Abstract: An image forming system including a printer that forms an image on a sheet and an inspection apparatus that inspects an image on a sheet delivered from the printer. A discharge mode for discharging a sheet inspected by the inspection apparatus and binding processing for binding a plurality of sheets as one bundle are set on a console panel. In a case where a first mode for discharging a first sheet determined as normal by the inspection apparatus to a first discharge destination, discharging a second sheet determined as defective by the inspection apparatus to a second discharge destination different from the first discharge destination, and discharging a third sheet conveyed subsequently to the second sheet, determined as normal by the inspection apparatus, to the first discharge destination is set as the discharge mode, and the binding processing is set, execution of the corresponding job is stopped.

Abstract: A method for generating a labeled set of images for use in machine learning based stray light characterization for space-related optical systems. The method includes (a) obtaining a set of images simulated for a space-related optical system, wherein the images of the set of images contain stray light simulated for the space-related optical system, (b) for each image of the set of images, identifying one or more clusters of light contained in the respective image and labeling the respective image by the one or more clusters of light, wherein the one or more clusters of light include at least one cluster of stray light, and (c) creating, based on the labeled images of the set of images, a plurality of new labeled images by applying transformations to the labeled images to generate an augmented set of labeled images.

Abstract: Techniques that facilitate three-dimensional (3D) delineation of tumor boundaries via one or more supervised machine learning algorithms are provided. An example embodiment includes a computer-implemented method that includes: extracting, by a computing system operatively coupled to a processor, one or more feature vectors from a time-series evolution of fluorescence distribution observed at a section of bodily tissue of interest, wherein the one or more feature vectors represent a physical model describing on-tissue dye dynamics of the section of bodily tissue; and generating, by the computing system, a classification attribute for the section of bodily tissue represented by the one or more feature vectors, wherein a pre-trained classifier designates the section of bodily tissue as a biopsy or a non-biopsy candidate through execution of the one or more supervised machine learning algorithms.

Abstract: A computer-implemented method for obfuscating sensitive information associated with mail delivery is disclosed. The computer-implemented method includes identifying that a piece of mail directed towards a potential recipient includes a particular type of sensitive information. The computer-implemented method further includes selecting a mail obfuscation policy for the particular type of sensitive information based on the particular type of sensitive information. The computer-implemented method further includes performing an obfuscation action with respect to the particular type of sensitive information based on the selected mail obfuscation policy.

Abstract: An image capture device may analyze visual content to determine a smile aggregation value. The smile aggregation value satisfying a smile aggregation criterion may indicate that people depicted in the visual content are smiling. When the smile aggregation value satisfies the smile aggregation criterion, the capture of visual content may be started. When the smile aggregation value fails to satisfy the smile aggregation criterion, the capture of visual content may be stopped.

Abstract: A image processing system which make it possible to reduce user time and effort in checking OCR error image data. The image processing system performs OCR processing on image data generated by an image forming apparatus in response to selection of an object by a user and stores the image data in a folder having a folder name formed by using a character string obtained by the OCR processing. The system includes a storage control unit configured to store the image data, in a case where the character string obtained by performing the OCR processing on the generated image data does not satisfy a predetermined condition, in a folder associated with the object.

Abstract: An apparatus and method for a printing system is provided which determines whether image data input by execution of an input job is a blank and, in a case where the image data is determined to be a blank, the printing system records information indicating the determination of a blank as history information of the input job. Thus, in a case where image data input by execution of an input job is not output by execution of an output job, the user is able to easily confirm the cause of the output failure of the input image data.

Abstract: Generating vector representations of visual objects is leveraged in a digital medium environment. For instance, a raster-based visual input object is encoded into a global latent code and individual path latent codes for visual components of the raster visual object are extracted from the global latent code. The path latent codes are decoded and used to generate vector representations of the original raster versions of the visual components. The vector representations are rasterized and composited to generate an output object that simulates a visual appearance of the input object.

Abstract: A first communication device may execute an output control process of externally outputting output information, the output information obtained using a public key of the first communication device, receive an authentication request from a second communication device that has obtained the public key, send a first authentication response to the second communication device via a wireless interface in a case where it is determined that a wireless connection has been established between the first communication device and a first access point, the first authentication response including first role information indicating that the first communication device is to serve a first role which is a role of sending first wireless setting information to the second communication device, and send the first wireless setting information to the second communication device.

Abstract: In some aspects, techniques for creating representative and informative training datasets for the training of machine-learning models are provided. For example, a risk assessment system can receive a risk assessment query for a target entity. The risk assessment system can compute an output risk indicator for the target entity by applying a machine learning model to values of informative attributes associated with the target entity. The machine learning model may be trained using training samples selected from a representative and informative (RAI) dataset. The RAI dataset can be created by determining the informative attributes based on attributes used by a set of models and further extracting representative data records from an initial training dataset based on the determined informative attributes. The risk assessment system can transmit a responsive message including the output risk indicator for use in controlling access of the target entity to an interactive computing environment.

Abstract: The shape and/or location of an organ may change in accordance with changes in the body shape and/or pose of a patient. Described herein are systems, methods, and instrumentalities for automatically determining, using an artificial neural network (ANN), the shape and/or location of the organ based on human models that reflect the body shape and/or pose the patient. The ANN may be trained to learn the spatial relationship between the organ and the body shape or pose of the patient. Then, at an inference time, the ANN may be used to determine the relationship based on a first patient model and a first representation (e.g., a point cloud) of the organ so that given a second patient model thereafter, the ANN may automatically determine the shape and/or location of the organ corresponding to the body shape or pose of the patient indicated by the second patient model.

Abstract: An apparatus includes a setting unit and a control unit. The setting unit performs a first print setting indicating a poster printing function that divides a single piece of image data into a plurality of pieces of image data and generates print jobs, and a second print setting indicating a correction function for correcting calculated gray values when an image processing apparatus converts a plurality of pieces of color image data to grayscale, based on a number of colors acquired from each of the plurality of pieces of color image data. The control unit restricts the setting unit from performing the first print setting and the second print setting. In a state where one of the first print setting and the second print setting is performed by the setting unit, the control unit controls the setting unit not to perform the other print setting.

Abstract: Techniques are described that facilitate training an artificial intelligence (AI) model in a manner that ensures equitable model performance across different sub-groups. According to an embodiment, a system is provided that includes a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components include a training component that trains a machine learning (ML) model on training data to perform an inferencing task using an equitable loss function that drives equitable performance of the ML model across different sub-groups represented by the training data, resulting in trained version of the ML model that provides a defined equitable performance level across the different sub-groups. The equitable loss function is “sub-group aware” and penalizes variation in model performance across the sub-groups during model training and validation.

Abstract: Methods, apparatuses, electronic devices, and mediums related to classification models are provided. The method may include: obtaining a set of positive samples and a set of negative samples, a sample in the set of positive samples including a pair of images from a first image set annotated as similar with each other, and a sample in the set of negative samples including a pair of images annotated as dissimilar with each other from the first image set; training a first sub-network based on the set of positive samples and the set of negative samples; constructing a classification model including a second sub-network, where initial weights of the second sub-network are weights of corresponding nodes of the trained first sub-network; and training the classification model using a second image set that includes at least one image that is common with at least one image in the first image set.

Abstract: An implement management system detects implement wear and monitors implement states to modify operating modes of a vehicle. The system can determine implement wear using the pull of the implement on the vehicle, the force and angle of which is represented by an orientation vector. The system may measure a current orientation vector and determine an expected orientation vector using sensors and a model (e.g., a machine learned model). Additionally, the implement management system can determine an implement state based on images of the soil and the implement captured by a camera onboard the vehicle during operation. The system may apply different models to the images to determine a likely state of the implement. The difference between the expected and current orientation vectors or the determined implement state may be used to determine whether and how the vehicle's operating mode should be modified.

Abstract: Systems and methods for viewing, storing, transmitting, searching, and editing application-specific audiovisual content (or other unstructured data) are disclosed in which edge devices generate content on the fly from a partial set of instructions rather than merely accessing the content in its final or near-final form. An image processing architecture may include a generative model that may be a deep learning model. The generative model may include a latent space comprising a plurality of latent codes and a trained generator mapping. The trained generator mapping may convert points in the latent space to uncompressed data points, which in the case of audiovisual content may be generated image frames. The generative model may be capable of closely approximating (up to noise or perceptual error) most or all potential data points in the relevant compression application, which in the case of audiovisual content may be source images.

Abstract: Systems and methods for handwriting recognition using language modeling facilitate improved results by using a trained language model to improve results from a handwriting recognition machine learning model. The language model may be a character-based language model trained on a dataset pertinent to field values on which the handwriting recognition model is to be used. A loss prediction module may be trained with the handwriting recognition model and/or the language model and used to determine whether a prediction from the handwriting recognition model should be refined by passing the prediction through the trained language model.

Abstract: A non-transitory recording medium storing a computer program for an application providing a Graphical User Interface (GUI) causing a computer to perform each step of a control method of the computer, the method including: a reception step of receiving a print ticket including a print setting according to activation of the program for the application by an operating system after a print instruction is given by a user; and a display control step of displaying a screen for authenticated printing based on the received print ticket in which the authenticated printing has been set.

Abstract: A method for determining a stereotactic brain target includes at least one target point, the method comprising the following steps: selecting patients for whom the result measured following treatment at at least one target point is greater than or equal to a threshold, postoperative imaging having been performed for each of the patients; processing the postoperative imaging so as to determine coordinates of the at least one target point; selecting brain marker points; processing the postoperative imaging so as to determine coordinates of the marker points; creating a learning database comprising the coordinates of the target points and the coordinates of the marker points determined for all of the selected patients; determining a prediction function giving the coordinates of at least one target point according to the coordinates of the marker points by using the learning database and a supervised statistical learning method; processing preoperative imaging of a new patient to be treated so as to determine coo

Abstract: In some example embodiments, a computer-implemented method may include training a machine learning model in a first database instance using a machine learning algorithm and a training dataset in response to receiving a request to train, serializing the trained machine learning model into a binary file in response to the training of the machine learning model, recreating the trained machine learning model in a second database instance using the binary file in response to receiving a request to apply the machine learning model, and generating an inference result by applying the recreated trained machine learning model on the inference dataset in the second database instance.