Abstract: A system receives a configuration request comprising an infrastructure definition that defines a set of resources, to be selected from a set of tenant-managed resources implemented on a tenant's premises, for implementing the compute target entity. The system generates a compute target entity associated with an addressable identifier. The compute target entity corresponds to the set of resources selected from the set of tenant-managed resources. The system receives an execution request for execution of a set of operations, where the execution request specifies the addressable identifier associated with the compute target entity for execution of the set of operations. The system maps the addressable identifier of the compute target entity to the set of resources. The system causes execution of the set of operations on the set of resources on the tenant's premises via the compute target entity.

Abstract: Methods, computing systems, and technology for optimizing a landing page of a website are presented. The system can receive, from a user device, a first web address associated with a first webpage of the website. The system can process, using the machine-learned assessment model, the first webpage to generate a first landing page score. The system can determine, based on the first landing page score and using a machine-learned optimization model, an actionable suggestion associated with the landing page. The system can cause, on a display of the user device, a presentation of the actionable suggestion.

Abstract: A facility providing systems and methods for discovering novel features to use in machine learning techniques. The facility receives, for a number of subjects, one or more sets of data representative of some output or condition of the subject over a period of time or capturing some physical aspect of the subject. The facility then extracts or computes values from the data and applies one or more feature generators to the extracted values. Based on the outputs of the feature generators, the facility identifies novel feature generators for use in at least one machine learning process and further mutates the novel feature generators, which can then be applied to the received data to identify additional novel feature generators.

Abstract: Example aspects of the present disclosure provide an example method. In some implementations, the example method can include receiving request data indicating a request for content. In some implementations, the example method can include determining a request context associated with the request data, wherein the request context is based on account data for a user device associated with the request. In some implementations, the example method can include determining, based on the request and the request context, a data object from a knowledge graph, wherein the data object comprises a subject and one or more attributes for the subject. In some implementations, the example method can include generating, using a machine-learned content generation model, content descriptive of the subject, the content generated based on the request, the request context, and the data object.

Abstract: Systems and methods are configured for preprocessing of images for further content based analysis thereof. Such images are extracted from a source data file, by standardizing individual pages within a source data file as image data files, and identifying whether the image satisfies applicable size-based criteria, applicable color-based criteria, and applicable content-based criteria, among others, utilizing one or more machine-learning based models. Various systems and methods may identify particular features within the extracted images to facilitate further image-based analysis based on the identified features.

Abstract: Various examples are provided related to virtual performance compatibility checking and management for modular construction. In one example, a method includes segmenting boundaries of as-built and as-planned models of a construction element; determining features of each boundary segment of the as-built and as-planned models; determining similarity ratios for matching pairs of boundary segments of the as-built and as-planned models; comparing a total similarity ratio based upon the similarity ratios for matching pairs of boundary segments with a specified threshold; and snapping the as-built model in position in a point cloud in response to the comparison. A system comprising computing or processing circuitry can execute a program or application to implement the similarity/compatibility checking methodology. The similarity/compatibility checking methodology can be implemented within a construction performance modeling and simulation (CPMS) framework.

Abstract: Embodiments described herein relate generally to a multipurpose smartphone device. More specifically, embodiments described herein disclose a smartphone that is capable of being used as a point-of-sale (POS) device.

Abstract: The present application relates to devices and components including apparatus, systems, and methods for configuring or utilizing rate matching patterns and interference measurements.

Abstract: The present disclosure presents control systems and related methods for accurate estimation of a system-level risk. One such method comprises obtaining, via at least one of one or more computing devices, a tree data structure of a system-level failure risk of a system plant, wherein a binary state failure probability of an internal node in the tree data structure is a function of its logic gate connection and its child nodes; where the tree data structure has only independent nodes, computing a binary state failure probability of a failure event represented as a top node of the tree data structure; where the tree data structure has multiple dependent chains having a common dependent node, evaluating, the binary state failure probability of each dependent chain separately; and outputting the binary state failure probability for the failure event represented as the top node of the tree data structure.

Abstract: The embodiments described herein relate generally to securely establishing an account and authentication metrics associated with a communication platform. An account associated with a communication platform may allow a user associated with the account to send and receive communications via the communication platform.

Abstract: In an embodiment, a distributed firewall that learns from traffic patterns to prevent attacks is configured to receive traffic comprising one or more uniform resource identifiers (URIs), where a URI of the one or more URIs includes one or more parameters and one or more corresponding values. The firewall is configured to classify the corresponding value(s) using a pre-configured classifier and obtain a statistical rule that specifies an allowable type and an allowable length for traffic containing the one or more parameters, where the statistical rule is generated based on the classification. The firewall is configured to apply the statistical rule to incoming traffic to allow or drop requests comprising the parameter(s).

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing multiple modalities to generate accurate two-dimensional floorplans based on sparse digital videos depicting three-dimensional space. In particular, in one or more embodiments, the disclosed systems extract both visual and audio information from sparse digital video coverage of portions of a three-dimensional space and utilize the extracted visual and audio information to generate a two-dimensional floorplan representing both viewed and unviewed portions of the three-dimensional space. For example, the disclosed systems utilize self-attention layers of a specialized machine learning model to maintain and leverage bi-directional relationships among sequences of visual and audio features to generate floorplan predictions associated with the three-dimensional space.

Abstract: Search guidance includes generating user interface data for at least a portion of a user interface including a user input element, a data-analytics request construct card, and a data-analytics guidance portion. The data-analytics request construct card includes text describing usage of a data-analytics request construct card grammatical function. The user interface data is output for presentation to a user. Data-analytic request construct card data expressing usage intent is received and updated user interface data is generated. The updated user interface data corresponds to an updated user input element in accordance with the data-analytic request construct card data and an updated data-analytics guidance portion in accordance with the data-analytic request construct card data. The updated user interface data is output. Resolved-request data is generated in accordance with the data-analytic request construct card data.

Abstract: An automated automobile claims fraud detector and method for automatically evaluating validity and extent of at least one damaged object from image data and detect possible fraud, the automated automobile claims fraud detector comprising the processing steps of: (a) receiving image data comprising one or more images of at least one damaged object; (b) processing said one or more images for existing image alteration using unusual pattern identification and providing a first fraud detection; (c) processing said one or more images for fraud detection using RGB image input, wherein the RGB values are used for (i) CNN-based pre-existing damage detection, (ii) parallel CNN-based color matching, and (iii) double JPEG compression (DJCD) detection using custom CNN; (d) processing output of (i) CNN-based pre-existing damage detection, (ii) parallel CNN-based color matching, and (iii) double JPEG compression detection using custom CNN as input for ML-based fraud identifier providing a second fraud detection; and (e) gen

Abstract: A method for authenticating a mobile device in real-time. The method includes detecting the mobile device, sending a text message containing a unique uniform resource locator (“URL”) to the mobile device, and detecting an access of the unique URL by the mobile device through a first communication path. In response to detecting the access of the unique URL, requesting and subsequently receiving, by the host system in real-time, a phone number and a subscriber identification ID associated with the mobile device through a second communication path distinct from the first communication path, and a device fingerprint of the mobile device through the first communication path. The method further includes initiating a risk analysis based on the phone number, the subscriber ID, and the device fingerprint and determining an authentication status of the mobile device based on the risk analysis.

Abstract: Methods, systems, and apparatus, including computer-readable media storing executable instructions, for enhancing robot learning. In some implementations, a robot stores first embeddings generated using a first machine learning model, and the first embeddings include one or more first private embeddings that are not shared with other robots. The robot receives a second machine learning model from a server system over a communication network. The robot generates a second private embedding for each of the one or more first private embeddings using the second machine learning model. The robot adds the second private embeddings to the cache of the robot and removes the one or more first private embeddings from the cache of the robot.

Abstract: The present application relates to devices and components including apparatus, systems, and methods for canceling reference signal interference in dynamic spectrum sharing networks.

Abstract: Deep machine learning methods and apparatus, some of which are related to determining a grasp outcome prediction for a candidate grasp pose of an end effector of a robot. Some implementations are directed to training and utilization of both a geometry network and a grasp outcome prediction network. The trained geometry network can be utilized to generate, based on two-dimensional or two-and-a-half-dimensional image(s), geometry output(s) that are: geometry-aware, and that represent (e.g., high-dimensionally) three-dimensional features captured by the image(s). In some implementations, the geometry output(s) include at least an encoding that is generated based on a trained encoding neural network trained to generate encodings that represent three-dimensional features (e.g., shape). The trained grasp outcome prediction network can be utilized to generate, based on applying the geometry output(s) and additional data as input(s) to the network, a grasp outcome prediction for a candidate grasp pose.

Abstract: A method for migrating an application includes representing the application as a vector including metadata about the application and at least one customer constraint, scoring a deviation of data consistency for the application related to each of a plurality of clouds, including a cloud in which the application is currently deployed and a plurality of potential target clouds, selecting one of the potential target clouds based on the scoring, wherein the selected cloud has an improved score over the cloud in which the application is current deployed, and migrating the application to the selected cloud.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing multiple modalities to generate accurate two-dimensional floorplans based on sparse digital videos depicting three-dimensional space. In particular, in one or more embodiments, the disclosed systems extract both visual and audio information from sparse digital video coverage of portions of a three-dimensional space and utilize the extracted visual and audio information to generate a two-dimensional floorplan representing both viewed and unviewed portions of the three-dimensional space. For example, the disclosed systems utilize self-attention layers of a specialized machine learning model to maintain and leverage bi-directional relationships among sequences of visual and audio features to generate floorplan predictions associated with the three-dimensional space.