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: Systems and methods for generating synthetic data are disclosed. For example, a 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 including time-series data. The operations may include generating a plurality of data segments based on the dataset, determining respective segment parameters of the data segments, and determining respective distribution measures of the data segments. The operations may include training a parameter model to generate synthetic segment parameters. Training the parameter model may be based on the segment parameters. The operations may include training a distribution model to generate synthetic data segments. Training the distribution model may be based on the distribution measures and the segment parameters.

Abstract: Systems and methods for generating synthetic data are disclosed. For example, a 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 including time-series data. The operations may include generating a plurality of data segments based on the dataset, determining respective segment parameters of the data segments, and determining respective distribution measures of the data segments. The operations may include training a parameter model to generate synthetic segment parameters. Training the parameter model may be based on the segment parameters. The operations may include training a distribution model to generate synthetic data segments. Training the distribution model may be based on the distribution measures and the segment parameters.

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: The disclosed systems and methods may receive a first salted password having a first password, a first user device identifier, and a first browser identifier, extract the first password, the first user device identifier, and the first browser identifier from the first salted password, and determine whether the first password, the first user device identifier, and the first browser identifier respectively match a stored first password, a stored first user device identifier, and a stored first browser identifier. The systems may grant the request to access the one or more resources for the first user device or perform other actions depending on whether the first password, the first user device identifier, and the first browser identifier respectively match the stored first password, the stored first user device identifier, and the stored first browser identifier.

Abstract: A device detects a communication involving a user associated with an account and a service representative, and sends, to a user device associated with the account, an authentication notification that causes the user device to display an authentication field for the user. The device sends, to a service representative device associated with the service representative, a message that indicates that the service representative is to request, via the communication, the user to enter personal information associated with the user into the authentication field, where the user device is configured to generate a first authentication code based on a user input received from the user device in the authentication field. The device generates a second authentication code based on personal information associated with the account from a data structure, receives the first authentication code, and performs an action based on the first authentication code and the second authentication code.

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: 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: 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 ATM fraud detection are presented. ATM fraud detection may be provided in the context of including a currency sensor in the ATM to count currency notes. ATM fraud detection may be obtained by using a currency sensor, such as a scanner, to image and count any currency notes that remain after being dispensed to a user, and determining if the count is correct for the currency notes dispensed. A count discrepancy resulting from removal of some but not all dispensed currency notes would indicate attempted theft or fraud.

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: A method includes receiving, by a processor, bias data categories. A data input from a user for classification in data categories is received. A classification machine learning model is utilized to classify the data input in at least one data category and determine a first confidence probability in a classification outcome. A bias filter machine learning model is utilized to determine a second confidence probability that the classification outcome of classifying the data input into the at least one data category is based on at least one bias characteristic associated with at least one bias data category. A gate machine learning model is utilized to determine when to output the classification outcome of classifying the data input into the at least one data category to a computing device of a user based at least in part on the first confidence probability, the second confidence probability, and a predefined bias threshold.

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: 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 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: 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: In various embodiments, a graph streaming application computes a first distance along a first potential playback path between a first playback position and a first media content block. The graph streaming application then computes a first score for the first media content block based on the first distance and a first probability associated with the first potential playback path. Similarly, the graph streaming application computes a second score for a second media content block based on a second playback position, a second potential playback path, and a second probability associated with the second potential playback path. The graph streaming application compares the first score and the second score to determine that the first media content block should be buffered by a client device. The graph streaming application then causes the first media content block to be stored in a playback buffer for subsequent playback on the client device.

Abstract: A block-request streaming system provides for low-latency streaming of a media presentation. A plurality of media segments are generated according to an encoding protocol. Each media segment includes a random access point. A plurality of media fragments are encoded according to the same protocol. The media segments are aggregated from a plurality of media fragments.

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: 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).