Abstract: A DevSecOps integration system that can analyze a source repository and provide recommendations on security services, along with supported security tools, that can be included as part of the DevOps pipeline. The DevSecOps integration system can automatically fit the selected security tools into the appropriate stage of the pipeline to optimize security protection. In some embodiments, the DevSecOps integration system automatically generates various and multiple CI/CD-specific integrations, scripts, and security code. In one example, the security tools are selected in response to user selection(s), such as an initial input file and code parameters, and automatically identifies where the scripts should be inserted in the development process.

Abstract: A device may receive user data identifying vitals of users when receiving treatments and dosages of the treatments, and may process the user data, with a divergence model, to determine divergence data identifying divergences between the users. The device may process the divergence data, with a clustering model, to group the users into clusters of users, and may train a first neural network model, with the user data, to generate a trained first neural network model. The device may train a second neural network model, with the user data, to generate a trained second neural network model, and may generate a treatment model based on the trained first and second neural network models. The device may process new user data identifying a new user, with the treatment model, to determine a recommended treatment for the new user, and may perform one or more actions based on the recommended treatment.

Abstract: A device may receive source code and a database to be migrated to a cloud computing environment, and may perform a first stage analysis of the source code to generate a first report. The device may cause a second stage analysis of the source code to be performed based on the first report and to generate refactored and rewritten code, and may perform a third stage analysis of the database to generate a second report. The device may cause a fourth stage analysis of the database to be performed and to generate a refactored and rewritten database, and may perform the first stage analysis of the refactored and rewritten code and the third stage analysis of the refactored and rewritten database to generate a final report. The device may generate a migration strategy based on the final report and may perform actions based on the migration strategy.

Abstract: A system and method for automating and improving tabular and list-based data extraction from a variety of document types is disclosed. The system and method detect and sort which documents include tables or lists, and performs row and column segmentation. In addition, the system and method apply Conditional Random Fields models to localize each table and semantic data understanding to map and export the extracted data to the desired format and arrangement.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing an olfaction training program by an online service executing on a server. A first object and an aroma of the first object is transmitted to the client device of a user that presents the first object in a virtual reality environment and disperses the first aroma. In response, the user interacts with the vital reality environment. The user response is transmitted back to the online service. The user response is stored in a database along with the first data and the first aroma. The online service determines a second object and an aroma of the second object based on the user response. The second object and the aroma of the second object is transmitted to the client device.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support application portfolio management. Input data may be received that is associated with a set of applications. The input data may indicate, for each application of the set of applications, a name of the application and a description of the application. For an application of the set of applications, a functional score, a cost, and a technical score may be determined based on the name of the application, the description of the application, or a combination thereof. A disposition recommendation for the application may be determined based on the functional score, the cost, and the technical score. In some implementation, an indication of the disposition recommendation of the application may be output via a graphical user interface.

Abstract: Methods, systems, and apparatus are provided for operating a contact center operation control assistant system. An oral request from a user is captured using a virtual assistant executing on a smart device. An intent of the oral request is identified using a natural language understanding (NLU) platform. A request to a contact center platform is generated by invoking an application programming interface (API) based on the identified intent. An API response is received from the contact center platform in response to the API request. The API response is packaged into a sentence and provided to the user of the smart device using the virtual assistant.

Abstract: This disclosure relates generally to the technical field of knowledge graphs, and in particular to automatic and intelligent link prediction. The proposed circuitry and system operate semantic analytics on an input knowledge graph to extract its ontology and derive a set of semantic rules. The extracted ontology and rules are then used for an automatic candidate generation strategy based on an input query for link prediction by filtering down from possible candidate triples to a reduced set of semantically plausible candidates. The semantically plausible candidate triples are then evaluated by a link prediction circuitry trained based on machine learning techniques. As such, the various disclosed implementations provide a refinement of the hypothesis triple set returned by the link prediction circuitry towards semantical plausibility, thereby reducing if not eliminating hallucinations (false hypotheses) in link prediction and at the same time improving practicality of link inference and testing.

Abstract: In some implementations, a collaborative knowledge system may receive a first set and a second set of privatized embeddings. The first set of privatized embeddings may be generated by a local model based on a first set of private documents associated with a first knowledge domain. The second set of privatized embeddings may be generated by a local model based on a second set of private documents associated with a second, different knowledge domain. The collaborative knowledge system may train, based on the first and second sets of privatized embeddings, a centralized model. The collaborative knowledge system may receive a query associated with the first knowledge domain or the second knowledge domain. The collaborative knowledge system may generate a response to the query based on processing the query with the centralized model. The collaborative knowledge system may provide the response to the query to a user device.

Abstract: Methods, systems, and apparatus for generating intensity modulated radiation therapy treatment plans.

Abstract: An automated, dynamic system and method of testing infrastructure-as-code (IaC). The system is configured to validate infrastructure provisioned in multi-cloud environments and is able to accommodate any cloud provider. Implementation of such as system can eliminate manual errors, as well as enable early detection of errors (i.e., before production deployment), thus empowering early ‘go live’. Furthermore, the proposed embodiments are configured to integrate with already existing devOps pipelines for rapid test execution and can run as many times as needed with minimal configuration, allowing for creation and execution of complex scenarios to test low-level validation upon cloud infrastructure setup.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support configuration of cloud-based functionality. A configuration device is provided and includes a data processing module, a modelling module, and a loading module. The data processing module provides functionality for compiling information for use in configuring the cloud-based functionality in a requirements compliant manner. The modelling module may include various machine learning modules configured to evaluate configuration workbooks for compliance with requirements specified by a user. The modelling module may output recommendations for improving compliance of the configuration workbooks and appropriate changes may be made. The loading module may be configured to obtain templates applicable to the cloud-based functionality being configured and to extract appropriate data from the (updated) configuration workbooks.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning (ML) to forecast energy demand and to generate an energy plan for one or more facilities of an organization. For example, a system may forecast an occupancy of the facilities for use with historical demand data in forecasting the energy demand. The forecasting may be performed by one or more trained ML models. Additional ML models may be trained to select energy resources that satisfy the forecasted energy demand and that prioritize constraint(s). The system may generate an energy plan that indicates information related to the selected energy resources, such as cost, energy type, environmental impact, etc., for use in increasing an amount of renewable energy resources used at the facilities. In some implementations, the system may recommend actions to reduce a negative environmental impact associated with the selected energy resources.

Abstract: Provided are systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to identify, diagnose, and mitigate occurrences of network faults or incidents within a network. Historical network incidents may be used to generate a model that may be used to evaluate real-time occurring network incidents, such as to identify a cause of the network incident. Clustering algorithms may be used to identify portions of the model that share similarities with a network incident and then actions taken to resolve similar network incidents in the past may be identified and proposed as candidate actions that may be executed to resolve the cause of the network incident. Execution of the candidate actions may be performed under control of a user or automatically based on execution criteria and the configuration of the fault mitigation system.

Abstract: A device may receive digital content and may process the digital content, with at least one of an optimization-based poisoning model or a statistical-based poisoning model, to generate at least one of first poisoning data or second poisoning data, respectively. The device may generate new digital content based on the digital content and the at least one of the first poisoning data or the second poisoning data. The device may provide the new digital content to one or more devices to be accessed by at least one deepfake model used to create fake digital content and may perform one or more actions based on the new digital content.

Abstract: A device may receive historical project data identifying experiences and/or work product from previous projects and client data identifying a client with a problem, and may process the historical project data and the client data, with machine learning models, to generate recommendations for the problem and confidence scores for the recommendations. The device may process the recommendations and the confidence scores, with an NLG model, to generate a solution to the problem and content for the solution, and may generate a digitized dynamic client solution to the problem based on the solution and the content. The device may provide the digitized dynamic client solution to a user device, and may receive feedback on the digitized dynamic client solution from the user device. The device may generate a final digitized dynamic client solution based on the feedback, and may perform actions based on the final digitized dynamic client solution.

Abstract: A code translation apparatus receives a source code including one or more code vulnerabilities and automatically generates remediated code. The source code provided to the code translation apparatus is converted to a source directional graph. The edges of the source directional graph are augmented with additional edge attributes. The source directional graph thus augmented is further converted into a source graph vector representation. The source graph vector representation is provided to an encoder of a trained code transformer. The remediated code is obtained from the decoder of the trained code transformer.

Abstract: Methods, systems, and apparatus for solving cost optimization problems. In one aspect, a method includes receiving data representing a cost optimization problem in a network, wherein i) the network is represented as a graph of nodes and edges, and ii) each edge comprises an associated cost; mapping the data representing the cost optimization problem in a network to a quadratic unconstrained binary optimization (QUBO) formulation of the cost optimization problem, the QUBO formulation comprising multiple variables with values determined by states of respective qubits, wherein each qubit corresponds to a respective edge of the graph of nodes and edges; obtaining data representing a solution to the cost optimization problem from a quantum computing resource; and initiating an action based on the obtained data representing a solution to the cost optimization problem.

Abstract: A first device may provide a request to establish a secure communication with a second device, and may hide public keys based on a commutative legacy compatible encryption process sharing a modulus and based on quasi-Carmichael numbers larger than the modulus with quadratic residuals. The first device may utilize variable extendable-output function hashing, based on the modulus, with bloom filtering to generate an output that prevents creation of classical rainbow tables, and may utilize a key derivation function to generate a symmetric key based on the output. The first device may establish the secure communication with the second device based on the symmetric key.

Abstract: Implementations are directed to processing a content object model through a ML model to provide a set of base content feature representations, processing a style object model through the ML model to provide a set of base style feature representations, executing iterations including: generating, by the ML model, a set of stylized feature representations for an initial stylized object model, the initial stylized object model having one or more adjusted parameters relative to a previous iteration, determining a total loss based on the set of stylized feature representations, the set of base content feature representations, and the sets of base style feature representations, and determining that the total loss is non-optimized, and in response, initiating a next iteration, executing an iteration including determining that the total loss is optimized, and in response providing the initial stylized object model as output of the iterative process.