Abstract: Systems and methods for synthetic data generation. A system includes at least one processor and a storage medium storing instructions that, when executed by the one or more processors, cause the at least one processor to perform operations including receiving a continuous data stream from an outside source, processing the continuous data stream in real-time, and using machine learning techniques to generating synthetic data to populate the dataset. The operations also include creating a plurality of bins, wherein the plurality of bins occupy a data range between the determined minimum and maximum values without overlapping; and determining a number of samples within each of the created bin, based on a bin edges, wherein the bin edges are bounds within the data range.

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 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: 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: Randomizations of a web page may be generated in advance and provided to a client. The client may store the randomizations in its cache. Multiple randomizations for the same web page may be provided to the client and stored in the client's cache. When a request for a web page is made, it is determined if the client has any cached randomizations. Randomizations for the probable next web page to be requested by the client may be provided to the client for storage in the cache. For example, the probability that a link will be clicked or a website visited may be determined. Those web pages and websites with higher probabilities may be determined. Randomizations for those web pages are then provided to the client for use.

Abstract: In some embodiments, gaze-tracking-based image downscaling for multi-party video communication may be provided. In some embodiments, a set of gaze locations may be received from a set of receiving devices during concurrent video communication sessions between the receiving devices and a sending device. Different collections of positions may be determined based on the gaze locations, and, for each such collection, a spatial indicator (e.g., a bounded region) may be determined. A first downscaled encoding of the source image (from the sending device) may be generated based on the first spatial indicator, and a second downscaled encoding of the source image may be generated based on the second spatial indicator, etc. The downscaled encodings may then be sent to the respective receiving devices during the concurrent video communication sessions.

Abstract: A device may receive a preauthorization associated with missed transaction prevention for a transaction account of a user, wherein the missed transaction prevention involves preventing a missed transaction associated with merchant accounts of the user. The device may monitor a transaction log of the transaction account and identify a transaction pattern associated with a merchant account based on a plurality of historical transactions identified in the transaction log related to the merchant account. The device may determine that a historical transaction of the plurality of historical transactions is not designated for automatic execution and that an execution of an upcoming transaction corresponding to the plurality of historical transactions is not scheduled. The device may cause an account transaction associated with the upcoming transaction to be automatically executed before a transaction period expiration, associated with the merchant account, passes.

Abstract: Disclosed herein are system, method, and apparatus for generating labels for k-means clusters. The method includes accessing a plurality of data records from a database repository, and storing the plurality of data records into at least one of primary or secondary memory associated with at least one computer processor performing the method, along with a cluster number for each data record. All data records having a same cluster number form a cluster, and each record has been categorized or designated a cluster number out of a total K number of clusters. The method includes for each of a plurality of classification features, performing cluster-based analysis for a first cluster with respect to a single feature to generate a single feature overlap score. The method includes sorting, grouping, and generating a naming label for the first cluster based on the predetermined number of features having the lowest overlap scores.

Abstract: Systems and methods of procuring real data items based on user affinity gauged via synthetic data items are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: utilizing a trained machine learning model to generate a synthetic data item based on real user data; presenting the synthetic data item to those users; obtaining indications identifying user responses to the synthetic data item; obtaining user-defined control parameters from the users; configuring a user-defined control mechanism to share a portion of the real user data based thereon; obtaining a subset of the real user data based the user-defined control parameters; providing to a particular third-party data source at least one of: data regarding the synthetic data item, the at least one portion of the real user data, and the indications of the users; and then receiving a second real data item from the particular third-party data source.

Abstract: Aspects described herein may relate to methods, systems, and apparatuses that provide new capabilities for recommending purchases to a user based on a new descriptor referred to as “purchase embeddings.” The purchase embedding may include a tuple in a new multi-dimensional search space for describing and tracking purchases of goods and services. Recommendations may be based on a distance between tuples in the search space, which provides a measure of similarity between items represented by the tuples.

Abstract: Disclosed embodiments may include a system for generating custom content. The system may generate a first webpage comprising first feature(s). The system may transmit the first webpage for display via a graphical user interface (GUI) of a user device. The system may iteratively, until a predetermined threshold is achieved: receive data corresponding to a first user; generate, by an encoder, encoded data based on the data; generate, by a decoder, second feature(s) based on the encoded data; modify the first webpage to generate a second webpage comprising the second feature(s); transmit the second webpage for display via the GUI of the user device; receive user feedback associated with the second webpage; and determine whether the user feedback exceeds the predetermined threshold. Responsive to the predetermined threshold being achieved, the system may transmit the latest version of the second webpage without requesting additional user feedback.

Abstract: Methods and systems described herein relate to authenticating users-based on generating images that have modified features. More specifically, the methods and systems generate these images by processing existing training data to identify a common feature in existing photos that may be modified, and then modifying that feature with the use of generative adversarial networks.

Abstract: Systems as described herein may label data to preserve privacy. An annotation server may receive a document comprising a collection of text representing a plurality of confidential data from a first computing device. The annotation server may convert the document to a plurality of text embeddings. The annotation server may input the text embeddings into a machine learning model to generate a plurality of synthetic images, and receive a label for each of the plurality of synthetic images from a third-party labeler. Accordingly, the annotation server may send the confidential data and the corresponding labels to a second computing device.

Abstract: Various embodiments are directed to a system that utilizes regular expression (regex) to recognize at least portions of characters, words, text, numbers, etc. in a structured or unstructured dataset, any patterns associated therewith, and/or similarities between the determined patterns. In examples, a regex-based pattern recognition platform may receive a dataset and determine whether at least a first regex pattern and a second regex pattern can be identified. The occurrences of the first and second regex patterns and the frequency of those occurrences may reveal something about the dataset itself or any patterns contained therein.

Abstract: In some implementations, a device may receive an activation signal transmitted by an activator device. The device may determine that a first signal pattern of the activation signal matches a second signal pattern associated with activating the Bluetooth capability of the device. The device may activate the Bluetooth capability of the device based on determining that the first signal pattern matches the second signal pattern. The device may communicate with a wireless communication device using the Bluetooth capability of the device based on activating the Bluetooth capability of the device.

Abstract: In some implementations, a system may monitor online activity of a user on a user device and determine that the user is about to perform an imminent interaction associated with a domestic interaction item. The system may identify a domestic interaction amount associated with the domestic interaction item. The system may search one or more international databases for an international interaction item corresponding to the domestic interaction item and based on a prioritized order of international countries associated with the international database(s). The system may determine, based on a currency exchange rate, that an international interaction amount associated with the international interaction item is less than the domestic interaction amount. The system may transmit, to the user device, international interaction item data indicating the international interaction item, the international interaction amount, and an associated international interaction party.

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: Various embodiments are generally directed to techniques for embedding a data object into a multidimensional frame, such as for training an autoencoder to generate latent space representations of the data object based on the multidimensional frame, for instance. Additionally, in one or more embodiments latent space representations of data objects may be classified, such as with a machine learning algorithm. Some embodiments are particularly directed to embedding a data object comprising a plurality of object entries into a three-dimensional (3D) frame.

Abstract: An exemplary system, method, and computer-accessible medium can include, for example, receiving an original dataset(s), receiving a synthetic dataset(s), training a model(s) using the original dataset(s) and the synthetic dataset(s), and evaluating the synthetic dataset(s) based on the training of the model(s). The model(s) can include a first model and a second model, and the first model can be trained using the original dataset(s) and the second model can be trained using the synthetic dataset(s). The synthetic dataset(s) can be evaluated by comparing first results from the training of the first model to second results from the training of the second model.

Abstract: Systems and methods for distorting CAPTCHA images with generative adversarial networks include an image distortion interface that can select an image record from a database, determine the size of the selected image, and apply an adversarial attack algorithm to create an array of pixels that is the same size as the selected image so that the majority of the pixels in the array are zero values and the remaining pixels in the array are in the red-green-blue value range. The image distortion interface can merge the array of pixels with the selected image to form a distorted image. A server can include a CAPTCHA test interface that can select a set of images having the same label, retrieve corresponding distorted images, and provide a CAPTCHA test using the distorted images. The CAPTCHA test can use distorted images with different applied adversarial attack algorithms.