Abstract: Vehicle cognitive data is collected using multiple devices. A user interacts with various pieces of technology to perform numerous tasks and activities. Reactions can be observed and cognitive states inferred from reactions to the tasks and activities. A first computing device within a vehicle obtains cognitive state data which is collected on an occupant of the vehicle from multiple sources, wherein the multiple sources include at least two sources of facial image data. At least one face in the facial image data is partially occluded. A second computing device generates analysis of the cognitive state data which is collected from the multiple sources. A third computing device renders an output which is based on the analysis of the cognitive state data. The partial occluding includes a time basis of occluding. The partial occluding includes an image basis of occluding. The cognitive state data from multiple sources is tagged.

Abstract: An apparatus, method and system are provided for sensing an individual's biometric information, and generating and transmitting an acoustic signal representative of the sensed biometric information. The acoustic signal may be transmitted as an audio signal or an ultrasonic signal to another apparatus in the system for authentication or verification of the individual's identity.

Abstract: A system includes: a light source that emits pulsed light that illuminates a user's head portion; a photodetector that detects at least part of pulsed light returning from the head portion and that outputs one or more signals corresponding to an intensity of the at least part; electrical circuitry; and a memory that stores an emotion model indicating a relationship between the one or more signals and emotions. Based on a change in the one or more signals, the electrical circuitry selects an emotion by referring to the model. The one or more signals include a first signal corresponding to an intensity of first part of the reflection pulsed light and a second signal corresponding to an intensity of second part of the reflection pulsed light. The first part includes part before a falling period is started; and the second part includes at least part in the falling period.

Abstract: A media source is configured to provide a media stream that includes an audio-video recording. A first algorithm detects a presence of synthetic media in a video based on a first feature type. A second algorithm detects the presence of synthetic media based on a second feature type. A device determines that the first feature type is presented in a selected portion of the image frames but the second feature type is not. In response, the first algorithm is executed using the selected portion of the image frames as an input. Based on the executed first algorithm, a probability is determined that the media stream provided by the media source includes synthetic media.

Abstract: Exemplary embodiments of the present disclosure are directed towards a system for analyzing graffiti content, tracking graffiti vandals who executed graffiti on different surfaces and report the graffiti vandal reports to law enforcement agent or public works department, comprising: a computing device is configured to allow a user to capture graffiti executed on a plurality of surfaces and uploads the captured graffiti to a graffiti analyzing and tracking module, the graffiti analyzing and tracking module parses out the graffiti content into data points and analyzes, reconfigures, and reports the data points to clearly reveal trends in categories on computing device, and a database configured to store information about graffiti crimes, locations, and allows the user to allocate resources, the computing device enables the graffiti analyzing and tracking module to display graffiti vandals and map the graffiti content to graffiti vandals that have appeared in graffiti renderings from the database.

Abstract: A learning device includes one or more processors. The processors generate a plurality of pieces of learning data to be used in a plurality of learning processes, respectively, to learn a parameter of a neural network using an objective function. The processors calculate a first partial gradient using a partial data and the parameter added with noise, with respect to at least a part of the learning data out of the plurality of pieces of learning data. The partial data is obtained by dividing the learning data. The first partial gradient is a gradient of the objective function relating to the parameter for the partial data. The noise is calculated based on a second partial gradient calculated for another piece of the learning data. The processors update the parameter using the first partial gradient.

Abstract: A neural network model training apparatus for enhancing image detail is provided. The apparatus includes a memory and at least one processor configured to obtain a low quality input image patch and a high quality input image patch, obtain a low quality output image patch by inputting the low quality input image patch to a first neural network model, obtain a high quality output image patch by inputting the high quality input image patch to a second neural network model, and train the first neural network model based on a loss function set to reduce a difference between the low quality output image patch and the high quality input image patch, and a difference between the high quality output image patch and the high quality input image patch. The second neural network model is identical to the first neural network model.

Abstract: A computer-implemented method for the safe, active training of a computer-aided model for modeling time series of a physical system using Gaussian processes, including the steps of establishing a safety threshold value ?; initializing by implementing safe initial curves as input values on the system, creating an initial regression model and an initial safety model; repeatedly carrying out the steps of updating the regression model; updating the safety model; determining a new curve section; implementing the determined new curve section on the physical system and measuring output variables; incorporating the new output values in the regression model and the safety model until N passes have been carried out; and outputting the regression model and the safety model.

Abstract: A system for sorting seeds based on their resistance to a stress is disclosed. Batches of purified seeds sorted using the system are also disclosed.

Abstract: A method is provided that includes generating a visual environment for interactive development of a machine learning (ML) model. The method includes accessing observations of data each of which includes values of independent variables and a dependent variable, and performing an interactive exploratory data analysis (EDA) of the values of a set of the independent variables. The method includes performing an interactive feature construction and selection based on the interactive EDA, and in which select independent variables are selected as or transformed into a set of features for use in building a ML model to predict the dependent variable. The method includes building the ML model using a ML algorithm, the set of features, and a training set produced from the set of features and observations of the data. And the method includes outputting the ML model for deployment to predict the dependent variable for additional observations of the data.

Abstract: A method and machine learning system for detecting adversarial examples is provided. A first machine learning model is trained with a first machine learning training data set having only training data samples with robust features. A second machine learning model is trained with a second machine learning training data set, the second machine learning training data set having only training data samples with non-robust features. A feature is a distinguishing element in a data sample. A robust feature is more resistant to adversarial perturbations than a non-robust feature. A data sample is provided to each of the first and second trained machine learning models during an inference operation. if the first trained machine learning model classifies the data sample with high confidence, and the second trained machine learning model classifies the data sample differently with a high confidence, then the data sample is determined to be an adversarial example.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: Embodiments of this disclosure provide an apparatus and method. As target detection is performed in the detected pedestrian area by directly using the deep learning model, various article belonging to a pedestrian can be accurately detected in an input image.

Abstract: An image recognition apparatus includes circuitry. The circuitry is configured to input an image of an object captured by a camera. The circuitry is further configured to divide, based on a predetermined positioning point, the image into a plurality of regions, set a process region that includes the respective divided region, and set a rotation of the respective process region so that a positional relationship between up and down of the object in the respective process region matches up. The circuitry is further configured to perform the rotation to the image corresponding to the respective process region and perform a recognition process to the image after rotation.

Abstract: An image analysis device has: an image analysis circuitry to analyze images input from each of cameras using instances of an image analysis program including a learned neural network model for object detection and learned neural network models for object recognition; inference processors to perform inference processes in the learned neural network model for object detection and each learned neural network model for object recognition; and a processor assignment circuitry to assign, from the inference processors, inference processors to be used for the inference process in the learned neural network model for object detection and the inference process in each learned neural network model for object recognition, based on inference time and frequency of use required for the inference process in each of the learned neural network models for object detection and object recognition included in each instance of the image analysis program.

Abstract: A system for customizing media presentation based on user's sentiments is disclosed. The system presents a media item to the user on a platform comprising a website. The system captures a first set of microexpressions of the user reacting to the media item. The system extracts a set of baseline features from the first set of microexpressions. The system determines whether the media item elicits positive or sentiment from the user. If the system determines that the media item elicits positive sentiment from the user, the system classifies the media item into a first class of media items that elicit positive sentiment from the user. The system adjusts contents of the platform to include media items from the first class of media items.

Abstract: Provided are an image processing method and device and an electronic device. The method may include: a first image and a second image are obtained; a facial key point of a target object in the first image is detected to obtain information of a first key point; a facial key point of a target object in the second image is detected to obtain information of a second key point; a conversion relationship is determined based on the information of the first key point and the information of the second key point; and faces of the target objects in the first image and the second image are fused based on the conversion relationship to obtain a third image.

Abstract: A method, apparatus, and/or computer program product can perform an analog defect simulation on an electronic device. The method, apparatus, and/or computer program product can generate a defect catalog which identifies a defect class relating to a defect and a modeling parameter that is associated with the defect class. The method, apparatus, and/or computer program product can receive a weight formula that identifies a weight for the defect class in relation to the modeling parameter. The method, apparatus, and/or computer program product can call a defect weight function to return the weight from the defect weight formula. The method, apparatus, and/or computer program product can perform the analog defect simulation on the electronic device. The method, apparatus, and/or computer program product can determine a simulation statistic relating to the analog defect simulation utilizing the weight.

Abstract: Disclosed herein are apparatuses and methods for classifying person type in a visual medium. The implementations include identifying a person in an image, generating a visual boundary around the identified person, and dividing the image into a plurality of image portions such that multiple images portions partially depict the identified person inside the visual boundary. The implementations include generating the divided image on a user interface that prompts a user to select each portion that at least partially depicts a person of a predetermined type. The implementations include receiving a selection of at least one portion. The implementations include generating, for a machine learning model and in response to determining that the selection comprises the identified person, a training input that comprises a training image and a tag indicating that the identified person is of the predetermined type. Additionally, the implementations include storing the training input in memory.

Abstract: This disclosure provides a highly scalable training data preparation pipeline for data cleaning and augmentation with the aim of extracting the most meaningful information while keeping the noise level low, as well as a highly efficient distributed trainer for the deep neural networks suitable for facial recognition. The goal is to train deeper and larger neural networks with larger and higher quality facial image datasets iteratively and frequently without incurring prohibitive costs and drastic delays.