Abstract: Apparatuses, methods, systems, and computer program products are presented for dynamic feature and performance testing and adjustment. An audit module is configured to dynamically test a plurality of image capture settings for a camera of a mobile device of an end user in an executable mobile application executing on the mobile device. A feature module is configured to select one of a plurality of image capture settings for a camera of a mobile device based on a dynamic test. An adjustment module is configured to dynamically configure, during runtime of an executable mobile application on a plurality of different mobile devices of different end users, the different mobile devices to use a selected one of a plurality of image capture settings.

Abstract: Apparatuses, methods, systems, and program products are presented for securing data based on randomization. An apparatus includes a security component that is configured to determine, during compile time of an application, at least one random value used to determine a partition size, a set of storage locations, and an access threshold for a data element during execution of the application. An apparatus includes a partition module that is configured to partition a data element into a plurality of data segments that each have a size that is randomly determined, during execution of an application, based on at least one random value to determine the partition size. An apparatus includes a storage module that is configured to randomly determine a first set of storage locations for a plurality of data segments, during execution of an application, based on at least one random value for the set of storage locations.

Abstract: Apparatuses, systems, methods, and computer program products are presented for a propensity module based optimization. An apparatus comprises a processor and a memory that stores code executable by the processor to receive an electronic submission for a pass/fail interface, identify information from the electronic submission to suggest to a user for entering into an input field for the pass/fail interface prior to submitting the electronic submission to the pass/fail interface to reduce a likelihood that the electronic submission will be rejected at the pass/fail interface, determine the likelihood that the electronic submission will be accepted by the pass/fail interface, and submit the electronic submission to the pass/fail interface in response to the likelihood satisfying a threshold.

Abstract: Apparatuses, methods, systems, and computer program products are presented for dynamic feature and performance testing and adjustment. An audit module is configured to dynamically test a plurality of image capture settings for a camera of a mobile device of an end user in an executable mobile application executing on the mobile device. A feature module is configured to select one of a plurality of image capture settings for a camera of a mobile device based on a dynamic test. An adjustment module is configured to dynamically configure, during runtime of an executable mobile application on a plurality of different mobile devices of different end users, the different mobile devices to use a selected one of a plurality of image capture settings.

Abstract: Apparatuses, methods, systems, and computer program products are presented for dynamic feature and performance testing and adjustment. An audit module is configured to dynamically test a plurality of image capture settings for a camera of a mobile device of an end user in an executable mobile application executing on the mobile device. A feature module is configured to select one of a plurality of image capture settings for a camera of a mobile device based on a dynamic test. An adjustment module is configured to dynamically configure, during runtime of an executable mobile application on a plurality of different mobile devices of different end users, the different mobile devices to use a selected one of a plurality of image capture settings.

Abstract: Apparatuses, methods, systems, and program products are presented for securing data based on randomization. An apparatus includes a security component that is configured to determine, during compile time of an application, at least one random value used to determine a partition size, a set of storage locations, and an access threshold for a data element during execution of the application. An apparatus includes a partition module that is configured to partition a data element into a plurality of data segments that each have a size that is randomly determined, during execution of an application, based on at least one random value to determine the partition size. An apparatus includes a storage module that is configured to randomly determine a first set of storage locations for a plurality of data segments, during execution of an application, based on at least one random value for the set of storage locations.

Abstract: Apparatuses, methods, systems, and program products are presented for securing data based on randomization. An apparatus includes a partition module that is configured to partition a data element into a plurality of data segments. An apparatus includes a storage module configured to randomly determine a first set of storage locations for a plurality of data segments such that the data segments are not stored in contiguous storage locations. An apparatus includes a threshold module configured to randomly determine an access threshold for a data element. An access threshold, in certain embodiments, comprises a number of times a data element can be accessed before determining different storage locations for a plurality of data segments, which may be reconstituted in response to a data access. A storage module randomly determines a second set of storage locations for a plurality of data segments in response to an access threshold being satisfied.

Abstract: Apparatuses, systems, methods, and computer program products are presented for a propensity module based optimization. An apparatus comprises a processor and a memory that stores code executable by the processor to receive an electronic submission for a pass/fail interface, identify information from the electronic submission to suggest to a user for entering into an input field for the pass/fail interface prior to submitting the electronic submission to the pass/fail interface to reduce a likelihood that the electronic submission will be rejected at the pass/fail interface, determine the likelihood that the electronic submission will be accepted by the pass/fail interface, and submit the electronic submission to the pass/fail interface in response to the likelihood satisfying a threshold.

Abstract: Apparatuses, systems, methods, and computer program products are presented for a propensity module based optimization. A mobile computing device comprises one or more of a camera, a network interface, and an optimization module. An optimization module is configured to process one or more images from a camera using machine learning to determine a likelihood that the one or more images will pass submission to a pass/fail interface over a network interface. An optimization module is configured to submit one or more images to a pass/fail interface over a network interface in response to a likelihood satisfying a threshold.

Abstract: Apparatuses, methods, systems, and computer program products are presented for dynamic feature and performance testing and adjustment. An audit module is configured to dynamically test a plurality of image capture settings for a camera of a mobile device of an end user in an executable mobile application executing on the mobile device. A feature module is configured to select one of a plurality of image capture settings for a camera of a mobile device based on a dynamic test. An adjustment module is configured to dynamically configure, during runtime of an executable mobile application on a plurality of different mobile devices of different end users, the different mobile devices to use a selected one of a plurality of image capture settings.

Abstract: Apparatuses, methods, systems, and program products are presented for securing data based on randomization. An apparatus includes a partition module that is configured to partition a data element into a plurality of data segments. An apparatus includes a storage module configured to randomly determine a first set of storage locations for a plurality of data segments such that the data segments are not stored in contiguous storage locations. An apparatus includes a threshold module configured to randomly determine an access threshold for a data element. An access threshold, in certain embodiments, comprises a number of times a data element can be accessed before determining different storage locations for a plurality of data segments, which may be reconstituted in response to a data access. A storage module randomly determines a second set of storage locations for a plurality of data segments in response to an access threshold being satisfied.

Abstract: Apparatuses, systems, methods, and computer program products are presented for a propensity module based optimization. A mobile computing device comprises one or more of a camera, a network interface, and an optimization module. An optimization module is configured to process one or more images from a camera using machine learning to determine a likelihood that the one or more images will pass submission to a pass/fail interface over a network interface. An optimization module is configured to submit one or more images to a pass/fail interface over a network interface in response to a likelihood satisfying a threshold.