Abstract: Examples of a character recognition system are provided. In an example, the system may receive an object detection requirement pertaining to a video clip. The system may identify a visual media feature map from visual media data to process the object detection requirement. The system may implement an artificial intelligence component to segment the visual media feature map into a plurality of regions, and identify a plurality of image proposals therein. The system may implement a first cognitive learning operation to allocate a human face identity for a human face and an object name for an object present in the video clip. The system may determine a face identity model for the human face present in the plurality of image proposals and generate a tagged face identity model. The system may implement a second cognitive learning operation to assemble the plurality of frames with an appurtenant tagged face identity model.

Abstract: In some examples, portable electronic device sales, provisioning, and user care may include authenticating a user associated with a user portable electronic device. A user portable electronic device connector may be used to communicatively connect to a portable electronic device receptacle of the user portable electronic device, and transfer data and/or configurations associated with the user portable electronic device to a data storage. Options to purchase a new portable electronic device may be displayed. Selection of a new portable electronic device may be received from a display of at least one new portable electronic device, and the selected new portable electronic device may be configured by transferring, from the data storage, the data and/or the configurations associated with the user portable electronic device to the selected new portable electronic device.

Abstract: Contextual graph matching based anomaly detection may include evaluating computer-generated log file data to create a master directed graph that specifies known events and transitions between the known events. The master directed graph may be processed to determine a plurality of decomposed master graph walks. Incoming computer-generated log file data may be evaluated to create an incoming directed graph that specifies unknown events and transitions between the unknown events. The incoming directed graph may be processed to determine a decomposed incoming walk. Overlap, distance difference, and correlation scores may be determined for each walk pair of a plurality of walk pairs including each of the plurality of decomposed master graph walks and the decomposed incoming walk. One of the decomposed master graph walks may be selected based on the overlap score, the difference score, and the correlation score, to detect an anomaly.

Abstract: A device may select an enterprise content management (ECM) system for monitoring. The ECM system may include a set of subsystems. The device may monitor the set of subsystems of the ECM system based on selecting the ECM system for monitoring. The device may perform a set of health checks on the set of subsystems based on monitoring the set of subsystems. The set of health checks may include a validation of a set of network addresses associated with the set of subsystems, an analysis of a set of log files associated with the set of subsystems, and a determination of a set of metrics associated with the set of subsystems. The device may generate a health check report based on the set of health checks. The device may provide the health check report.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing performance engineering are disclosed. In one aspect, a method includes the actions of providing, to a cloud computing system that is configured to implement multiple different types of virtual computing nodes, an instruction to execute an application on a virtual computing node that is a first type of virtual computing node of the multiple different types of virtual computing nodes. The actions further include determining computing performance parameters of the virtual computing node. The actions further include determining to execute the application on both the virtual computing node and an additional virtual computing node. The actions further include selecting, from among the multiple different types of virtual computing nodes, a second type of virtual computing node.

Abstract: Methods, systems, and apparatus for improving exchange systems. In one aspect, a method includes receiving data representing an exchange problem; determining, from the received data, an integer programming formulation of the exchange problem; mapping the integer programming formulation of the exchange problem to a quadratic unconstrained binary optimization (QUBO) formulation of the exchange problem; obtaining data representing a solution to the exchange problem from a quantum computing resource; and initiating an action based on the obtained data representing a solution to the exchange problem.

Abstract: A device may instrument a codebase associated with a software application. The device may execute a test on the instrumented codebase as the instrumented codebase executes, wherein the instrumented codebase generates test data based on the test being executed. The device may generate, based on the test data, a live graphical model of the codebase from a composite graphical model of the codebase, wherein the composite graphical model includes historical information, associated with the codebase, mapped to a graphical model of the codebase. The device may generate an extended reality rendered view of the live graphical model. The device may provide, to an extended reality device, the extended reality rendered view of the live graphical model for display by the extended reality device.

Abstract: A device may receive, from sources, data objects identifying values relating to entities for which a risk indicator is to be determined, and may process the data objects to generate an analytical record that identifies relationships between values of different data objects. The device may determine, based on the analytical record, the risk indicator corresponding to one or more entities. The risk indicator may be determined based on at least one of: a comparison between the analytical record and a data structure that identifies expected values of one or more of the data objects; an identification of a group of the entities, and an outlier from the group of the entities based on the analytical record; or an identification of a change in behavior of the one or more entities based on the analytical record. The device may perform an action based on determining the risk indicator.

Abstract: A machine learning multi-dimensional acoustic feature vector authentication system, according to an example of the present disclosure, builds and trains multiple multi-dimensional acoustic feature vector machine learning classifiers to determine a probability of spoofing of a voice. The system may extract an acoustic feature from a voice sample of a user. The system may convert the acoustic feature into multi-dimensional acoustic feature vectors and apply the multi-dimensional acoustic feature vectors to the multi-dimensional acoustic feature vector machine learning classifiers to detect spoofing and determine whether to authenticate a user.

Abstract: A device obtains prescription information relating to a medication in a container. The device causes a camera device of the device to obtain image data relating to the medication and a weighing device of the device to obtain weight data relating the medication. The device sends the prescription information, the image data, and the weight data to a different device to cause the different device to verify the medication using a machine learning model. The device receives information concerning the medication and automatically generates, based on the information concerning the medication, a message concerning the medication, wherein the message includes instructions on how much of the medication a user of the device is to take. The device causes the device or an additional device to present the message.

Abstract: A device may determine probabilities for test scripts associated with a test to be executed on a software element, where a respective probability is associated with a respective test script, indicates a likelihood that the respective test script will be unsuccessful in a test cycle, and is determined based on historical test results, associated with the software element, for the respective test script. The device may generate, based on the probabilities, a test script execution order, of the test scripts, for the test cycle, and may execute, based on the test script execution order, the test on the software element in the test cycle. The device may dynamically generate, based on results for the test in the test cycle, an updated test script execution order, and may execute, based on the updated test script execution order, the test on the software element in the test cycle.

Abstract: This document describes systems, methods, devices, and other techniques for accessing a first video showing a first two-dimensional scene of an environment and captured by a first camera located in the environment having a first field of view; detecting one or more objects shown in the first video; analyzing the first video to determine one or more features of each of the detected objects shown in the first video; accessing a second video showing a second 2D scene of the environment and captured by a second camera located in the environment having a second field of view; detecting one or more objects shown in the second video; analyzing the second video to determine one or more features of each of the detected objects shown in the second video; and correlating one or more objects shown in the first video with one or more objects shown in the second video.

Abstract: An automatic environment provisioning system receives a user request for auto provisioning of a development environment and automatically creates an environment per the user's request on a cloud platform. The user's request includes a selection of a technology platform on which to create the environment in addition to other system requirements. The user's input for generating the environment including the selection of a technology platform is employed for generating a template. The template can reference individual identifiers of various images corresponding to the software resources associated with the user's selected technology platform. The template enables instantiating the images with identifiers associated with the user-selected technology platform in order to enable creating the auto-provisioned environment. One or more users can be provided access to the auto-provisioned environment to of develop, test and/or implement the solution.

Abstract: A framework for interactive VR content items provides for user interaction via placement of interaction points within VR content items that would otherwise be passively viewed by users on HMD devices. The interaction points are defined by positional metadata that contains information regarding when and where the interaction points should be displayed during the interactive VR content play. The interaction points are also defined by action metadata that determines one or more actions to be executed when the user selects the interactions points. Selection of the interaction points via one or more of a user gesture and a voice input is also enabled by the framework.

Abstract: An Artificial Intelligence (AI) based resource identification system enables identifying available resources in response to receiving a request for resources. The request attributes are mapped to the attributes of the resources in a resource pool. Matching index scores are calculated and resources that match the request can be selected based on the matching index scores. If no resources are available suitable alternate resources with lower matching index scores are suggested so that a user has the choice to filter this alternate resources based on a threshold score. Particular suggestions to change one or more of the request attributes can be provided based on analysis of the mapping of the request and the resource attributes which enables the resource identification system to suggest changes to one or more of the skill attributes, level attributes, time attributes or location attributes of the request.

Abstract: In some examples, artificial intelligence based corpus enrichment for knowledge population and query response may include generating, based on annotated training documents, an entity and relation annotation model, identifying, based on application of the entity and relation annotation model to a document set that is to be annotated, entities and relations between the entities for each document of the document set to generate an annotated document set, and categorizing each annotated document into a plurality of categories. Artificial intelligence based corpus enrichment may include determining whether an identified category includes a specified number of annotated documents, and if not, additional annotated documents may be generated for the identified category that may represent a corpus.

Abstract: A distributed ledger technology (DLT) network may include a plurality of participant nodes. The participant nodes may respectively validate a distributed ledger based on a consensus protocol. A blockchain export system may send, to a data receiver not included in the DLT network, a participant public key for a participant node of the DLT network. The blockchain export system may receive, from the data receiver, a request to share token data stored in a blockchain of the distributed ledger. The blockchain export system may generate a certification that the token data is stored on the blockchain. The certification may include a digital signature based on a participant private key paired to the participant public key. The blockchain system may transmit the certification to the data receiver. The data receiver may verify the certification based on the participant public key.

Abstract: A machine learning (ML) based intermittent data processing system accesses a collection of intermittent data points, determines a data distribution associated with the collection and generates one or more calculated values based on the data distribution. A simulation can be employed to determine the accuracy of the calculated values based on which, the calculated values can be employed for further processing. The collection of intermittent data points is initially processed to determine if one or more of the data distribution identification, bootstrapping or variability capping techniques are to be applied in order to obtain the calculated values. The calculated values are used to generate visualizations and recommendations.

Abstract: In an example, in response to occurrence of a report generation event, a report may be generated. An aspect of the report requiring a description may be identified, the aspect being at least one of a trend noted in the report and a discrepancy in the report. A hypothesis explaining the aspect of the report may be generated. Further, an evidence may be collected for the hypothesis, based on a relation between entities of the hypothesis, the relation being determined based on a knowledge data having information pertaining to the entities. The hypothesis may be evaluated generate an evaluation score, based on the collected evidence. Based on the evaluation score, the hypothesis may be selected for explaining the aspect. The selected hypothesis may be provided in natural language text explaining the aspect of the report, based on at least one of the domain and user knowledge and linguistic knowledge.

Abstract: Examples of employee concierge are provided. In an example, an issue may be determined for an employee. The issue may be determined based on a query shared by the employee or upon occurrence of an unusual event. The unusual event may be indicative of a deviation in behaviour and routine of the employee. A session may be initiated and the issue may be parsed to determine a context. A bot may be selected from multiple bots for the issue where each bot includes information relating to a solution to address the issue. Data associated with the issue may be collected from a central database and other bots. The data may then be analyzed to determine a solution. The solution comprises a response to the query and a suggestion to mitigate the unusual event.