Abstract: Aspects of the disclosure relate to a machine learning based decentralized business planning system. A computing platform may identify an event likely to impact one or more business operations. Subsequently, the computing platform may receive, for the event, data from one or more sources of data. Then, the computing platform may generate a data structure including a plurality of nodes, where the plurality of nodes corresponds to the received data. Then, the computing platform may authenticate, by utilizing a distributed ledger-based verification system, the plurality of nodes. Subsequently, the computing platform may perform, based on business rules applicable to the one or more business operations, analytics on the plurality of authenticated nodes. Then, the computing platform may generate, based on the analytics, a contingency plan to mitigate the impact to the one or more business operations, and may provide, via an interactive graphical user interface, the contingency plan.

Abstract: Systems, computer program products, and methods are described herein for secure data transmission using fully homomorphic encryption. The present invention is configured to electronically retrieve a data file from a source computing device, wherein the data file in encrypted using a public key; initiate a homomorphic engine on the data file, wherein the homomorphic engine comprises one or more homomorphic encryption algorithms; generate, using a first homomorphic encryption algorithm, a header and a trailer for the data file; generate, using the first homomorphic encryption algorithm, a unique row for the data file; generate an evaluation key based on at least generating the header, the trailer, and the unique row for the data file; append the header, the trailer, and the unique row to the data file to generate an appended data file; and transmit the appended data file to a target computing device.

Abstract: The present invention generally relates to the field of automated and flexible information extraction for assisted and streamlined development of computer code. The invention provides for accommodating coding representations of reusable utilities in a technology agnostic pattern so that, based on a specified coding stack, the technology agnostic embeddings can be decoded and deployed into developers' integrated development environment. The present invention includes a technologic agnostic digital wallet for developers capable of storing reusable components either from open source repositories or user-defined functions in an embedded pattern in a centralized storage platform such as cloud or hosted virtual desktop.

Abstract: Systems, computer program products, and methods are described herein for character recognition in a digital image processing environment.

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.

Abstract: A guided sampling tool guides the sampling of datapoints in large datasets. Generally, the guided sampling tool applies a machine learning algorithm to a database of historical issues encountered by an organization to guide the sampling of a large dataset. The guided sampling tool can evaluate and change provided variables and weights for performing a sampling. After the datapoints are sampled, the guided sampling tool compares the historic transactions represented by those datapoints to baseline images to determine if the historic transactions encountered a problem or issue, which would affect the overall quality assessment.

Abstract: A software code optimizer automatically detects inefficiencies in software code and corrects them. Generally, the software code optimizer converts software code into a graph representing the workflows and relationships in the software code. The graph is then converted into vectors that represent each workflow in the software code. The vectors are assembled into a matrix that represents the software code. The matrix may be stored in a cluster in a database as an example of optimized software code or be compared with other matrices stored as clusters in the database to determine whether the software code is optimized. The software code optimizer can change the software code to be more efficient if a matrix for an optimized version of the software code is found in the database.

Abstract: A software code optimizer automatically detects inefficiencies in software code and corrects them. Generally, the software code optimizer converts software code into a graph representing the workflows and relationships in the software code. The graph is then converted into vectors that represent each workflow in the software code. The vectors are assembled into a matrix that represents the software code. The matrix may be stored in a cluster in a database as an example of optimized software code or be compared with other matrices stored as clusters in the database to determine whether the software code is optimized. The software code optimizer can change the software code to be more efficient if a matrix for an optimized version of the software code is found in the database.

Abstract: A device configured to obtain an architecture diagram that includes features that are configured to form a workflow for a computer system. The device is further configured to identify the features within the architecture diagram and their metadata. The device is further configured to convert the features into vector points based on the metadata and to generate a vector map that associates vector points with their metadata. The device is further configured to input the vector points into a machine learning model and to obtain classification results for the vector points. The device is further configured to identify non-compliant features that correspond with vector points that are associated with a non-compliant classification. The device is further configured to identify alternative features for the non-compliant features, to update the vector map with the alternative features, and to update the architecture diagram based on the updated vector map.

Abstract: Systems, computer program products, and methods are described herein for cognitive resource identification using swarm intelligence. The present invention is configured to receive one or more resource requirements; receive metadata associated with one or more resources; generate a superimposed unified resource ontological (URO) graph based on at least the resource requirements and the metadata associated with the resources; initiate an ant colony optimization (ACO) algorithm on the superimposed URO graph; generate, using the ACO algorithm, one or more primary resource selection parameters; initiate a fuzzy resource selection engine on the primary resource selection parameters; determine the resources in a descending order of applicability for the resource requirements; and transmit control signals configured to cause the computing device of the user to display the resources in the descending order of applicability to the resource requirements.

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/or 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 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 device configured to obtain an architecture diagram that includes features that are configured to form a workflow for a computer system. The device is further configured to identify the features within the architecture diagram and their metadata. The device is further configured to convert the features into vector points based on the metadata and to generate a vector map that associates vector points with their metadata. The device is further configured to input the vector points into a machine learning model and to obtain classification results for the vector points. The device is further configured to identify non-compliant features that correspond with vector points that are associated with a non-compliant classification. The device is further configured to identify alternative features for the non-compliant features, to update the vector map with the alternative features, and to update the architecture diagram based on the updated vector map.

Abstract: An adaptive gamified portal builder is provided. The portal builder may include a camera that captures video input and a microphone that captures audio input. The portal builder may identify affective features from the captured audio and video inputs. Using generative adversarial networks (GAN), the portal builder may generate a user interface (UI) output based on the affective features. Using a generator neural network, the portal builder may generate an image. Using a discriminator neural network, the portal builder may access a repository of gamified portal features associated with the cluster of affective vectors and validate the image. The portal builder may iterate through the generator network and the discriminator network to enhance the image. The portal builder may generate a UI image output comprising the enhanced image. Using a UI encoder, the portal builder may generate source code for the UI image output.

Abstract: A tool uses a graph-based approach to analyze scripts to determine whether the scripts pose security threats when executed. The tool breaks down scripts into component steps and generates a graph based on those steps. The tool then converts the graph into a vector and compares that vector with clusters of other vectors. Based on that comparison, the tool determines whether the script will cause a security vulnerability. If the script causes a security threat when executed, the script may be prevented from executing.

Abstract: An Artificial Intelligence (AI)-initiated customized/user-specific computer security training. Users' computing activity data is monitored and captured that relates to most, if not all, computing activities, functions and interactions performed by a user. A behavior model is created based on the captured computing activity data and, based on the behavior model, AI including Reinforcement Learning (RL) is implemented to determine computing activity features or patterns that define the user and computing anomalies/incidents. Multimedia security training is generated on a per-user basis based at least on the identified computing activity features/patterns and anomalies associated with a specific user.

Abstract: Systems, computer program products, and methods are described herein for generating an execution sequence using learning reinforcement. The present invention is configured to electronically receive one or more requirement modules from one or more computing devices associated with one or more resource development teams; store the one or more requirement modules in a resource development pipeline, wherein the one or more requirement modules are in a first order of execution; initiate a reinforcement learning algorithm on the one or more requirement modules; determine, using the reinforcement learning algorithm, a final order of execution for the one or more requirement modules; initiate a reordering of the one or more requirement modules from the first order of execution to a final order of execution; and initiate an execution of the one or more requirement modules in the final order of execution.

Abstract: Systems, methods and apparatus are provided for an intelligent server platform using genetic algorithms to execute a server migration based on fitness. The platform may locate hardware and software artifacts and map functional relationships between artifacts. Artifacts may be clustered based on interdependency to ensure that functionally related artifacts are migrated as a unit. The platform may apply a fitness protocol to generate a fitness score for each cluster and select clusters based on fitness score. The platform may apply a crossover protocol to optimize selected clusters for compliance with enterprise standards. The platform may iterate through the crossover protocol and modify a convergence goal based on populations of successive generations. The platform may rank artifacts and generate a protocol for migration. The platform may execute the migration in accordance with the migration protocol.

Abstract: Systems, computer program products, and methods are described herein for dynamic allocation of navigation tools based on learned user interaction. The present invention is configured to generate a training dataset based on at least the information associated with the interaction of the user with the one or more GUI grids, information associated with the one or more interactions of the one or more peers with the one or more GUI grids, information associated with the user, and information associated with the one or more peers; initiate one or more machine learning algorithms on the training dataset; receive, via the user computing device, a user selection of an unseen navigation tool for placement on the GUI; and classify the unseen navigation tool using the first set of parameters to predict a placement of the unseen navigation tool in at least one of one or more GUI grids associated with the GUI.

Abstract: An apparatus configured to generate responses to a multi-encrypted email message. The apparatus is configured to receive an email message comprising a first portion having a first level of encryption and a second portion having a second level of encryption. The apparatus is configured to receive an indication that a first portion of a reply message is in response to the first portion of the received email message. The apparatus is further configured to receive an indication that a second portion of the reply message is in response to the second portion of the received email message. The apparatus encrypts the first portion of the reply message using a first encryption key. The apparatus further encrypts the second portion of the reply message using a second encryption key. Finally, the apparatus transmits the reply message to a server.