Abstract: An apparatus configured to construct an email message addressed to a plurality of recipients. The apparatus is further configured to apply a cipher and a first encryption key to a first portion of the email message, which will be viewable by each of the recipients. The apparatus applies the cipher and a second encryption key to a second portion of the email message, which will be viewable by a first recipient from among the recipients. The apparatus further applies the cipher and a third encryption key to a third portion of the mail message, which will be viewable by a second recipient from among the recipients. The apparatus then transmits the email message to a server.

Abstract: Aspects of the disclosure relate to generating a pseudo-code from a text summarization based on a convolutional neural network. A computing platform may receive, by a computing device, a first document comprising text in a natural language different from English. Subsequently, the computing platform may translate, based on a neural machine translation model, the first document to a second document comprising text in English. Then, the computing platform may generate an attention-based convolutional neural network (CNN) for the second document. Then, the computing platform may extract, by applying the attention-based CNN, an abstractive summary of the second document. Subsequently, the computing platform may generate, based on the abstractive summary, a flowchart. Then, the computing platform may generate, based on the flowchart, a pseudo-code. Subsequently, the computing platform may display, via an interactive graphical user interface, the flowchart, and the pseudo-code.

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: 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: 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: 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: A device is further configured to determine a location within a spatial domain for a first program. The device is further configured to determine a first distance threshold value that corresponds with a first distance away from the location of the first program within the spatial domain. The device is further configured to determine distances between the location of the first program and locations of other programs from the plurality of programs and to identify one or more programs from the plurality of programs that are less than the first distance threshold value. The device is further configured to identify the one or more programs from the plurality of programs that are less than the first distance threshold value.

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: Systems, computer program products, and methods are described herein for integration of a hexagonal convolutional neural network (H-CNN) within an image processing technical environment. The present invention is configured to initiate a hexagonal convolutional neural network (H-CNN); train the H-CNN using the first set of digital images; train the S-CNN in the image processing technical environment using the first set of digital images; classify a second set of digital images using the S-CNN using the second set of parameters; electronically receive an indication that the S-CNN has misclassified at least one of the second set of digital images; transform the at least one of the second set of digital images misclassified by the S-CNN as additional training data for the H-CNN; and re-train the H-CNN with the additional training data, wherein re-training the H-CNN further comprises modifying the first set of parameters for classification.

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

Abstract: A system is configured for converting an unstandardized architecture diagram into a braille language diagram is disclosed. The system receives the unstandardized architecture diagram which includes a plurality of architecture components. The system receives a standardized model that includes features to depict the architecture components of the unstandardized architecture diagram in a standard format. The system determines the architecture components, their connections, and their sequences from the unstandardized architecture diagram. The system determines the features to depict the architecture components of the unstandardized architecture diagram in the standard format. The system applies the identified features on the identified architecture components in the unstandardized architecture diagram. The system determines a standardized graphical representation of the unstandardized architecture diagram.

Abstract: Systems, computer program products, and methods are described herein for intelligent machine learning engine in a cloud computing environment. The present invention is configured to implement, at a local computing environment, the first set of skewness variables on records in a first direction to generate skewed records; transmit the skewed records to a cloud computing environment; initiate, using the cloud computing environment, machine learning algorithms on the skewed records; train, using the machine learning algorithms, the skewed records to generate a training model, wherein the training model comprises a first set of parameters; implement, at the local computing environment, the first set of skewness variables on the first set of parameters in a direction opposite to the first direction to generate a first set of skewed parameters.

Abstract: Systems, computer program products, and methods are described herein for implementing data analytics in a mainframe environment. The present invention is configured to determine one or more data analytics resources associated with natural language processing algorithms; initiate one or more compiler protocols on the one or more data analytics resources to build one or more executable code for the one or more data analytics resources capable of being executed on a mainframe environment; establish a communication link with a job control language (JCL) subsystem associated with the mainframe environment; transmit the one or more executable code for the one or more data analytics resources to the JCL subsystem; generate one or more job control statements configured to be executable on the mainframe environment; generate a log of the one or more job control statements; and initiate an execution of the one or more job control statements on the mainframe environment.

Abstract: Systems, computer program products, and methods are described herein for integration of a hexagonal convolutional neural network (H-CNN) within an image processing technical environment. The present invention is configured to initiate a hexagonal convolutional neural network (H-CNN); train the H-CNN using the first set of digital images; train the S-CNN in the image processing technical environment using the first set of digital images; classify a second set of digital images using the S-CNN using the second set of parameters; electronically receive an indication that the S-CNN has misclassified at least one of the second set of digital images; transform the at least one of the second set of digital images misclassified by the S-CNN as additional training data for the H-CNN; and re-train the H-CNN with the additional training data, wherein re-training the H-CNN further comprises modifying the first set of parameters for classification.

Abstract: Systems, computer program products, and methods are described herein for implementing cognitive self-healing in knowledge based deep learning models. The present invention is configured to receive, via the real-time resource transmission session, one or more query strings from the user; transform the one or more query strings into one or more multi-dimensional query vectors; electronically retrieve one or more multi-dimensional resource vectors from a resource repository; determine a similarity index between the one or more multi-dimensional query vectors and one or more multi-dimensional resource vectors; determine a first multi-dimensional resource vector associated with at least one of the one or more multi-dimensional resource vectors; rasterize the first multi-dimensional resource vector into one or more grid of pixels to generate a first resource; and display, via the real-time resource transmission session, the first resource.

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 model designer creates models for machine learning applications while focusing on reducing the carbon footprint of the machine learning application. The model designer can automatically extract features of a machine learning application from requirements documents and automatically generate source code to implement that machine learning application. The model designer then uses computing statistics of previous models and machine learning applications to determine hardware limitations or restrictions to be placed on machine learning application or model. The designer then adds or adjusts the source code to enforce these hardware limitations and restrictions.

Abstract: Systems, computer program products, and methods are described herein for system for dynamic generation of a transmission interface bridge for computing platforms.

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: An analysis tool receives an image as an input matrix. A first convolutional kernel is determined by performing 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 exclusive nor operations between the first layer 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.