Abstract: The complexity of implementing machine learning models in software systems can be reduced using an abstraction system. The abstraction system functions as an intermediary between a machine learning service and a client process. The abstraction system provides a unified API by which the client process can submit client requests targeting a machine learning model and also abstracts the complexity of configuring model requests in the appropriate form for a particular machine learning service and model. The abstraction system also provides a standard mechanism for delivering results to the client process in an actionable format and for tracking outcomes of any actions that the results trigger. The abstraction system therefore greatly simplifies the process of employing machine learning models within a software system.

Abstract: As part of implementing a recognition and reward system, a communications platform can employ a unique set of data structures, APIs and a rules engine that abstract the definition of rewards from the definition of rules for determining when the rewards should be made available. Accordingly, boosters may interface directly with the communications platform to offer rewards to participants but need not be aware of or involved in the process of defining the rules that will be used to distribute the rewards. Likewise, administrators may interface directly with the communications platform to define rules for making rewards available without needing to be aware of the rewards themselves. In this way, a communications platform can integrate boosters and their rewards into a recognition and rewards system without requiring tight coupling between the rules for determining when rewards should be made available and the rewards themselves.

Abstract: A platform can be employed to implement a personalized learning system that is simple to use, streamlined and scalable thereby enabling such systems to be seamlessly implemented in any learning environment. The platform can be implemented in a client-server environment in which a server or servers maintain a number of data structures which can be used to define students, assignments, classes, flashcards, videos, and learning standards definitions, among many others. A number of backend processes, websites, and web APIs can be configured to allow users to access the content of these data structures as well as to create new entries in these data structures to thereby facilitate the implementation of a personalized learning system that incorporates automation and machine learning in a school, workplace or other learning environment.

Abstract: As part of implementing a recognition and reward system, a communications platform can employ a unique set of data structures, APIs and a rules engine that abstract the definition of rewards from the definition of rules for determining when the rewards should be made available. Accordingly, boosters may interface directly with the communications platform to offer rewards to participants but need not be aware of or involved in the process of defining the rules that will be used to distribute the rewards. Likewise, administrators may interface directly with the communications platform to define rules for making rewards available without needing to be aware of the rewards themselves. In this way, a communications platform can integrate boosters and their rewards into a recognition and rewards system without requiring tight coupling between the rules for determining when rewards should be made available and the rewards themselves.

Abstract: The complexity of implementing machine learning models in software systems can be reduced using an abstraction system. The abstraction system functions as an intermediary between a machine learning service and a client process. The abstraction system provides a unified API by which the client process can submit client requests targeting a machine learning model and also abstracts the complexity of configuring model requests in the appropriate form for a particular machine learning service and model. The abstraction system also provides a standard mechanism for delivering results to the client process in an actionable format and for tracking outcomes of any actions that the results trigger. The abstraction system therefore greatly simplifies the process of employing machine learning models within a software system.

Abstract: A platform can be employed to implement a personalized learning system that is simple to use, streamlined and scalable thereby enabling such systems to be seamlessly implemented in any learning environment. The platform can be implemented in a client-server environment in which a server or servers maintain a number of data structures which can be used to define students, assignments, classes, flashcards, videos, and learning standards definitions, among many others. A number of backend processes, websites, and web APIs can be configured to allow users to access the content of these data structures as well as to create new entries in these data structures to thereby facilitate the implementation of a personalized learning system that incorporates automation and machine learning in a school, workplace or other learning environment.

Abstract: A platform can be employed to implement a personalized learning system that is simple to use, streamlined and scalable thereby enabling such systems to be seamlessly implemented in any learning environment. The platform can be implemented in a client-server environment in which a server or servers maintain a number of data structures which can be used to define students, assignments, classes, flashcards, videos, and learning standards definitions, among many others. A number of backend processes, websites, and web APIs can be configured to allow users to access the content of these data structures as well as to create new entries in these data structures to thereby facilitate the implementation of a personalized learning system that incorporates automation and machine learning in a school, workplace or other learning environment.

Abstract: By rendering a special test image and applying flat-field correction for a device under test (DUT) non-uniformity, the E-O response of a reflective LCOS microdisplay can be quickly determined through an image processing algorithm. The measurement is made in a spatial domain instead of in a temporal domain. From the measurement, the driving voltage of maximum brightness, Vbright, can be determined. The use of Vbright enhances the visibility of pixel and sub-pixel defects to the test system. Other defect visibility enhancements are achieved through appropriate sampling rate, optical axis rotation and improved parallelism between the DUT and the CCD sensor camera. By modeling a sub-pixel defect as a local non-uniformity, a near neighborhood algorithm may be used for detection. The neighborhood algorithm does not rely on the alignment between the display pixels and the camera pixels.