The present invention relates to an improved method of providing identification of a user or authentication of a user's identity. More particularly, the present invention relates to an improved method of providing identification of a user or authentication of a user's identity using conditional behavioural biometrics. The present invention seeks to provide an enhanced method of authenticating and/or identifying a user identity using conditional behavioural biometrics. According to a first aspect of the present invention, there is provided a method of generating a user profile for use in identifying and/or authenticating a user on a device, the device equipped with one or more sensors, the method comprising: generating a set of data points from sensory data collected by the one or more sensors; clustering the set of data points to produce a set of data clusters; developing a first classifier for the data clusters, the first classifier being operable to assign a further data point derived from a further user interaction with the computing device to one of the data clusters; and developing one or more further classifiers for at least one of the data clusters, the further classifier operable to identify and/or authenticate a user identity based on the further data point.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model to segment magnified images of tissue samples. The method includes obtaining a magnified image of a tissue sample; processing an input comprising: the image, features derived from the image, or both, in accordance with current values of model parameters of a machine learning model to generate an automatic segmentation of the image into a plurality of tissue classes; providing, to a user through a user interface, an indication of: (i) the image, and (ii) the automatic segmentation of the image; determining an edited segmentation of the image, comprising applying modifications specified by the user to the automatic segmentation of the image; and determining updated values of the model parameters of the machine learning model based the edited segmentation of the image.

The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging. The method includes identifying at least one quantitative measurement of a subject using ultrasound imaging, the at least one quantitative measurement included as part of quantitative information of the subject gathered based on the ultrasound imaging, comparing the at least one quantitative measurement to a first predetermined standard to determine a first initial value, the first predetermined standard falling within a first range of quantities, identifying at least one qualitative measurement of the subject using the ultrasound imaging, the at least one qualitative measurement included as part of qualitative information of the subject gathered based on the ultrasound imaging, comparing the at least one qualitative measurement to a second predetermined standard to determine a second initial value, the second predetermined standard falling within a second range of quantities; and correlating at least the quantitative information and the qualitative information using the first initial value and the second initial value to determine a final value that is used in predicting a disease state of the subject.

Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.

A computer system document according to one embodiment includes a generation unit and an update unit. The generation unit performs first machine learning on a target document, as input data, to which a correct path in a tree structure where each node indicates a document category is given, and thereby generates a classification model indicating a right path to a leaf node for the target document. The update unit performs second machine learning that applies, to the classification model, the target document to which the correct path is not given, and when a path from an Nth level node to an (N+1)th level node is different from the correct path, updates the classification model by setting a modified path from the (N+1)th level node to an (N+2)th level node different from a child node of the (N+1)th level node based on the correct path.

System and method for calculating interaction between feature amounts, including a model construction unit for acquiring data including a feature amount vector which is a set of numerical values of feature amounts as an explanatory variable, and information of an event as an objective variable, and constructing a classification and prediction model having a tree structure for classifying and predicting the event based on the feature amount vector, an interaction score calculation unit for calculating an interaction score indicating a degree of association of interaction between the feature amounts with the event is based on a position of the feature amount appearing in a node constituting the classification and prediction model, and a position of the feature amount in the classification and prediction model in which the position of the feature amount appearing in the node has been shuffled, and an output processing unit for outputting the calculated interaction score.

Aspects of the present disclosure describe systems, methods and structures providing wide-area traffic monitoring based on distributed fiber-optic sensing (DFOS) that employs deep neural network(s) for denoising noisy waterfall traces measured by the DFOS. Such systems, methods, and structures according to aspects of the present disclosure may advantageously monitor multiple highways/roadways using a single interrogator and optical fiber switch(es) which provides traffic information along every sensing point of existing, deployed, in-service optical telecommunications facilities.

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.

A method of fault criticality assessment using neural twins includes converting a netlist into a neural twin by replacing each circuit element of the netlist with a neural-network-readable cell equivalent; and replacing each wire with a neural connection. Bias value adders are inserted at locations in the neural twin; and these bias value adders are used to apply a bias that represents a perturbation in the signal propagated by that connection. For each perturbed bias at a corresponding site selected to be perturbed, a loss value is calculated for the neural twin; and the site is classified, using a neural-twin-trained classifier, as critical or benign based on that loss value.

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.