Abstract: Systems and methods are provided that may be implemented to automatically discover intended and/or unintended changes as they occur in an IT environment, such as a network that includes multiple endpoint information handling systems. Intended changes may include, but are not limited to, changes in user and device or endpoint system level experience metrics that occur on one or more of the endpoint information handling systems within the IT environment after a planned action occurring in the IT environment. Unintended changes to similar metrics may occur when no activities are planned or initiated for the IT environment. These IT environment changes may be discovered automatically based on individual device level telemetry data and/or fleetwide aggregations derived of benchmarking various telemetry metrics.

Abstract: A method for managing a plurality of network devices of a network includes determining, by one or more processors, a causality map for the plurality of network devices according to an intent. The method further includes receiving, by the one or more processors, an indication of a network service impact and determining, by the one or more processors, a relevant portion of the causality map based on the network service impact. The method further includes determining, by the one or more processors, one or more candidate root cause faults based on the relevant portion of the causality map and outputting, by the one or more processors, an indication of the one or more candidate root cause faults.

Abstract: Provided herein are method, apparatus, and computer program products for facilitating capture and play back of interface event data. One exemplary method may comprise providing, as a function of at least profile data, environmental data, transaction data, relevance data, and implicit data derived therefrom, a dynamic application environment, displaying the dynamic application environment on an dynamic application interface, capturing interface event data, the interface event data being based on user interaction with the dynamic application interface, and comprising of one or more interface events and a particular time at which each of the one or more interface events occurred, providing data to the play back device configured for initializing a play back environment, such that the play back environment receives the interface event data and performs play back, and outputting the one or more interface events in a relative time to the particular time.

Abstract: An apparatus and a method for testing a multi-channel high-speed low-latency interconnect interface (HLII) for a silicon interposer are provided. The apparatus includes: a standard test port configured to exchange a test instruction; an asynchronous bypass port configured to directly access an input/output (I/O) port of a channel of a physical layer of the interconnection interface; a built-in self-test (BIST) engine configured to implement inter-level loopback testing and data verification; a redundant data channel configured to repair a damaged data channel; and a delay chain testing circuit configured to test a function and linearity of a delay chain. The apparatus embeds test and repair logic into the physical layer and a link layer, achieving internal test control without any external controller. In this way, a sample can be tested and quickly screened to ensure its performance.

Abstract: An error detection circuit and a method for performing a cyclic redundancy check on a clock gated register signal are disclosed. The error detection circuit comprising a first register, a check bit processing logic and an error detection module. The first register is a clock gated register configured to be updated with a data signal (x) in response to a clock enabling signal. The check bit processing logic configured to, in response to a control signal, update a second register with a check bit, wherein the control signal (b) is the same as the clock enabling signal. The error detection module configured for calculating an indication bit based on at least the output of the first register and the output of the second register.

Abstract: A learning unit of an operation management system extracts, according to performance information and event logs of a device to be managed, a first abnormality occurrence period using the performance information, calculates a first TF-IDF vector created using event logs in the first abnormality occurrence period, and stores at least the first TF-IDF vector as a learning model. A recommendation section calculates, using the performance information and event logs newly acquired by the device, a second abnormality occurrence period and a second TF-IDF vector, calculates a first degree of similarity between the first TF-IDF vector in the learning model and the second TF-IDF vector, and outputs recommendation information regarding the calculated second abnormality occurrence period when the first degree of similarity is equal to or less than a predetermined value.

Abstract: Disclosed is an approach for analyzing a computer network to identify attack paths using a software representation that embodies network configuration and policy data for security management. The software representation comprises a state machine where different states can be reached using respective transitions or properties which are possible as determined based on the network configuration and network policy data. The states correspond to respective entities on the network which may comprise resources that are identifiable for protection in the software representation using crash statements. The software representation can then be stimulated using software analysis tools such as fuzzers to identify sequences of state-to-state transitions that could be used to compromise a protected resource on the computer network.

Abstract: As a technology for an observation device and an inspection device, a technology capable of reducing a work effort related to generation of a recipe including alignment information is provided. An observation device 1 includes an observation unit 103 that obtains an image for observing a sample 101 on a stage 102. A computer system 2 of the observation device 1 acquires the image from the observation unit 103, specifies a period of a pattern-formed unit region repeatedly formed on a surface of the sample 101 from the image, and generates a recipe including observation or inspection alignment positions of the sample 101 using the specified period.

Abstract: Techniques are described for deploying a fault tolerant data center by determining that the physical infrastructure deployment of the data center meets the fault tolerance levels and the fault domains specified for the data center. Techniques are described for obtaining configuration information related to various infrastructure resources deployed in a data center. A resource graph for the data center is generated based on the configuration information. The resource graph represents a logical representation of a set of vertices representing the physical and logical resources used to power a data center and a set of edges that connect the set of vertices. The resource graph is used to determine if a set of infrastructure nodes deployed in the data center meet the fault tolerance levels and fault domains specified for the data center. Results indicative of whether a deployed data center is fault tolerant are then transmitted to a user.

Abstract: Embodiments predict errors using database validation rules. Validation rules can be defined that include business logic for validating transactions performed on a database with a data model. Transactions can be performed using the database, where the database is in a post-transaction state after performance of the transactions. The database can be validated in the post-transaction state by performing the defined business logic for a subset of validation rules, where at least one validation rule fails to validate. Using a trained machine learning model, one or more errors for one or more future transactions can be predicted, the predicted errors being based on the at least one failed validation rule.

Abstract: A method includes determining a gap between a difference in a first health characteristic value and a second health characteristic value of blocks of memory cells and a health threshold associated with the blocks of memory cells, determining the gap is greater than or equal to a gap threshold from the health threshold, performing a pseudo media management operation on the blocks of memory cells, and determining an updated first health characteristic value of the blocks of memory cells.

Abstract: Efficient systems and methods for multiple-term searching and product ordering. A method includes receiving a plurality of independent search requests from a user and executing a product search for each of the plurality of independent search requests to generate a plurality of independent product results. The method includes providing at least a portion of the plurality of independent product results to the user simultaneously. The method includes receiving a product selection from the user and adding the product selection to a virtual shopping cart for the user while continuing to display at least a portion of the plurality of independent product results.

Abstract: Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. In response, embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using incident signatures for the software application that are generated by using a natural language processing machine learning framework, a structured data processing machine learning model, a feature combination machine learning model, and a clustering machine learning model.

Abstract: A plurality of web pages in a web browser are presented to a user. One or more web pages of the plurality of web pages are navigated in the web browser by following at least one hyperlink. A browser history of the user is stored as a chronological list or recently presented web pages. A determination is made whether a dwell time of the user on a first web page has exceeded a threshold time. In response to determining that the dwell time exceeded the threshold, an of-user-interest tag is added to the first web page stored to the browsing history.

Abstract: In some embodiments, a system for resource transportation comprises a routing command subsystem. In some embodiments, the routing command subsystem is configured to be communicably coupled to a first input device at a first location, the first input device configured to determine a first resource factor of a resource at the first location and a location input device associated with a transporter, the transporter configured to transport the resource, the location input device configured to determine a transporter location. In some embodiments, the routing command subsystem is further configured to change a first endpoint of a transporter route to a first alternate location based at least in part on the first resource factor and the transporter location.

Abstract: Performing controlled access to data stored in a secure partition is described herein, including: associating a predetermined exception with an exception handling program in an operating system; restricting a user program to execution by a normal privilege user; and designating a secure partition and restricting the secure partition to be accessible by a highest privilege user; wherein, when executed in user space corresponding to the normal privilege user, the user program generates the predetermined exception, and wherein the predetermined exception triggers execution of the exception handling program in kernel space, and the exception handling program is configured to read data from the secure partition and deliver the data after processing to the user program.

Abstract: An error cause estimation device comprising: a data pre-processing unit that uses data to be processed and generates training data that has an appropriate format for input to a machine learning model; and a model tree generation unit that generates error detection models that are training models for detecting errors and uses the training data as inputs therefor, and generates a model tree that expresses the relationship between error detection models by using a tree structure that has the error detection models as node therefor. Thus, it is possible to generate a training model that detects errors for each of a plurality of types of errors that occur, even when there has been no prior annotation of error causes.

Abstract: An event graph schema for a technology landscape may be determined, where the technology landscape is characterized using scores assigned to performance metrics. The event graph schema may include a plurality of nodes corresponding to the performance metrics and the scores, and directional edges connecting node pairs of the plurality of nodes, with each directional edge having a score-dependent validity criteria defined by scores of a corresponding node pair. Anomalous scores associated with an event within the technology landscape may be used to find anomalous nodes. Valid edges connecting two of the anomalous nodes and satisfying the score-dependent validity criteria thereof may be used to determine at least one path that includes the valid edges and connected anomalous nodes. In this way, it is possible to traverse the at least one path to identify at least one of the connected anomalous nodes as a root cause node of the event.

Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.

Abstract: Systems, apparatuses, methods, and computer program products for parallel processing of real-time data and accumulated data are provided. For example, a method provided herein may include determining that a computing device is affected by a fault during a first time period. The method may include receiving real-time data from the computing device via a network during a second time period. The method may include receiving accumulated data from the computing device via a network during the second time period. The method may include directing the real-time data to a real-time data processing unit and the accumulated data to an accumulated data processing unit. The method may include processing the real-time data with the real-time data processing unit and the accumulated data with the accumulated data processing unit. The method may include routing the real-time data and the accumulated data following the processing to one or more databases.

Abstract: An information processing device that executes an arithmetic process includes a first processing circuit and a second processing circuit. The first processing circuit executes the arithmetic process N times consecutively. The second processing circuit executes the arithmetic process N times consecutively. N is an integer of 2 or more. The first processing circuit and the second processing circuit continue to operate according to a match between at least one result among the results of the N arithmetic processes executed by the first processing circuit and at least one result among the results of the N arithmetic processes executed by the second processing circuit. As a result, it is possible to suppress an increase in cost required for hardware and to suppress a temporary stop due to a temporary failure.

Abstract: The disclosure provides an approach for database query management. Embodiments include receiving, by a service operating on a server, a request for data stored in a database. Embodiments also include determining, by the service, whether to handle the request as an internal request or an external request. Embodiments include, in response to determining to handle the request as an internal request: sending, by the service, a query for at least a portion of the data to the database; receiving, by the service, the at least the portion of the data, and storing query metadata of the request in local memory of the server and not in the database, the query metadata comprising parameters of the request.

Abstract: Techniques manage log data. Such techniques involve receiving log data describing a state of an application system at a target time point. Such techniques further involve determining whether a level of the log data satisfies a predetermined level. Such techniques further involve, in response to determining that the level of the log data satisfies the predetermined level, storing log data within a predetermined range adjacent to the target time point to a log repository of the application system, the predetermined range indicating the amount of log data expected to be stored.

Abstract: Disclosed are a method for generating billing information, a terminal, a server, and a billing system. The method for generating billing information comprises: receiving an end billing request transmitted by a terminal, the end billing request comprising location information of the terminal and bus information; and executing, on the basis of the location information of the terminal and of the bus information, an end billing operation with respect to a first terminal from which the distance to a bus is greater than a distance threshold.

Abstract: A system comprises an enterprise network system and engine. The engine has a discovery module coupled to a switch device, an AI and machine learning based monitoring and detection module coupled to the switch device, and a remediation module coupled to the switch device. The remediation module is configured to initiate a remediation process based upon the detection of at least one of the bot anomalies from the flow of data.

Abstract: A network device includes processing circuitry configured to cause the network device to: partition a flow graph for an application to generate a partitioned graph of nodes and edges, each of the nodes including computations mapped to an execution unit to execute at least a portion of the application, and each of the edges denoting communications between execution units; determine whether the partitioned graph is an irreducible graph; and schedule the computations and the communications for execution in response to determining that the partitioned graph is an irreducible graph.

Abstract: A work order initiation and tracking system is coupled between various plant asset diagnostic systems and a work or business management system within a process plant. The work order initiation and tracking system includes a user interface that enables a user to review diagnostic data, recommendations, and other information from one or more of the diagnostic systems regarding potential or recommended changes to plant assets, and to initiate the generation of one or more work orders to implement the recommendations or corrections based on information from the diagnostic systems.

Abstract: A computer-implemented method for defect analysis is provided. The computer-implemented method includes calculating a plurality of weight-of-evidence (WOE) scores respectively for a plurality of device operations with respect to defects occurred during a fabrication period, a higher WOE score indicating a higher correlation between a defect and a device operation; and ranking the plurality of WOE scores to obtain a list of selected device operations highly correlated with the defects occurred during the fabrication period, device operations in the list of selected device operations having a WOE score greater than a first threshold score. A respective one of the plurality of device operations is a respective device defined by a respective operation site at which the respective device perform a respective operation.

Abstract: A margin tester including an identification reader configured to receive an adaptor identifier of an adaptor, an interface configured to connect to a device under test through the adaptor, and one or more processors configured to assess a margin, such as an electrical margin or an optical margin, of a device under test and tag the assessment with the adaptor identifier. Assessing the margin can include assessing the margin based on an expected margin that is predicted or provided based on the adaptor identifier.

Abstract: Automated computer-implemented methods and systems for resolving performance problems with objects executing in a data center are described. The automated methods use machine learning to obtain rules defining relationships between probabilities of event types of in log messages and performance problems identified by a key performance indictor (“KPI”) of the object. When a KPI violates a corresponding threshold, the rules are used to evaluate run time log messages that describe the probable root cause of the performance problem. An alert identifying the KPI threshold violation, and the log messages are displayed in a graphical user interface of an electronic display device.

Abstract: The disclosure herein describes the detection of malware campaigns based on analysis of attributes of telemetry data. Telemetry data associated with malware campaign detection includes multiple attributes and is associated with a first time interval. Statistics of a target statistic set are calculated based on a composite time series of the multiple attributes of the telemetry data. The target set is compared to a historical statistic set based on a second time interval and, based on the target set exceeding a statistic threshold of the historical set, peak detection analysis of the target set is performed. Based on the analysis indicating the presence of a valid peak result, a notification of detection of a malware campaign is sent, wherein the notification includes data indicative of the valid peak result and enables a receiver of the notification to take corrective action.

Abstract: A programming order of memory dies of a metablock is typically fixed. However, in some storage architectures, this may cause performance bottlenecks. As such, the programming order of the memory dies may be altered to reduce or eliminate performance bottlenecks.

Abstract: An automatic incident dispatcher calculates an incident signature of a new incident, calculates a degree of variance (DoV) of the new incident from an incident signature of the previously classified incident, compares the calculated DoV to a predetermined threshold, and determines that the new incident belongs to a same class as a class of the previously classified the new incident if the calculated DoV is less than or equal to the threshold.

Abstract: Techniques are described for error log anomaly detection. In an implementation, error logs from an application are processed to generate training data. The error logs, for instance, are processed to remove personal information and other data such as numerical strings. The processed error logs are converted into embeddings to generate the training data. The training data is utilized to train an anomaly detection model. For instance, as part of training the anomaly detection model, an anomaly threshold is defined based on a loss value determined from output of the anomaly detection model. Further error logs from the application are then processed by the trained anomaly detection model to determine which of the further error logs are error anomalies, such as based on comparing loss values for the further error logs to the anomaly threshold.

Abstract: A manufacturing condition output apparatus of an embodiment is a manufacturing condition output apparatus which outputs a manufacturing condition of a product. The manufacturing condition output apparatus outputs change degree information which is information regarding degrees of change of values regarding defect probabilities for a plurality of variables relating to manufacturing of the product from model information of a model generated through machine learning on a basis of manufacturing data of the product and inspection result data of the product, as a manufacturing condition.

Abstract: Early indications of application programming interface (API) usage are identified by correlation to particular issues with the API including singular and mutual consistency, completeness, accuracy, and staleness. Analysis of API input and output along with data type and formatting information facilitates identification of the API issues. Establishing a correlation between API usage and issues supports early detection of potential usage reduction on a case-by-case level. Corrective action to resolve identified issues may be performed in a timely manner to maintain usage levels.

Abstract: Automated, computer-implemented methods and systems for resolving performance problems with objects executing in a data center are described. The automated methods use machine learning to train a model that comprises rules defining relationships between probabilities of event types of in log messages and values of a key performance indictor (“KPI”) of the object over a historical time period. When a KPI violates a corresponding threshold, the rules are used to evaluate run time log messages that describe the probable root cause of the performance problem. An alert identifying the KPI threshold violation, and the log messages are displayed in a graphical user interface of an electronic display device.

Abstract: A vector entry of a signaling vector is registered to a buffer summary group. The buffer summary group includes one or more summary indicators for one or more buffers assigned to the buffer summary group. A command is processed that sets a vector indicator in the vector entry and based on setting the vector indicator in the vector entry, a summary indicator of the one or more summary indicators is set in the buffer summary group.

Abstract: Provided is an apparatus for processing containers, in particular plastic containers and plastic parisons, including at least one processing device which processes the containers in a predetermined manner, and including at least one transfer device, which transfers the containers, individually, to the processing device in a transfer region and/or to which the containers are transferred from the processing device in a transfer region. At least one inspection device is provided, which is suitable and intended for visually inspecting at least a part of the processing device and or the transfer region at least at sections and completely, at least temporarily during a transfer process of a container.

Abstract: A method in an illustrative embodiment of the present disclosure includes determining, utilizing a first diagnosis model deployed in a storage system, whether a cause of a fault belongs to environmental factors. The method further includes determining, responsive to determining that the cause of the fault belongs to the environmental factors, whether the fault can be solved locally in the storage system. The method further includes sending, responsive to determining that the fault cannot be solved locally in the storage system, the fault to a second diagnosis model, wherein the first diagnosis model is obtained by distilling the second diagnosis model. According to the method for fault diagnosis of the present disclosure, particular faults can be diagnosed and solved locally in a storage system, so that the workload of a customer support team of the storage system in a cloud can be reduced.

Abstract: Systems and methods of displaying product information relating to multiple purchase options are provided. A method includes displaying, in a first portion of a user interface, a web browser and displaying, in a second portion of the user interface, a decision organizing tool comprising a candidate icon portion comprising a plurality of candidate icons and an active icon portion. The method also includes receiving instructions to transfer of one of the plurality of candidate icons from the candidate icon portion to the active icon portion and, in response to the receiving, transferring the one of the plurality of candidate icons to the active icon portion, where the one of the plurality of candidate icons provides a link to an item currently displayed in the web browser and offered for sale. Effectively becoming a common shopping cart for multiple websites simultaneously.

Abstract: Embodiments relate to monitoring an information technology (IT) environment having a plurality of domains through key performance indicator (KPI) data. In response to detection of a technical health problem, a first KPI related to the problem is identified. A root cause analysis is performed on the identified KPI generating a knowledge graph. A second KPI related to the first KPI is identified through the discovery of a correlation between the two identified KPIs. A diagnosis is generated for the technical health problem within the IT environment based on the discovered hidden correlation between the first KPI and second KPI. The generated diagnosis includes the root cause of the technical health issue.

Abstract: The present teachings relate to a method comprising a plurality of sensors, and one or more functionally connected processing units, the method comprising: providing, at any of the one or more processing units, time-series residual data of a sensor object; the sensor object being a group of at least some of the sensors from the plurality of sensors, and wherein the residual data comprises, for each of the sensors of the sensor object, a residue signal which is a difference between the sensor's measured output and the sensor's expected output, monitoring, via any of the one or more processing units, a level signal; wherein the level signal is indicative of a collective time-based variation of the time-series residual data, monitoring, via any of the one or more processing units, an association signal; wherein the association signal is indicative of the variation and/or association structure of the time-series residual data, generating, via any of the one or more processing units, an anomaly event signal when a

Abstract: The claimed system and method describes a root cause analysis system for a radio access network. Some aspects include automatic identification of possible causes for network issues, their ranking, determination of the root (main) cause and execution of related best actions, alerts and reporting in order to automatically identify, mitigate or eliminate the problem.

Abstract: A consumer is identified engaged in an activity at a specific physical location. A context is formulated for the consumer based on the activity, the location, preferences associated with the consumer, and a history associated with the consumer. A natural language voice-based assistance session is initiated with the consumer. The session follows the consumer as the consumer travels and remains engaged in the activity. In an embodiment, the session is seamlessly maintained and is seamlessly transferred from a plurality of different devices as the consumer is engaged in the activity and travels. In an embodiment, the session is initiated without the consumer requesting the session.

Abstract: A method may include detecting an error during execution of a function of an application executing on a system; inputting an identifier of the error into a first client-side telemetry data filter model; based on the inputting, receiving an output value from the first client-side telemetry data filter model; determining that the output value is above an importance threshold; and in response to the determining, transmitting telemetry data associated with the error to a server computing device.

Abstract: A system and related method identify a weakness of a workflow in a complex system. The method collects runtime data about the complex system. The complex system comprises a plurality of subcomponents, and the method identifies an abnormal operation in the complex system. The method constructs a multi-dimensional cause-and-effect relation matrix among the plurality of subcomponents, and filters one or more related operations using the multi-dimensional cause-and-effect relation matrix.

Abstract: An information analysis apparatus according to an embodiment includes an input unit that inputs information indicating an apparatus that is a fault occurrence location in a communication network and a factor of a fault, a storage unit that stores a restoration handling method rule in which the apparatus that is the fault occurrence location, the factor of the fault, and an appropriate restoration handling method for the fault are associated, and an analyzer that analyzes an appropriate restoration handling method for the apparatus and the factor indicated by the information that is input, based on information related to a past fault for the apparatus that is the fault occurrence location, or a condition inherent in the apparatus that is the fault occurrence location, from the restoration handling method rule.

Abstract: A system for generating a set of rules for detecting Controller Area Network (CAN) messages anomalies, the system comprising a processing resource configured to: obtain a training set including a plurality of CAN messages, each CAN message having properties; train a model, using the training set, the model characterizing statistical relationships between one or more first types of CAN messages of respective first CAN message type and one or more second types of CAN messages each of respective second CAN message type, wherein the statistical relationships are based on one or more of the properties of the CAN messages of the training set; wherein the model is usable for identifying anomalies within a sequence of input CAN messages.

Abstract: An example system includes a processor that can receive conversation logs of a dialogue system to be analyzed. The processor can train a predictive machine learning model using a training set of the conversation logs on a selected feature to obtain feature values with associated importance values. The processor can select a number of feature values using a significance score calculated based on the associated importance values. The processor can generate an interactive user interface including the selected number of feature values.