Abstract: An example method includes receiving historical usage data associated with computing services provided by distributed servers and an inference model. The inference model is configured to receive a request and make an inference based on the request. The method further includes training a machine learning model to determine a correlation between usage of a first computing service of the and usage of the inference model. The correlation indicates that a first spike in usage of the first computing service precedes a second spike in usage of the inference model. The method further includes receiving, in real-time, current usage data associated with the first computing service. The method further includes determining, based on the current usage data and the correlation, that the current usage data is indicative of the first spike in usage of the first computing service that precedes the second spike in usage of the inference model.

Abstract: An exemplary system, method and computer-accessible medium can include receiving information regarding a purchase by a person, receiving financial account information regarding a financial account associated with the person that was used to fund the purchase, generating rewards information associated with the purchase, generating an authentication token(s) based on the financial account information, sending the rewards information and the authentication token(s) over a network, and receiving a confirmation, over the network, that a rewards account associated with the person has been updated.

Abstract: A method for generating a dual-class dataset is disclosed. A single-class dataset and a context dataset are obtained. The context dataset can be labeled. A model can be trained using the combination of the single-class dataset and the labeled context dataset. The model can be run on the context dataset. The data points that are classified the same as the data points included in the single-class dataset, can be removed from the labeled context dataset and added to the single-class dataset. These steps can be repeated until no data points are classified by the model.

Abstract: Embodiments of the present disclosure provide a device, system, methods and components related to a modular card. Example components of the modular card include a thin rectangular rear plate, a thin rectangular front plate overlying the rear plate, a first plurality of fasteners fixed to the thin rectangular front plate, a second plurality of fasteners fixed to the thin rectangular rear plate, the second plurality of fasteners being configured to be removably engaged with the first plurality of fasteners; a magnetic strip, and/or an integrated circuit. In operation a user may remove and/or replace various components to customize the modular card. Some customizations include altering the accounts to which the modular card relates. In some example, the modular card may include components which relate to different accounts.

Abstract: A computer-implemented method for facilitating identification of object coverage information using prior acquisition data, that includes detecting an object acquisition, determining a grouping of the object acquisition from a plurality of groupings, and identifying a corresponding object acquisition within the same grouping as the object acquisition. The method further includes determining the corresponding object acquisition includes coverage protection, and transmitting an alert of the corresponding object acquisition and the coverage protection.

Abstract: A method includes storing, by one or more processors of one or more computing devices, a plurality of datasets in a non-transitory computer memory associated with the one or more computing devices. A plurality of index representations is generated where each one of the plurality of index representations includes a compressed representation of a respective one of the plurality of datasets. The plurality of index representations is stored in the non-transitory computer memory. A sample dataset is received by the one or more processors. A sample dataset representation is generated that includes a compressed representation of the sample dataset. A determination that at least one of the plurality of datasets is most similar to the sample dataset based on the sample dataset representation and the plurality of index representations is performed.

Abstract: Disclosed herein are system, method, and apparatus for generating labels for k-means clusters. The method includes accessing a plurality of data records from a database repository, and storing the plurality of data records into at least one of primary or secondary memory associated with at least one computer processor performing the method, along with a cluster number for each data record. All data records having a same cluster number form a cluster, and each record has been categorized or designated a cluster number out of a total K number of clusters. The method includes for each of a plurality of classification features, performing cluster-based analysis for a first cluster with respect to a single feature to generate a single feature overlap score. The method includes sorting, grouping, and generating a naming label for the first cluster based on the predetermined number of features having the lowest overlap scores.

Abstract: Aspects described herein may allow for deploying third-party callback to be performed on a mobile device. For example, the mobile device may configure a third-party callback to be performed after a triggering event is detected by a remote device. The triggering event may relate to a remote device associated with the mobile device. A determination may be made as to whether the mobile device is within a pre-determined proximity of the remote device. If the two devices are in proximity and if the mobile device receives a message that indicates the triggering event has been detected, the mobile device may perform the third-party callback. In this way, a user's need to request services conveniently and accurately is facilitated.

Abstract: A secure method for resetting the password for an account is disclosed. During the setup of the account, the user can provide the service provider with a media file, and when the user asks the service provider to reset the password for the account, the user will be prompted with several media files. The user can be asked to identify the media file that the user provided to the service provider at the time of the setup of the account. If the user properly identifies the media file, the password will be reset.

Abstract: Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.

Abstract: An exemplary system, method, and computer-accessible medium can include, for example, (a) receiving a dataset(s), (b) determining if a misclassification(s) is generated during a training of a model(s) on the dataset(s), (c) generating a synthetic dataset(s) based on the misclassification(s), and (d) determining if the misclassification(s) is generated during the training of the model(s) on the synthetic dataset(s). The dataset(s) can include a plurality of data types. The misclassification(s) can be determined by determining if one of the data types is misclassified. The dataset(s) can include an identification of each of the data types in the dataset(s).

Abstract: Screen intake assemblies and related methods of fabrication and operation that improve both operational performance and structural strength. The screen intake assembly can include one or more screen portions that run substantially and continually along the length of the screen assembly to provide increase fluid intake and uniform flow. The screen intake assembly can comprise a central manifold having an exterior manifold screen portion to provide additional filtering capacity. The screen intake assembly can be used alone or in combination with internal flow modifier structures to increase fluid intake and uniformity. Internal flow modifiers can include a perforated flow modifier pipe, an internal flow modifier pipe, and/or converging flow modifiers. The screen intake assembly can include cleaning systems that operate continuously or on demand.

Abstract: A cloud computing system can be configured to generate data models. A model optimizer of the cloud computing system can provision computing resources of the cloud computing system with a data model. A dataset generator of the cloud computing system can generate a synthetic dataset for training the data model. The computing resources can train the data model using the synthetic dataset. The model optimizer can store the data model and metadata of the data model in a model storage. The cloud computing system can receive production data from a data source by a production instance of the cloud computing system using a common file system. The production data can be processed using the data model by the production instance. The computing resources, the dataset generator, and the model optimizer can be hosted by separate virtual computing instances of the cloud computing system.

Abstract: Methods and Systems for providing a change to a voice interacting with a user are described. Information indicating a change that can be made to the voice can be received. The voice can be changed based on the information.

Abstract: A system for performing one or more steps of a method is disclosed. The method includes receiving a first legal clause, generating, using a segmentation algorithm, a first hidden Markov chain comprising a plurality of first nodes based on the first legal clause, each of the plurality of first nodes corresponding to an element of the first legal clause, generating, using the segmentation algorithm, a second hidden Markov chain comprising a plurality of second nodes based on the second legal clause, each of the plurality of second nodes corresponding to an element of the second legal clause, comparing each of the plurality of first nodes with each of the plurality of second nodes to identify a difference for each of the plurality of first nodes, determine, based on the comparison, whether the difference for each of the plurality of first nodes exceeds a predetermined difference threshold.

Abstract: Methods and systems for the dynamically re-clustering of nodes in clusters to provide optimal performance and/or the most efficient use of resources through the use of machine learning models. Specifically, the methods and systems may determine a cluster that optimally performs and/or has the most efficient use of resources based on a first machine learning model. The methods and system may then retrieve available substitute nodes from other domains and/or networks that may lie outside the cluster, but may nonetheless be available to, or accessed by the cluster. The methods and systems may then generate an additional plurality of clusters using one or more of the original nodes of the originally selected clusters and/or one or more of the available substitute nodes.

Abstract: A spectrometer is provided. In one implementation, for example, a spectrometer comprises an excitation source, a focusing lens, a movable mirror, and an actuator assembly. The focusing lens is adapted to focus an incident beam from the excitation source. The actuator assembly is adapted to control the movable mirror to move a focused incident beam across a surface of the sample.

Abstract: Apparatuses, systems and methods are described herein for separately collecting and storing form contents. Different information may be collected from a user on a form. Based on determining that portions of the form request private information, an extended reality (XR) environment may be provided to the user. The user may provide private information via an input method in the XR environment. The user may provide non-private information using a different input method outside of the XR environment. The private information may be processed and stored in a different database as compared to the non-private information. The database storing private information may have a higher security standard than the database storing non-private information.

Abstract: Techniques are disclosed for improving user experience of multimedia streaming over computer networks. More specifically, techniques presented herein reduce (or eliminate) latency in playback start time for streaming digital media content resulting from digital rights management (DRM) authorizations. A streaming media client (e.g., a browser, set-top box, mobile telephone or tablet “app”) may request a “fast-expiring” license for titles the streaming media client predicts a user is likely to begin streaming. A fast-expiring license is a DRM license (and associated decryption key) which is valid for only a very limited time after being used for playback. During the validity period of such a license, the client device requests a “normal” or “regular” license to continue accessing the title after the fast-expiring license expires.

Abstract: Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.