Abstract: A computer-implemented method includes receiving, by one or more processors, Process Instruction (PI) information containing instructions for adjudicating a healthcare service request based on a primary fact source; dividing, by the one or more processors and using an Artificial Intelligence (AI) model, the instructions into one or more instruction sets; generating, by the one or more processors and the AI model, an input-output mapping for first ones of the one or more instruction sets having a complexity that does not satisfy a complexity threshold; generating, by the one or more processors and the AI model, code for second ones of the one or more instructions sets having the complexity that satisfies the complexity threshold; generating, by the one or more processors and the AI model, a validation input set for the input-output mapping and for the code; and applying, by the one or more processors and the AI model, the validation input set to the input-output mapping and the code to generate a validation ou

Abstract: A firewall intelligence system, includes a data storage storing a set of firewall rules for a network; a recommendation engine that receives, from a log service, traffic logs detailing traffic for the network and firewall logs detailing the usage of firewall rules in response to the traffic for the network, accesses, from the data storage, the set of firewall rules for the network; processes the set of firewall rules to evaluate the firewall rules against a set of quantitative evaluation rules to determine one or more firewall rule recommendations, wherein each firewall rule recommendation is a recommendation to change at least one of the firewall rules in the set of firewall rules; and a front end API that provides data describing the one or more firewall rule recommendations to a user device.

Abstract: Methods and apparatus to manage infrastructure as code (IaC) implementations are disclosed, A disclosed example system to manage a shared computing resource includes programmable circuitry; and machine readable instructions to cause the programmable circuitry to: determine an IaC type associated with a request corresponding to the shared computing resource; select a template from a plurality of IaC templates based on the IaC type; and service the request based on the template.

Abstract: Methods and apparatus to configure virtual machines (VMs) are disclosed. Am example system to manage a plurality of virtual machines of a shared computing resource, the system includes interface circuitry, programmable circuitry, and machine readable instructions to cause the programmable circuitry to at least one of scan or monitor the plurality of virtual machines, determine whether a master application corresponding to the virtual machines has accepted a minion application corresponding to a first one of the virtual machines, and in response to the determination that the master application has not accepted the minion application, cause the master application to accept the minion application.

Abstract: An example apparatus comprises memory, first instructions, and programmable circuitry to be programmed by the first instructions to associate a first portion of metadata with a first category, the metadata corresponding to a cloud resource of a cloud account, associate a second portion of the metadata with a second category, and determine a template based on the first portion being greater than the second portion, the template associated with the first category, the template including second instructions to define a target state to be enforced on the cloud account.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed for converting enforcement policy information into provisioning template information by instantiating or executing machine-readable instructions to determine a type of a first placeholder of a provisioning template with a plurality of placeholders, copy enforcement policy data corresponding to the determined type of the first placeholder, fill the first placeholder of the provisioning template with the copied enforcement policy data, and save the provisioning template.

Abstract: Systems and methods are disclosed for allocating and distributing power management budgets for subsystems of a computer system. For each of a plurality of subsystems, a power management system may generate a long-term power budget and a closed-loop power budget, and then determine a final power budget to provide to the subsystem, e.g., by applying a min function, a weighted sum, or some other function to the long-term power budget and the closed-loop power budget. The closed-loop power budget is determined based on observations of power draw over a past period of time, and therefore cannot respond immediately to large changes in power. The long-term power budget is generated based on a prediction of battery capability over an upcoming window of time, and may therefore provide a power cap before the system is under duress.

Abstract: Various approaches for the deployment and coordination of network operation processing, communications, and mobile device positioning, in connection with backhaul of a radio access network (RAN), are disclosed. An example method of operation of backhaul communications used with a radio access network (RAN) includes: obtaining measurements corresponding to wireless communications of a radio access network (RAN) operating with a backhaul, the measurements based on in-phase and quadrature (IQ) data of the wireless communications; performing a comparison of the measurements to an expected operational state of the RAN, with the expected operational state being established from a baseline of the IQ data collected in the RAN; and modifying the wireless communications of the backhaul based on the comparison of the measurements to the expected operational state.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to generate and monitor security infrastructure. An example apparatus comprises programmable circuitry to at least one of instantiate or execute the machine readable instructions to: provision the security infrastructure based on a security infrastructure generation request; detect a global infrastructure template based on the security infrastructure generation request; and generate the security infrastructure based on the provisioned security infrastructure, the global infrastructure template, and an identified security infrastructure generation authorization, the security infrastructure generation authorization to indicate whether the generation of the security infrastructure is permitted.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to tag cloud resources. An example apparatus includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to obtain first accesses of a cloud resource, the first accesses defining a first usage pattern associated with the cloud resource, associate the cloud resource with a business environment based on the first accesses, compare the first usage pattern to a second usage pattern corresponding to historical accesses of reference cloud resources associated with the business environment, and when the first usage pattern is different from the second usage pattern, limit second accesses of the cloud resource based on the second usage pattern.

Abstract: Methods and systems for entity state-based stage-wise formal specification of life science processes are provided.

Abstract: Techniques are provided for using one or more machine learning systems to process input data including image data. The input data including the image data can be obtained, and at least one machine learning system can be applied to at least a portion of the image data to determine at least one color component value for one or more pixels of at least the portion of the image data. Based on application of the at least one machine learning system to at least the portion of the image data, output image data for a frame of output image data can be generated. The output image data includes at least one color component value for one or more pixels of the frame of output image data. Application of the at least one machine learning system causes the output image data to have a reduced dimensionality relative to the input data.

Abstract: A system for simulating network configurations includes data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that when executed on the data processing hardware cause the data processing hardware to perform operations. The operation includes receiving one or more parameter changes for a production network model of a network. The operations also include generating a simulation network model including the one or more parameter changes. Another operation includes analyzing the simulated network flow within the simulation network model. The operations also include generating a report including an impact of the parameter changes on the network. The operations may also include receiving a production network log including a recorded workflow for the production network model and simulating the production workflow of the production network log within the simulation network model to generate a simulated network log.

Abstract: Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing one-click driver image labeling to facilitate the assignment of drive time to a driver. Various aspects of the driver and/or passengers may be determined, which may include a driver identifier previously associated with the driver, whether the driver is wearing a facial covering, whether an image of the driver is partially occluded, whether the driver is facing in the direction of the camera, and whether an image of the driver is expected to be easily recognizable to a person tasked with assigning representative driver images to driver identifiers. These determinations may be used to improve the efficacy and ease-of-use of a visual login system.

Abstract: Techniques are provided for using one or more machine learning systems to process input data including image data. The input data including the image data can be obtained, and at least one machine learning system can be applied to at least a portion of the image data to determine at least one color component value for one or more pixels of at least the portion of the image data. Based on application of the at least one machine learning system to at least the portion of the image data, output image data for a frame of output image data can be generated. The output image data includes at least one color component value for one or more pixels of the frame of output image data. Application of the at least one machine learning system causes the output image data to have a reduced dimensionality relative to the input data.

Abstract: In some instances, a method for inaccuracy detection and prevention within prescription information is provided.

Abstract: Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing one-click driver image labeling to facilitate the assignment of drive time to a driver. Various aspects of the driver and/or passengers may be determined, which may include a driver identifier previously associated with the driver, whether the driver is wearing a facial covering, whether an image of the driver is partially occluded, whether the driver is facing in the direction of the camera, and whether an image of the driver is expected to be easily recognizable to a person tasked with assigning representative driver images to driver identifiers. These determinations may be used to improve the efficacy and ease-of-use of a visual login system.

Abstract: A method of estimating a level of one or more water parameters using trained machine learning (ML) based regression trees, comprising receiving physical features and spectral features relating to one or more sample of waters, propagating the water sample(s) through a regression trees comprising a plurality of nodes arranged in a plurality of branches propagating from a root node to a plurality of leaf nodes, each of the plurality of nodes is associated with an ML model trained to estimate a level of one or more water parameters in water samples, identifying a certain leaf node to which the water sample(s) propagated based on the plurality of physical features, estimating a level of one or more of the water parameters in the water sample(s) by applying the ML model associated with the certain leaf node to the spectral features, and initiating action(s) based on the water parameter(s) estimated level.

Abstract: In some instances, a method for inaccuracy detection and prevention within prescription information is provided.

Abstract: A firewall intelligence system, includes a data storage storing a set of firewall rules for a network; a recommendation engine that receives, from a log service, traffic logs detailing traffic for the network and firewall logs detailing the usage of firewall rules in response to the traffic for the network, accesses, from the data storage, the set of firewall rules for the network; processes the set of firewall rules to evaluate the firewall rules against a set of quantitative evaluation rules to determine one or more firewall rule recommendations, wherein each firewall rule recommendation is a recommendation to change at least one of the firewall rules in the set of firewall rules; and a front end API that provides data describing the one or more firewall rule recommendations to a user device.