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: A block-request streaming system provides for improvements in the user experience and bandwidth efficiency of such systems, typically using an ingestion system that generates data in a form to be served by a conventional file server (HTTP, FTP, or the like), wherein the ingestion system intakes content and prepares it as files or data elements to be served by the file server. A client device can be adapted to take advantage of the ingestion process. The client device might be configured to optimize use of resources, given the information available to it from the ingestion system. This may include configurations to determine the sequence, timing and construction of block requests based on monitoring buffer size and rate of change of buffer size, use of variable sized requests, mapping of block requests to underlying transport connections, flexible pipelining of requests, and/or use of whole file requests based on statistical considerations.

Abstract: Systems, methods, and computer-readable storage media for ensuring electronic communications have not been intercepted and manipulated. An exemplary device generates a public/private pair of keys, and transmits the public key to another device with information about the data to be shared. The second device encrypts associated data, while also executing a hash function on at least a portion of the data. The first device receives the encrypted data, decrypts it, and verifies its accuracy using a third party. The third party also executes the hash function on the data received from the first device, and transmits the output of that hash function to the first device. Both the first device and second devices and display the hash values, allowing users to visually determine if the data has been manipulated during the transaction.

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 multi-dispersive spectrometer is provided in which the spectrometer comprises an optical system configured to direct an excitation signal from an excitation light source toward a sample, receive a spectroscopy signal from the sample, and direct the spectroscopy signal toward the detector. The optical system comprises a movable optical component adapted to move the spectroscopy signal relative to at least one sensor of the detector and the detector is adapted to detect a plurality of discrete shifted spectroscopy signals. A method of obtaining a Raman spectrum from a sample is also provided. The method comprises directing an excitation signal from an excitation light source toward a sample; receiving a spectroscopy signal from the sample; and directing the spectroscopy signal toward a detector, wherein the spectroscopy signal is moved relative to at least one sensor of the detector to provide a plurality of discrete shifted spectroscopy signals.

Abstract: An error notification system includes a plurality of data production systems in communication with a monitoring server. Each data production system has a data processor configured to receive input data from a first set of data production systems, process the input data to produce output data, and make the output data accessible to a second set of data production systems. The monitoring server is configured to monitor data transmissions between the data production systems and to identify, for each data transmission, originating and receiving systems. The monitoring server is further configured to map data flow from each originating source system to identify all downstream data production systems. Upon identification of a data error in the originating source system, the monitoring server obtains data error information, assembles a data error notification, and transmits the data error notification to data production systems meeting system notification criteria.

Abstract: Aspects of the disclosure include computer-implemented methods and systems for providing generative adversarial network (GAN) digital image data. GAN digital image data corresponding to a suggested transaction for an identified customer can be determined.

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: Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

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 a first type of information, an extended reality (XR) environment may be provided to the user. The user may provide the first type information via an input method in the XR environment. The user may provide a second type of information using a different input method outside of the XR environment. The first type of information may be processed and stored in a different database as compared to the second type of information. The database storing the first type of information may have a higher security standard than the database storing second type of information.

Abstract: A playback application is configured to analyze audio frames associated with transitions between segments within a media title to identify one or more portions of extraneous audio. The playback application is configured to analyze the one or more portions of extraneous audio and then determine which of the one or more corresponding audio frames should be dropped. In doing so, the playback application can analyze a topology associated with the media title to determine whether any specific portions of extraneous audio are to be played outside of a logical ordering of audio samples set forth in the topology. These specific portions of extraneous audio are preferentially removed.

Abstract: Systems and methods for route mapping with familiar routes include striking a balance between optimal routes from standard navigation systems minimizing time and distance and a mapping component that suggests familiar routes based on a user's route history. New routes including one or more familiar routes may be suggested to the user when they are not too far out of the way or take too long compared to the optimal routes and when they may be preferable or more comfortable.

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 playback application is configured to dynamically generate topology for an interactive media title. The playback application obtains an initial topology and also collects various data associated with a user interacting with the feature. The playback application then modifies the initial topology, based on the collected data, to generate a dynamic topology tailored to the user. The dynamic topology describes the set of choices available to the user during playback as well as which options can be selected by the user when making a given choice. In addition, the playback application also selectively buffers different portions of the interactive media title, based on the collected data, in anticipation of the user selecting particular options for available choices.

Abstract: Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

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: 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 changes to a live voice stream of a person are disclosed. A change to be made to the live voice stream based on user information can be identified. The live voice stream can be changed based on the user information.