Abstract: Methods, systems, and computing devices for visualizing natural language processing algorithm processes are described herein. A plurality of categories may be determined. Each color of a plurality of colors may correspond to the categories. Text content may be processed using a natural language processing algorithm. Confidence values indicating, for each of a plurality of portions of the text content, a degree of confidence corresponding to one or more of the plurality of categories may be determined. Display colors may be determined based on the confidence values. A user interface comprising a visualization of the text content may be displayed, and the user interface may be configured to show each portion of the text content using a display color such that the user interface indicates changes in confidence across the plurality of characters.

Abstract: Disclosed herein are system, method, and computer program product embodiments for a text-to-speech system. An embodiment operates by identifying a portion of content of the document, that is to be replaced with a summary associated with the content responsive to a request for an audible version of the document. A first request for the audible version of the document is received. A first summary of the content at a first level of detail is generated. The first summary is audibly output. A second request for additional information is received. A second summary of the content at different level of detail is generated. The second summary of the content at the second level of detail is audibly output.

Abstract: Methods and systems are described herein for modifying scheduled events based on bandwidth availability. To modify the scheduled event, the system determines a plurality of user devices corresponding to an event. The system receives first network conditions for a first user device and generates a feature input based on the first network conditions. The system inputs the feature input into an artificial intelligence model to determine a first available bandwidth metric and aggregates the first available bandwidth metric with respective available bandwidth metrics for other user devices of the plurality of user devices to determine a composite available bandwidth metric. The system can compare the composite available bandwidth metric to a threshold available bandwidth metric and in response to determining that the composite available bandwidth metric equals or exceeds the threshold available bandwidth metric, the system generates for display, on a user device interface, a recommendation for modifying the event.

Abstract: Systems and methods for classifying data are disclosed. For example, a system may include at least one memory storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving training data comprising a class. The operations may include training a data classification model using the training data to generate a trained data classification model. The operations may include receiving additional data comprising labeled samples of an additional class not contained in the training data. The operations may include creating a synthetic data generator. The operations may include training the synthetic data generator to generate synthetic data corresponding to the additional class. The operations may include generating a synthetic classified dataset comprising the additional class. The operations may include retraining the trained data classification model using the synthetic classified dataset.

Abstract: Systems and methods for architecture embeddings for efficient dynamic synthetic data generation are disclosed. The disclosed systems and methods may include a system for generating synthetic data configured to perform operations. The operations may include retrieving a set of rules associated with a first data profile and generating, by executing a hyperparameter search, a plurality of hyperparameter sets for generative adversarial networks (GANs) that satisfy the set of rules. The operations may include generating mappings between the hyperparameter sets and the first data profile and storing the mappings in a hyperparameter library. The operations may include receiving a request for synthetic data, the request indicating a second data profile and selecting, from the mappings in the hyperparameter library, a hyperparameter set mapped to the second data profile. The operations may include building a GAN using the selected hyperparameter set and generating, using the GAN, a synthetic data set.

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 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: Systems and methods for formatting data are disclosed. For example, a system may include at least one memory storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving data comprising a plurality of sequences of data values and training a recurrent neural network model to output conditional probabilities of subsequent data values based on preceding data values in the data value sequences. The operations may include generating conditional probabilities using the trained recurrent neural network model and the received data. The operations may include determining a data format of a subset of the data value sequences, based on the generated conditional probabilities, and reformatting at least one of the data value sequences according to the determined data format.

Abstract: A system encodes/compresses at least some portion of website data and transmits the encoded website data to a remote user. A storage device stores original website data representing an original webpage. Retrieval logic retrieves the original website data from the storage device. The retrieval may be responsive to a request from an electronic user device. A parser parses the original website data to detect and tag at least one type of website code within the website data. An encoder encodes the tagged website data and produces encoded website data representing the original website data. A transmitter transmits the encoded website data to the electronic user device. The electronic user device has previously been provided a decoder from the system to decode the encoded website data to recover the original website data. This allows the electronic user device to display the website on the electronic user device.

Abstract: The present disclosure provides systems and methods for synthetic data generation. A recurrent neural network can be trained for synthetic data generation by obtaining a sequence of elements and determining, using a classifier, that the sequence corresponds to a token. In response to the determination, a recurrent neural network configured to use a first vocabulary including the elements can be modified to use a second vocabulary, the second vocabulary including the token and the first vocabulary. The modified recurrent neural network can be trained using the token and the sequence of elements. The trained recurrent neural network can be used to generate synthetic data. A classifier can detect sequences of elements in the synthetic data corresponding to tokens. The tokens can replace the sequences of elements in the generated synthetic data and can be provided to the trained recurrent neural network to continue synthetic data generation.

Abstract: Systems and methods are disclosed for assisting users in assessing costs of transactions. For instance, method may include: determining a cost value of at least one transaction associated with a user; classifying the user into a category, based on consumer interest characteristics of the user and consumer interest characteristics associated with the category; determining a relatable product based on the consumer interest characteristics associated with the category, the relatable product being a product purchased by a plurality of persons classified in the category; determining a relatable cost value, the relatable cost value being a representation of the cost value of the at least one transaction using a quantity of the respective relatable product; and presenting the relatable cost value to the user.

Abstract: In a method for activating account-variable transaction cards having private and public card encryption keys stored therein, private and public personal keys are associated with an account and transmitted to an account holder device. A request for activation of an account-variable transaction card is received by a central processor from the user device. The request includes information encrypted using at least the private personal key and the private card key. The central processor associates an account function with the account-variable transaction card and initiates via a first node in a distributed consensus network, an account blockchain associated with the account and the account-variable transaction card.

Abstract: Computer-implemented methods and systems are provided for locally freezing a user account in a geographic or digital space. Consistent with disclosed embodiments, locally freezing a user account in a geographic or digital space includes receiving fraud data associated with the user account, the fraud data including a location where a fraud associated with the user account has occurred, wherein the fraud location includes at least one of a digital location or a geographical location; receiving account data associated with the user account, the account data including non-fraudulent account transaction information; generating a pattern of fraud based on the fraud data; generating a pattern of use associated with the user account based on the account data; determining a geodigital area for a localized account freeze based on the pattern of fraud and the pattern of use; and performing a localized account freeze on the user account based on the determined geodigital area.

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: Methods and systems may be used for transfer learning of neural networks. According to one example, a method includes: grouping data objects of a first training set into a plurality of clusters; training a base model using a first cluster of the plurality of clusters, the base model being a neural network having a plurality of nodes; generalizing the base model to obtain a generalized base model, the generalizing the base model including setting a portion of the plurality of nodes to have random or predetermined weights; determining that the first cluster is, out of the plurality clusters, most similar to a second training set; and training the generalized base model using the second training set to obtain a trained model.

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: A browser extension application is configured to collect data relating to the user's browsing activity and display notifications on a user interface. A user can instruct a web browsing application to navigate to a website. The browser extension application can detect a type of the webpage, and based on the type of the webpage, collect certain information relating to what the webpage is asking the user to provide and what the user is providing to the webpage. The browser extension application can transmit this information to a browser extension server. The browser extension server can determine a likelihood that the website is associated with instances of hacking online accounts. The browser extension server can transmit a signal to the browser extension application of the user's computing device. The browser extension application can take an action, e.g., direct the user to another website or log out of the user's account.

Abstract: An exemplary system, method, and computer-accessible medium can include, for example, establishing a unique rule-identifier in one-to-one correspondence with at least one set of unknown time-variable rules against which data is to be made compliant, obtaining at least one set of data marked compliant against the one or more set of rules, obtaining meta-data from the compliant data, obtaining at least one set of data marked non-compliant against the set of unknown time-variable rules, extracting meta-data from the non-compliant data, joining the set of compliant and non-compliant metadata to generate a set of estimated rules corresponding to the rule-identifier based at least one of (i) the meta-data of the joined set and (ii) machine learning algorithms.