Abstract: One embodiment of the invention sets forth a mechanism for encoding video streams associated with the same digital content such that switch points staggered across two video streams occur at every offset temporal distance. The offset temporal distance is less than the distance between two consecutive key frames in a given video stream. This enables a content player to switch to a video stream having a playback quality up or down one level from a current video stream at the offset temporal distance from the most recently played key frame. In effect, the content player does not wait the entire key frame temporal distance before switching.

Abstract: Computer systems and methods for verifying a route taken by a communication are disclosed. In one implementation, a device for verifying a route taken by a communication may include one or more processors configured to obtain a communication transmitted by a source entity. The communication may include data and digital signatures, and each of the digital signatures may be generated based on at least the data. Further, the digital signatures may include a digital signature associated with the source entity, and a set of digital signatures associated with at least a subset of intermediate entities on a route taken by the communication. The one or more processors may be further configured to verify the digital signatures included in the communication, verify whether the entities associated with the digital signatures form an expected route for the communication, and process the data.

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.

Abstract: Systems and methods for determining neural network brittleness are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a modeling request comprising a preliminary model and a dataset. The operations may include determining a preliminary brittleness score of the preliminary model. The operations may include identifying a reference model and determining a reference brittleness score of the reference model. The operations may include comparing the preliminary brittleness score to the reference brittleness score and generating a preferred model based on the comparison. The operations may include providing the preferred model.

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 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: In some embodiments, apparatuses and methods pertaining to a dispensing container with a dispensing cap, and cartridge are provided herein. In one aspect, a dispensing container includes a bottle with a primary fluid, a dispensing cap with a receptacle; and a cartridge insertable into the receptacle thereof and including a secondary fluid within a cavity of the cartridge, Methods of manufacturing the dispensing container, cap, and cartridge are further provided, as well as methods of using the dispensing cap and container to dispense two different fluids.

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: Systems and methods for managing indexing and tagging datasets using a plurality of nodes are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a dataset comprising a plurality of columns and applying a series of nodes to the dataset. Applying the series of nodes may compose applying a first node comprising a machine learning model to generate a first probability, appending a first tag based on the first probability, and selecting second nodes subsequent in the series based on the first probability. Applying the series may include iteratively applying the selected second nodes to generate second probabilities and second tags. The operations may include generating a data structure comprising the first and second probabilities and first and second tags. The operations may include outputting metadata.

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: A system for determining candidate answers during an interview. The system may include a memory storing executable instructions, and at least one processor configured to execute the instructions to perform operations. The operations may include capturing, by an image sensor, a plurality of images comprising a plurality of candidate answers; detecting, based on the captured images, a first bounding box comprising the plurality of the candidate answers; detecting, based on the captured images, a second bounding box comprising a plurality of alphanumeric characters, a first one of the alphanumeric characters being located on a line of software code; calculating a distance from a side of a first bounding box to a side of a second bounding box; and determining, based on the calculated distance, a source code alignment.

Abstract: Systems and methods for determining neural network brittleness are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a modeling request comprising a preliminary model and a dataset. The operations may include determining a preliminary brittleness score of the preliminary model. The operations may include identifying a reference model and determining a reference brittleness score of the reference model. The operations may include comparing the preliminary brittleness score to the reference brittleness score and generating a preferred model based on the comparison. The operations may include providing the preferred 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: 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: 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: The disclosed computer-implemented methods and systems can splice targeted content such as advertisements into a live stream of a real-time event. For example, the methods and systems discussed herein determine targeted content items for splicing into a live stream by generating a computing model of targeted content recommendations. In one or more examples, the computing model generates targeted content recommendations that are specific to a length of a targeted content break, the viewer of the live stream, and the player where the live stream is being viewed. The systems and methods discussed herein further determine the placement and duration of targeted content breaks based on signals and markers that are inserted into the live stream. Various other methods, systems, and computer-readable media are also disclosed.

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: The disclosed computer-implemented methods and systems can splice targeted content such as advertisements into a live stream of a real-time event. For example, the methods and systems discussed herein determine targeted content items for splicing into a live stream by generating a computing model of targeted content recommendations. In one or more examples, the computing model generates targeted content recommendations that are specific to a length of a targeted content break, the viewer of the live stream, and the player where the live stream is being viewed. The systems and methods discussed herein further determine the placement and duration of targeted content breaks based on signals and markers that are inserted into the live stream. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In various embodiments, a congestion control module within a transport stack limits the rate at which packets are transmitted from a server to a client device based on a percentage of the available capacity of a network path through which the packets are transmitted. In some embodiments, the available network path capacity can be determined by first performing a linear regression using (1) send durations over which packets associated with encoded frames are transmitted, and (2) corresponding reception durations over which the packets associated with the encoded frames are received, in order to determine a line that relates send duration and reception duration. After the line is determined, the available network path capacity can be computed as an estimated intersection between the determined line and the line y=x, with the intersection being approached as a limit.

Abstract: Systems and methods mediate permissions for applications on user devices using predictive models. Data communications are monitored on a user device for permission requests and responses. A predictive model is trained with these permission requests and responses until a threshold is met. Then, a default permission response is provided on behalf of the user device in response to a permission request.