Abstract: A degraded user experience, such as a user having to wait for an unresponsive application, can be automatically detected and classified. A user experience degradation detection network detects a degraded user experience based on a state of the computing system and a user interaction state. The computing system state can be based on telemetry data provided by the operating system, processor units, and other computing system components and resources, and the user interaction state can be based on user interactions with one or more input devices (e.g., keyboard, touchpad, mouse, touchscreen). A root cause of the degradation event (e.g., hardware, memory, network, or general responsiveness issue) can be classified using a multi-label classifier. An output report can include a snapshot of the system telemetry and user interaction data before, during, and after the degradation event.

Abstract: Systems, apparatus, articles of manufacture, and methods to generate digitized handwriting with user style adaptations are disclosed. An example apparatus includes at least one memory, and processor circuitry to train a machine learning model to generate a first digitized handwriting sequence based on a stored handwriting sample. To train the machine learning model, the processor circuitry is to cause a parameterization of a first portion of the machine learning model; and cause a reparameterization of a second portion of the machine learning model. The processor circuitry to re-train the trained machine learning model to generate a second digitized handwriting sequence based on a user handwriting sample.

Abstract: A method controls display of an image by dividing a source image into a plurality of M×N blocks of pixels, selecting a first one of the blocks, and transferring the pixels in the first block from a source memory to a display memory, the pixels in the first block transferred based on orientation change information. The selecting and transferring steps are then repeated to transfer pixels in remaining ones of the blocks to the display memory. Each block corresponds to only a portion of the source image, where any given portion represents less than a full line of pixels in the source image.

Abstract: A method for controlling display of an image includes generating a list of non-sequential random numbers, each number corresponding to a respective one of a plurality of lines in a source image. The lines in the source image are then transferred in non-sequential order from a first memory to a second memory based on the random numbers in the list and orientation change information. The resulting image in the second memory is then displayed in the desired orientation.

Abstract: A method controls display of an image by dividing a source image into a plurality of M×N blocks of pixels, selecting a first one of the blocks, and transferring the pixels in the first block from a source memory to a display memory, the pixels in the first block transferred based on orientation change information. The selecting and transferring steps are then repeated to transfer pixels in remaining ones of the blocks to the display memory. Each block corresponds to only a portion of the source image, where any given portion represents less than a full line of pixels in the source image.

Abstract: A method for controlling display of an image includes generating a list of non-sequential random numbers, each number corresponding to a respective one of a plurality of lines in a source image. The lines in the source image are then transferred in non-sequential order from a first memory to a second memory based on the random numbers in the list and orientation change information. The resulting image in the second memory is then displayed in the desired orientation.