Abstract: A method for controlling access to one or more of a plurality of target systems includes receiving profile data that defines one or more features associated with a plurality of individuals with one or more entitlements of those individuals. Each entitlement is indicative of target system access. The method further includes generating a model that relates the one or more features and the one or more entitlements of the plurality of individuals. Profile data that defines one or more features associated with a target individual is received from a first user management system. A listing that includes one or more entitlements associated with the target individual, and confidence values associated with the one or more entitlements is generated based on the profile data and the model. Each confidence value is indicative of whether the target individual should be granted a corresponding entitlement.

Abstract: The disclosure relates to methods, systems, and apparatuses, including computer programs on a computer storage medium, for generating a dynamic user interface and playing a video within the dynamic user interface. A dynamic computer user guidance system includes a user interface generation module that causes a display of a dynamic user interface. The dynamic user interface provides a plurality of instructions via one or more dynamic elements. The user interface generation module generates a user interface status indicator. The system includes a video control module that an integrated video to play in the dynamic user interface in response to receipt of the user interface status indicator. The video control module generates a video status indicator. A synchronization module communicably coupled to the user interface generation module and the video control module synchronize a state change in one or more dynamic elements in response to receipt of the video status indicator.

Abstract: A device receives first network information associated with a first portion of a network, and second network information associated with a second portion of the network, and determines, based on the first network information and the second network information, an alarm condition for the network. The device determines, based on correlation rules, whether the alarm condition relates to a currently handled alarm condition or a previously handled alarm condition. The device correlates the alarm condition with network inventory information, network topology information, and network service information, when the alarm condition does not relate to the currently handled alarm condition or the previously handled alarm condition, to generate a correlated alarm condition. The device automatically generates a resolution for the correlated alarm condition based on the correlated alarm condition, and automatically performs one or more actions based on the resolution for the correlated alarm condition.

Abstract: An optimized test data selection strategy references a sampling file that identifies data attributes that serve as the basis of the test data selection strategy. By analyzing fields and the corresponding field values of the sample imprint, a total number of test data selected for inclusion into a sample dataset is reduced. The test data selection strategy provides an efficient methodology for implementing a data comparison testing process.

Abstract: A system for improving network capabilities is provided for enabling diagnostics and deployment of hybrid cloud technology. The system may obtain a plurality of display prompts. The display prompts may be associated with a stack of respective software criteria indicative of software capabilities and network functions. The system generates a prompt interface including a plurality of display prompts. The system may include an analytics engine to analyze the inputs responsive to the stack interface. The output of the analytics engine may include a capability score representing a measurement of satisfaction of a software criterion by a network node. The system may identify, based on a capability metric, a recommendation text associated with the software criterion. The system may generate a report interface comprising the recommendation text. The recommendation text may include a suggested modification to the network node including hybrid cloud architecture transformation and virtualization management deployment.

Abstract: A system for migrating data from a legacy system to a target system includes an input/output (IO) processor configured to receive legacy data from a plurality of different types of legacy systems and to communicate target data to a plurality of different types of target systems; a staging area database configured to store legacy data according to a common database schema; a localized database configured to store target data according to a target schema that is associated with a target system type; a processor in communication with the interface, the staging area database, and the localized database; and non-transitory computer readable media in communication with the processor that stores instruction code.

Abstract: A mechanism automatically reconfigures and later restores the network configuration and settings for network devices installed at one location according to configuration and settings of corresponding network devices installed at another location. This enables a user to access these network devices the same way in both locations. This mechanism further avoids having to perform any network reconfiguration of mobile devices that may be carried between the two locations.

Abstract: Implementations are directed to methods for providing an enhanced encounter via a holographic virtual assistant, including detecting, by one or more processors, an encounter request from a user, selecting a first encounter including a first holographic representation and a first dialog output, providing the first encounter for presentation to the user on the holographic virtual assistant, receiving, from the user, a first user reaction, the first user reaction including a first user dialog input and a first user engagement input, and training, using the first user reaction, a conversational goal model.

Abstract: Methods, systems, and apparatus for generating workplace movement visualizations. A method includes receiving a request for a user interface presentation that presents time spent by individuals in productive areas of a work site; receiving data specifying the presence of a plurality of personal wireless beacons within each of a plurality of areas of the worksite, wherein a first area of the plurality of areas is designated a productive area and wherein a second area of the plurality of areas is designated a nonproductive area; —computing, for each of a plurality of time periods, a respective count of individuals within the first area designated as a productive area; generating a user interface presentation that presents the respective counts of individuals within the productive area; and providing the generated user interface presentation in response to the request.

Abstract: Techniques are described for routing service requests in a computer-implemented service environment. A received service request may be initially analyzed to determine a priority of the request. In some implementations, one or more actions may be automatically performed to provide an initial response to the requester. The text of the request may be analyzed to automatically determine a category of the request. In some implementations, a classification engine may determine the category of the request through use of a classification model that has been trained using one or more machine learning (ML) techniques and/or that employs Natural Language Processing (NLP). Based on the category, the request may be routed to agent(s) for handling. Routing may include generating a ticket that includes the request, the category, the priority, and/or other information, and the ticket may be provided to the appropriate agent(s) through a ticketing service.

Abstract: Techniques are described for receiving data generated by multiple platforms of different types, and managing the data in multiple stages of a data lifecycle associated with a super-platform. An end-user (e.g., data discloser) may interact with multiple individual (e.g., siloed) platforms of different types. The individual platforms may generate data describing, and/or resulting from, these interactions with end-user(s). The data from the various individual platforms may be received, ingested, stored, analyzed, aggregated, and/or otherwise processed by a super-platform during various stages of a data lifecycle. In some implementations, the end-user, the super-platform, and/or the individual platform(s) may provide one or more restrictions on how the data may be handled in each of the stages of the data lifecycle.

Abstract: A machine learning (ML) based asset monitoring system that automatically determines damage mechanisms (DMs) and generates automatically updated visualizations of assets that include equipment and lines of a processing plant is disclosed. The asset monitoring system is communicatively coupled to the assets of the plant and continuously receives process parameters associated with the various processes and equipment in the plant. Corrosion loops (CLs) are identified and automatically demarcated by the asset monitoring system. DMs are predicted for each of the assets using a ML model based on the process parameters and the corrosion loops. The data regarding the DMs, CLs and the process parameters are used to obtain equipment risk rankings for the assets. Multi-dimensional visualizations of the assets that display the state of the plant assets in real-time are generated.

Abstract: Systems and methods may reduce bias in an artificial intelligence model. The system may receive word embedding model generated based on a corpus of words. The system may determine a bias definition vector in an embedding space of the word embedding model. The system may receive bias classification criteria. The bias classification criteria may include logic to group word vectors in the word embedding model based on a distance measurement from the bias definition vector. The system may identify, in the word embedding model, a first group of vectors and a second group of vectors based on the bias classification criteria and the bias definition vector. The system may generate a debiased artificial intelligence model. The debiased artificial intelligence model may include associations between words and metrics. The system may weight the metrics for the words associated with the first and second group of vectors with a non-zero penalization factor.

Abstract: A device may receive first user input indicating creation of a first software component for inclusion in a first software application, the first software component being selected for creation in a first software framework associated with the first software application. The device may generate, based on the first software component, a second software component for inclusion in a second software application, the second software component being generated in a second software framework associated with the second software application. In addition, the device may cause display of the second software component in the second software application. The device may also provide component data to a second device, the component data defining at least one property associated with the first software component.

Abstract: A device receives a first request indicating that a first portion of code of a first code type is to be converted to a first portion of code of a second code type. The device consolidates data structure access to a single point to retrieve rules associated with relevant data for further processing of the first request and accesses, from the data structure, the rules associated with the relevant data. The device validates the rules associated with the relevant data and the first request and performs, based on validating the rules, calculations associated with the relevant data and the first request to generate results. The device updates the results of the calculations associated with the relevant data and the first request to generate the first portion of code of the second code type and provides the first portion of code of the second code type.

Abstract: A device may receive event information identifying a container event associated with one or more shipping containers. The container event may indicate a time to be used to determine demurrage or detention data for the one or more shipping containers. The device may determine, based on the event information, baseline information associated with generating demurrage or detention data for the one or more shipping containers. The device may determine, based on the container event and the baseline information, that demurrage or detention has been triggered in association with the one or more shipping containers. The device may generate the demurrage or detention data for the one or more shipping containers using the baseline information and the event information, and based on determining that demurrage or detention has been triggered in association with the one or more shipping containers. The device may provide the demurrage or detention data.

Abstract: A device may obtain test case information for a set of test cases. The test case information may include test case description information, test case environment information, and/or test case defect information. The device may determine a set of field-level similarity scores by using a set of similarity analysis techniques to analyze a set of test case field groups associated with the test case information. The device may determine a set of overall similarity scores for a set of test case groups by using a machine learning technique to analyze the set of field-level similarity scores. The device may update a data structure that stores the test case information to establish one or more associations between the test case information and the set of overall similarity scores. The device may process a request from a user device using information included in the updated data structure.

Abstract: Implementations directed to providing a computer-implemented method for automating vehicle feature updates, the method being executed by one or more processors and comprising receiving telematics data identifying an actual usage of a vehicle; performing a gap analysis between the actual usage of the vehicle and an expected usage of the vehicle; determining a feature update based on the gap analysis; providing the feature update to a product engineering module when the feature cannot be implemented by a software update; and providing the feature update to an onboard computer system when the feature can be implemented by a software update.

Abstract: A rating prediction engine builds and applies models to predict ratings based on an analysis of textual reviews and comments. The engine can build multiple models simultaneously through distributed parallel model building that employs deep convolutional neural networks (CNNs). The engine can also incorporate user moment feature data, including user status and context information, to provide better performance and more accurate predictions. The engine can also employ heuristic unsupervised pre-training and/or adaptive over-fitting reduction for model building. In some instances, the techniques described herein can be used in a service to predict personalized ratings for reviews or other published items, in instances where the original author of the item did not include a rating and/or in instances where the publication channel does not provide a mechanism to enter ratings.

Abstract: A device may receive user preference data and company preference data associated with one or more bookable items. The device may obtain external preference data associated with the one or more bookable items. The external preference data may be different from the user preference data and the company preference data. The device may generate a list of schedules based on the user preference data, the company preference data, and the external preference data. The device may generate one or more category scores for a plurality of schedules included in the list of schedules based on the company preference data. The device may rank the list of schedules based on the one or more category scores. The device may select a schedule, from the ranked list of schedules, based on the one or more category scores. The device may perform an action based on selecting the schedule.