Abstract: A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a plurality of transaction parameters and a risk score, where the risk score is generated by a machine-learning model based on the plurality of transaction parameters; for each transaction parameter of the plurality of transaction parameters, perturbing a value of the transaction parameter and re-analyzing the first payment transaction with the machine-learning model to generate a perturbed risk score based on the perturbed transaction parameter; determining at least one impact parameter from the plurality of transaction parameters by comparing the perturbed risk scores generated for each of the plurality of transaction parameters; and generating an inquiry response message based on the at least one impact parameter.

Abstract: Systems, methods, and computer program products calculate a historical feature similarity point distribution associated with a first machine learning model, calculate a real-time feature similarity point distribution associated with a second machine learning model, and automatically provide, based on a comparison of the historical feature similarity point distribution to the real-time feature similarity point distribution, an indication of whether the second machine learning model is aligned with the first machine learning model.

Abstract: A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a plurality of transaction parameters and a risk score, where the risk score is generated by a machine-learning model based on the plurality of transaction parameters; for each transaction parameter of the plurality of transaction parameters, perturbing a value of the transaction parameter and re-analyzing the first payment transaction with the machine-learning model to generate a perturbed risk score based on the perturbed transaction parameter; determining at least one impact parameter from the plurality of transaction parameters by comparing the perturbed risk scores generated for each of the plurality of transaction parameters; and generating an inquiry response message based on the at least one impact parameter.

Abstract: Provided are methods for multi-task learning (MTL) in deep neural networks. An exemplary method may include receiving an MTL model; receiving a testing data set comprising testing data items for the MTL model, each testing data item comprising a plurality of elements, each element associated with a respective feature; grouping the features into a plurality of groups based on an impact of each feature on the tasks of the MTL model, determining an overall accuracy score and task-specific accuracy scores based on inputting the testing data to the MTL model; applying feature reduction evaluation (FRE) to provide a feature score for each feature; and adjusting the feature scores based on a respective grouping associated with the respective feature and at least one of the overall accuracy score, the task-specific accuracy scores, or any combination thereof to provide an adjusted feature score. Systems and computer program products are also disclosed.

Abstract: A cold cathode fluorescent lamp includes a lamp base and a light tube coupled to the lamp base. The light tube includes a light tube body having two end portions, and a getter mercury dispensing electrode sealed into each end portion. The light tube body has a relatively large diameter of about 4 mm to about 10 mm, and the getter mercury dispensing electrode has a dimension configured to fit inside the light tube body.