Abstract: A method for detecting and/or localizing keyboard accessibility failures (KAFs) in a web page is disclosed. A document object model of a web page is read. A keyboard navigation flow model is generated from the document object model of the web page based on interactions of a user with the web page. The model includes states representing user interfaces displayed by the web page, nodes representing keyboard inputs in the states, and edges representing transitions that occur in the web page between the nodes. KAFs are detected based on an analysis of the keyboard navigation flow model. A report of the detected one or more KAFs on the web page is produced. The KAFs include unintuitive navigation failures, reflow related failures, and dialog related failures.

Abstract: In one embodiment, an issue analysis service obtains telemetry data from a plurality of devices in a network across a plurality of time intervals. The service detects a failure event in which a device in the network is in a failure state. The service clusters the telemetry data obtained prior to the failure event into rounds according to time intervals in which the telemetry data was collected. Each round corresponds to a particular time interval. The service applies a machine learning-based classifier to each one of the rounds of clustered telemetry data to identify one or more common traits appearing in the telemetry data for each round. The service generates a trait change report indicating a change in the one or more common traits appearing in the telemetry data across the rounds leading up to the failure event.

Abstract: A method for identifying gestures in an area of real space is provided. The method including using a plurality of sensors to produce respective sequences of frames of corresponding fields of view in the area of real space, detecting subjects in the area of real space, using a sequence of frames produced by a corresponding sensor in the plurality of sensors in a first inference engine to identify inventory items carried by the detected subjects in the sequence of frames, using outputs of the first inference engine over a period of time in a second inference engine to identify gestures of the detected subjects, and storing the identified gestures in a database.

Abstract: A deep learning fault diagnosis method includes the following steps: a fault diagnosis data set X is processed based on sliding window processing, to obtain a picture-like sample data set {tilde over (X)}, and obtain an attention matrix A of the picture-like sample data set {tilde over (X)}; and a 2D-CNN model is constructed to process the picture-like sample data set {tilde over (X)} to obtain a corresponding feature map F, and in the meantime, the feature map F is processed based on channel-oriented average pooling and channel-oriented maximum pooling to obtain an output P1 of the average pooling and an output P2 of the maximum pooling, and a weight matrix W is obtained based on the attention matrix A, the output P1 of the average pooling, and the output P2 of the maximum pooling, so that an output of the model is a feature map {tilde over (F)} based on an attention mechanism, where {tilde over (F)}=WF.

Abstract: An example computer-implemented method is for managing a compute system with FRUs. The computer-implemented method includes receiving status information corresponding to the FRUs. The status information is collected from the FRUs in response to the compute system being powered on. The computer-implemented method also includes, in response to a predetermined condition being met, sending for each of the FRUs, one or more instructions to erase a dynamic portion of electrically erasable programmable read-only memory (EEPROM) located at a respective FRU; and sending for each of the FRUs, one or more instructions to write a portion of the status information corresponding to the respective FRU in the dynamic portion of the EEPROM at the respective FRU. Moreover, the EEPROM at each FRU includes the dynamic portion and a read-only portion.

Abstract: An input/output module (IOM) for use within a network storage system mounted within a rack enclosure. The IOM includes a switching component configured to provide top-of-rack (TOR) switching for data to be routed from input connectors to data storage devices within the rack enclosure. The IOM also includes a protocol interface configured to convert a protocol of the data from an input data protocol (e.g., Ethernet, Fibre Channel or InfiniBand™) to a protocol for use with the storage devices (e.g., nonvolatile memory express (NVMe) and Peripheral Component Interconnect Express (PCIe)). Among other features, the IOM allows switching to be dis-aggregated from a TOR switch and distributed throughout the data network of the rack.

Abstract: Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and/or software components. To manage the operation of the data processing system, a data processing system manager may obtain logs for components of the data processing system. The logs may record information that describe and reflect the historical and/or current operation of these components. Inference models may be implemented to predict likely future component failures (e.g., a predicted mean time to first failure (MTFF) of the components) using information recorded in the logs and component specification information from component vendors. The likely future component failures may be analyzed to reduce the likelihood of the data processing system becoming impaired.

Abstract: A system for predicting performance of building equipment includes one or more processors and memory storing instructions. When executed by the one or more processors, the instructions cause the one or more processors to perform operations. The operations include obtaining sensor data generated by one or more sensors while operating the building equipment to affect one or more environmental conditions of a building, generating a predicted performance of the building equipment using a machine learning model if the machine learning model comprises prior data similar to the sensor data, and requesting feedback from an expert related to the predicted performance of the building equipment if the sensor data are different from the prior data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, a using a bot management framework to provide bot management solutions for software bots deployed in a robotic process automation (RPA) system. In one aspect, a method includes the actions of receiving, from a RPA system, information regarding a software bot deployed within the RPA system to perform an assigned task, the information including incident data from an anomaly in the RPA system while the software bot performed the assigned task; mapping the received incident data according to a common data model; determining a resolution for the anomaly based an analysis through a trained artificial intelligence model of the mapped incident data; and executing the determined resolution.

Abstract: A hardware failure prediction and avoidance system executing on a unified endpoint management platform information handling system comprising a network interface device (NID) to receive failed operational telemetries for client devices including power and software analytics, and event viewer error logs, and an error associated with a hardware type, a processor to execute a classification supervised learning algorithm on the failed operational telemetries to determine that a hardware or software configuration will likely co-occur with a future error of the hardware type, identify the hardware or software configuration as problematic, the processor to identify the problematic system configuration in a current operational telemetry by correlating the current operational telemetry with a portion of the failed operational telemetries, and the NID to transmit a recommendation to the first client device to adjust the problematic system configuration in order to avoid occurrence of the error at the first client devic

Abstract: A method in an illustrative embodiment includes determining, by a first deep learning model on a storage device, whether a fault is caused by an environmental factor. The method further comprises: determining, by a second deep learning model on the storage device in response to determining that the fault is caused by the environmental factor, whether the fault can be solved locally in the storage device. The method further comprises: processing the fault according to a knowledge base in response to determining that the fault can be solved locally in the storage device. The method further comprises: sending the fault to a third deep learning model on a cloud device in response to determining that the fault cannot be solved locally in the storage device, the first deep learning model and the second deep learning model being obtained by model distillation of the third deep learning model.

Abstract: A method includes processing event data to detect a status of a network function. The event data is processed based on two or more conditions defined by a correlation policy. The correlation policy includes a non-deterministic finite automata tree (NFAT) structure correlation policy having a policy type. The method also includes determining a first value of a first condition of the two or more conditions. The method further includes determining a second value of a second condition of the two or more conditions. The method additionally includes determining the policy type of the NFAT structure correlation policy. The method also includes determining whether the first value is greater than a first preset value indicative of whether the first condition is satisfied. The method further includes determining whether the second value is greater than a second preset value indicative of whether the second condition is satisfied.

Abstract: A double-blind comparison is performed between prognostic-surveillance systems, which are located on a local system and a remote system. During operation, the local system inserts random faults into a dataset to produce a locally seeded dataset, wherein the random faults are inserted into random signals at random times with variable fault signatures. Next, the local system exchanges the locally seeded dataset with a remote system, and in return receives a remotely seeded dataset, which was produced by the remote system by inserting different random faults into the same dataset. Next, the local system uses a local prognostic-surveillance system to analyze the remotely seeded dataset to produce locally detected faults. Finally, the local system determines a performance of the local prognostic-surveillance system by comparing the locally detected faults against actual faults in the remotely seeded fault information. The remote system similarly determines a performance of a remote prognostic-surveillance system.

Abstract: A replay tool configured in a learning mode is used to replay a recorded interaction workflow to obtain respective learning-mode test data responsive to a request from a client device to a server. A baseline response template is obtained from the respective learning-mode test data. A baseline response time of the request is also obtained from the respective learning-mode test data. The recorded interaction workflow is replayed in a testing mode to obtain testing-mode test data. Responsive to determining that a response body included in the testing-mode test data is inconsistent with the baseline response template, a first anomaly message is output. Responsive to determining that the response time included in the testing-mode test data is not within a tolerance of the baseline response time, a second anomaly message is output.

Abstract: A rotational speed measurement system includes a video device configured to obtain a video of a pulley of a conveyor belt system. The rotational speed measurement system also includes a strobe configured to generate a stroboscopic effect on the obtained video. The rotational speed measurement system also includes a memory device. The rotational speed measurement system also includes a network interface. The rotational speed measurement system also includes one or more processors configured to: generate a stroboscopic window using the strobe and the obtained video, synchronize the strobe with the obtained video using the stroboscopic window, determine a measured rotational speed for the pulley based on the synchronized strobe, and determine belt slippage based on the measured rotational speed.

Abstract: Network throughput can be increased and the message failure rate can be reduced in 5G and 6G communications by use of AI-based fault mitigation: that ism detection, localization, and correction of faulted message elements in real-time. A receiver provides the demodulated message, along with amplitude and phase measurements of each message element, directly to a properly trained artificial intelligence model. The model determines the most-likely faulted message elements, and in some cases can indicate the most probable correct value of the faulted message elements. The AI model can also determine the fault probability of each message element. The expected message content (such as value ranges and predetermined format) can also be provided to the AI model, for further corruption sensitivity. By correcting faulted messages in less time than required for a retransmission, the system can save time, reduce backgrounds, and greatly reduce dropped messages.

Abstract: A system having a plurality of marking and/or coding devices and a computing system coupled to the devices and having processor configured to: receive sensor data associated with each device; and store printer metadata and batch job metadata in a backup file for each device. The processor is configured to classify a fault condition of a respective one device based on one or more of received sensor data, device self-test data, current operational data and historical device condition data. The processor is configured to determine autonomously a repair process recommendation in response to the classified fault condition and based on an estimated time to repair (ETR) the device using a self-repair recovery process relative to a line recovery time (LRT) threshold. The processor is configured to recommend as the repair process recommendation the self-repair recovery process in response to the ETR being less than or equal to the LRT threshold.

Abstract: Systems and methods for network device management are described. An indication of a failed network connection between a first user device and a second network device can be received. Using data from the first network device, the first user device, at least one second network device, and/or at least one second user device, one or more characteristics of the first network device and/or the at least one second network device can be determined. Modification of the determined one or more characteristics can be caused.

Abstract: The disclosed technology relates identifying causes of an observed outcome. A system is configured to receive an indication of a user experience problem, wherein the user experience problem is associated with observed operations data including an observed outcome. The system generates, based on the observed operations data, a predicted outcome according to a model, determines that the observed outcome is within range of the predicted outcome, and identifies a set of candidate causes of the user experience problem when the observed outcome is within range of the predicted outcome.

Abstract: A failure prediction method of predicting a failure of a component of a robot including a robot arm having the component and a detection section that detects information on vibration characteristics when the robot arm moves, includes generating a failure prediction model for prediction of the failure of the component by machine learning based on the information on vibration characteristics, and predicting the failure of the component based on an estimated value of failure prediction output by the generated failure prediction model when the information on vibration characteristics is input to the generated failure prediction model.

Abstract: Systems and methods are disclosed herein for monitoring, detecting, and mitigating hardware and software failures. An error detection module monitors the execution of software processes and detects failures of the monitored processes. The error detection module may monitor reboot events and correlate reboot events with failures of the monitored software processes. If a monitored process fails, the error detection module may log the failure and its cause. If the same process has failed numerous times, causing the user device to experience a reboot loop, remedial action may be taken based on the cause of the failure.

Abstract: A system can include instructions for generating a machine-learning model, including accessing, from a memory device, training data including two or more first commands. The first commands can be manually generated in response to a utilization exceeding a preset threshold and two or more first conditions, including a date and time the utilization exceeded the preset threshold. The training data can also include second commands generated by the machine-learning model in response to the utilization exceeding the preset threshold. The system may train the machine-learning model using a machine-learning algorithm using the training data and the second commands. The system may receive an indication of a utilization exceeding a preset threshold and determine, using the machine-learning model, an action responsive to the utilization. A command can be outputted to execute the action responsive to the utilization.

Abstract: Various embodiments of the teachings herein include a fault processing method comprising: receiving two historical faults similar to a target fault; searching keywords in a description of the target fault and each historical fault, wherein the keywords are classified into N grades, and for each system component in a grade, the grade comprises at least one keyword for describing the component, wherein N is an integer no less than 2; for each of the N grades, counting a quantity of identical system components represented by the keywords in the text description of each historical fault and the target fault; and comparing a degree of similarity of each historical fault to the target fault according to the quantity of identical system components counted in each grade of the N different grades, wherein a historical fault relating to a larger number of high-grade identical system components has a higher degree of similarity to the target fault.

Abstract: A video playback method executed in a computer system includes measuring a bitrate in transport stream (TS) file units, and selecting the image quality of the next TS file in consideration of the bitrate and the amount of buffer that has already been loaded.

Abstract: A method for detecting and/or localizing keyboard accessibility failures (KAFs) in a web page is disclosed. A web page is rendered in a browser on a display portion of a computing system. A keyboard navigation flow graph based on based on keyboard-based navigation of a user with the web page. A second point-click navigation flow graph based on mouse-based navigation of the user with the web page. One or more KAFs are determined based on a comparison of the first graphical model with the second graphical model.

Abstract: A system configuration suitable for carrying out video recognition by machine learning in a video system including an imaging device and a video processing device is provided. One aspect of the video system according to the present invention includes an imaging device and a video processing device. The imaging device includes an imaging unit configured to generate video data by imaging, a front side NN unit including multiple layers of neural networks from an input layer to a predetermined intermediate hidden layer (hereinafter referred to as “intermediate hidden layer”) that are configured to perform recognition processing of the video data; and an imaging side control unit configured to transmit an output of the intermediate hidden layer of the front side NN unit (hereinafter referred to as “intermediate feature quantity”).

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: Various embodiments of the teachings herein include an industrial network behavior analysis method. The method may include: defining a first time window for a target industrial control system, for the target industrial control system to perform a control behavior; respectively determining an execution probability deviation for each control instruction within the first time window and using the execution probability to characterize the ratio of the number of times the corresponding control instruction is executed within a time period to the total number of times the control instruction within the time period; defining a second time window according to the control instruction characterizing a time period when the system performs the corresponding control behavior, and the control behavior is the same as that performed in the first time window.

Abstract: Artificial Intelligence (AI) can rapidly evaluate a faulted message in 5G or 6G, calculate a likelihood that each message element is faulted, and optionally suggest a most probable corrected version for each of the likely faulted message elements. To do so, the AI takes in numerous factors besides the message itself, such as the modulation quality of each message element, the proximity and quality of a nearest demodulation reference, a signal-to-noise ratio of the message element, a measure of current electromagnetic noise during the message element, an expected format or expected codewords based on prior messages or convention, and other factors. The AI model can then provide guidance as to mitigation, such as choosing whether to request a retransmission or attempting to vary the likely faulted message elements. The AI model can be adapted to fixed-site computers or to the more limited computers of a mobile user device.

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: In one embodiment, an issue analysis service obtains telemetry data from a plurality of devices in a network across a plurality of time intervals. The service detects a failure event in which a device in the network is in a failure state. The service clusters the telemetry data obtained prior to the failure event into rounds according to time intervals in which the telemetry data was collected. Each round corresponds to a particular time interval. The service applies a machine learning-based classifier to each one of the rounds of clustered telemetry data to identify one or more common traits appearing in the telemetry data for each round. The service generates a trait change report indicating a change in the one or more common traits appearing in the telemetry data across the rounds leading up to the failure event.

Abstract: A system and method to reduce weight storage bits for a deep-learning network includes a quantizing module and a cluster-number reduction module. The quantizing module quantizes neural weights of each quantization layer of the deep-learning network. The cluster-number reduction module reduces the predetermined number of clusters for a layer having a clustering error that is a minimum of the clustering errors of the plurality of quantization layers. The quantizing module requantizes the layer based on the reduced predetermined number of clusters for the layer and the cluster-number reduction module further determines another layer having a clustering error that is a minimum of the clustering errors of the plurality of quantized layers and reduces the predetermined number of clusters for the another layer until a recognition performance of the deep-learning network has been reduced by a predetermined threshold.

Abstract: An information processing system executes a series of processes. The information processing system includes a computer which manages, among multiple programs for executing the series of processes, a first program included in the information processing system and one or more second programs registered in the information processing system. The computer controls execution of the series of processes including a process executed by the second program and controls a virtual server for executing the second program.

Abstract: Embodiments of the present disclosure relate to a method for providing log information, an electronic device, and a computer program product.

Abstract: Disclosed are apparatus and methods for automatically training a sensor node to detect anomalies in an environment. At the sensor node, an indication is received to initiate training by the sensor node to detect anomalies in the environment based on sensor data generated by a sensor that resides on such sensor node and is operable to detect sensor signals from the environment. After training is initiated, the sensor node automatically trains a model that resides on the sensor to detect anomalies in the environment, and such training is based on the sensor data. After the model is trained, the model to detect anomalies in the environment is executed by the sensor node.

Abstract: Storage management is performed. For example, a computing device may determine that a fault belongs to one of a plurality of predefined fault categories based on description information of the fault of a storage system. Then, the computing device may determine at least one fault cause associated with the fault category at a first level of a hierarchical structure of predetermined fault causes. Further, the computing device may determine a first fault cause that causes the fault among the at least one fault cause. After that, the computing device may determine a target fault cause at the deepest level that causes the fault based on the first fault cause. As a result, the root cause of a fault of a storage system may be accurately and efficiently determined, thereby providing the possibility of fundamentally eliminating the fault.

Abstract: Embodiments include a system, method, and a computer program product for notifying a technician when a repair task the technician is addressing may be negatively impacted by a nearby fault and/or if the technician is qualified to repair a nearby fault. The technician is qualified if the technician has the necessary skills, parts (e.g., materials), and/or equipment (test equipment) required to repair the nearby fault. In addition, embodiments include receiving and responding to queries from a technician to: determine whether any of the repair tasks associated with the technician's assigned tickets are futile tasks based on newly received faults or pending tickets; and determine whether the querying technician is qualified to address (e.g., repair) any newly received faults or pending tickets.

Abstract: Techniques for processing a request may include: providing tasks to a state machine framework, wherein the tasks perform processing of a workflow for servicing the request; generating, by the state machine framework, a state machine for processing the request, wherein the state machine includes states associated with the tasks, wherein generating the state machine may include automatically determining a first state transition of the state machine between a first and a second of the states; receiving the request; and responsive to receiving the request, performing first processing using the state machine to service the request. The framework may automatically generate triggers that drive the state machine to determine subsequent states in accordance with defined state transitions. State machine internal state information may be persistently stored and used in restoring the state machine to one of its states in connection processing of the command.

Abstract: Systems and methods are described identify a database metric value associated with a database instance storing a dataset associated with a user system. A database issue is detected in view of a determination that the database metric value satisfies a condition. In response to satisfaction of the condition, a set of user action metrics associated with the user system is collected from one or more data monitoring systems. At least one notification communication is generated including at least a portion of the set of user action metrics and information identifying the database issue. The at least one notification communication is transmitted to a remediation execution system configured to execute, using the at least a portion of the set of user action metrics and information identifying the database issue, a remedial action in response to the database issue.

Abstract: A security manager configured to generate a plurality of learned security policies and provide at least one learned security policy and a security agent to a client machine for enforcement of the at least one learned security policy by the security agent on the client machine. The security manager configured to receive alerts from the security agent indicating anomalous behavior on the client machine.

Abstract: An information processing apparatus includes a processor configured to execute a reset process for resetting a watchdog timer, execute a recovery process of the information processing apparatus, in a case where the watchdog timer is not able to be reset within a time limit, and sets the time limit to a longer time, in a case where an apparatus load which is at least one of a current processing load per unit time of the information processing apparatus or a processing amount of a processing request which is input to the information processing apparatus is large, as compared to a case where the apparatus load is small.

Abstract: Systems, methods, and computer-readable media for fault code aggregation across application-centric dimensions. In an example embodiment, a system obtains respective fault codes corresponding to one or more network devices in a network and maps the one or more network devices and/or the respective fault codes to respective logical policy entities defined in a logical policy model of the network, to yield fault code mappings. The system aggregates the one or more of the fault code mappings along respective logical policy dimensions in the network to yield an aggregation of fault codes across respective logical policy dimensions and, based on the aggregation, presents, for each of the respective logical policy dimensions, one or more hardware-level errors along the respective logical policy dimension.

Abstract: Computer implemented method for training a software infrastructure based on machine learning techniques and intended for analysis of data obtained from a three-dimensional tomographic inspection of objects of a predetermined type, such as logs, with the aim of determining information about internal characteristics of interest of the self-same objects, wherein, once a training set comprising a plurality of objects of the same predetermined type has been selected, for each object the software infrastructure is supplied with training input data and corresponding training output data, which are processed by the software infrastructure for setting internal processing parameters of the software infrastructure which correlate the training input data with the training output data; where the training input data comprise data obtained from a three-dimensional tomographic inspection of the object, and the training output data comprise information about internal characteristics of interest assessed at internal points of

Abstract: In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

Abstract: An online system optimizes for longer attribution window conversions with an additive decomposition model by predicting the probability that a predefined action happens given an impression/click. The online system receives a content item from a content provider for display to a target user, and predicts a probability that a target user will convert given an interaction with the content item by the target user. The online system computes, by a first trained model, a short-term conversion probability of a conversion event happening within a first conversion window after the interaction. The online system computes, by a second trained model, a long-term conversion probability of the a conversion event happening within a second conversion window after the interaction, the second conversion window being longer than the first conversion window. The online system computes the conversion probability given the interaction based on the short-term conversion probability and the long-term conversion probability.

Abstract: A neural network system includes a primary input layer configured to acquire data in both a learning mode and an operation mode, an auxiliary input layer configured to acquire data solely in the learning mode, a first partial network configured to carry out learning using both an output of the primary input layer and an output of the auxiliary input layer and to subsequently carry out learning solely using the output of the primary input layer so as to produce a same calculation result as a calculation result produced using both the output of the primary input layer and the output of the auxiliary input layer, and a second partial network configured to carry out calculations in the learning mode and the operation mode upon receiving an output of the first partial network.

Abstract: Remote on-demand site link testing is provided. A site link tester (SLT) system includes an SLT connected to a customer's VoIP phone system. The SLT is configured to communicate with a front end client application operating remotely on a user's computing device. The packet-capture application receives instructions from the client application to perform a packet capture in association with the SLT's network interface and/or to operate as an emulated VoIP endpoint and conduct a test call (e.g., to confirm the customer's VoIP system's compliance with 911-associated legislation or to troubleshoot a VoIP issue). Results of the packet capture may be sent to the client application and analyzed for remotely diagnosing and troubleshooting VoIP-related problems. Using the SLT system, the technician is enabled to perform 911-associated legislation compliance and diagnose VoIP issues on-demand from a remote location, which can reduce or eliminate the need for a technician to be on-site.

Abstract: A device may receive information associated with media content that is capable of being provided by a first content delivery network and a second content delivery network. The first content delivery network may include a first host device and a second host device. The device may determine scores associated with the first content delivery network and the second content delivery network. The device may provide, based on the scores, information regarding the first content delivery to a user device to cause the user device to request the media content from the first content delivery network. The device may determine scores associated with the first host device and the second host device. The device may provide information regarding the first host device to a network device to cause the network device to route requests for the media content to the first host device.

Abstract: An issue tracking system configured to determine similarity between issue content in an issue request issue content in a previously submitted issue record. Based on the similarity, the issue tracking system may determine a likelihood that a given issue is related to at least one additional issue. In response to a determination that likelihood exceeds a threshold, data can be extracted from the related issue to provide the user with a suggested supplemental issue request to be submitted to the issue tracking system.

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.