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: An integrated portable extremely fast charger (XFC) may be installed for heavy-duty off-road vehicles, such as tractors and combine harvesters. The XFC shortens the charging period of plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) by providing >500 kW in a single charger. The proposed XFC integrates a solar farm and local energy storage system (ESS) such that the local grid only needs to provide the power gap between the vehicle battery and the local ESS during charging events, yielding a low-cost XFC installation and higher renewable energy penetration. Two design approaches for the XFC, conductive and wireless, are based on multiphase interleaved dc-dc converter circuit, permitting flexible access to electricity. Smart fault protection mechanism is also proposed to achieve high safety during charging events. The hexagonal prism charger may be integrated into the power electronic devices with a transformer, yielding a high compactness and power density.

Abstract: Devices and techniques are generally described for determining named entity recognition tags. In various examples, first input data representing a natural language input may be determined. In some examples, a first machine learned model may determine first data comprising a first encoded representation of the first input data. In various examples, second data representing a grouping of text of the first input data may be determined based at least in part on the first data. In some examples, first entity data may be determined by searching a memory layer using the second data. In at least some examples, the first entity data and the first data may be combined to generate third data. In various examples, output data comprising a predicted named entity recognition tag may be generated for the grouping of text based at least in part on the third data.

Abstract: The present disclosure relates to a method to improve the encapsulation efficiency and physicochemical stability of ginsenosides Rg3 and CK nano-emulsion, belonging to the field of functional emulsions. The method to improve the encapsulation efficiency and physicochemical stability of ginsenosides Rg3 and CK nano-emulsion, includes the following steps: (1) WPI, Tween 80 and water were mixed evenly according to the amount ratio of 0.5 g:0.5-0.7 g:50 mL to obtain the aqueous phase; (2) Mixing a saponin extract containing the minor ginsenosides Rg3 and CK with edible oil evenly to obtain the oil phase; (3) Mixing the aqueous phase with the oil phase, carrying out shearing dispersion to obtain coarse emulsion, and then allowing the coarse emulsion to be subjected to microfluidization homogenization to obtain an oil-in-water nano-emulsion containing the minor ginsenosides Rg3 and CK.

Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises converting received data from a time-based domain to a frequency-based domain using a signal processing algorithm; identifying transactional noise within the converted data by identifying contextual factors based on a determined pattern, wherein the transactional noise is data associated with an identified fraudulent transaction; filtering the identified transactional noise by removing datapoints within the converted data that reaches a predetermined threshold of signal strength using the signal processing algorithm; and generating a line graph depicting removal of the data that is indicative of the identified transactional noise from the converted data.

Abstract: Computer vision and deep learning techniques are leveraged to detect behavior patterns in transaction histories. A transaction timeline is built for a series of transactions, e.g., financial, and a graphic image is constructed representing the transaction timeline. The graphic image is then matched to a known behavior pattern using a cognitive system. The cognitive system is trained with historical timeline images having associated labels. In one example the graphic image is a bar chart and each financial transaction is represented as a bar in the bar chart having a height proportional to a transaction amount, the bar being located along a time axis of the bar chart according to the transaction date and being color coded according to the transaction type.

Abstract: A detection modeling system has a processing device and a memory coupled to the processing device. The detection modeling system is configured to obtain health value data associated with an analytical model, determine a time period at which the model was trained based on the obtained health value data, and identify a survival time period of the model based on the determined time period at which the model was trained and a failure time period of the model. The detection modeling system is further configured to repeat these steps to determine a survival time period for a plurality of analytical models, and perform a survival analysis based on the survival time period for the plurality of analytical models.

Abstract: A detection modeling system performs a distribution analysis to alert to model degradation. The detection modeling system may have a distribution analysis module configured to perform an alerting process in conjunction with a processing device. The distribution analysis module may select, by the processing device, model metrics for analysis, the model metrics being a measure of a parameter associated with the analytical model and determine normal distributions for model metric results for each of the selected model metrics. The detection modeling system may further receive model metric values for each of the selected model metric, compare, by the processing device, the model metric values to the normal distributions for model metric results for each of the received model metric value, and alert, by the processing device, to model degradation of the analytical model based on the comparison of the model metric values to the normal distributions for model metric results.

Abstract: A system, platform, program product, and/or method for generating new composite insight templates that includes: running a machine learning model on a data set to obtain for each of a plurality of entities a risk score and feature-based insights; generating a list of top “n” features input to the machine learning model that contributes to the risk score for each entity; grouping entities based upon similar features input to the machine learning model that contributes to the risk score for each entity; generating a decision tree for at least one of the group of entities; extracting, from the decision tree generated for the at least one of the group of entities, one or more feature-based insights; generating, by applying subject matter input, a new composite insight based upon the one or more feature-based insights; and adding the new composite insight to insight templates.

Abstract: A method, system, and computer program product for computer vision modeling are provided. The method identifies a set of transactions. A set of categorical behavior transaction types are determined for the set of transactions. The set of categorical behavior transaction types are mapped to a set of colors in a color coordinate system. The method scales color component values of the set of colors in the color coordinate system to generate a pattern of colorized units at intervals along a timespan of the set of transactions. The method generates a fraud detection model based on the set of transactions and the color component values.

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: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises converting received data from a time-based domain to a frequency-based domain using a signal processing algorithm; identifying transactional noise within the converted data by identifying contextual factors based on a determined pattern, wherein the transactional noise is data associated with an identified fraudulent transaction; filtering the identified transactional noise by removing datapoints within the converted data that reaches a predetermined threshold of signal strength using the signal processing algorithm; and generating a line graph depicting removal of the data that is indicative of the identified transactional noise from the converted data.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises receiving transactional data from at least two users in a plurality of users; determining a pattern within the received transactional data based on a frequency-based domain conversion, wherein the pattern is associated with a determined periodicity; determining a delay within the received transactional data by identifying contextual factors associated with the received transactional data and measuring an amount of time between each identified contextual factors within a plurality of identified contextual factors using a signal processing algorithm; aligning the received transactional data from the at least two users by placing at least two signal forms associated based on the determined delay within the received transactional data at a same point; and generating a line graph depicting the aligned transactional data.

Abstract: A system, computer program product, and method are presented for facilitating determinations of risk including behavior classifications and predictions through timeline reshaping and rescoring of structured data. One embodiment of the method includes receiving, for one or more target focal objects, at least a portion of a transaction history including a plurality of sequential transactions, where the portion of the transaction history is associated with a first temporal range. The method also includes generating a first transaction timeline image representative of the portion of the transaction history, where the first temporal range includes a first temporal scaling. The method further includes labeling, through a machine learning (ML) model, the first transaction timeline image. The method also includes reshaping the first transaction timeline image, including rescaling the first temporal range, thereby generating a rescaled transaction timeline image, and labeling the rescaled transaction timeline image.

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: One or more computer processors determine a plurality of models to incorporate a plurality of determined features from a received dataset. The one or more computer processors generate an aggregated prediction utilizing each model, in parallel, in the determined plurality of models subject to stop criteria, wherein stop criteria includes a prediction duration threshold. The one or more computer processors calculate a confidence value for the aggregated prediction.

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: In an approach for evaluating system generated historical transaction timeline images for a computer vision deep learning process, a processor provides one or more chart evaluation factors for evaluating historical timeline images for a deep learning of patterns based on the historical timeline images. A processor generates a quantitative metric based on the one or more chart evaluation factors using a quantitative technique. A processor determines a score for an input timeline image based on the quantitative metric. A processor filters input space based on the score. A processor, in response to receiving a feedback, adjusts a chart setting based on the one or more chart evaluation factors.