Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate abnormal document self-discovery. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a fairness component, an identification component, a removal component, and an evaluation component. The fairness component can receive a dataset for a tree-based model and calculates a first fairness, and the identification component can identify root to leaf paths in the tree-based model for one or more records of the dataset and one or more corresponding perturbed records. The removal component can remove at least one record of the one or more records having similar root to leaf paths to the other records of the one or more records.

Abstract: Classification of cell data includes obtaining a target dataset and an artificial intelligence (AI) model trained to identify relationship(s) between cells of a row and classify whether a focus cell of the row is erroneous based on the identified relationship(s), and applying the AI model to the target dataset to identify erroneous cell(s) thereof. The applying includes selecting a row of cells of the target dataset, inputting the selected row of cells to the AI model with an identification of a focus cell, the focus cell to be classified by the AI model, classifying the focus cell to obtain a classification of the focus cell, the classifying identifying whether the focus cell is erroneous, and outputting an indication of the classification of the focus cell.

Abstract: Disclosed embodiments provide techniques for monitoring application program interface (API) health based on design-time checks and runtime performance. The health of an API can include multiple factors. The factors may include correctness of the data returned/output by the API. A high amount of incorrect data may correlate to a lower health metric. A low amount of incorrect data may correlate to a higher health metric. If a health metric value falls below a predetermined threshold, it may indicate that the API health is poor, and hence, should not be used, or should be used with caution, depending on the application. In embodiments, an alternate API may be automatically selected as a replacement for an API in response to a health metric falling below a predetermined threshold.

Abstract: A computer-implemented method includes: receiving, by a computing device and from a user device, a request to validate an application program interface (API); validating, by the computing device, the API by performing a fetch analysis using different user profiles; returning, by the computing device and to the user device, a result of the fetch analysis; validating, by the computing device, the API by performing an insert/update analysis using the different user profiles; and returning, by the computing device and to the user device, a result of the insert/update analysis

Abstract: This application discloses rodenticide compositions containing artificial sweeteners. The disclosed compositions have improved acceptability to the rodents and longer storage life.