Abstract: Disclosed are techniques for quantifying physical qualities of a ball returned by a player using a loop drive technique, such as in table tennis, and generating a corresponding quantitative summary of the overall quality of the loop drive technique based on the quantified physical qualities. Image processing techniques are applied to historical video recordings of balls returned using loop drive techniques to quantify physical properties of said balls. A machine learning model is generated using the quantified physical properties to determine relative significance of specific qualities and their corresponding quantified values to the overall quality or success of loop drive techniques, such as in table tennis matches.

Abstract: Disclosed are techniques for quantifying body postures of a player employing a loop drive technique to strike a ball, such as performed in table tennis activities. A video recording of a player striking a ball with a loop drive technique is received and divided, using image processing techniques, into two segments: the first concerning player body postures before the ball is hit, and the second concerning body postures from the moment of impact between the ball and racket and the subsequent follow-through body postures. Then, image processing techniques are again leveraged to isolate and quantify specific body postures contributing to a loop drive technique in a given segment.

Abstract: Disclosed are techniques for leveraging machine learning to generate posture adjustment values for specific body postures of a player to improve loop drive techniques, such as in table tennis. Video clips of a player hitting a ball with a loop drive technique are analyzed to determine values for specific body postures and qualities of the ball after being hit. A machine learning model is generated to analyze relationships between body posture values and ball qualities. Upon receiving a video clip of a live session of a player hitting a ball using a loop drive technique, the machine learning model is used to generate adjustment values for body postures of the player to impart improved loop drive qualities to the ball, such as faster topspin.

Abstract: A system receives transaction parameters which indicate a type of fraud. The system generates a set of sample transactions based on the parameters. The set of sample transactions generated by the system include at least one fraudulent transaction consistent with the type of fraud indicated by the parameters. The system can then send the transaction to an analyzer. Upon receiving results from the analyzer, the system evaluates performance of the analyzer.

Abstract: A system, computer program product, and method are presented for classifying behaviors and predictions through processing temporal financial features with a recurrent neural network (RNN). The method includes receiving, by a RNN model, first financial transaction events. The method also includes classifying non-fraudulent behavioral patterns and potentially fraudulent behavioral patterns resident within the first financial transaction events and training the RNN model therewith. The method further includes receiving, by the RNN model, second financial transaction events over a predetermined period of time. The method also includes normalizing the second financial transaction events, including partitioning the predetermined period of time into a plurality of first equal temporal segments. Some of the plurality of first equal temporal segments are representative of the second financial transaction events residing therein.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for improving data simulation using reinforcement learning. The method includes one or more processors generating a first simulated data set based on a first parameter set. The method further includes generating a second parameter set, by modifying one or more parameters of the first parameter set, and then generating a second simulated data set based on the second parameter set. The method further includes determining data discrepancies between the first simulated data set and a target data set and determining data discrepancies between the second simulated data set and the target data set. The method further includes selecting between the first and second simulated data sets, a first data set that corresponds to fewer data discrepancies relative to the target, then comparing data discrepancies of the selected first data set to a data discrepancy threshold.

Abstract: Disclosed are techniques for quantifying physical qualities of a ball returned by a player using a loop drive technique, such as in table tennis, and generating a corresponding quantitative summary of the overall quality of the loop drive technique based on the quantified physical qualities. Image processing techniques are applied to historical video recordings of balls returned using loop drive techniques to quantify physical properties of said balls. A machine learning model is generated using the quantified physical properties to determine relative significance of specific qualities and their corresponding quantified values to the overall quality or success of loop drive techniques, such as in table tennis matches.

Abstract: Disclosed are techniques for leveraging machine learning to generate posture adjustment values for specific body postures of a player to improve loop drive techniques, such as in table tennis. Video clips of a player hitting a ball with a loop drive technique are analyzed to determine values for specific body postures and qualities of the ball after being hit. A machine learning model is generated to analyze relationships between body posture values and ball qualities. Upon receiving a video clip of a live session of a player hitting a ball using a loop drive technique, the machine learning model is used to generate adjustment values for body postures of the player to impart improved loop drive qualities to the ball, such as faster topspin.

Abstract: Disclosed are techniques for quantifying body postures of a player employing a loop drive technique to strike a ball, such as performed in table tennis activities. A video recording of a player striking a ball with a loop drive technique is received and divided, using image processing techniques, into two segments: the first concerning player body postures before the ball is hit, and the second concerning body postures from the moment of impact between the ball and racket and the subsequent follow-through body postures. Then, image processing techniques are again leveraged to isolate and quantify specific body postures contributing to a loop drive technique in a given segment.