Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for a parameter archival storage system for image processing models. The system is configured for read-optimized compression storage of machine-learning neural-network based image processing models with reduced storage by separately storing weight filter bits. The system is configured to construct weigh parameter objects associated with the plurality of neural network layers of an image processing model, such that the image processing model can be reconstructed from the weigh parameter objects. The system may discard the hierarchical linked architecture of the second image processing model and store the second image processing model at the at least one hosted model versioning system repository by storing only the weigh parameter objects.

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: Embodiments of the invention are directed to systems, methods, and computer program products for a unique platform for analyzing, classifying, extracting, and processing information from images. In particular, the novel present invention provides a unique platform for analyzing, classifying, extracting, and processing information from images using deep learning image detection models with the use of convolutional neural networks. Embodiments of the inventions are configured to provide an end to end automated solution for extracting data from images that can be securely distributed to multiple entities and third parties while maintaining the ability to deter unauthorized copying of the platform via embedded steganographic watermarking introduced during the encoding process and only removable using an additional step between the encoding and decoding processes.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for approaching less spatial resolution documents with parameterized and evolution linear units and momentum-driven SGD to accelerate the training phase of the system further by transformation of electronic documents by low-resolution intelligent up-sampling. An up-sampling layer is integrated with dots-per-inch (DPI) to validate whether the desirable output is obtained.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for a security vulnerability analysis and management platform utilizing deep learning technology and knowledge graph database structures. The system is configured to receive software code and metadata corresponding to existing issues and defects present in the software code or associated with the implementation of the software code. By applying a deep learning technique to extract data from the open source software code, wherein the data corresponds to potential issues and defects in the open source software code, the system is configured to populate a knowledge graph database and build a unified cybersecurity ontology that can later be organized and queried based on user input.

Abstract: Embodiments of the invention are directed to metamorphic relationship based code testing using mutant generators. The system is configured for identifying and remediating defects in an original program based on constructing at least one mutated program by distorting the original program, and analyzing expression of mutants in test results. In particular, the system receives a request to perform defect analysis of an original program. In response, the system constructs a first mutated program by embedding one or more mutants in the original program code. Moreover, the system typically maps the one or more mutants to one or more metamorphic relationships of process functions of the original program. The system may then implement tests of the original program and the first mutated program by providing one or more predetermined test cases as input to both, to determine whether the original program comprises at least one defect.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for a unique platform for analyzing, classifying, extracting, and processing information from images. In particular, the novel present invention provides a unique platform for analyzing, classifying, extracting, and processing information from images using deep learning image detection models with the use of convolutional neural networks. Embodiments of the inventions are configured to provide an end to end automated solution for extracting data from images that can be securely distributed to multiple entities and third parties while maintaining the ability to deter unauthorized copying of the platform via embedded steganographic watermarking introduced during the encoding process and only removable using an additional step between the encoding and decoding processes.

Abstract: Embodiments of the present invention provide a system for providing a centralized advanced security provisioning platform to create reliable machine learning models and also to enhance the existing machine learning models. The system is configured for executing instructions in the privacy module to monitor and control data privacy and data usage, executing instructions in the security module to preserve the authenticity of data that is used by the machine learning models to predict an outcome, executing instructions in the equality module to detect and prevent biasing of the machine learning models, executing instructions in the transparency module to provide transparency associated with the decision making process employed by the machine learning models, and executing instructions in the accuracy enhancement module to enhance the accuracy of machine learning models.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for a unique platform for analyzing, classifying, extracting, and processing information from graphical representations. Embodiments of the inventions are configured to provide an end to end automated solution for extracting data from graphical representations and creating a centralized database for providing graphical attributes, image skeletons, and other metadata information integrated with a graphical representation classification training layer. The invention is designed to receive a graphical representation for analysis, intelligently identify and extract objects and data in the graphical representation, and store the data attributes of the graphical representation in an accessible format in an automated fashion.

Abstract: Embodiments of the present invention provide a system for generating capsule neural networks for enhancing image processing platforms. The system is configured for generate capsule neural network based on instructions received form at least one user, transfer learning from an existing image processing platform to train the capsule neural network, receive input from one or more devices and provide the input to the existing image processing platform comprising a convolutional neural network, wherein the convolutional neural network processes the input, activate the capsule neural network to validate the processing of the convolutional neural network, and retrain the capsule neural network based on the validations associated with the convolutional neural network.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for cross-technology code analysis for redundancy identification and functionality recognition. In particular, the novel present invention provides a unique platform for analyzing software code across multiple coding language using a unique approach involving the use of denoising autoencoders. Embodiments of the inventions are configured to leverage a marginalized stacked denoising autoencoder approach to analyze software code, identify code redundancies, and improve efficiency for code storage and query ability by the use of a trained autoencoding module to autoencode software code attributes into vectorized data that can be compared to determine cross-platform functionality and redundancy within a software library.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for user interface construction based on image segmentation, transformation of user interface image segments, and construction of user interface objects. The system is configured to capture a static image of a visual representation of an user interface (UI), wherein the static UI image comprises a visual representation of one or more UI image components of the UI. The system is further configured to segment the static UI image into one or more UI image segments, wherein each UI image segment is associated with a UI image component of the one or more UI image components, and construct an operational user interface by embedding the constructed first UI component object into the operational user interface.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for user interface construction based on image segmentation, transformation of user interface image segments, and construction of user interface objects. The system is configured to capture a static image of a visual representation of an user interface (UI), wherein the static UI image comprises a visual representation of one or more UI image components of the UI. The system is further configured to segment the static UI image into one or more UI image segments, wherein each UI image segment is associated with a UI image component of the one or more UI image components, and construct an operational user interface by embedding the constructed first UI component object into the operational user interface.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for a security vulnerability analysis and management platform utilizing deep learning technology and knowledge graph database structures. The system is configured to receive software code and metadata corresponding to existing issues and defects present in the software code or associated with the implementation of the software code. By applying a deep learning technique to extract data from the open source software code, wherein the data corresponds to potential issues and defects in the open source software code, the system is configured to populate a knowledge graph database and build a unified cybersecurity ontology that can later be organized and queried based on user input.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for approaching less spatial resolution documents with parameterized and evolution linear units and momentum-driven SGD to accelerate the training phase of the system further by transformation of electronic documents by low-resolution intelligent up-sampling. An up-sampling layer is integrated with dots-per-inch (DPI) to validate whether the desirable output is obtained.

Abstract: Embodiments of the invention are directed to metamorphic relationship based code testing using mutant generators. The system is configured for identifying and remediating defects in an original program based on constructing at least one mutated program by distorting the original program, and analyzing expression of mutants in test results. In particular, the system receives a request to perform defect analysis of an original program. In response, the system constructs a first mutated program by embedding one or more mutants in the original program code. Moreover, the system typically maps the one or more mutants to one or more metamorphic relationships of process functions of the original program. The system may then implement tests of the original program and the first mutated program by providing one or more predetermined test cases as input to both, to determine whether the original program comprises at least one defect.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for user interface construction based on image segmentation, transformation of user interface image segments, and construction of user interface objects. The system is configured to capture a static image of a visual representation of a user interface (UI), wherein the static UI image comprises a visual representation of one or more UI image components of the UI. The system is further configured to segment the static UI image into one or more UI image segments, wherein each UI image segment is associated with a UI image component of the one or more UI image components, and construct an operational user interface user interface construction based on transforming interface image segments static images into actionable user interface components.

Abstract: A system and a method for computing infrastructural damages is disclosed. In particular, the present invention provides for identifying one or more potential areas to be impacted during a predicted calamity and classifying the one or more potential areas based on severity of impact in said areas. Further, a first group of datasets associated with one or more potential areas are generated. A pre-calamity data is generated based on the first group of datasets using one or more processing techniques. Further, the present invention provides for generating a post-calamity data based on a second group of datasets associated with respective one or more geographical areas actually affected by the predicted calamity. The damage associated with each of the said properties is computed based on at least one of a comparison between the pre-calamity and the post-calamity data, or based on the post-calamity data.