Abstract: A system is disclosed for securely communicating between a user device and a target device, which includes a user input receiving user inputs and a user device memory for storing at least one fixed dataset having a plurality of data bits and an inherent entropy. At least one predetermined harvest process is stored in ser device memory, which is operable within a main harvest process to distill the dataset to a predetermined bit length to define a private key of the user at a predetermined key length.

Abstract: A method for encoding/decoding a message with One Time Pad (OTP) process, includes receiving a message to be encoded and an ordered dataset. A PRNG is parameterized with an initial seed that is used in a harvesting operation, which selects data from the dataset to create a current entropy plane of the selected data. In an encoding operation a portion of the received message is selected. The current entropy plane is scanned and data from which data is selected. The location of the selected data from the entropy plane is determined and marked as used and then assembled in an encoded message. This operation is repeated until no unmarked data is present in the current entropy plane. A new entropy plane is generated, and the scanning operation repeated until all portions of the received message have been processed. The process is reversed for a decoding operation.

Abstract: A system is disclosed for securely communicating between a user device and a target device, which includes a user input receiving user inputs and a user device memory for storing at least one fixed dataset having a plurality of data bits and an inherent entropy. At least one predetermined harvest process is stored in ser device memory, which is operable within a main harvest process to distill the dataset to a predetermined bit length to define a private key of the user at a predetermined key length.

Abstract: System and method for transformer-based adversarial active learning system. A machine learning system includes a generator, a transformer encoder, a classifier, and a discriminator all working in combination to generate and select unlabeled data points for labeling. The system utilizes a generative adversarial network paired with an active learning framework to optimize text embedding and feature encoding according to distribution of training data.

Abstract: A method for encoding/decoding a message with One Time Pad (OTP) process, includes receiving a message to be encoded and an ordered dataset. A PRNG is parameterized with an initial seed that is used in a harvesting operation, which selects data from the dataset to create a current entropy plane of the selected data. In an encoding operation a portion of the received message is selected. The current entropy plane is scanned and data from which data is selected. The location of the selected data from the entropy plane is determined and marked as used and then assembled in an encoded message. This operation is repeated until no unmarked data is present in the current entropy plane. A new entropy plane is generated, and the scanning operation repeated until all portions of the received message have been processed. The process is reversed for a decoding operation.

Abstract: A method for encoding/decoding a message with One Time Pad (OTP) process, includes receiving a message to be encoded and an ordered dataset. A PRNG is parameterized with an initial seed that is used in a harvesting operation, which selects data from the dataset to create a current entropy plane of the selected data. In an encoding operation a portion of the received message is selected. The current entropy plane is scanned and data from which data is selected. The location of the selected data from the entropy plane is determined and marked as used and then assembled in an encoded message. This operation is repeated until no unmarked data is present in the current entropy plane. A new entropy plane is generated, and the scanning operation repeated until all portions of the received message have been processed. The process is reversed for a decoding operation.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. An application server or another device may receive a set of input data associated with an activity between an actor and an electronic communication message (e.g., a marketing email). From the input data, the application server may identify a set of features associated with the activity (an open rate, a click rate, etc.) and a set of source network addresses of respective, known automated scanners. The application server may input the features and source network addresses into a positive-and-unlabeled (PU) learning model, which may output a classification result that indicates a probability that the activity is associated with an automated scanner.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A modeling service may generate a set of candidate segments using a set of cluster models and based on a seed segment and entity data. Based on respective features associated with the segments, the service may generate candidate segment fingerprints and a seed segment fingerprint, where a segment fingerprint may indicate a distribution of entities within a segment based on similarities between features associated with entities within the segment. That is, a segment fingerprint may depict how similar entities are in a candidate segment based on different features. The service may calculate similarity scores between the seed segment and the candidate segments using the segment fingerprints, and rank entities in terms of their similarity. The highest ranking entities may be identified from the candidate segments and included in a lookalike segment corresponding to the seed segment.

Abstract: A system is disclosed for securely communicating between a user device and a target device, which includes a user input receiving user inputs and a user device memory for storing at least one fixed dataset having a plurality of data bits and an inherent entropy. At least one predetermined harvest process is stored in ser device memory, which is operable within a main harvest process to distill the dataset to a predetermined bit length to define a private key of the user at a predetermined key length.

Abstract: A system is disclosed for securely communicating between a user device and a target device, which includes a user input receiving user inputs and a user device memory for storing at least one fixed dataset having a plurality of data bits and an inherent entropy. At least one predetermined harvest process is stored in ser device memory, which is operable within a main harvest process to distill the dataset to a predetermined bit length to define a private key of the user at a predetermined key length.

Abstract: A system is disclosed for securely communicating between a user device and a target device, which includes a user input receiving user inputs and a user device memory for storing at least one fixed dataset having a plurality of data bits and an inherent entropy. At least one predetermined harvest process is stored in ser device memory, which is operable within a main harvest process to distill the dataset to a predetermined bit length to define a private key of the user at a predetermined key length.

Abstract: A system is disclosed for securely communicating between a user device and a target device, which includes a user input receiving user inputs and a user device memory for storing at least one fixed dataset having a plurality of data bits and an inherent entropy. At least one predetermined harvest process is stored in ser device memory, which is operable within a main harvest process to distill the dataset to a predetermined bit length to define a private key of the user at a predetermined key length.

Abstract: A system is disclosed for securely communicating between a user device and a target device, which includes a user input receiving user inputs and a user device memory for storing at least one fixed dataset having a plurality of data bits and an inherent entropy. At least one predetermined harvest process is stored in ser device memory, which is operable within a main harvest process to distill the dataset to a predetermined bit length to define a private key of the user at a predetermined key length.

Abstract: A system is disclosed for securely communicating between a user device and a target device, which includes a user input receiving user inputs and a user device memory for storing at least one fixed dataset having a plurality of data bits and an inherent entropy. At least one predetermined harvest process is stored in ser device memory, which is operable within a main harvest process to distill the dataset to a predetermined bit length to define a private key of the user at a predetermined key length.

Abstract: A system is disclosed for securely communicating between a user device and a target device, which includes a user input receiving user inputs and a user device memory for storing at least one fixed dataset having a plurality of data bits and an inherent entropy. At least one predetermined harvest process is stored in ser device memory, which is operable within a main harvest process to distill the dataset to a predetermined bit length to define a private key of the user at a predetermined key length.