Abstract: A system and non-transitory storage medium for performing object classification using a trained machine learning algorithm (MLA). The MLA includes an embedding layer and a classification layer. A byte representation of an object is received. A set of embedding indices is generated based on the byte representation of the object. The MLA embeds, using the embedding layer, the set of embedding indices to obtain an input vector and predicts an estimated class based on the input vector. In some implementations, the set of embedding indices is generated by parsing the byte representation to obtain byte n-grams and by applying a hash function on the byte n-grams.

Abstract: A method and a system for training a machine learning algorithm (MLA) for object classification. The machine learning algorithm includes an embedding layer and a classification layer. A set of embedding indices representing a reference object is received. The set of embedding indices has been generated based on a byte representation of the reference object. A label associated with the reference object indicative of a reference class the objects belongs to is received. The MLA is iteratively trained to classify objects by embedding the set of embedding indices to obtain an input vector and by predicting an estimated class based on the input vector, and updating a parameter of at least one of the embedding layer and the updated embedding layer. The set of embedding indices is generated by parsing the byte representation to obtain byte n-grams and by applying a hash function on the byte n-grams.

Abstract: A method for determining a series of gaze positions of at least one eye over time is provided. The method comprises capturing a video of a user's face simultaneously with displaying a stimulus video on a screen and extracting at least one color component for each one of a plurality of images obtained from the video of the user's face. Based on the at least one color component for each one of the plurality of images, the series of gaze positions of the user's face over the time of the video is determined. A system for determining a series of gaze positions of at least one eye over time is also provided.

Abstract: Persistent storage may contain typed data of a plurality of types, directional relationships between pairs of the plurality of types, and a conditional dependency structure for the typed data. One or more processors may be configured to: generate an essential graph from the conditional dependency structure; orient the edges of the essential graph such that they are directed in accordance with the directional relationships; generate typed directed acyclic graphs (DAGs) that can be found in the essential graph; form a t-essential graph from a union of the typed DAGs; identify an event represented as a first vertex in the t-essential graph, wherein the first vertex is of a first type; trace backward from the first vertex and through the t-essential graph to identify a second vertex of a second type; and provide a representation of the second vertex as a cause of the event.

Abstract: The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.

Abstract: The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.

Abstract: A method and a system for training a machine learning algorithm (MLA) for object classification. The machine learning algorithm includes an embedding layer and a classification layer. A set of embedding indices representing a reference object is received. The set of embedding indices has been generated based on a byte representation of the reference object. A label associated with the reference object indicative of a reference class the objects belongs to is received. The MLA is iteratively trained to classify objects by embedding the set of embedding indices to obtain an input vector and by predicting an estimated class based on the input vector, and updating a parameter of at least one of the embedding layer and the updated embedding layer. The set of embedding indices is generated by parsing the byte representation to obtain byte n-grams and by applying a hash function on the byte n-grams.

Abstract: A method for determining a series of gaze positions of at least one eye over time is provided. The method comprises capturing a video of a user's face simultaneously with displaying a stimulus video on a screen and extracting at least one color component for each one of a plurality of images obtained from the video of the user's face. Based on the at least one color component for each one of the plurality of images, the series of gaze positions of the user's face over the time of the video is determined. A system for determining a series of gaze positions of at least one eye over time is also provided.

Abstract: A method for training a neural network for determining a gaze position of at least one eye in an initial image comprising the at least one eye. A plurality of training initial images are obtained, of which at least one training color component image is extracted, each of the training initial images respectively comprising at least one eye and a known gaze position. Those are fed into a neural network outputting a respective internal representation for each one of the at least one component image. The neural network is trained by readjusting weights in the neural network to have the respective internal representation for each one of the at least one training color component image more consistent with a respective one of the known gaze position. Once trained, the neural network is used to determine the estimated gaze position relative to a screen of an electronic device.

Abstract: A method for training a neural network for determining a gaze position of at least one eye in an initial image comprising the at least one eye. A plurality of training initial images are obtained, of which at least one training color component image is extracted, each of the training initial images respectively comprising at least one eye and a known gaze position. Those are fed into a neural network outputting a respective internal representation for each one of the at least one component image. The neural network is trained by readjusting weights in the neural network to have the respective internal representation for each one of the at least one training color component image more consistent with a respective one of the known gaze position. Once trained, the neural network is used to determine the estimated gaze position relative to a screen of an electronic device.

Abstract: A computer-implemented method for determining a gaze position of a user, comprising: receiving an initial image of at least one eye of the user; extracting at least one color component of the initial image to obtain a corresponding at least one component image; for each component image, determining a respective internal representation; determining an estimated gaze position in the initial image by applying a respective primary stream to obtain a respective internal representation for each of the at least one component image; and outputting the estimated gaze position. The processing of the component images is performed using a neural network configured to, at run time and after the neural network has been trained, process the component images using one or more neural network layers to generate the estimated gaze position. A system for determining a gaze position of a user is also provided.

Abstract: A computer-implemented method for determining a gaze position of a user, comprising: receiving an initial image of at least one eye of the user; extracting at least one color component of the initial image to obtain a corresponding at least one component image; for each component image, determining a respective internal representation; determining an estimated gaze position in the initial image by applying a respective primary stream to obtain a respective internal representation for each of the at least one component image; and outputting the estimated gaze position. The processing of the component images is performed using a neural network configured to, at run time and after the neural network has been trained, process the component images using one or more neural network layers to generate the estimated gaze position. A system for determining a gaze position of a user is also provided.

Abstract: A computer-implemented method for determining a gaze position of a user, comprising: receiving an initial image of at least one eye of the user; extracting at least one color component of the initial image to obtain a corresponding at least one component image; for each component image, determining a respective internal representation; determining an estimated gaze position in the initial image by applying a respective primary stream to obtain a respective internal representation for each of the at least one component image; and outputting the estimated gaze position. The processing of the component images is performed using a neural network configured to, at run time and after the neural network has been trained, process the component images using one or more neural network layers to generate the estimated gaze position. A system for determining a gaze position of a user is also provided.

Abstract: A computer-implemented method for determining a gaze position of a user, comprising: receiving an initial image of at least one eye of the user; extracting at least one color component of the initial image to obtain a corresponding at least one component image; for each component image, determining a respective internal representation; determining an estimated gaze position in the initial image by applying a respective primary stream to obtain a respective internal representation for each of the at least one component image; and outputting the estimated gaze position. The processing of the component images is performed using a neural network configured to, at run time and after the neural network has been trained, process the component images using one or more neural network layers to generate the estimated gaze position. A system for determining a gaze position of a user is also provided.