Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for electronic query engine for an image processing model database. The system is configured is configured for constructing a model abstraction layer for machine-learning neural-network based image processing models configured for selection, mutation and construction of the image processing models. Here, the system is configured to receive and process a user input query comprising a plurality of discrete input language elements, wherein each of the plurality of discrete input language elements comprises a character string. The system is also configured to construct a second image processing model by mutating a first image processing model, in accordance with the discrete input language elements.

Abstract: Systems, computer program products, and methods are described herein for implementing auto didactic content generation using reinforcement learning. The present invention is configured to retrieve a user interaction portfolio of a user associated with a completion of a first task; determine requirements associated with the first task; determine an interaction score associated with the user; determine a target interaction score associated with the first task; determine that the interaction score associated with the user is less than the target interaction score; electronically receive, from a knowledge repository, a first video file demonstrating the one or more interaction requirements; generate a modified first video file; and transmit control signals configured to cause a computing device of the user to display the modified first video file to the user.

Abstract: The present invention generally relates to the field of automated and flexible information extraction for review and analysis of computer code. In particular, the novel present invention provides a unique platform for analyzing, classifying, extracting, and processing information using multichannel input from user devices and optical tracking sensors and employing the use of behavioral cloning network (BCN) technology. Embodiments of the inventions are configured to provide an end to end automated solution for extracting data from code review processes that can be used to automate and accelerate the code review and validation methods.

Abstract: Embodiments of the present invention provide a system for intelligently optimizing the utilization of clusters. The system is configured to continuously gather real-time hardware telemetric data associated with one or more entity systems via a hardware telemetric device, continuously convert the real-time hardware telemetric data into a first color coded representation, receive one or more tasks associated with one or more entity applications, queue the one or more tasks associated with the one or more entity applications, determine hardware requirements associated with the one or more tasks, determine one or more attributes associated with the one or more tasks, convert the hardware requirements and the one or more attributes of the one or more tasks into a second color coded representation, and allocate the one or more tasks to the one or more entity systems based on the first color coded representation and the second color coded representation.

Abstract: Systems, computer program products, and methods are described herein for testing application resiliency via context aware auditing equilibrium. The present invention is configured to receive an indication to test application resiliency of an application; initiate a resiliency test engine on the application; determine, using the resiliency test engine, one or more artifacts associated with the application to be tested; determine a first subset of the one or more test scripts is able to capture a behavior of a first subset of the one or more artifacts; initiate a context-based classification engine on the first subset of the one or more test scripts and the first subset of the one or more artifacts; classify the first subset of the one or more test scripts into a supporting behavior class and an opposing behavior class; generate a graphical representation of the classification; and display the graphical representation of the classification.

Abstract: Systems, computer program products, and methods are described herein for implementing federated containerization platform using LiFi (Light Fidelity). The present invention is configured to electronically receive, from a container orchestration engine, a request to allocate one or more resources to one or more applications to execute a first task; retrieve one or more resource requirements associated with the one or more applications; determine, using a machine learning model, one or more resources to be allocated to the one or more applications; retrieve, from a resource repository, the one or more resources to be allocated to the one or more applications; and allocate the one or more retrieved resources to the one or more applications.

Abstract: A machine learning training device having a clustering device configured to group machine learning training requests into a consolidated training block. The clustering device includes an isomorphism engine and an energy estimator configured to construct the consolidated training block based upon an amount of energy that will be saved by training the machine learning training requests together, thereby reducing an overall energy consumption by machine learning processes.

Abstract: Systems, computer program products, and methods are described herein for decentralized edge computing enablement in robotic process automation. The present invention is configured to receive an indication that a hosted virtual desktop (HVD) has received a first set of instructions for execution from a controller hosted virtual desktop (CHVD); electronically receive, from the HVD, an indication that the first set of instructions have been executed by the HVD; predict, using the edge computing enablement engine, a second task to be executed by the HVD; determine, using the quantum database search algorithm, a location of the second task in the knowledge repository; retrieve a second set of instructions associated with the second task from the location of the second task in the knowledge repository; and receive, from the HVD, an indication that the second set of instructions have been executed by the HVD.

Abstract: A language proficiency analyzer automatically evaluates a person's language proficiency by analyzing that person's oral communications with another person. The analyzer first enhances the quality of an audio recording of a conversation between the two people using a neural network that automatically detects loss features in the audio and adds those loss features back into the audio. The analyzer then performs a textual and audio analysis on the improved audio. Through textual analysis, the analyzer uses a multi-attention network to determine how focused one person is on the other and how pleased one person is with the other. Through audio analysis, the analyzer uses a neural network to determine how well one person pronounced words during the conversation.

Abstract: A system for prioritizing a plurality of requests received from a plurality of clients is disclosed. The system receives the plurality of requests. For each request from the plurality of requests, the system extracts features of the request, where the extracted features provide information regarding a priority in performing the request. The extracted features correspond to a numerical representation of the request, such that if a priority level associated with the request is high the numerical representation comprises higher numerical values compared to another request that is associated with a low priority level. The system determines a prioritization in performing the plurality of requests by ranking a plurality of extracted features representing the plurality of requests based on ranking numerical values associated with the plurality of extracted features.

Abstract: An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to generate, based on a given role of a plurality of roles within an enterprise, a policy for a new user assigned to the given role. The policy includes recommendations of virtual desktop resources of an enterprise system to provide to the new user. The machine learning algorithm was trained using information identifying virtual desktop resources used by a set of existing users of the system, each of whom is assigned to a role of the plurality of roles within the enterprise. The processor receives a request to provide a new user with access to the enterprise system. In response, the processor implements the machine learning algorithm to generate the policy for the new user, and subsequently provisions the new user with the one or more virtual desktop resources recommended by the policy.

Abstract: Systems, computer program products, and methods are described herein for generation of data content based on learning reinforcement. The present invention is configured to receive a video file demonstrating regulatory compliance requirements; display the video file in one or more interactive application environments stored thereon; initiate a reinforcement learning algorithm on the video file; initiate an optimization policy generation engine on the user inputs to generate an optimization policy, wherein the optimization policy generation engine is configured to encode the one or more user inputs into shaping rewards; initiate an implementation of the optimization policy on the video file to generate a modified video file based on at least the optimization policy; initiate a validation engine on the modified video file to validate one or more changes implemented on the video file; and initiate a deployment of the modified video file to the one or more users.

Abstract: Systems, methods and apparatus are provided for an end to end control automation workflow using a distributed network. The data segregation layer may assimilate data from a variety of enterprise sources. A trust engine may validate the data from enterprise sources against enterprise security standards. The system may use a distributed network to validate the data from the various sources and populate a distributed hash table. The distributed network may be a Holochain® framework. The system may include an automation layer that uses robotic processing automation scripts to validate data against a system of record and flag exceptions. The automation layer may provide data to a user interface layer. The user interface may include an interactive dashboard for presenting a range of detailed reports.

Abstract: An FPGA device receives an input matrix. A first convolutional kernel is determined by performing the exclusive nor operations between the input matrix and a first weight vector. A first binary kernel is determined based on the first convolutional kernel. A first layer feature map is determined by convoluting the input matrix using the first binary kernel. A second convolutional kernel is determined by performing the exclusive nor operations between the first feature map and the second weight vector. A pooled kernel is determined based on the second convolutional kernel. A second binary kernel is determined based on the pooled kernel. A second layer feature map is determined by convoluting the first layer feature map using the second binary kernel. A probability is determined that the input matrix is associated with a predetermined class of images. If the probability is greater than a threshold, classification results are provided.

Abstract: Systems, computer program products, and methods are described herein for dynamically determining performance benchmarking parameters based on reinforcement learning.

Abstract: Embodiments of the present invention provide a system for intelligently optimizing the utilization of clusters. The system is configured to continuously gather real-time hardware telemetric data associated with one or more entity systems via a hardware telemetric device, continuously convert the real-time hardware telemetric data into a first color coded representation, receive one or more tasks associated with one or more entity applications, queue the one or more tasks associated with the one or more entity applications, determine hardware requirements associated with the one or more tasks, determine one or more attributes associated with the one or more tasks, convert the hardware requirements and the one or more attributes of the one or more tasks into a second color coded representation, and allocate the one or more tasks to the one or more entity systems based on the first color coded representation and the second color coded representation.

Abstract: A code converter uses machine learning to determine conflicts and redundancies in software code. Generally, the code converter uses machine learning to convert software code into vectors that represent the code. These vectors may then be compared with other vectors to determine similarities between code. The similarities may be used to detect conflicts and/or redundancies created during the development process (e.g., when a developer attempts to change the code).

Abstract: Systems, computer program products, and methods are described herein implementing content retrofitting using information vectorization. The present invention is configured to retrieve a user interaction portfolio of a user associated with a completion of a first task; determine requirements associated with the first task; determine an interaction score associated with the user; determine a target interaction score associated with the first task; determine that the interaction score associated with the user is greater than the target interaction score associated with the first task; generate one or more second image frames based on at least the one of the one or more user interactions that did not meet the one or more interaction requirements; generate at least one modified video file based on at least the one or more second image frames; and store the at least one modified video file in a knowledge repository.

Abstract: A system accesses a first digital audio file that includes a plurality of spoken instructions. The system converts the first digital audio file to a first spectrogram image, applies a filter to determine whether an image quality of the first spectrogram image is below a predetermined image quality, and in response, generates a second spectrogram image from the first spectrogram image using a training model. The system converts the second spectrogram image to a second digital audio file and converts the second digital audio file into multiple vectors that each correspond to a particular spoken instruction. The system identifies related vectors and concatenates the related vectors together in order to create a plurality of concatenated vectors. The system generates, using the plurality of concatenated vectors, a third digital audio file that includes concatenated spoken instructions from the first digital audio file.

Abstract: Systems, computer program products, and methods are described herein for scalable encryption framework using virtualization and adaptive sampling. The present invention is configured to receive metadata associated with one or more intrusion types from an intrusion data lake; initiate an adaptive instance sampling engine on the metadata associated with the one or more intrusion types to generate a sampled intrusion data lake; initiate one or more simulations of atomic intrusion on a firewall; generate one or more prioritized combination of the one or more sampled intrusion types; initiate one or more simulations of cumulative intrusion on the firewall using the one or more prioritized combination of the one or more sampled intrusion types; determine an atomic performance metric and a cumulative performance metric of the firewall; and generate a robustness report for the firewall.