Abstract: A system and method for an infrastructure as code (IaC) environment includes automatically generating different infrastructure code for different timepoints based at least in part on changes in infrastructure configurations at the different timepoints of a first virtual private cloud (VPC) so as to execute one of the different infrastructure code for a second VPC to cause deployment of one of the infrastructure configurations; and includes automatically generating or updating a script based at least in part on changes in the infrastructure code, the script to be used to deploy at least a version of the infrastructure configurations with the changes in a second VPC of a different geographical location than associated with the first VPC.

Abstract: Described are examples for providing service level monitoring for a network hosting applications as a cloud service. A service level monitoring device may receive end-to-end measurements of service usage collected at user devices for a plurality of applications hosted as a cloud services. The service level monitoring device may determine degraded applications of the plurality of applications based on anomalies in the measurements. The service level monitoring device may determine a service level metric based on an aggregation of the degraded applications. In some examples, the service level monitoring device may detect a network outage affecting the service.

Abstract: Operational metrics of a distributed collection of servers in a cloud environment are analyzed by a service to intelligently machine learn which operational metric is highly correlated to incidents or failures in the cloud environment. To do so, metric values of the operational metrics are analyzed over time by the service to check whether the operation metrics exceed a particular metric threshold. If so, the service also checks whether such spikes in the operation metric above the metric thresholds occurred during known cloud incidents. Statistics are calculated reflecting the number of times the operational metrics spiked during times of cloud incidents and spiked during times without cloud incidents. Correlation scores based on these statistics are calculated and used to select the correlated operational metrics that are most correlated to cloud failures.

Abstract: Described are examples for providing service level monitoring for a network hosting applications as a cloud service. A service level monitoring device may receive end-to-end measurements of service usage collected at user devices for a plurality of applications hosted as a cloud services. The service level monitoring device may determine degraded applications of the plurality of applications based on anomalies in the measurements. The service level monitoring device may determine a service level metric based on an aggregation of the degraded applications. In some examples, the service level monitoring device may detect a network outage affecting the service.

Abstract: Embodiments described herein are directed to a programming language trigger mechanism. The trigger mechanism is a small piece of code that a software developer utilizes in a computer program. The trigger mechanism enables computing operations or tasks to be performed asynchronously and in a parallel fashion. In particular, logic (e.g., operations or tasks) associated with the trigger mechanism are provided to a plurality of resources for processing in parallel. Each resource asynchronously processes the task provided thereto and asynchronously provides the result. The results are asynchronously returned as an enumeration. The enumeration enables the software developer to enumerate through the returned elements as a simple stream of results as they are calculated.

Abstract: Example aspects include techniques for detecting, for one or more instances of a dependency call from a service to a dependency in the cloud computing platform, the one or more instances of the dependency call having a common set of dependency call inputs, that a value of a dependency call performance metric of the dependency call is outside of a threshold range, providing, to a machine learning (ML) model and based on detecting that the value is outside of the threshold range, the common set of dependency call inputs for the one or more instances of the dependency call, obtaining, from the ML model and based on the common set of dependency call inputs, an expected value for the dependency call performance metric, and determining, based on comparing the value to the expected value, the entity causing the value to be outside of the threshold range.

Abstract: Described are examples for providing service level monitoring for a network hosting applications as a cloud service. A service level monitoring device may receive end-to-end measurements of service usage collected at user devices for a plurality of applications hosted as a cloud services. The service level monitoring device may determine degraded applications of the plurality of applications based on anomalies in the measurements. The service level monitoring device may determine a service level metric based on an aggregation of the degraded applications. In some examples, the service level monitoring device may detect a network outage affecting the service.

Abstract: Example aspects include techniques for shifting left database degradation detection. These techniques may include identifying a database query in application code in a pre-production environment and predicting, via a prediction model corresponding to a production environment, a resource cost of the database query, the prediction model trained on database activity resulting from execution of a plurality of database queries over a production database system within the production environment. In addition, the techniques may include presenting, via a user interface, a notification corresponding to the resource cost.

Abstract: Described are examples for providing service level monitoring for a network hosting applications as a cloud service. A service level monitoring device may receive end-to-end measurements of service usage collected at user devices for a plurality of applications hosted as a cloud services. The service level monitoring device may determine degraded applications of the plurality of applications based on anomalies in the measurements. The service level monitoring device may determine a service level metric based on an aggregation of the degraded applications. In some examples, the service level monitoring device may detect a network outage affecting the service.

Abstract: Methods for intelligent automatic merging of source control queue items are performed by systems and apparatuses. Project changes are submitted in build requests to a gated check-in build queue requiring successful builds to commit changes to a code repository according to source control. Multiple pending build requests in the build queue are intelligently and automatically merged into a single, pending merged request based on risk factor values associated with the build requests. For merged requests successfully built, files in the build requests are committed and the build requests are removed from the queue. Merged requests unsuccessfully built are divided into equal subsets based on updated risk factor values using information from the unsuccessful build. Successful builds of subsets allow for committing of files and removal from the build queue, while unsuccessful builds are further divided and processed until single build requests are processed to identify root cause errors.

Abstract: Disclosed is a method of operating an Alkaline Membrane Fuel Cell (AMFC) with direct ammonia feeding. The method may include providing AMFC comprising an anode inlet for receiving ammonia and a cathode inlet for receiving oxygen containing gas; operating the AMFC at an operation temperature of above 80° C.; providing the oxygen containing gas; to a cathode of the AMFC at a pressure above the equilibrium vapor pressure of water at the operation temperature; maintaining the pressure during the operation of the AMFC as to maintain water in substantially liquid phase near the cathode; and providing the ammonia to an anode of the AMFC.

Abstract: Operational metrics of a distributed collection of servers in a cloud environment are analyzed by a service to intelligently machine learn which operational metric is highly correlated to incidents or failures in the cloud environment. To do so, metric values of the operational metrics are analyzed over time by the service to check whether the operation metrics exceed a particular metric threshold. If so, the service also checks whether such spikes in the operation metric above the metric thresholds occurred during known cloud incidents. Statistics are calculated reflecting the number of times the operational metrics spiked during times of cloud incidents and spiked during times without cloud incidents. Correlation scores based on these statistics are calculated and used to select the correlated operational metrics that are most correlated to cloud failures.

Abstract: Embodiments described herein are directed to a programming language trigger mechanism. The trigger mechanism is a small piece of code that a software developer utilizes in a computer program. The trigger mechanism enables computing operations or tasks to be performed asynchronously and in a parallel fashion. In particular, logic (e.g., operations or tasks) associated with the trigger mechanism are provided to a plurality of resources for processing in parallel. Each resource asynchronously processes the task provided thereto and asynchronously provides the result. The results are asynchronously returned as an enumeration. The enumeration enables the software developer to enumerate through the returned elements as a simple stream of results as they are calculated.

Abstract: Operational metrics of a distributed collection of servers in a cloud environment are analyzed by a service to intelligently machine learn which operational metric is highly correlated to incidents or failures in the cloud environment. To do so, metric values of the operational metrics are analyzed over time by the service to check whether the operation metrics exceed a particular metric threshold. If so, the service also checks whether such spikes in the operation metric above the metric thresholds occurred during known cloud incidents. Statistics are calculated reflecting the number of times the operational metrics spiked during times of cloud incidents and spiked during times without cloud incidents. Correlation scores based on these statistics are calculated and used to select the correlated operational metrics that are most correlated to cloud failures.

Abstract: Disclosed is a method of operating an Alkaline Membrane Fuel Cell (AMFC) with direct ammonia feeding. The method may include providing AMFC comprising an anode inlet for receiving ammonia and a cathode inlet for receiving oxygen containing gas; operating the AMFC at an operation temperature of above 80° C.; providing the oxygen containing gas; to a cathode of the AMFC at a pressure above the equilibrium vapor pressure of water at the operation temperature; maintaining the pressure during the operation of the AMFC as to maintain water in substantially liquid phase near the cathode; and providing the ammonia to an anode of the AMFC.

Abstract: An imaging device captured images of a scene that includes at least one shooter. Each shooter of the at least one shooter operates an associated firearm to discharge one or more projectile. A positioning mechanism positions an infrared filter in and out of a path between the imaging device and the scene. A processing system processes images of the scene when the infrared filter is positioned in the path to detect projectile discharges in response to each shooter of the at least one shooter firing the associated firearm. The processing system processes images of the scene captured when the infrared filter is positioned out of the path to identify, for each detected projectile discharge, a shooter of the at least one shooter that is associated with the detected projectile discharge.

Abstract: Non-limiting examples of the present disclosure relate to automated presentation control during presentation of one or more digital documents. Processing operations described herein may be configured to work with any type of application/service, where examples described herein extend to presentation of any type of digital document(s) including slide-based presentations. Speech signals may be detected while a digital document is being presented. The detected speech signals may be converted to text. The converted text may be matched with contextual data (e.g., pre-processed content) associated with the digital document. Probability scoring for determination of a next placement position of a digital document may be generated based on the matching of converted text with the contextual data of the digital document. The next placement position may be selected based on the generated probability scoring. Display of the digital document may be automatically updated to reflect the selected next placement position.

Abstract: A system has at least one processor, a control subsystem, and an end unit with at least one image sensor. The control subsystem switches operation of the system between first and second modes. In the first mode, an image sensor captures a visible light image of a scene, and a processor analyzes the visible light image to identify a target in the scene and extract target spatial information. In the second mode, an image sensor captures a series of IR images of the scene, and a processor analyzes the IR images to identify firearm projectile strikes on the target based on the extracted spatial information and comparisons between the IR images. In certain embodiments, the same image sensor is used for visible light and IR image, and an IR filter is selectively positioned in an optical path from the scene to the image sensor according to the mode of operation.

Abstract: Methods for intelligent automatic merging of source control queue items are performed by systems and apparatuses. Project changes are submitted in build requests to a gated check-in build queue requiring successful builds to commit changes to a code repository according to source control. Multiple pending build requests in the build queue are intelligently and automatically merged into a single, pending merged request based on risk factor values associated with the build requests. For merged requests successfully built, files in the build requests are committed and the build requests are removed from the queue. Merged requests unsuccessfully built are divided into equal subsets based on updated risk factor values using information from the unsuccessful build. Successful builds of subsets allow for committing of files and removal from the build queue, while unsuccessful builds are further divided and processed until single build requests are processed to identify root cause errors.

Abstract: A system has at least one processor, a control subsystem, and an end unit with at least one image sensor. The control subsystem switches operation of the system between first and second modes. In the first mode, an image sensor captures a visible light image of a scene, and a processor analyzes the visible light image to identify a target in the scene and extract target spatial information. In the second mode, an image sensor captures a series of IR images of the scene, and a processor analyzes the IR images to identify firearm projectile strikes on the target based on the extracted spatial information and comparisons between the IR images. In certain embodiments, the same image sensor is used for visible light and IR image, and an IR filter is selectively positioned in an optical path from the scene to the image sensor according to the mode of operation.