Abstract: Described herein are systems and methods for improving defect detection in an automated production process. The system comprises a memory that stores executable components and a processor, operatively coupled to the memory, that executes the executable components. The executable components comprise an automation defect component and a machine learning component. The automation defect component retrieves parametric data associated with the production process. The automation defect component provides the parametric data to a machine learning algorithm. The machine learning component generates common attributes between the defective items. The machine learning component identifies a set of common attributes shared between the defective items and a non-defective item. The machine learning component modifies the set of the common attributes shared between the defective items and the non-defective item. The machine learning component generates defect indicators based on the common attributes.

Abstract: A holder for a roll of paper which has a base shaped to prevent tipping over and which is provided with an upstanding pole for holding a roll of paper in place between a cap and the base, upstanding parallel spaced guides guide the sheets of paper for removal and impeding material is placed on the base and pole to impede removal of the sheets.