Abstract: In an example, a computer implemented method can include extracting a plurality of functions from assembly code representative of machine code compiled based on obfuscated source code (e.g., legacy source code), causing one or more functions of the plurality of functions to be grouped based on relationships between the plurality of functions, and defining a class for each grouping of functions. Each defined class can include a subset of functions of the plurality of functions. The method can include causing a diagram model to be generated based on the plurality of classes. The diagram model can characterize the obfuscated source code.

Abstract: Systems, methods and media are provided for optimizing similar item recommendations in a semi-structured environment. In one embodiment a system includes at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising, at least identifying a seed item; retrieving a subset of recommended items relevant to the seed item; and ranking the subset of recommended items based on an item conversion probability, wherein the ranking of the subset of recommended items is based on a machine learning technique, and wherein a binary or multi-class label is used as training input to the machine learning technique.

Abstract: The present disclosure relates generally to the field of tubes for carrying fluids. More specifically, the present disclosure relates to a tube having an oval shaped cross-section. In some embodiments, the tube with oval cross-section comprises a tube within a cavity in a gas turbine engine.

Abstract: A method of making a granulation comprising the steps of (a) combining sucralose, a polar solvent, a wettable material and an active agent, thereby forming a mixture; and (b) drying the mixture, thereby forming the granulation.