Abstract: Described is a digital platform for facilitating insurance-related transactions using an integrated application programming interface (API). Insurance may include workers' compensation insurance. The integrated API may include a representational state transfer (REST) API that integrates many different transactions into the platform. Transactions may include transactions between insurance agencies and agents, comparative rating entities, companies having insurance needs, or direct consumers having insurance needs. Transactions may include providing of quotes (quoting), finalizing or binding of quotes, managing policies, or pulling of support data. Actions associated with transactions may include creating a quote, getting quote status, getting policy status, or getting class appetite.

Abstract: Nonwoven fabrics are provided that include a first nonwoven layer comprising a first plurality of fine fibers. The first plurality of fine fibers comprises a first polymeric material including (i) a first polymer component and (ii) optionally one or more first additives, wherein the first polymer component comprises a first recycled-polypropylene (rPP) comprising a visbroken spunbond grade polypropylene having a first melt flow rate (MFR) before visbreaking and second MFR after visbreaking, and wherein the second MFR is larger than the first MFR.

Abstract: In some embodiments, a method can include receiving first images of produce. The method can further include executing a first machine learning model to generate second images of produce based on the first images of produce. The first images of produce can include (1) images of non-bagged produce or (2) images of bagged produce. The second images of produce can include the other of (1) images of non-bagged produce or (2) images of bagged produce. The method can further include training a second machine learning model based on the first images of produce and the second images of produce. The method can further include executing, after the training, the second machine learning model to classify as a bagged produce or a non-bagged produce an image not included in the first images and not included in the second images.

Abstract: A contactless photobooth system configured with a viewing screen, user interface, and light sources. The contactless photobooth system to allow a client representative to schedule and obtain photos of a subject different than the client with controlled lighting parameters without requiring the subject to travel to a studio.

Abstract: In some embodiments, a method includes receiving an image of produce and an image of marking. The image of produce has a set of pixels, each associated with a position and a color value. The method further includes generating a grayscale image from the image of produce. The method further includes cropping out a portion from the grayscale image. The method further includes locating a marking position pixel on the image of produce by: (a) producing a list of pixels that are part of the cropped portion, (b) selecting, from the list of pixels, a subset of pixels having grayscale pixel values above a threshold, and (c) randomly selecting the marking position pixel from the subset of pixels. The method further includes overlaying the image of marking on the image of produce by coinciding a pixel of the image of marking with the marker position pixel.

Abstract: A contactless photobooth system configured with a viewing screen, user interface, and light sources. The contactless photobooth system to allow a client representative to schedule and obtain photos of a subject different than the client with controlled lighting parameters without requiring the subject to travel to a studio.

Abstract: A systematic mechanism for visualizing functions or capabilities that an application has. One or more user experience objects are generated corresponding to an application. An application definition is obtained for that application, and then multiple user experience templates are identified based on that application definition. Information from the application definition is then used to populate at least one of the user experience templates to generate at least one object experience object. The user may then review visualizations of the user experience objects to determine the general capabilities of the application, and thereby determine whether to install or open the application, and how best to use the application.

Abstract: The disclosed computer-implemented methods and systems provide solutions for mitigating misdetection of display devices in connection with a subscription service. In some examples, the described methods and systems receive a content request from a display device, where the content request is correlated with a particular subscription service account. In some examples, the systems and methods further determine that the display device is not associated with the particular subscription service account. To verify the display device, the systems and methods utilize display timings and scan timings associated with a verification token to prove that the display device is validly located and can therefore be trusted in connection with the subscription service account. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Aspects of the present disclosure relate to image data verification. In examples, a discrete cosine transform (DCT) is performed on image data and used to generate a set of DCT coefficients. In some instances, multiple block sizes are used when processing the image data, such that a set of DCT coefficients is generated for each block size. The resulting sets of DCT coefficients for the plurality of block sizes may be processed according to an expected distribution to determine whether the content of the image data is likely to be authentic or likely to be altered, such that an indication may be provided in association with the image data accordingly.

Abstract: In some embodiments, a method can include receiving first images of produce. The method can further include executing a first machine learning model to generate second images of produce based on the first images of produce. The first images of produce can include (1) images of non-bagged produce or (2) images of bagged produce. The second images of produce can include the other of (1) images of non-bagged produce or (2) images of bagged produce. The method can further include training a second machine learning model based on the first images of produce and the second images of produce. The method can further include executing, after the training, the second machine learning model to classify as a bagged produce or a non-bagged produce an image not included in the first images and not included in the second images.

Abstract: Systems, treatment and prognostic methods, and kits for risk stratification and development of treatment options for diffuse large B-cell lymphoma patients. The systems, methods, and kits comprise determining, detecting, and evaluating gene expression values for at least ALDOC, ASIP, ATP8A1, CD IE, DUSP16, FAF1, FAM223A1FAM223B, GAREM, GNG8, LM02, LPPR4, LY75, NMAEL, PAD 12, PDK1, PPP1R7, SCN1A, SLAMF1, SSTR2, TNFRSF9, USH2A, VEZF1, WDR91, ADRA2B, ECT2, ELOVL6, IGSF9, NEK3, PDK4, PES1, PUSL1, TAD A2A, and ZMYND19, or a subset thereof, detected in a biological sample from the patient and determining a risk score associated with the gene signature panel, which can be used to guide treatment of the patient.

Abstract: In some embodiments, a method can include executing a first model to extract a first region of interest (ROI) image and a second ROI image from an image that shows an item and an indication of information associated to the item. The first ROI image can include a portion of the image showing the item and the second ROI image can include a portion of the image showing the indication of information. The method can further include executing a second model to identify the item from the first ROI image and generate a representation of the item. The method can further include executing a third model to read the indication of information associated to the item from the second ROI image and generate a representation of information.

Abstract: A contactless photobooth system configured with a viewing screen, user interface, and light sources. The contactless photobooth system to allow a client representative to schedule and obtain photos of a subject different than the client with controlled lighting parameters without requiring the subject to travel to a studio.

Abstract: In some embodiments, a method can include receiving first images of produce. The method can further include executing a first machine learning model to generate second images of produce based on the first images of produce. The first images of produce can include (1) images of non-bagged produce or (2) images of bagged produce. The second images of produce can include the other of (1) images of non-bagged produce or (2) images of bagged produce. The method can further include training a second machine learning model based on the first images of produce and the second images of produce. The method can further include executing, after the training, the second machine learning model to classify as a bagged produce or a non-bagged produce an image not included in the first images and not included in the second images.

Abstract: In some embodiments, a method can include capturing images of produce. The method can further include generating simulated images of produce based on the images of produce. The method can further include associating each image of produce from the images of produce and each simulated image of produce from the simulated images of produce with a category indicator, an organic type indicator, and a bag type indicator, to generate a training set. The method can further include training a machine leaning model using the training set such that when the machine learning model is executed, the machine learning model receives an image and generates a predicted category indicator of the image, a predicted organic type indicator of the image, and a predicted bag type indicator of the image.

Abstract: In some embodiments, a method includes receiving an image of produce and an image of marking. The image of produce has a set of pixels, each associated with a position and a color value. The method further includes generating a grayscale image from the image of produce. The method further includes cropping out a portion from the grayscale image. The method further includes locating a marking position pixel on the image of produce by: (a) producing a list of pixels that are part of the cropped portion, (b) selecting, from the list of pixels, a subset of pixels having grayscale pixel values above a threshold, and (c) randomly selecting the marking position pixel from the subset of pixels. The method further includes overlaying the image of marking on the image of produce by coinciding a pixel of the image of marking with the marker position pixel.

Abstract: In some embodiments, a method can include capturing images of produce. The method can further include generating simulated images of produce based on the images of produce. The method can further include associating each image of produce from the images of produce and each simulated image of produce from the simulated images of produce with a category indicator, an organic type indicator, and a bag type indicator, to generate a training set. The method can further include training a machine leaning model using the training set such that when the machine learning model is executed, the machine learning model receives an image and generates a predicted category indicator of the image, a predicted organic type indicator of the image, and a predicted bag type indicator of the image.

Abstract: A remote eye examination method includes initiating a remote chat session between a local computing device coupled to a phoropter, and a remote computer, and initializing the phoropter for remote control by the remote computer. Then, a multi-colored lamp is activated in a first color until a determination that a refractory examination utilizing the phoropter has commenced. Thereafter, responsive to a determination that the examination has commenced, the multi-colored lamp changes to a second color, and movement of portions of the phoropter are commanded as directed by the remote computer, data is recorded into a patient record and a progression of the examination monitored. Finally, on condition that a threshold period of time before a conclusion of the refractory examination is detected, the multi-colored lamp changes to a third color and upon conclusion of the examination, a message transmitted to a remote account indicating access to the patient record.

Abstract: In some embodiments, a method can include capturing images of produce. The method can further include generating simulated images of produce based on the images of produce. The method can further include associating each image of produce from the images of produce and each simulated image of produce from the simulated images of produce with a category indicator, an organic type indicator, and a bag type indicator, to generate a training set. The method can further include training a machine leaning model using the training set such that when the machine learning model is executed, the machine learning model receives an image and generates a predicted category indicator of the image, a predicted organic type indicator of the image, and a predicted bag type indicator of the image.