Abstract: In a method for embedding user information in webpages, user information of a user who logs into a target website is obtained. The user information is converted into color values. The color values are embedded in a target webpage of the target website. If a webpage is leaked, a user who leaks the webpage can be traced.

Abstract: In a method for embedding user information in webpages, user information of a user who logs into a target website is obtained. The user information is converted into color values. The color values are embedded in a target webpage of the target website. If a webpage is leaked, a user who leaks the webpage can be traced.

Abstract: The present disclosure relates to training and utilizing an image exposure transformation network to generate a long-exposure image from a single short-exposure image (e.g., still image). In various embodiments, the image exposure transformation network is trained using adversarial learning, long-exposure ground truth images, and a multi-term loss function. In some embodiments, the image exposure transformation network includes an optical flow prediction network and/or an appearance guided attention network. Trained embodiments of the image exposure transformation network generate realistic long-exposure images from single short-exposure images without additional information.

Abstract: A method for displaying screen information includes obtaining visitor information of a visitor of a display device. Once a page to be visited by the visitor is determined, a current color scheme for at least one controllable element of the page to be visited is determined according to the visitor information and a page type of the page to be visited.

Abstract: A method includes determining whether an inference framework for a deep learning inference framework supports a first data arrangement scheme of a machine learning inference model; determining, in response to the inference framework not supporting the first data arrangement scheme, a data arrangement scheme conversion strategy of input data and output data of an inference operator of the inference framework, based on a dimension of the input data received by the inference operator, a dimension of the output data output corresponding to the input data, and a correlation between the inference operator and the data arrangement scheme; and converting either a data arrangement scheme of the input data or the output data of the inference operator based on the determined data arrangement scheme conversion strategy.

Abstract: Consumer and industrial 2-in-1 alkaline ware washing compositions providing both detergency and rinseability in a single cleaning composition are disclosed. Further benefits can be achieved with low levels of rinse aid surfactants in a rinse additive following the alkaline ware washing composition, including levels of rinse additive surfactant of about 500 ppm surfactant or less. Alkaline ware washing compositions and methods of using the same provide user-friendly, solid, ware washing compositions without the need for using a separate rinse aid composition.

Abstract: Predicting patch displacement maps using a neural network is described. Initially, a digital image on which an image editing operation is to be performed is provided as input to a patch matcher having an offset prediction neural network. From this image and based on the image editing operation for which this network is trained, the offset prediction neural network generates an offset prediction formed as a displacement map, which has offset vectors that represent a displacement of pixels of the digital image to different locations for performing the image editing operation. Pixel values of the digital image are copied to the image pixels affected by the operation.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate refined segmentation masks for digital visual media items. For example, in one or more embodiments, the disclosed systems utilize a segmentation refinement neural network to generate an initial segmentation mask for a digital visual media item. The disclosed systems further utilize the segmentation refinement neural network to generate one or more refined segmentation masks based on uncertainly classified pixels identified from the initial segmentation mask. To illustrate, in some implementations, the disclosed systems utilize the segmentation refinement neural network to redetermine whether a set of uncertain pixels corresponds to one or more objects depicted in the digital visual media item based on low-level (e.g., local) feature values extracted from feature maps generated for the digital visual media item.

Abstract: An analysis apparatus analyses a video image signal comprising successive frames of imaging an endoscopy procedure. A machine learning block analyses the video image signal using a machine learning technique that classifies regions of the frames as belonging to one of plural classes corresponding to respective types of image artefact. The classes include a motion blur class corresponding to motion blur of the image, at least one erroneous exposure class corresponding to a type of erroneous exposure of the image, and at least one noise artefact class corresponding to a type of image artefact that is noise. A quality score block derives quality scores representing image quality of the successive frames based on the classified regions.

Abstract: Systems and methods for locating a medical device in a body lumen are provided. A first flexible elongate instrument comprises a plurality of imaging markers, and a location information sensor is disposed at the first flexible elongate instrument or at a second flexible elongate instrument configured for relative movement with respect to the first flexible elongate instrument. A processor is configured to establish a reference coordinate system based on the plurality of imaging markers, which are visible in a medical image comprising the first flexible elongate instrument disposed in a body lumen, receive diagnostic scan or therapeutic delivery information at a plurality of locations of the body lumen from the first or second flexible elongate instrument, and correlate the information with the imaging markers. A display configured to display a composite image comprising the correlated diagnostic scan or therapeutic delivery information and the imaging markers.

Abstract: A digital mirroring method includes creating a digital model for each physical device of a plurality of physical devices located in a physical space. Once a related data of each physical device is obtained, the related data of each physical device is mapped to corresponding digital model.

Abstract: A data query method includes receiving query data from a query device and verifying whether the query data is authorized, obtaining shared data corresponding to the query data in response that the query data is authorized, performing a logical judgment on the query data and the shared data corresponding to the query data and obtaining a query result corresponding to the query data, encrypting the query result corresponding to the query data, and sending the encrypted query result to the query device. The query device decrypts the query result according to a decryption rule. The query result is a preset logical judgment result. A shared device and a query device of a blockchain system are also provided.

Abstract: A method for performing efficient mixed-precision search for an artificial neural network (ANN) includes training the ANN by sampling selected candidate quantizers of a bank of candidate quantizer and updating network parameters for a next iteration based on outputs of layers of the ANN. The outputs are computed by processing quantized data with operators (e.g., convolution). The quantizers converge to optimal bit-widths that reduce classification losses bounded by complexity constrains.

Abstract: Digital image completion by learning generation and patch matching jointly is described. Initially, a digital image having at least one hole is received. This holey digital image is provided as input to an image completer formed with a dual-stage framework that combines a coarse image neural network and an image refinement network. The coarse image neural network generates a coarse prediction of imagery for filling the holes of the holey digital image. The image refinement network receives the coarse prediction as input, refines the coarse prediction, and outputs a filled digital image having refined imagery that fills these holes. The image refinement network generates refined imagery using a patch matching technique, which includes leveraging information corresponding to patches of known pixels for filtering patches generated based on the coarse prediction. Based on this, the image completer outputs the filled digital image with the refined imagery.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate refined segmentation masks for digital visual media items. For example, in one or more embodiments, the disclosed systems utilize a segmentation refinement neural network to generate an initial segmentation mask for a digital visual media item. The disclosed systems further utilize the segmentation refinement neural network to generate one or more refined segmentation masks based on uncertainly classified pixels identified from the initial segmentation mask. To illustrate, in some implementations, the disclosed systems utilize the segmentation refinement neural network to redetermine whether a set of uncertain pixels corresponds to one or more objects depicted in the digital visual media item based on low-level (e.g., local) feature values extracted from feature maps generated for the digital visual media item.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for accurately and efficiently removing objects from digital images taken from a camera viewfinder stream. For example, the disclosed systems access digital images from a camera viewfinder stream in connection with an undesired moving object depicted in the digital images. The disclosed systems generate a temporal window of the digital images concatenated with binary masks indicating the undesired moving object in each digital image. The disclosed systems further utilizes a 3D to 2D generator as part of a 3D to 2D generative adversarial neural network in connection with the temporal window to generate a target digital image with the region associated with the undesired moving object in-painted. In at least one embodiment, the disclosed systems provide the target digital image to a camera viewfinder display to show a user how a future digital photograph will look without the undesired moving object.

Abstract: In some embodiments, an image manipulation application receives an incomplete image that includes a hole area lacking image content. The image manipulation application applies a contour detection operation to the incomplete image to detect an incomplete contour of a foreground object in the incomplete image. The hole area prevents the contour detection operation from detecting a completed contour of the foreground object. The image manipulation application further applies a contour completion model to the incomplete contour and the incomplete image to generate the completed contour for the foreground object. Based on the completed contour and the incomplete image, the image manipulation application generates image content for the hole area to generate a completed image.

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for generating artistic images by applying an artistic-effect to one or more frames of a video stream or digital images. In one or more embodiments, the disclosed system captures a video stream utilizing a camera of a computing device. The disclosed system deploys a distilled artistic-effect neural network on the computing device to generate an artistic version of the captured video stream at a first resolution in real time. The disclosed system can provide the artistic video stream for display via the computing device. Based on an indication of a capture event, the disclosed system utilizes the distilled artistic-effect neural network to generate an artistic image at a higher resolution than the artistic video stream. Furthermore, the disclosed system tunes and utilizes an artistic-effect patch generative adversarial neural network to modify parameters for the distilled artistic-effect neural network.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing a machine learning model trained to determine subtle pose differentiations to analyze a repository of captured digital images of a particular user to automatically capture digital images portraying the user. For example, the disclosed systems can utilize a convolutional neural network to determine a pose/facial expression similarity metric between a sample digital image from a camera viewfinder stream of a client device and one or more previously captured digital images portraying the user. The disclosed systems can determine that the similarity metric satisfies a similarity threshold, and automatically capture a digital image utilizing a camera device of the client device. Thus, the disclosed systems can automatically and efficiently capture digital images, such as selfies, that accurately match previous digital images portraying a variety of unique facial expressions specific to individual users.