Abstract: Provided are techniques for assisting remote education learners. Data is collected for an actual base environment, a simulated base environment, and an exam environment for a learner taking an exam. The collected data is used to generate a first behavior pattern for the learner in the simulated base environment and a second behavior pattern for the learner in the exam environment. In response to the second behavior pattern deviating beyond a threshold from the first behavior pattern, a classification of non-cognitive learners class is determined for the learner. A de-bias technique is applied to the classification to generate a final classification. In response to the final classification being the non-cognitive learners class, education material for a subject covered in the exam is selected, and the educational material is played on a remote learner computer of the learner.

Abstract: A system and method including building propensity inclination dimensions of an affinity cluster, the propensity inclination dimensions maintained at a group profile level of the affinity cluster, identifying next best priority signals based on influencing events to determine a next best priority, applying a predictive algorithm to derive an influencing index, which is used to determine a tolerance level of the affinity cluster, deriving a duration of time of the tolerance level, and determining a critical mass of the affinity cluster required to achieve an objective of an organization.

Abstract: A method, computer program product, and system include a processor(s) obtaining, environmental data comprising captured audio data and captured image data. The processor(s) generates, based on the environmental data, a user profile for the user, by cognitively analyzing the environmental data to perform a binary valuation of one or more pre-defined core attributes. The processor identifies, based on the environmental data, one or more entities within the vicinity of the user. The processor(s) generates a subject profile for each entity of the one or more entities by cognitively analyzing the environmental data to perform the binary valuation of the one or more pre-defined core attributes. The processor(s) predicts perceived positive or negative outcome of the user initiating a contact with each entity of the one or more entities. The processor(s) generates a recommendation to initiate the contact with the at least one entity and transmits the recommendation.

Abstract: A method, system, and computer program product for implementing a digital conversion process is provided. The method includes receiving network data and text data associated with a text presentation being presented via a hardware device of a user. Semantic attributes of the text data are analyzed, and associated concepts and premises of the text data are determined. The network data is analyzed, and the user is associated with a plurality of users associated with the text presentation resulting in the formation of a group of users. A propensity inclination is determined for the group of users with respect to specialized digital dimension and retrieved images are associated with the text data. The retrieved images are presented to the user.

Abstract: A method, computer program product, and system include a processor(s) obtaining, environmental data comprising captured audio data and captured image data. The processor(s) generates, based on the environmental data, a user profile for the user, by cognitively analyzing the environmental data to perform a binary valuation of one or more pre-defined core attributes. The processor identifies, based on the environmental data, one or more entities within the vicinity of the user. The processor(s) generates a subject profile for each entity of the one or more entities by cognitively analyzing the environmental data to perform the binary valuation of the one or more pre-defined core attributes. The processor(s) predicts perceived positive or negative outcome of the user initiating a contact with each entity of the one or more entities. The processor(s) generates a recommendation to initiate the contact with the at least one entity and transmits the recommendation.

Abstract: A system, method and program product for application landscape processing. A system is disclosed that includes: a contextual analysis engine that analyzes structured and unstructured data from external source information, internal source information, and application usage patterns to identify performance indicators; a system for storing metadata for each application in an application landscape, wherein metadata for each application specifies a set of application parameters and associated values; and a priority calculator that calculates a priority score for applications in the application landscape, wherein the priority score for a selected application is determined by evaluating performance indicators that correlate to metadata of the selected application.

Abstract: A system and method for automatically transforming non-Unicode supporting files into Unicode supporting files via one or more reusable maps. The system comprises a reusable source type tree organized from a schema, wherein the schema is created from a file; and one or more reusable maps, structured to automatically map one or more rules onto the reusable source type tree to transform the reusable source type tree into a reusable target type tree. The method comprises determining a type of data to be supported; defining one or more rules to support the type of data; and applying the one or more rules automatically to a reusable source type tree to transform the reusable source type tree into a reusable target type tree.

Abstract: A system and method for automatically transforming non-Unicode supporting files into Unicode supporting files via one or more reusable maps. The system comprises a reusable source type tree organized from a schema, wherein the schema is created from a file; and one or more reusable maps, structured to automatically map one or more rules onto the reusable source type tree to transform the reusable source type tree into a reusable target type tree. The method comprises determining a type of data to be supported; defining one or more rules to support the type of data; and applying the one or more rules automatically to a reusable source type tree to transform the reusable source type tree into a reusable target type tree.