Abstract: A system, platform, computer program product, and/or method to analyze a computer-implemented video conference includes: a plurality of participant devices, and a central processing server. Each participant device is configured to form a video snippet for a time interval of the video conference having audio data and video data; generate a transformed video snippet by embedding extracted participant data and/or metadata into the video snippet; and send each transformed video snippet to the central processing server. The central processing server receives each transformed video snippet; performs analytics on each transformed video snippet; and transmits to at least one of the participant devices, results of the performed analytics. Participant devices can display one or more results of the performed analytics.

Abstract: A computer-implemented system, method and computer program product for providing access to a network of computing nodes that includes: requesting, by a client, access into a host node in the network, preferably a private network; selecting a digital certificate issuer; verifying, by the digital certificate issuer, the identity of the client's token; adding, by the certificate issuer, a nonce to a distributed ledger; and granting the client access to the host node in the network. The computing nodes in an embodiment are ranked based upon CPU capacity, and computing nodes with highest CPU capacity ranking are selected to participate in a proof-of-capacity consensus to solve for the nonce.

Abstract: A system, platform, computer program product, and/or method for managing UAV resources is disclosed that includes: receiving UAV specifications to provision one or more UAVs; converting the UAV specifications to UAV specification metadata; transforming the UAV specification metadata to UAV configuration metadata to configure UAV software for the one or more UAVs; and configuring, using the UAV configuration metadata, the UAV software for the one or more UAVs. The system, platform, program product, and method can further include assembling, using the UAV configuration metadata, the UAV hardware of the one or more UAVs; deploying the one or more UAVs from a base location to a new location; transferring control over operation of the one or more UAVs to a third-party operator; creating and configuring a network connection to the one or more UAVs; and/or remotely logging into an operating system of the one or more UAVs.

Abstract: A system, platform, computer program product, and/or method to analyze a computer-implemented video conference includes: a plurality of participant devices, and a central processing server. Each participant device is configured to form a video snippet for a time interval of the video conference having audio data and video data; generate a transformed video snippet by embedding extracted participant data and/or metadata into the video snippet; and send each transformed video snippet to the central processing server. The central processing server receives each transformed video snippet; performs analytics on each transformed video snippet; and transmits to at least one of the participant devices, results of the performed analytics. Participant devices can display one or more results of the performed analytics.

Abstract: A computer-implemented system, method and computer program product for providing access to a network of computing nodes that includes: requesting, by a client, access into a host node in the network, preferably a private network; selecting a digital certificate issuer; verifying, by the digital certificate issuer, the identity of the client's token; adding, by the certificate issuer, a nonce to a distributed ledger; and granting the client access to the host node in the network. The computing nodes in an embodiment are ranked based upon CPU capacity, and computing nodes with highest CPU capacity ranking are selected to participate in a proof-of-capacity consensus to solve for the nonce.

Abstract: A computer-implemented system, method and computer program product for predicting cloud computing resources to add to a cloud computing system that includes: determining a total current size of available computing resources; determining a predicted size of faulty computing resources; determining a confirmed size of computing resources already provisioned; and/or determining a potential size of needed computing resources based upon analysis of customer-specific data; and predicting the cloud computing resources needed to be added to the cloud computing system using the total current size of available computing resources; the predicted size of faulty computing resources; the confirmed size of computing resources already provisioned; and/or the potential size of needed computing resources based upon analysis of customer-specific data.

Abstract: A computer-implemented system, method and computer program product for provisioning cloud computing resources that includes: converting a hand sketch representation of cloud computing resources to a digital image with metadata; generating a cloud deployment representation from the digital image with metadata; preparing cloud deployment manifest and configuration files from the cloud deployment representation; converting the cloud deployment manifest and configuration files into input data to be used to provision cloud computing resources; and transmitting the input data to a cloud computing vendor to provision the cloud computing resources.

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: Customizing computing resource allocation based on machine learning is provided. A plurality of current users of a service are evaluated based on attributes of each current user. A user value score is generated for each current user based on the attributes. The plurality of current users is clustered into a plurality of user groups based on the user value score. A likelihood of each prospective user becoming a paying customer is predicted based on collected behavior data corresponding to each prospective user. A likelihood of an application corresponding to the service and associated with a particular user will misbehave is predicted based on resource usage and features of the application. Resource allocation to the application corresponding to the service and associated with the particular user is customized based the likelihood that the application will misbehave and whether the particular user is a current user or a prospective user.

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.

Abstract: Customizing computing resource allocation based on machine learning is provided. A plurality of current users of a service are evaluated based on attributes of each current user. A user value score is generated for each current user based on the attributes. The plurality of current users is clustered into a plurality of user groups based on the user value score. A likelihood of each prospective user becoming a paying customer is predicted based on collected behavior data corresponding to each prospective user. A likelihood of an application corresponding to the service and associated with a particular user will misbehave is predicted based on resource usage and features of the application. Resource allocation to the application corresponding to the service and associated with the particular user is customized based the likelihood that the application will misbehave and whether the particular user is a current user or a prospective user.

Abstract: A prioritization system and method may include receiving a customer support ticket from a user, wherein a default severity level associated with the customer support ticket is assigned, calculating, by the processor, a user sentiment score and a user personality score by applying a sentiment analysis and a personality analysis to user-specific data, applying, by the processor, a weighting scheme to the user sentiment score and the user personality score to generate a weighted priority score associated with the customer support ticket, adjusting, by the processor, the default severity level according to the weighted priority score to determine an adjusted severity level of the customer support ticket, and prioritizing, by the processor, the customer support ticket among other customer support tickets based on the adjusted severity level of the customer support ticket.

Abstract: A method, and associated computer system and computer program product. One or more processors of a computer system receive an upgrade request to upgrade a base operating system (OS) of a virtual machine (VM). In response to receiving the upgrade request, the one or more processors store metadata of the VM into a resource registry. The one or more processors load a new version of the base OS onto the VM. The one or more processors retrieve, from the resource registry, the stored metadata for configuring the VM.

Abstract: A method, and associated computer system and computer program product. One or more processors of a computer system receive an upgrade request to upgrade a base operating system (OS) of a virtual machine (VM). In response to receiving the upgrade request, the one or more processors store metadata of the VM into a resource registry. The one or more processors load a new version of the base OS onto the VM. The one or more processors retrieve, from the resource registry, the stored metadata for configuring the VM.