A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

A method and apparatus are provided that analyzing sequence-to-sequence data, such as sequence-to-sequence speech data or sequence-to-sequence machine translation data for example, by minimum Bayes risk (MBR) training a sequence-to-sequence model and within introduction of applications of softmax smoothing to an N-best generation of the MBR training of the sequence-to-sequence model.

Aspects of the invention include includes identifying a respective estimated clinical risk score for each of a first group of patients and a second group of patients. An alternative probability estimate is generated using a same set of inputs used to determine each respective estimated clinical risk score. An unreliability of a patient's clinical risk score is determined based at least in part on a feature of the patient and on a difference between the alternative probability estimate and the determined respective estimated clinical risk score.

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

A method of detecting gaits of an individual with a sensor worn by the individual. The sensor includes an accelerometer and a processing unit. The method includes obtaining an signal representing one or more sensor acceleration values; sampling the signal to obtain a sampled signal; segmenting the sampled signal into windows to obtain a segmented acceleration signal; extracting a feature set from the segmented acceleration signal; determining a probability value, for a respective window, n, where n is a positive integer greater than zero, the probability value giving an estimated probability value of gait occurrence for the individual during the respective window; modifying the estimated probability value by using a histogram of previously detected gait durations to obtain a modified probability value; and determining, based on the modified probability value, and by using a determination threshold whether or not the respective window represents gait occurrence.

A computer assesses language attributes of web application display text elements. The computer receives access to a selected web application. The computer parses hypertext markup language content of the web application and generating a parse tree representing the content. The computer identifies, using the parse tree, display text elements within the content and determining associated element selector queries that identify respective display text elements within the parse tree. The computer processes a set of display text elements, using a plurality of Natural Language Processing classifier models, each of the classifier models generates a relevant language prediction for the processed display text element. The computer collects, for each text element, groups of classifiers associated with substantially-similar predictions and indexed by relevant text element selector. The computer determines a target language match condition for each group. The computer initiates a corresponding at least one corrective action associated with the match condition.

Aspects relate to apparatuses and methods for generating queries and transcribing video resumes. An exemplary apparatus includes at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive, from a posting generator, a plurality of posting inputs from a plurality of postings, receive a video resume from a user, generate a plurality of queries as a function of the video resume based on a plurality of posting categories, transcribe, as a function of the plurality of queries, a plurality of user inputs from the video resume, wherein the plurality of user inputs is related to attributes of a user, and classify the plurality of user inputs to the plurality of posting inputs to match the user to the plurality of postings.

Machine learning classification models which are robust against label noise are provided. Noise may be modelled explicitly by modelling “label flips”, where incorrect binary labels are “flipped” relative to their ground truth value. Distributions of label flips may be modelled as prior and posterior distributions in a flexible architecture for machine learning systems. An arbitrary classification model may be provided within the system. The classification model is made more robust to label noise by operation of the prior and posterior distributions. Particular prior and approximating posterior distributions are disclosed.

Methods may include creating a fracture set from a collection of intersecting fractures in a borehole image log recorded within a subterranean formation; classifying the fracture set into groups of fully and partially intersecting fractures; calculating one or more of the elongation ratio and the rotation angle of the partially intersecting fractures; determining a probability of full intersection of fractures from the fracture set; and determining a fracture size or a parametric distribution of fracture sizes from the fracture set using the calculated one or more of the elongation ratio and the rotation angle and the determined probability of full intersection of formation fractures within the borehole.

An evaluation device is a device that evaluates, by Bayesian optimization, an unknown characteristic point corresponding to a candidate experimental point based on a known characteristic point corresponding to an experimented experimental point, the device including: a reception controller that acquires experimental result data indicating the experimented experimental point and the known characteristic point, objective data indicating an optimization objective, and constraint-condition data indicating a constraint condition; an evaluation value calculator that calculates an evaluation value of an unknown characteristic point based on the experimental result data, the objective data, and the constraint-condition data; and an evaluation value output unit that outputs the evaluation value, in which the evaluation value calculator gives weighting according to a degree of compatibility of the constraint condition to an evaluation value for at least one objective characteristic.