Abstract: A blockchain / distributed ledger based approach for traversing distributed ledger data structures for generating cryptographically verifiable presentation data objects is proposed herein. The approach can be utilized as a mechanism for using a combination of cryptographically generated verifiable credential data objects with cryptographically generated verifiable presentation data objects, verifying characteristics of a user or a computational process, such as whether a user is a graduate of a particular educational institution, whether the user has a transaction history that warrants a score greater than a threshold, or whether the user has sufficient access credentials to access a controlled resource. Zero knowledge proofs are proposed as a mechanism for generating verifiable proofs to protect sensitive verifiable credential data objects. The score can be generated using a black box machine learning model.

Abstract: Machine learning based computer devices, systems and methods are proposed for automating the evaluation and visual testing of graphical user interface (GUI) designs using a combination of image transformations for scoring the GUI designs and machine learning data architectures with a set of logical and conditional rules. The approach describes an automated process that transforms the GUI designs into clusters of pixels before using a chained series of image transformations to obtain similarity scores and underlying distributions for the GUI designs and then uses a machine learning data architecture in combination with a set of logical and conditional rules to computationally generate a prediction of error estimates based on the underlying distributions of the GUI designs.