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.