Abstract: In some examples, scalable source code vulnerability remediation may include receiving source code that includes at least one vulnerability, and receiving remediated code that remediates the at least one vulnerability associated with the source code. At least one machine learning model may be trained to analyze a vulnerable code snippet of the source code. The vulnerable code snippet may correspond to the at least one vulnerability associated with the source code. The machine learning model may be trained to generate, for the vulnerable code snippet, a remediated code snippet to remediate the at least one vulnerability associated with the source code. The remediated code snippet may be validated based on an analysis of whether the remediated code snippet remediates the at least one vulnerability associated with the source code.

Abstract: Implementations of the present disclosure include receiving analytical attack graph data representative of an analytical attack graph, the analytical attack graph including: one or more rule nodes each representing a network configuration rule; and one or more impact nodes each representing an impact of one or more respective network configuration rules; converting the analytical attack graph to a tactic graph including one or more tactic nodes, each tactic node representing at least one rule node and at least one impact node; determining one or more paths of the tactic graph that lead to a particular network impact; generating a process model based on the paths that lead to the particular network impact, the process model representing network activity for execution of a process that leads to the particular network impact; and executing one or more remedial actions based on the process model to mitigate cyber-security risk to the enterprise network.

Abstract: A self-learning automated application testing system automatically generates test scripts during the execution of an application using an automatic test script generator plugged into the application. The test scripts are generated by capturing event data of events emitted during the execution of the application. The test scripts are compared to the test scripts stored in a test script repository and those test scripts that are determined to be duplicates of the existing test scripts are discarded while the remaining test scripts are stored as new test scripts in the test script repository. An application tester runs regression tests on the application per the new test scripts and logs the results to a test results repository. A dashboard is also provided that enables a user to view and edit the test scripts from the test scripts repository, change configuration settings from a configuration repository, and view test results from the test results repository.

Abstract: In some examples, structure-based multi-intent email classification may include receiving an email thread that includes a plurality of emails including an email that is to be classified, and identifying, for the email thread, a process associated with the email thread, Based on the process associated with the email thread and for each sentence of the email that includes a plurality of sentences, a corresponding classifier may be determined from a plurality of classifiers, and applied to each sentence of the email to generate classified sentences. At least one entity may be extracted from each of the classified sentences of the email. Based on the at least one extracted entity, the email may be classified.

Abstract: In some examples, machine learning based semantic structural hole identification may include mapping each text element of a plurality of text elements of a corpus into an embedding space that includes embeddings that are represented as vectors. A semantic network may be generated based on semantic relatedness between each pair of vectors. A boundary enclosure of the embedding space may be determined, and points to fill the boundary enclosure may be generated. Based on an analysis of voidness for each point within the boundary enclosure, a set of void points and void regions may be identified. Semantic holes may be identified for each void region, and utilized to determine semantic porosity of the corpus. A performance impact may be determined between utilization of the corpus to generate an application by using the text elements without filling the semantic holes and the text elements with the semantic holes filled.

Abstract: Systems and methods for smart incentivization for achieving collaborative machine learning are disclosed. A system receives local model parameters from plurality of client devices in a network, for global model corresponding to collaborative machine learning. The system determines an optimum score for each client device using pre-trained Conditional Variational Auto Encoder (CVAE), based on local model parameter. The system computes contribution score for each client device by determining relative distance value of optimum score corresponding to each client device with optimum score corresponding to another client device from the plurality of client devices, and a global model optimum score of global model. The system updates global model with local model parameter received from the selected set of client devices of the plurality of client devices corresponding to good class, average class, and bad class.

Abstract: In some examples, simulation-based software design and delivery attribute tradeoff identification and resolution may include receiving requirements specification, and generating, based on an analysis of the requirements specification, canonical sustainability requirements. Based on an analysis of the canonical sustainability requirements, sustainable software attribute decisions and an attribute optimization score may be generated, and used to generate a sustainable software attribute balance score and a tradeoff attributes list. Based on an analysis of the sustainable software attribute balance score and the tradeoff attributes list, a green quotient may be generated and used to generate an architecture document. Further, based on an analysis of the architecture document, software quality rules may be generated, and used to generate a software application.

Abstract: A device may receive and transform metric data and share of voice data, associated with digital marketing by an entity, into transformed data, may generate model data from the transformed data, and may divide the model data into training data, test data, and validation data. The device may train models, with the training data, to generate training results, and may process the test data, with the models, to generate test results. The device may process the validation data, with the models, to generate validation results, and may select a first model, a second model, and a third model based on the results. The device may utilize the first model to predict a share of voice, and may utilize the second model to predict a click through rate. The device may utilize the third model to predict a conversion rate, and may perform actions based on the predicted data.

Abstract: Implementations are directed to configuring a set of applications and one or more modules associated with each application, wherein the one or more modules of an application comprise functional components that are bundled into the application, wherein each application is associated with a site of an on-premise system where the application is to be deployed; creating a process flow that includes a plurality of nodes, each node corresponding to a process executed at the site; associating a collection of applications to each node included in the process flow, wherein the collection of applications are selected from the set of applications, wherein the set of applications are categorized based on a relevance score of each application; and deploying the process flow and the collection of applications associated with each node to corresponding on-premise edge devices of the on-premise system based on the site of each application.

Abstract: Automated response generation systems and methods are disclosed. The systems can include a deep learning model specially configured to apply inferencing techniques to redesign natural language querying systems for use over knowledge graphs. The disclosed systems and methods provide a model for inferencing referred to as a Hierarchical Recurrent Path Encoder (HRPE). An entity extraction and linking module as well as a data conversion and generation module process the content of a given query. The output is processed by the proposed model to generate inferred answers.

Abstract: A mobile unit that includes a compressed gas canister, a brake hose coupled to the compressed gas canister, a radar, and a processor and a storage device. The storage device stores instructions that are operable, when executed by the processor, to cause the processor to perform operations of obtaining radar data from the radar, determining, based on the radar data, a distance of the mobile unit to an object, determining that the distance of the mobile unit to the object satisfies a braking criteria, and based on determining that the distance of the mobile unit to the object satisfies the braking criteria, releasing gas from the compressed gas canister through the brake hose coupled to the compressed gas canister and into the brake pipe of a train car.

Abstract: Methods, systems, and computer-readable storage media for receiving data representative of a physical entity, generating an initial knowledge graph representative of a process that is executed by the physical entity based on the data, enriching the initial knowledge graph to provide a process aware energy consumption (PAEC) digital twin of the process as an enriched knowledge graph, providing at least two permutations based on the PAEC digital twin, executing analytics at least partially based on the at least two permutations to provide one or more recommendations, and executing at least one recommendation to optimize energy consumption of the physical entity.

Abstract: Springs can provide energy return and have a conductivity that changes in relation to an amount of strain or deformation of the spring. In some embodiments, the springs are made by multi-material 3D printing (additive manufacturing). Such springs made by multi-material 3D printing may include a first material that is electrically non-conductive and a second material that electrically conductive. The extent of deformation or strain of the spring may be determined or estimated by measuring the conductivity or resistivity of the electrically conductive material portion of the spring.

Abstract: Systems and methods for inclusive product design are disclosed. The system obtains likeness score for product attributes for product using survey before design phase of product, from user(s), and determines impact and relative contribution of each product attribute, for user, to inclusivity score, using multi-level machine learning models. The system segregates product attributes and inclusivity score at persona level, and determines feature importance score of each feature in product attributes for each user. System calculates risk score for each user indicating sensibility towards product designer choices, and provides what-if analysis capabilities to product designer for analyzing, based on risk score, risk of each user with sensibility towards product designer choices and receives multisensory review from user.

Abstract: Systems and methods are provided to identify security vulnerabilities related to containerization platforms. Container images may be received from a repository, and scanned for security vulnerabilities. Containers may be automatically generated and updated with security updates when the images are extracted and identified. Updated versions of images may be generated based on the updated containers. Stored security vulnerability may be automatically updated with CVE information received from external databases at regular intervals, or upon receiving a scan request. Scan results may be generated, stored and compared. Vulnerability comparisons may be generated for an initial version of an image and an updated version of the image that includes the implemented security updates that rectify the identifiable security vulnerabilities.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an aromatic simulation platform, a recipe including descriptions indicative of ingredients of a consumable, processing the recipe through a first neural network to provide a recipe embedding, processing an ingredients profile to determine an aroma compounds profile representing the ingredients of the ingredients profile, processing the aroma compounds profile through a second neural network to provide an aroma embedding, and processing the recipe embedding and the aroma embedding through a third neural network to provide an aroma profile representative of the consumable of the recipe.

Abstract: Systems and methods for establishing user persona from audio interactions are disclosed, including a voice-based conversational AI platform having an acoustic analytical record engine and audio signal codification optimizer. The engine receives an audio sample indicative of voice conversation between an end user and a bot and transforms it into quantifiable and machine-ingestible power spectrum and acoustic indicators that uniquely represent the audio sample in the form of a feature vector. The optimizer ingests and processes the indicators to estimate likelihood of an attribute value representing the audio sample by constructing a convolutional neural network model for each attribute category. The optimizer establishes user persona attribute values across different attribute categories for the audio sample based on the estimated likelihood.

Abstract: Methods, systems, and computer-readable storage media for receiving a AAG from computer-readable memory, generating from logical network ontology data, asset inventory data, and asset communication data, a logical topology of the enterprise network as a computer-readable data structure, defining, at least partially by executing community detection over the logical topology, a sub-set of groups within the enterprise network, each group representing a process of a plurality of process, each process being at least partially executed by one or more assets within the enterprise network, processing the AAG based on the sub-set of groups and data from one or more contextual data sources to provide the process aware AAG, the process aware AAG defining a mapping between an infrastructure-layer of the enterprise network and a process-layer of the enterprise network, and executing one or more remedial actions in the enterprise network in response to analytics executed on the process aware AAG.

Abstract: A method and system for cloud distributed hybrid data storage and normalization are disclosed. The method may include obtaining a data set comprising data entities. A data entity may comprise data fields each containing a data element. The method may further include determining policy constraint meta-data for each of the data elements based on the storage policy constraint. The policy constraint meta-data may include a first meta-tag indicating the storage policy constraint for the data element. The method may further include determining whether a server satisfies the storage policy constraint based on the first meta-tag for the data element. When the server satisfies the storage policy constraint, the method may further include transmitting the data element to the server to store the data element on the server. When the server fails to satisfy the storage policy constraint, the method may further include, storing the data element on the client.

Abstract: A code remediation system accesses a programming code including vulnerabilities such as potential secrets and remediates at least a subset of the potential secrets to generate modified programming code wherein the subset of potential secrets which are determined to be actual secrets are replaced with access mechanisms to storage locations on a vault wherein the actual secrets are secured. To identify the subset of potential secrets forming the actual secrets to be remediated, the code remediation system is configured to filter out false positives among the potential secrets and identify true positives. When an application executing the modified code encounters an access mechanism, it accesses the vault to retrieve the actual secrets.