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: 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.

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: 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 computing system may comprise a monitoring server system, a target server system, and client devices that may be connected via one or more networks. The monitoring server system may identify client devices to perform a test on the target server system, and may send a payload with instructions for performing the test to the clients. The client devices may determine that the payload is authentic and execute the instructions in the payload, as part of a non-malicious botnet, to perform the test on the target server system. The monitoring server system may receive client-side results from the client devices, and server-side results from the target server system. The monitoring server system may generate a report based on the received results. The report may indicate one or more issues, points of failure and/or recommendations for mitigating the issues and/or points of failure.

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: In certain embodiments, reference identifiers may be generated and embedded into a website's content. A request for a reference identifier to be embedded into first content on a first website may be obtained based on a user accessing the first website. Based on the request, interaction data related to the first website may be retrieved indicating that a prior user interacted with text on the first website and subsequently accessed a second website. A reference identifier related to the second website may be caused to be embedded into the text on the first website based on: (i) the second website comprising second content related to the text, (ii) the prior user interacting with the text on the first website, and (iii) the prior user accessing the second website after interacting with the first content on the first website.

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 are described herein for improvements to authenticate users, particularly authenticating a user based on data known to the user. For example, methods and systems allow for users to be securely authenticated based on data known to the users over remote communication networks without storing the data known to the users. Specifically, methods and systems authenticate users by requiring users to select images that are known to the users. For example, the methods and systems may generate synthetic images based on the user's own images and require the user to select the synthetic image, from a set of a set of images, that is known to the user to authenticate the user. Moreover, the methods and systems alleviate storage and privacy concerns by not storing the data known to the users.

Abstract: A system can include, for example, a secure data module(s) configured to store sensitive data regarding the user(s), a synthetic dataset generating module(s) configured to generate the synthetic dataset based on the sensitive data, and a control module configured to receive a request from an application for a dataset related to the user(s), provide the request to the synthetic dataset generating module(s), receive the synthetic dataset from the synthetic dataset generating module(s), and provide the synthetic dataset to the application. The synthetic dataset generating module(s) can be configured to generate the synthetic dataset based on the dataset.