Abstract: In certain embodiments, a method includes receiving, at a framework controller executing, a test execution request comprising test parameters and an expected test outcome, wherein the test execution request comprises a request for testing a microservice architecture implemented in a cloud infrastructure; providing, by the framework controller, the test execution request and the test parameters to a framework injector executing on the one or more processors; providing, by the framework controller, the expected test outcome to a framework monitor executing on the one or more processors; executing, by the framework injector, a test corresponding to the test execution request using a test injector of a plurality of test injectors implemented by the framework injector; monitoring, by the framework monitor, a response of the microservice architecture of the cloud infrastructure to the test; and providing, by the framework controller and from the framework monitor, a test result based on the response.

Abstract: This document describes techniques and systems that enable an interface for communicating a threshold in a camera. An electronic device recognizes an in-camera, drag gesture that triggers a camera application to switch modes from a real-time display mode (displaying real-time preview images in a viewfinder) to a buffer-display mode, which displays frames recorded in the camera buffer. During the motion of the drag gesture, the electronic device provides dynamic visual feedback indicating a relation between a drag distance of the drag gesture and a target threshold for the drag gesture. For simplicity and conciseness, the visual feedback can be combined with the virtual shutter control. After meeting the threshold, the user releases the touch input of the drag gesture and the system triggers the camera application to switch modes. This allows capture of a “missed” moment that was recorded in the camera buffer but not stored in non-volatile memory.

Abstract: An example method includes displaying, by a graphical user interface of a computing device, an image comprising a target region. The target region may be smaller than an entirety of the image. The method includes providing, by the graphical user interface, a user-adjustable control to adjust a desired local brightness exposure level for the target region. The method includes receiving, by the user-adjustable control, a user indication of the desired local brightness exposure level for the target region. The method includes adjusting the local brightness exposure level for the target region in the image in response to the user indication.

Abstract: In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.

Abstract: In some examples, processors of an infotainment system receives a request to refresh a graphical user interface of the application being displayed by a display of the infotainment system. The processors determine whether the one or more processors received the request during a refresh limit period. Responsive to determining that the one or more processors received the request during a refresh limit period, the processors delay processing of the request. Responsive to expiration of the refresh limit period, the processors process the request, and, responsive to processing the request, initiate the refresh limit period.

Abstract: A method is provided comprising storing, in a first subscriber data server, subscriber data for each of a plurality of wireless devices, the subscriber data enabling wireless communication services for the plurality of wireless devices in a wireless network; determining to migrate the subscriber data from the first subscriber data server to a second subscriber data server; upon determining to migrate the subscriber data, initiating a migration process for the subscriber data from the first subscriber data server to a second subscriber data server by copying the subscriber data from the first subscriber data server to the second subscriber data server; and while copying and one or more new wireless devices are being provisioned for the wireless communication services in the wireless network, adding new subscriber data for the one or more new wireless devices to both the first subscriber data server and to the second subscriber data server.

Abstract: In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.

Abstract: To optimize energy production of energy production sites, such as solar farms, there are a variety of maintenance and management factors that may be addressed to ensure optimal performance of energy production equipment on the energy production sites. Artificial intelligence may be employed to assist with identifying problems of energy production of common energy production equipment, physical properties, such as vegetation and/or energy production equipment, for example. The identified problems may be remediated, thereby reducing downtime and costs while optimizing energy production. As part of the analysis, in determining remediation of identified problems using artificial intelligence, predictive analyses of weather and other factors versus cost to perform certain remedial efforts may be performed.

Abstract: This document describes techniques and systems that enable an interface for communicating a threshold in a camera. An electronic device recognizes an in-camera, drag gesture that triggers a camera application to switch modes from a real-time display mode (displaying real-time preview images in a viewfinder) to a buffer-display mode, which displays frames recorded in the camera buffer. During the motion of the drag gesture, the electronic device provides dynamic visual feedback indicating a relation between a drag distance of the drag gesture and a target threshold for the drag gesture. For simplicity and conciseness, the visual feedback can be combined with the virtual shutter control. After meeting the threshold, the user releases the touch input of the drag gesture and the system triggers the camera application to switch modes. This allows capture of a “missed” moment that was recorded in the camera buffer but not stored in non-volatile memory.

Abstract: An example method includes displaying, by a graphical user interface of a computing device, an image comprising a target region. The target region may be smaller than an entirety of the image. The method includes providing, by the graphical user interface, a user-adjustable control to adjust a desired local brightness exposure level for the target region. The method includes receiving, by the user-adjustable control, a user indication of the desired local brightness exposure level for the target region. The method includes adjusting the local brightness exposure level for the target region in the image in response to the user indication.

Abstract: Examples described herein relate to migrating data in a system having multi-tiered physical storage. In an example, relative weights corresponding to a plurality of data access parameters may be determined based on a ranking associated with each of the plurality of data access parameters. Further, a priority metric corresponding to each of a plurality of candidate types may be determined based on the relative weights of the plurality of data access parameters. Furthermore, one or more candidate types may be selected from the plurality of candidate types based on the priority metric corresponding to each of the plurality of candidate types. Moreover, data containers corresponding to the selected one or more candidate types may be migrated from a first tier storage to a second tier storage.

Abstract: Examples described herein relate to migrating data in a system having multi-tiered physical storage. In an example, relative weights corresponding to a plurality of data access parameters may be determined based on a ranking associated with each of the plurality of data access parameters. Further, a priority metric corresponding to each of a plurality of candidate types may be determined based on the relative weights of the plurality of data access parameters. Furthermore, one or more candidate types may be selected from the plurality of candidate types based on the priority metric corresponding to each of the plurality of candidate types. Moreover, data containers corresponding to the selected one or more candidate types may be migrated from a first tier storage to a second tier storage.

Abstract: Sketch completion using machine learning in a digital medium environment is described. Initially, a user sketches a digital image, e.g., using a stylus in a digital sketch application. A model trained using machine learning is leveraged to identify and describe visual characteristics of the user sketch. The visual characteristics describing the user sketch are compared to clusters of data generated by the model and that describe visual characteristics of a set of digital sketch images. Based on the comparison, digital sketch images having visual characteristics similar to the user sketch are identified from similar clusters. The similar images are returned for presentation as selectable suggestions for sketch completion of the sketched object.

Abstract: Sketch completion using machine learning in a digital medium environment is described. Initially, a user sketches a digital image, e.g., using a stylus in a digital sketch application. A model trained using machine learning is leveraged to identify and describe visual characteristics of the user sketch. The visual characteristics describing the user sketch are compared to clusters of data generated by the model and that describe visual characteristics of a set of digital sketch images. Based on the comparison, digital sketch images having visual characteristics similar to the user sketch are identified from similar clusters. The similar images are returned for presentation as selectable suggestions for sketch completion of the sketched object.

Abstract: A system for generating a plurality of surveillance and compliance scenarios includes a first user interface comprising a graphical modeling tool for writing fraud detection logic in a first language and a second user interface for creating one or more configurable threshold parameters, one or more general parameters, as well as one or more configurable subsets of filters to be applied to the detection logic. The system is configured to interpret the fraud detection logic in the first language and translate the fraud detection logic into a second language that is capable of performing the fraud detection on a subset of transactions data after application of the one or more configurable subsets of filters to the detection logic, as well as limiting a number of alerts that can be generated after application of the threshold and general parameters.

Abstract: A system for generating a plurality of surveillance and compliance scenarios includes a first user interface comprising a graphical modeling tool for writing fraud detection logic in a first language and a second user interface for creating one or more configurable threshold parameters, one or more general parameters, as well as one or more configurable subsets of filters to be applied to the detection logic. The system is configured to interpret the fraud detection logic in the first language and translate the fraud detection logic into a second language that is capable of performing the fraud detection on a subset of transactions data after application of the one or more configurable subsets of filters to the detection logic, as well as limiting a number of alerts that can be generated after application of the threshold and general parameters.