Abstract: A device may receive, from a user device, a request to access a virtual reality (VR) learning environment that includes an identifier associated with a program that supports the VR learning environment. The device may identify a set of objects to use within the VR learning environment by searching a data structure using the identifier associated with the program that supports the VR learning environment. The device may provide the VR learning environment to the user device. The device may receive, from the user device, information associated with interactions within the VR learning environment. The device may identify one or more additional objects to use within the VR learning environment by using one or more natural language processing techniques to analyze the information associated with the interactions within the VR learning environment. The device may provide the one or more additional objects to the user device.

Abstract: A device may receive historical software data associated with a software development platform, and may train a neural network model, with the historical software data, to generate a trained neural network model. The device may receive, from the software development platform, historical test configuration data, and may train an execution model, with the historical test configuration data, to generate a trained execution model. The device may receive software data identifying software to be tested, and may process the software data, with the trained neural network model, to predict a set of test cases to execute for testing the software. The device may process the set of test cases, with the trained execution model, to identify configurations, scripts, and test targets for executing the set of test cases, and may perform one or more actions based on the set of test cases, the configurations, the scripts, and the test targets.

Abstract: The systems and methods described herein may generate multi-modal embeddings with sub-symbolic features and symbolic features. The sub-symbolic embeddings may be generated with computer vision processing. The symbolic features may include mathematical representations of image content, which are enriched with information from background knowledge sources. The system may aggregate the sub-symbolic and symbolic features using aggregation techniques such as concatenation, averaging, summing, and/or maxing. The multi-modal embeddings may be included in a multi-modal embedding model and trained via supervised learning. Once the multi-modal embeddings are trained, the system may generate inferences based on linear algebra operations involving the multi-modal embeddings that are relevant to an inference response to the natural language question and input image.

Abstract: A device may process configuration data to identify category data that identifies possible categories of data included in the configuration data. The device may process the category data, after identifying the category data, using a natural language processing technique to generate a set of terms to be processed using an artificial intelligence module. The device may utilize the artificial intelligence module to process the set of terms to predict a set of configuration actions to be performed with respect to the configuration data and to determine an order in which to perform the set of configuration actions. The artificial intelligence module may be associated with translating the configuration data into a computer-readable form. The device may generate an execution script based on the set of configuration actions or the order in which to perform the set of configuration actions. The device may perform the set of configuration actions.

Abstract: Implementations of the present disclosure are directed to dispensing beverages from a beverage dispensing machine and include determining, from computer-readable media, pour parameters for a beverage of a plurality of beverages, the pour parameters being specific to the beverage, at least one pour parameter including a tilt value, and automatically, by the beverage dispensing machine: rotating a drum that holds a vessel to achieve the tilt value, initiating dispensing of the beverage into the vessel from a tap, automatically rotating the drum to reduce the tilt value of the vessel during dispensing of the beverage, and ceasing dispensing of the beverage into the vessel from the tap, the tilt value being substantially zero upon ceasing.

Abstract: Techniques are described for unified identity and access management (IAM) across multiple applications in a distributed (e.g., cloud) computing environment. Implementations provide for IAM across multiple applications through use of a single, unified IAM tool including an IAM interface. The IAM tool can manage user identity and user access rights for multiple applications in the platform environment. The IAM tool can also employ a unified IAM database, which stores user profiles that each describes user access rights for a user in one or more applications. Through use of the unified IAM tool, an operator can access a single interface to manage user identity and access privileges across multiple applications which may have their own identity management interfaces, with user roles in different applications mapped through use of an equivalency matrix in some cases. The IAM tool can be used to add, remove, or edit user access privileges for multiple applications.

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 device may receive a first sample. The device may identify a plurality of second samples that are similar to the first sample. The plurality of second samples may correspond to service tickets that have previously been processed. The device may obtain or determine log information identifying a plurality of events associated with the first sample. The device may determine respective probabilities of occurrence corresponding to the plurality of second samples. A probability of occurrence, for a particular second sample, may identify a likelihood of receiving a corresponding service ticket within a particular time period of occurrence of the plurality of events. The device may assign ranks to the plurality of second samples based on the respective probabilities of occurrence. The device may provide information identifying the ranks and the plurality of second samples.

Abstract: Implementations of the present disclosure include receiving a content image depicting a first set of objects and a style image depicting a second set of objects and a collective style, processing, by the design generation assistant, the content image using one or more ML models to determine a first sub-set of objects, to which a style is to be transferred from the style image, the first sub-set of objects including fewer objects than the first set of objects, processing, by the design generation assistant, the style image using the ML models to determine a second sub-set of objects, from which the style is to be transferred, and generating, by the design generation assistant, an output image with stylized objects, each stylized object including an object in the first sub-set of objects having the style of the one or more objects in the second sub-set of objects.

Abstract: An aerial monitoring system and method for identification and location of object features is disclosed. The aerial monitoring system and method includes training an image processing engine to identify predefined object features in images. Training involves the image processing engine generating a model for identifying predefined object features in images. Identifying the predefined object features includes using a drone outfitted with a drone camera to capture and geotag monitoring images of target objects. The monitoring images are both infrared and non-infrared. The image processing engine applies the model to the monitoring images to determine whether the monitoring images include object features that fit within one of multiple categories. The image processing engine uses a fuzzy clustering process to group objects into cluster locations. The image processing system outputs the identification and location of the object features. The output is used for maintenance planning related to the objects.

Abstract: The system and method generally include identifying whether an utterance spoken by a user (e.g., customer) is a complete or incomplete sentence. For example, the system may include a partial utterance detection module that determines whether an utterance spoken by a user is a partial utterance. The detection process may include providing a detection advice code that gives a recommendation for handling the utterance of interest. If it is determined that the utterance is an incomplete sentence, then the system and method can identify the type of utterance. For example, the system may include a partial utterance classification module that predicts the class of a partial utterance. The classification process may include providing a classification advice code that gives a recommendation for handling the utterance of interest.

Abstract: Methods, systems, and apparatus for solving optimization tasks. In one aspect, a method includes receiving input data comprising (i) data specifying an optimization task to be solved, and (ii) data specifying task objectives for solving the optimization task, comprising one or more local task objectives and one or more global task objectives; processing the received input data to obtain one or more initial solutions to the optimization task based on the local task objectives, wherein at least one initial solution is obtained from a first quantum computing resource; and processing the generated one or more initial solutions using a second quantum computing resource to generate a global solution to the optimization task based on the global task objectives.

Abstract: Cloud service based on container technology provides a lightweight, portable, but robust architecture for deploying service applications. Service volume and resource requirement for a typical modern-day application may fluctuate dramatically in time. A dynamic resource allocation scheme based on machine learning algorithms and executed by the cloud for applications deployed as independent containers allows for efficient resource usage and improvement of quality of service for the applications.

Abstract: A device receives image data that depicts a target that is subject to a security check, and receives transactional data identifying a characteristic of a transaction associated with the target. The device identifies the target within the image data and identifies a first set of target attributes of the target. The device determines a risk level that represents a likelihood of the characteristic of the transaction having a correct value by analyzing the set of target attributes using one or more attribute recognition techniques and/or using a data model that has been trained using machine learning. The device determines whether the risk level satisfies one or more threshold risk levels. The device provides an alert to another device cause the other device to perform actions based on whether at least one of the one or more threshold risk levels are satisfied.

Abstract: Implementations directed to identifying variables affecting a cost of healthcare include actions of receiving data from a plurality of data sources, the data relating to cost of providing healthcare, providing a data model based on the data, processing the data model using non-parametric analysis to provide a non-parametric result, the non-parametric result including a first and second variable, automatically processing the data model to correlate at least the first variable of the non-parametric result to the second variable of the non-parametric result to provide a correlation result, processing the correlation result using parametric analysis to provide a parametric result including at least one variable that affects the cost more than one or more other variables of a plurality of variables of the data model, and providing at least one data visualization for display, the at least one data visualization providing at least one graphical representation of the parametric result.

Abstract: Systems, methods, and apparatus, including computer programs encoded on computer storage media, for obtaining, processing, and presenting data related to security events, and for implementing courses of action to protect assets in response to the security events. An event management module identifies malicious activity present on a first network domain and/or a second network domain based on received network domain activity. A threat intelligence module receives data identifying the malicious activity in first data constructs of a predefined data structure. The threat intelligence module obtains additional data related to the identified malicious activity and generates second data constructs that include enriched data regarding the malicious activity. The enriched data includes data describing a campaign in which at least a portion of the malicious activity is involved and one or more courses of action. A course of action module receives the second data constructs and implements a given course of action.

Abstract: A device receives documents from various sources, and processes the documents, with an optical character recognition engine, to generate digitized documents. The device processes the digitized documents, with a first machine learning model, to detect languages utilized in the digitized documents, and processes the digitized documents, in other languages that are different than a common language and with a second machine learning model, to translate the digitized documents, in the other languages, into the common language and to generate translated digitized documents. The device processes the translated digitized documents and untranslated digitized documents, with a classification model, to generate classified documents, and processes the classified documents, with a third machine learning model, to generate extracted information from the classified documents.

Abstract: Systems, apparatuses, and methods are directed towards identifying that an adversarial patch image includes a plurality of pixels. The systems, apparatuses, and methods include dividing the adversarial patch image into a plurality of blocks, that each include a different group of the pixels in which the pixels are contiguous to each other, and assigning a first plurality of colors to the plurality of blocks to assign only one of the first plurality of colors to each pixel of one of the plurality of blocks.

Abstract: A participant node of a distributed ledger network may identify a distributed federated learning (DFL) smart contract stored on a blockchain. The DFL smart contract may include an aggregation sequence. The aggregation sequence may include an ordered sequence of participant node identifiers. The participant node may generate a trained model by training a global model with training data. The participant node may detect, on the blockchain, a first transition token indicative of a first model previously aggregated by another participant node. The participant node may receive the first model. The participant node may aggregate the first model with the trained model to generate a second model. The participant node may store, on the blockchain, a second transition token indicative of the second model. A successor node identified in the aggregation sequence may further aggregate the second model with an additional model in response to detection of the second transition token.

Abstract: A device may obtain ticket data relating to a set of tickets, and process the ticket data to generate a ticket analysis model that is a clustering based natural language analysis model of natural language text associated with tickets of the set of tickets. The device may classify the set of tickets using the ticket analysis model, may determine an automation plan for at least one class of ticket determined based on classifying the set of tickets, and may implement the automation plan to configure an automatic ticket resolution or ticket generation mitigation procedure for the at least one class of ticket. The device may receive a ticket after configuring the automatic ticket resolution or ticket generation mitigation procedure, may classify, using the ticket analysis model, the ticket into the at least one class of ticket, and may automatically implement a response action for the ticket based on classifying the ticket and using the automatic ticket resolution or ticket generation mitigation procedure.