Abstract: A computing device receives a request for a design of a combinatorial test for a test system. The device receives a run indication of a total quantity of test cases for the design, a factor indication of a total quantity of factors, and/or a strength indication for a covering array. The device generates an updated design by: selecting test case(s) to remove from a first design; or adding test case(s) to the first design. The first design comprises a set of test cases that represent the covering array according to the strength indication. The updated design is constrained to the total quantity of test cases as indicated by the run indication. The device outputs a respective setting for each test condition for at least one test case of the updated design for testing the test system.

Abstract: A computer obtains image frames. The computer identifies a chip within the image frames, the chip having a position and dimensions determined based on a lane width. Based on a speed and a length of a vehicle passing through a field of view of the camera, the computer selects a subset of the image frames. The computer takes, from each of the image frames in the subset, the identified chip for use as input to an artificial neural network (ANN). The computer individually provides each taken chip as input to the ANN to generate an ANN output. Based on a combination of the ANN outputs, the computer identifies a shape, a number of axles, and a number of segments of the vehicle. The computer provides a tuple representing the vehicle shape, the number of axles, and the number of segments.

Abstract: A computing device for training an artificial neural network model includes: a model analyzer configured to receive a first artificial neural network model and split the first artificial neural network model into a plurality of layers; a training logic configured to calculate first sensitivity data varying as the first artificial neural network model is pruned, calculate a target sensitivity corresponding to a target pruning rate based on the first sensitivity data, calculate second sensitivity data varying as each of the plurality of layers is pruned, and output, based on the second sensitivity data, an optimal pruning rate of each of the plurality of layers, the optimal pruning rate corresponding to the target pruning rate; and a model updater configured to prune the first artificial neural network model based on the optimal pruning rate to obtain a second artificial neural network model, and output the second artificial neural network model.

Abstract: Techniques are described herein for generating and using a unified shape representation that encompasses features of different types of shape representations. In some embodiments, the unified shape representation is a unicode comprising a vector of embeddings and values for the embeddings. The embedding values are inferred, using a neural network that has been trained on different types of shape representations, based on a first representation of a three-dimensional (3D) shape. The first representation is received as input to the trained neural network and corresponds to a first type of shape representation. At least one embedding has a value dependent on a feature provided by a second type of shape representation and not provided by the first type of shape representation. The value of the at least one embedding is inferred based upon the first representation and in the absence of the second type of shape representation for the 3D shape.

Abstract: Systems, computer program products, and methods are described herein for preserving image and acoustic sensitivity using reinforcement learning. The present invention is configured to initiate a file editing engine on the audiovisual file to separate the audiovisual file into a video component and an audio component; initiate a convolutional neural network (CNN) algorithm on the video component to identify one or more sensitive portions in the one or more image frames; initiate an audio word2vec algorithm on the audio component to identify one or more sensitive portions in the audio component; initiate a masking algorithm on the one or more image frames and the audio component; generate a masked video component and a masked audio component based on at least implementing the masking action policy; and bind, using the file editing engine, the masked video component and the masked audio component to generate a masked audiovisual file.

Abstract: A method, computer system, and a computer program product for generating deep learning model insights using provenance data is provided. Embodiments of the present invention may include collecting provenance data. Embodiments of the present invention may include generating model insights based on the collected provenance data. Embodiments of the present invention may include generating a training model based on the generated model insights. Embodiments of the present invention may include reducing the training model size. Embodiments of the present invention may include creating a final trained model.

Abstract: A method may include identifying a first image for training a deep learning network, wherein the first image includes at least one target object associated with at least one location in the first image, and wherein the first image is associated with a mask image; determining a set of deformations to create a training set of deformed images, wherein the training set is to be used to train the deep learning network; generating the training set of deformed images by applying the set of deformations to the first image; and generating a set of deformed mask images by applying the set of deformations to the mask image, wherein each deformed image of the training set of deformed images is associated with a respective mask image to identify the location of the at least one target object in each deformed image.

Abstract: A method and an electronic device for selecting deep neural network hyperparameters are provided. In an embodiment of the method, a plurality of testing hyperparameter configurations are sampled from a plurality of hyperparameter ranges of a plurality of hyperparameters. A target neural network model is trained by using a training dataset and the plurality of testing hyperparameter configurations, and a plurality of accuracies corresponding to the plurality of testing hyperparameter configurations are obtained after training for preset epochs. A hyperparameter recommendation operation is performed to predict a plurality of final accuracies of the plurality of testing hyperparameter configurations. A recommended hyperparameter configuration corresponding to the final accuracy having a highest predicted value is selected as a hyperparameter setting for continuing training the target neural network model.

Abstract: A method is for determining a processing sequence for processing an image, the processing sequence including a plurality of algorithms, each respective algorithm of the plurality of algorithms being configured to perform an image processing process on the image to generate a respective output. In an embodiment, the method includes determining one or more required outputs from the processing sequence; and determining, using a data processing system, the processing sequence based on the one or more required outputs determined, the data processing system being configured based on sequences previously determined.

Abstract: The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium. The method comprises: acquiring an iris image group comprising at least two iris images to be compared; detecting iris locations in the iris images and segmentation results of iris areas in the iris images; performing multi-scale feature extraction and multi-scale feature fusion on an image area corresponding to the iris locations, to obtain iris feature maps corresponding to the iris images; performing comparison using the segmentation results and the iris feature maps respectively corresponding to the at least two iris images, and determining whether the at least two iris images correspond to the same object based on a comparison result of the comparison. Embodiments of the present disclosure realize accurate comparison of iris images.

Abstract: A method, apparatus, computer system, and computer program product for managing a device. The method detects, by a computer system, a physical handling of the device to form a physical handling pattern for the device. The method determines, by the computer system, a baseline physical handling pattern for the device, wherein the baseline physical handling pattern for the device meets a set of handling metrics for the device. The method initiates, by the computer system, a set of actions in response to the physical handling pattern for the device deviating from the baseline physical handling pattern for the device.

Abstract: In accordance with one or more embodiments herein, a system for creating a decision support material indicating damage to at least a part of an anatomical joint of a patient, wherein the created decision support material comprises one or more damage images, is provided.

Abstract: A training method, system, and computer program product include computing a matrix norm over a product of a weight matrix and a transpose of the weight matrix and using the matrix norm to constrain the L2 non-expansive neural network.

Abstract: Systems and methods are presented for detecting physical contacts effectuated by actions performed by an entity participating in an event. An action, performed by the entity, is detected based on a sequence of pose data associated with the entity's performance in the event. A contact with another entity in the event is detected based on data associated with the detected action. The action and the contact detections are employed by neural-network based detectors.

Abstract: Systems and methods are presented for generating statistics associated with a performance of a participant in an event, wherein pose data associated with the participant, performing in the event, are processed in real time. Pose data associated with the participant may comprise positional data of a skeletal representation of the participant. Actions performed by the participant may be determined based on a comparison of segments of the participant's pose data to motion patterns associated with actions of interests.

Abstract: A classifier network has at least two distinct sets of refined data, wherein the first two sets of refined data are sets of numbers representing the features values data received from sensors or a manufactured part. Performing, via at least two distinct types of support vector machines using an associated feature selection process for each classifier independently in a first layer, anomaly detection on the manufactured part. Then, using the stored data including refined data of at least two different types of data transforms and performing, via at least a two distinct types of support vector machines in a second layer, an associated feature selection process for each classifier independently. Forming at least four distinct compound classifier types for anomaly detection on the part using the stored data or coefficients. The ensemble of second layer support vector machine outputs compare the results to determine the presence of an anomaly.

Abstract: The technology disclosed relates to creating user-defined interaction spaces and modalities in a three dimensional (3D) sensor space in response to control gestures. It also relates to controlling virtual cameras in the 3D sensor space using control gestures and manipulating controls of the virtual cameras through the control gestures. In particular, it relates to defining one or more spatial attributes of the interaction spaces and modalities in response to one or more gesture parameters of the control gesture. It also particularly relates to defining one or more visual parameters of a virtual camera in response to one or more gesture parameters of the control gesture.

Abstract: Certain embodiments involve techniques for automatically identifying sounds in an audio recording that match a selected sound. An audio search and editing system receives the audio recording and preprocesses the audio recording into audio portions. The audio portions are provided as a query to the neural network that includes a trained embedding model used to analyze the audio portions in view of the selected sound to estimate feature vectors. The audio search and editing system compares the feature vectors for the audio portions against the feature vector for the selected sound and the feature vector for the negative samples to generate an audio score that is a numerical representation of the level of similarity between the audio portion and the selected sound and uses the audio scores to classify the audio portions into a first class of matching sounds and a second class of non-matching sounds.

Abstract: A system receives an image including a live facial image of the user and an identity document including a photograph of the user. Moreover, the system calculates a facial match score by comparing facial features in the live facial image to facial features in the photograph. The system recognizes data objects and characters in the identity document using optical character recognition (OCR) and computer vision, and then identifies, based on the recognized data objects and characters, a type of the identity document. Further, the system calculates a document validity score by comparing the recognized characters and data objects to character strings and data objects known to be present in the identified type of the identity document. Additionally, the system determines and outputs the user's identity verification status based on comparing the facial match score to a facial match threshold and comparing the document validity score to a document validity threshold.

Abstract: The disclosure provides a method and an apparatus for predicting flight delay, a device and a storage medium. The method includes: acquiring flight historical data, where the flight historical data includes take-off amount and delay amount of flights during each of a plurality of time periods; determining prior knowledge of each of the plurality of time periods according to the take-off amount and the delay amount of the flights during each of the plurality of time periods; constructing a SVM prediction model according to the prior knowledge and a standard SVM model; and predicting a flight delay situation according to the SVM prediction model. The prediction of the flight delay situation for each of the plurality of time periods is realized.