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: 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: Certain aspects of the present disclosure provide techniques for releasing an RRC connection by a user equipment (UE). A method that may be performed by the UE includes establishing an RRC connection, determining a time duration for a release timer, resetting the release timer, monitoring the RRC connection, and releasing the RRC connection.

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.

Abstract: Various approaches for the deployment and coordination of network operation processing, compute processing, and communications for 5G networks, including with the use of fingerprint-based vRAN cell integrity monitoring, are discussed. In an example, analyzing a state of a 5G network includes: obtaining initial fingerprint reference data of a network state between a virtualized radio access network (vRAN) node and at least one fingerprint reference unit (FRU) device wirelessly connected to the vRAN node; comparing the initial fingerprint reference data to subsequent fingerprint data of the network state between the vRAN node (e.g., operating as vRAN gNB, or as an IAB Donor or IAB Node) and the at least one FRU device to detect a changed network condition; and performing an action at the vRAN node to modify or disable a component of the 5G network, in response to detection of the changed network condition.

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: 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: Techniques and mechanisms for selectively increasing or decreasing an amount of cache resources which are to be available for use in the provisioning of decoded micro-operations in a processor. In an embodiment, a processor core comprises both a first cache which is dedicated to caching micro-operations, and a second cache which is coupled to receive data, or non-decoded instructions. The core further comprises circuitry to monitor one or more cache performance characteristics of the core. Based on the one or more cache performance characteristics, the circuitry performs an evaluation to determine whether to increase—or alternatively, to decrease—the size of a pool of one or more caches which are to be available to receive micro-operations. In another embodiment, the second cache is added to the pool based on an indication of an overutilization of the first cache.

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: 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 digital wallet verification system in a blockchain ecosystem providing enhanced due diligence for cryptocurrency. The system includes receiving a first profile and a digital wallet. The system determines a status for the first profile, based on a verification information. Further, a score for the digital wallet is calculated based on the status of a second profile performing a blockchain transaction in the digital wallet associated with the first profile. The system provides certain private KYC information of the second profile to the first profile and indicating the status of the first profile, the second profile and/or the score associated with any digital wallet publicly on the internet at large.