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: 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: 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: Techniques to apply machine learning to schedule events of interest for a device user. As described herein, a typical device user is inundated with information suggesting activities and other things to do. Using these techniques, the information is categorized according to the activity and those activities that are most likely to be engaged in by the device user are recommended to the device user (via their device) as events of interest. If the device user selects an event of interest, the device is updated to reflect that selection. For instance, a calendar application may be updated to include an event description at the event's date and time. Other embodiments are described and claimed.

Abstract: A computer-implemented method may include: receiving first data that may include information associated with an error in an execution of code; based on the first data, determining: for each contributor in a predetermined list of contributors: a respective familiarity value indicative of an amount of association between the contributor and the error in the code; and a respective set of task management statistics; and an urgency value of the error; using an optimization model, selecting at least one contributor from the predetermined list of contributors to assign to the error, the optimization model based on the respective familiarity values and the respective sets of task management statistics for each contributor, and the urgency value of the error; and transmitting a notification indicative of the error to a computing device associated with the at least one contributor assigned to the error.

Abstract: Disclosed herein are system, method, and computer program product embodiments for a text-to-speech system. An embodiment operates by receiving text to be converted into audible output and identifying a webpage address within the text. A webpage corresponding to the webpage address is retrieved, wherein the retrieved webpage is not displayed on a user interface. Metadata of the retrieved webpage corresponding to the webpage address is identified. A portion of the metadata is selected to replace the webpage address in an audible version of the text. The webpage address is replaced with the selected portion of metadata of the retrieved webpage corresponding to the webpage address in the audible version of the text. The audible version of the text including the selected portion of metadata is provided for audible output.

Abstract: Techniques for providing a real-time service that protects personal data of clients from customer service agents are provided. Customer data that includes personal data indicative of sensitive information of a customer can be received from the customer. The personal data within the received customer data can be detected and a token that does not include the sensitive information of the customer can be generated. The personal data and the generated token can be stored along with data indicating a relationship between the token and the personal data. The personal data in the received customer data can be replaced by the token to form modified customer data. The modified customer data can be provided to a customer service representative. The token within the modified customer data can later be detected and associated with the personal data without revealing the personal data to the customer service representative.

Abstract: Techniques and apparatus for providing peer-based management of user accounts are described. In one embodiment, for example, an apparatus may include at least one memory and logic coupled to the at least one memory. The logic may be configured to receive a request from at least one first user account to unlock a second user account locked responsive to a fraud event, determine a safe authentication value for the fraud event, and unlock the second user account responsive to the at least one first user account being a safe authentication account and the safe authentication value being over a safe authentication threshold value. Other embodiments are described.

Abstract: Various embodiments are generally directed to techniques utilizing computers to process service billing data, generate one or more groups of customers based on one or more attributes of the data, and train a recurrent neural network (RNN) to predict future bill amounts for customers. Embodiments further include techniques to predict the future bill amounts of the customers and providing an indication of the bill amounts to the customers.

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: 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 server device may receive information identifying a user of a first device participating in a screen sharing session with a second device and associate the information identifying the user of the first device with information indicating a sequence of interactions with an application at the first device. The server device may determine one or more user interface elements that correspond to a current view of the application at the first device based on the sequence of interactions, and transmit, to the second device, information identifying the one or more user interface elements that correspond to the current view to permit the second device to reconstruct the current view based on the information identifying the one or more user interface elements that correspond to the current view and stored information related to a plurality of views associated with the application.

Abstract: Systems and methods for redeeming digital files are disclosed. In particular, the systems and methods relate to localized sharing of digital files such that the digital file is degraded when the file is redeemed. The digital file can include a plurality of bits, and bits of the digital file can be removed upon each transfer and/or access of the digital file. When a quantity of bits in the digital file falls below a predetermined threshold, the digital file can be deactivated. The systems can include an application that degrade the digital file. The degradation can include file compression, bitrate reduction, and/or removal of parity bits from the digital file. Security measures, such as private/public encryption keys, are also disclosed herein.

Abstract: A method for generating a dual-class dataset is disclosed. A single-class dataset and a context dataset are obtained. The context dataset can be labeled. A model can be trained using the combination of the single-class dataset and the labeled context dataset. The model can be run on the context dataset. The data points that are classified the same as the data points included in the single-class dataset, can be removed from the labeled context dataset and added to the single-class dataset. These steps can be repeated until no data points are classified by the model.

Abstract: Embodiments of the present disclosure provide a device, system, methods and components related to a modular card. Example components of the modular card include a thin rectangular rear plate, a thin rectangular front plate overlying the rear plate, a first plurality of fasteners fixed to the thin rectangular front plate, a second plurality of fasteners fixed to the thin rectangular rear plate, the second plurality of fasteners being configured to be removably engaged with the first plurality of fasteners; a magnetic strip, and/or an integrated circuit. In operation a user may remove and/or replace various components to customize the modular card. Some customizations include altering the accounts to which the modular card relates. In some example, the modular card may include components which relate to different accounts.

Abstract: A computer-implemented method for facilitating identification of object coverage information using prior acquisition data, that includes detecting an object acquisition, determining a grouping of the object acquisition from a plurality of groupings, and identifying a corresponding object acquisition within the same grouping as the object acquisition. The method further includes determining the corresponding object acquisition includes coverage protection, and transmitting an alert of the corresponding object acquisition and the coverage protection.

Abstract: An error notification system includes a plurality of data production systems in communication with a monitoring server. Each data production system has a data processor configured to receive input data from a first set of data production systems, process the input data to produce output data, and make the output data accessible to a second set of data production systems. The monitoring server is configured to monitor data transmissions between the data production systems and to identify, for each data transmission, originating and receiving systems. The monitoring server is further configured to map data flow from each originating source system to identify all downstream data production systems. Upon identification of a data error in the originating source system, the monitoring server obtains data error information, assembles a data error notification, and transmits the data error notification to data production systems meeting system notification criteria.

Abstract: A method for training a neural network model includes generating a training dataset with a plurality of data types and word samples belonging to each data type. A plurality of character strings stored in a plurality of data fields in a first data file are received where the plurality of character strings includes at least one word belonging to at least one data type in the plurality of data types. The at least one word from each of the plurality of character strings in each of the data fields are split and matched to the at least one data type using the neural network model. An ad hoc second data file with a plurality of data vectors is constructed based on a user selection of data field labels where each data vector includes words matched to a data type with a respective data field label.

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: Described herein are embodiments for managing comments in a program code file. A system may select program code and compile it to an intermediary code. The system may compare the intermediary code to a library of intermediary code snippets associated with comments. Based on the comparison, a system may recognize the code to be obsolete. In some embodiments, a system may generate one or more recommendations to update a code. Based on received feedback regarding a recommendation, a system may accordingly update a code.