The method includes extracting data from the plurality of historical support tickets to generate training data. The method trains a complexity model to predict a complexity value of a task associated with a support ticket using the training data. The method predicts, using the complexity model, the complexity value of a new task associated with a new support ticket.

Abstract: A model generation apparatus (2000) acquires component failure data in which a usage status is associated with a failure record of a component. The model generation apparatus (2000) generates, for each of a plurality of component groups, a prediction model for predicting the number of failures of each component included in the component group by using the component failure data relating to the component belonging to the component group. The prediction model computes a prediction value of the total number of failures of the components belonging to a corresponding component group from the usage status, and computes a prediction value of the number of failures of each component belonging to the component group from the computed prediction value of the total number of failures.

Abstract: An embodiment includes a method for use in managing a system comprising one or more computers, each computer comprising at least one hardware processor coupled to at least one memory. The method comprises a computer-implemented manager: detecting that the system is in an unhealthy state; determining a set of one or more possible actions to remedy the unhealthy state of the system; selecting at least one action of the set of one or more possible actions; and constructing a service request implementing the selected at least one action; wherein at least one of the detecting, determining, selecting, and constructing is based at least in part on applying a reinforcement learning algorithm.

Abstract: An anomaly location identification device includes a determination unit configured to determine presence or absence of an anomaly by inputting part or all of information items output from a plurality of devices into an anomaly detection algorithm; a calculation unit configured to calculate, in response to a determination made by the determination unit that an anomaly is present, with respect to one of the information items, an index indicating a degree of contribution to the anomaly; and an identification unit configured to perform calculation by an analysis algorithm using a causal model receiving the index as input, to identify an anomalous device, to improve the precision and calculation speed related to identification of an anomaly location.

Abstract: A system and method for connected vehicle cybersecurity. The method includes creating a normal behavior model based on a first set of data, the first set of data including at least one first event with respect to at least one connected vehicle, wherein the first set of data is collected from a plurality of data sources; detecting an anomaly based on the normal behavior model and a second set of data, the second set of data including a second event with respect to the at least one connected vehicle, wherein each of the first set of data and the second set of data includes vehicle data related to operation of the at least one connected vehicle, wherein each event represents a communication with the at least one connected vehicle; determining, based on the detected anomaly, at least one mitigation action; and causing implementation of the at least one mitigation action.

Abstract: Disclosed are various embodiments for managing the state of client devices using device-driven management workflows. A computing device can be evaluated to determine the current state of the computing device. Then, the current state of the computing device is compared to an expected state of the computing device. The expected state of the computing device may be based at least in part on a result of execution of at least one device-driven management workflow by the computing device. In response to a determination that the current state of the computing device fails to match the expected state of the computing device, the device-driven management workflow can be executed to resolve the discrepancy between the expected state and the current state.

Abstract: An operation data collection system comprises circuitry configured to: acquire, from an industrial device, configuration information on a connection destination device connected to the industrial device; execute a setting for collecting operation data on the connection destination device based on the configuration information; and collect the operation data from the industrial device based on the setting.

Abstract: A system and method for connected vehicle cybersecurity. The method includes creating a normal behavior model based on a first set of data, the first set of data including at least one first event with respect to at least one connected vehicle, wherein the first set of data is collected from a plurality of data sources; detecting an anomaly based on the normal behavior model and a second set of data, the second set of data including a second event with respect to the at least one connected vehicle, wherein each of the first set of data and the second set of data includes vehicle data related to operation of the at least one connected vehicle, wherein each event represents a communication with the at least one connected vehicle; determining, based on the detected anomaly, at least one mitigation action; and causing implementation of the at least one mitigation action.

Abstract: In an exemplary embodiment, computer circuitry determines term characterization values for terms in computer performance logs and generates vectors that indicate the term characterization values. The computer circuitry determines vector similarity scores for these vectors. The computer circuitry aggregates the computer performance logs into aggregated logs based on the vector similarity scores. The computer circuitry selects rare logs from these aggregated logs and obtains computer performance anomaly labels for the rare logs. The computer circuitry matches new computer performance logs with the rare logs to detect the labeled computer performance anomalies.

Abstract: A computer-implemented method of generating functional safety data for a design of an electronic component includes receiving attribute data for elements in an electronic component. The attribute data include element data for the elements, wherein element data for a given element include an identity of the given element and a failure rate characteristic for the given element. The attribute data include functionality data for element functionalities, wherein functionality data for a given element functionality include an identity of a use case, an identity of an element, an identity of a fault characterization for providing information about one or more fault models and a configurable data for controlling selection and use of the given functionality data when generating the functional safety data. The method also includes generating the functional safety data using the attribute data and storing a report including the functional safety data.

Abstract: A device for determining whether a defect of a component is determinable based on results of predefined diagnostic options, including: storing, in a matrix, parameters which, for each combination of one component defect and one diagnosis result, provide a measure of whether the respective defect is possible for the respective diagnosis result, the matrix rows being assigned to one of the diagnosis results, the matrix columns being assigned to one of the defects; selecting at least two matrix rows which, for a diagnosis result consistent with the defect to be identified, include a parameter greater than a predefined first limiting value; calculating a result vector by the element-wise multiplication of the selected matrix rows; and determining whether the result vector includes at least one element not smaller than a predefined second limiting value, the result vector elements, which are smaller, not being greater than a predefined third limiting value.

Abstract: System and techniques for computer vision assisted item search are described herein. A composite image, including visual data and depth data, may be obtained. The composite image may be filtered to isolate a clothing article represented in the composite image. A classifier may be applied to the depth data to produce a set of clothing attributes for the clothing article. The clothing attributes may then be provided to a remote device.

Abstract: Artificial intelligence procedures are disclosed for localizing faults in corrupted messages in 5G and 6G, and for correcting those faults based on measured parameters such as backgrounds and message signals. Message faults can be caused by noise or interference from a variety of sources with a wide range of properties. An AI model with multiple adjustable variables may be “trained” using a large number of message events, including faulted messages, to determine which message elements are likely faulted, based on input parameters such as modulation quality, SNR, and other signal properties. The receiving entity can then attempt a grid search to correct the faulted message elements, or request a retransmission. For field use by base stations and user devices, an algorithm may be developed based on the AI model, and configured to predict which message elements are likely faulted.

Abstract: A process for analyzing an incident includes setting up an alert for a high error rate on a particular endpoint. Once the alert is triggered, a set of traces for transactions exhibiting errors on the offending endpoint is queried. All traces for other services/operations that include errors on the offending endpoint are also enumerated. A set of baseline transactions that involve the offending endpoint, but do not result in error may be utilized to determine whether the errors are always present, or are distinctive for certain offending transactions. All traces are ranked based on a statistic. Once the traces have been ranked, they may be traced down to a deepest/most terminal error. A set of transactions that correlate to the terminal error may also be analyzed to determine infrastructure causes.

Abstract: A probabilistic data structure may be queried to test whether text of a received log message is present in the probabilistic data structure. The representative log messages may be included in respective existing clusters are included in the probabilistic data structure. Based on whether the text is present, whether to create a new cluster for the received log message may be determined.

Abstract: Techniques for monitoring Voice-over-IP (VoIP) network services over the Internet are disclosed. In some embodiments, a system, process, and/or computer program product for monitoring and/or troubleshooting VoIP network services over the Internet includes performing VoIP call initiation testing using a source agent and a target agent; performing synthetic VoIP call quality testing using the source agent and the target agent over the Internet; and generating a report based on the VoIP call initiation testing and the synthetic VoIP call quality testing over the Internet.

Abstract: Examples described herein generally relate to receiving a query context for service events occurring on one or more networks, determining, based on the query context, a set of service events occurring on the one or more networks, querying multiple layers of a multiple-layer relational graph to determine one or more other service events having a defined relationship with the set of service events at one or more of the multiple layers, where the multiple layers include a configuration layer, an observation layer, and learned layer, defining relationships between services or service events, and indicating, via a user interface and in response to the query context, the one or more other service events.

Abstract: An evaluation device of an embodiment includes a storage, a data generator, a class definer, a characteristic data divider, and an evaluator. The data generator is configured to generate a set of characteristic data from both a set of first data and at least a set of second data, the at least set of second data being associated in time information with the set of first data, the set of characteristic data representing a plurality of characteristics. The characteristic data divider is configured to divide the plurality of sets of characteristic data into a plurality of groups on the basis of the plurality of classes defined by the class definer and condition of operations included in the set of first data. The evaluator is configured to evaluate a operating state using a first model defined for each of the plurality of groups divided by the characteristic data divider.

Abstract: Techniques for monitoring Voice-over-IP (VoIP) network services over the Internet are disclosed. In some embodiments, a system, process, and/or computer program product for monitoring and/or troubleshooting VoIP network services over the Internet includes performing VoIP call initiation testing using a source agent and a target agent; performing synthetic VoIP call quality testing using the source agent and the target agent over the Internet; and generating a report based on the VoIP call initiation testing and the synthetic VoIP call quality testing over the Internet.

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 learning method detects anomalies and is implemented on a microcontroller including at least one memory, the microcontroller being configured to receive data sets coming from at least one sensor, the memory being configured to store a maximum number of categories, a category including at least a signature and an occurrence.

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions. The instructions may cause the processor to receive a call request for a cloud-based service and retrieve a first policy corresponding to the cloud-based service, the first policy identifying a policy reroute. The instructions may also cause the processor to determine which second policy of a plurality of second policies that corresponds to the policy reroute and retrieve the second policy that the corresponds to the policy reroute. The instructions may further cause the processor to apply the second policy on the cloud-based service and respond to the call request following the application of the second policy.

Abstract: A method for providing computer-related support to an end user is described. The method includes receiving, from a mobile device, an image of a user-readable message that is displayed by a computing device. The method also includes identifying one or more solutions using text extracted from the image of the user-readable message. The method further includes sending the one or more solutions to the mobile device for display on the mobile device.

Abstract: Systems and methods are described herein for logging system events within an electronic machine using an event log structured as a collection of tree-like cause and effect graphs. An event to be logged may be received. A new event node may be created within the event log for the received event. One or more existing event nodes within the event log may be identified as having possibly caused the received event. One or more causal links may be created within the event log between the new event node and the one or more identified existing event nodes. The new event node may be stored as an unattached root node in response to not identifying an existing event node that may have caused the received event.

Abstract: A computer-implemented method for detecting compromised automated teller machines is provided. The method includes receiving activity data from an ATM, activity data including user input data and machine data, wherein user input data includes instructions input by a user at the ATM during a transaction, and wherein machine data is associated with processes performed by the ATM; storing operating parameter rules in the memory, the operating parameter rules configured to indicate whether the received activity data is within a normal activity range for the ATM; applying the received activity data to the operating parameter rules; generating an activity score for the ATM based on the applying, wherein the activity score is configured to indicate a likelihood that the ATM is compromised; and initiating a response based on the activity score.

Abstract: An intelligent target object detection and alerting platform may be provided. The platform may receive a content stream from a content source. A target object may be designated for detection within the content stream. A target object profile associated with the designated target object may be retrieved from a database of learned target object profiles. The learned target object profiles may be associated with target objects that have been trained for detection. At least one frame associated with the content stream may be analyzed to detect the designated target object. The analysis may comprise employing a neural net, for example, to detect each target object within each frame. A parameter for communicating target object detection data may be specified. In turn, when the parameter is met, the detection data may be communicated.

Abstract: An alert management system for a configuration management database (CMDB) platform includes a client instance configured to: apply a first filter of an alert rule to CMDB alerts to select a first alert based on context of the first alert and apply a second filter of the alert rule to the CMDB alerts to select a second alert based on context of the second alert, wherein the context of the first and second alerts includes details regarding the first and second alerts and associated configuration items (CIs). The client instance is also configured to: evaluate at least one condition of the alert rule using the context of the first and/or second alert; and in response to evaluating the at least one condition of the alert rule to be true, performing at least one action of the alert rule using the context of the first and/or second alert.

Abstract: A capacitive touch sensor is disclosed for use with input signal. The capacitive touch sensor includes a number n of input/output lines. Each of the number n of input/output lines is electrically disconnected from every other of the number n of input/output lines. Each of the number n of input/output lines is arranged to cross every other of the number n of input/output lines. Each of a number ? of positions includes one of the number n of input/output lines crossing another of the number n of input/output lines.

Abstract: A method and detector for detecting anomalies among media event sequences are disclosed. One method includes obtaining a first profile of media event data over a first time period, where the first profile is a first distribution of different media event sequences. Each sequence is associated with a number of occurrences of the sequence and the first profile having a first profile vector. The method further includes obtaining a second profile of media event data over a second time period less than the first time period. The second profile is a second distribution of the different media sequences and the second profile having a second profile vector. The method also includes comparing the first profile vector and the second profile vector, and determining one of a presence and absence of at least one anomaly in the second profile vector of media event data based on the comparison.

Abstract: The present disclosure relates generally to facilitating routing of communications across external systems. More specifically, techniques are provided to dynamically route issue tracking tickets to disparate endpoints based on the content of the ticket.

Abstract: An infrastructure backup system includes an infrastructure manager system coupled to infrastructure devices via a network. A first infrastructure device includes a management controller database storing component dependency information identifying dependenc(ies) between a first component in the first infrastructure device and a second component in a second infrastructure device. A management controller device in the first infrastructure device receives a predicted failure for the first component from the infrastructure manager system and performs first backup operations to generate first component backup data based on the identification of the predicted failure for the first component. Based on the component dependency information that identifies the dependency between the first component and the second component, the management controller device also performs second backup operations to generate second component backup data.

Abstract: A processor-readable storage device storing instructions that cause a processor to perform operations including, subsequent to determining, at a first device based on a first file attribute associated with a file, that a classification for the file is unavailable at the first device, sending the first file attribute from the first device to a second device to determine whether the classification for the file is available at the second device. The operations include receiving a notification at the first device from the second device that the classification for the file is unavailable at the second device. The operations include, determining the classification for the file by performing, at the first device, an analysis of a second file attribute based on a trained file classification model. The operations include sending the classification from the first device to the second device and to a third device.

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: A computer-implemented method performed in a computerized system incorporating a central processing unit, a localization signal receiver, a plurality of sensors, separate and distinct from the localization signal receiver, and a memory, the computer-implemented method involving: using the central processing unit to initialize a plurality of particles based on an information on a map graph; using the central processing unit to repeatedly execute a particle filter loop, wherein the particle filter loop includes: using the central processing unit to perform a motion update of the plurality of particles; using the central processing unit to perform a measurement update of the plurality of particles; and using the central processing unit to perform a resampling of the plurality of particles based on particle importance weights and the map graph information. The location of the computerized system is subsequently determined based on the plurality of particles.

Abstract: For incident management, parsing, responsive to an incident ticket being opened relative to a first application. The parsing identifying a set of incident data. Identifying, using a dependency graph, a set of applications, wherein each application in the set of applications is dependent on the first application through at least one dependency relationship. Notifying, responsive to the incident ticket, a subset of a set of users of a second application about the incident ticket related to the first application, the second application being a member of the set of applications, the subset of the set of users of the second application performing a type of transaction with the second application wherein the type of transaction is indicated in a dependency relationship between the first application and the second application. Preventing a user in the subset of users from creating a second incident ticket.

Abstract: Aspects of the present disclosure provide a method and apparatus for maintenance recommendations. Embodiments include receiving data related to a plurality of fault codes. Embodiments include determining, for each respective fault code of the plurality of fault codes, a unique subset of parameters for the respective fault code based on frequencies of the parameters within the plurality of fault codes in the data. Embodiments include receiving a plurality of fault records and a plurality of maintenance records. Embodiments include, for each respective maintenance record of the plurality of maintenance record, identifying one or more candidate fault records of the plurality of fault records and correlating a subset of the one or more candidate fault records with the respective maintenance record based on the unique subset of the parameters for the respective fault code corresponding to each respective candidate fault record of the one or more candidate fault records.

Abstract: In one embodiment, an issue analysis service determines that an issue exists with a device in a network. The service searches a decision tree for a solution to the issue, wherein branch nodes of the decision tree comprise diagnostic checks. The service clusters, based on a determination that a solution to the issue does not exist in the decision tree, telemetry for the device with telemetry for one or more other devices that also experienced the issue. The service uses a neural network to identify a difference between the clustered telemetry and telemetry from one or more devices for which the issue was resolved. The service adds a leaf node to the decision tree with the identified difference as a solution to the issue.

Abstract: A method including receiving events from different data sources for a service automatically executing in an enterprise system. A first event is enriched by providing the first event with first metadata that associates the first event with a first application used by the service. The first event is assigned to a time slice associated with the first application. A second event is enriched in a similar manner. A correlation graph of nodes and edges is built using the enriched events, with nodes representing the events and edges indicating relationships between the edges. A third event indicating a fault in the first application associated with the first node is received. The source of the error for the third event is identified using the second updated correlation graph and the time slice. The source of error is then mitigated.

Abstract: A system may include (i) a database disposed within a computational instance of a remote network management platform that manages a managed network and (ii) server device(s) associated with the computational instance. The database is configured to store records related to issues with configuration items of the managed network, each record assigned to one of a plurality of states including: a first state for newly-opened records, a second state for records undergoing assessment, a third state for records undergoing root cause analysis, a fourth state for records with a fix in progress, a fifth state for resolved records, and a sixth state for closed records. Additionally, the server device(s) are configured to generate and provide graphical user interface panes to a client device associated with the managed network, each of the graphical user interface panes corresponding to one of the plurality of states for a particular record.

Abstract: A system retrieves cluster data for a cluster including one or more nodes and one or more pods. The system displays, on a visual interface, a cluster visualization based on the retrieved cluster data. The cluster visualization includes, for each of the one or more nodes, a node spoke graphical element. Each node spoke graphical element includes a pod region indicating pods of the node and a health region indicating one or more health metrics of the node. A particular pod of the one or more pods in the cluster data is represented in the cluster visualization by a pod graphical element at the pod region of a node graphical element of a node corresponding to the particular pod. A graphical feature of the pod graphical element is indicative of at least one of one or more health metrics of the particular pod and one or more health metrics of a pod element within the particular pod.