In one example, a computer-based system determines a relationship between a first job and a second job at one or more companies, by using a title data store, a training module, and a prediction module, wherein the title data store accepts job-related information characterizing at least one job-related position that includes at least one of title, corporate entity, job description, and job-related interest data. The training module accepts input data from the title data store, calculates or generates a set of coefficients and a set of job-related vectors from the input data, and stores the coefficients into a database. The prediction module may accept: a first set of data including at least one of a first title, a first corporate designation data, a second set of data including at least one of a second title and a second corporate designation data, and the coefficients from the training module; and then a similarity between the first set of data and the second set of data may be calculated.

An imaging quality control system (80) employing an imaging quality controller (84) and a monitor (81). In operation, the imaging quality controller (84) executes an image processing of subject image data of the anatomical object (e.g., subject non-segmentation-based or segmentation-based image registration of US, CT and/or MRI anatomical images), and assessing an accuracy of the image processing of the subject image data of the anatomical object as a function of a subject Eigen weight set relative to a training Eigen range set (e.g., previously registered or segmented US, CT and/or MRI anatomical images). The subject Eigen weight set is derived from the subject image data of the anatomical object, and the training Eigen range set is derived from training image data of anatomical object. The monitor (81) displays the assessment of the accuracy of the image processing of the subject image data of the anatomical object by the imaging quality controller (84).

A sensor data stream is provided consisting of feature vectors acquired by sensors of rotating equipment, similar feature vectors are aggregated in microclusters. For newly arriving feature vectors, a correlation distance measure between the new feature vector and each microcluster is calculated. If there is no microcluster in range, then a new microcluster is created. Otherwise, the feature vector is assigned to the best fitting microcluster, and the necessary statistical information is incorporated into the aggregation contained in the microcluster. In other words, similar feature vectors are aggregated in the same microclusters. The microclusters thus provide a generic summary structure that captures the necessary statistical information of the incorporated feature vectors. At the same time, the loss of accuracy is quite small. Clustering the sensor data stream with microclusters has the benefit that the computational complexity can be reduced significantly.

The present disclosure provides a method for recognizing a human motion, a method for recognizing a user action and a smart terminal. The method for human motion recognition comprises: collecting human motion data to train to obtain a feature extraction parameter and a template data sequence; in one human motion recognition, collecting data for performing human motion recognition to obtain an original data sequence; using the feature extraction parameter to perform feature extraction on the original data sequence, reducing the number of data dimensions of the original data sequence, and obtaining a test data sequence after the dimension reduction; matching the test data sequence with the template data sequence, and confirming that a human motion corresponding to the template data sequence associated with the test data sequence occurs when a successfully-matched test data sequence exists. By performing dimension reduction on the test data sequence, the present disclosure lowers requirements for human motion postures and cancels noise, then matches the data after the dimension reduction with the template, realizes accurate recognition of human motions while reducing the computing complexity, and improves the user experience.

A computer implemented method includes accessing a multivariate time series set of samples collected by multiple biological sensors sensing a first biological function over a first period of time, dividing the data set into windows, calculating statistical dependencies between the samples of the timeseries data collected by each sensor, generating a relationship matrix as a function of the statistical dependencies, and transforming the relationship matrix to generate a first feature vector for each window of time that captures the statistical dependencies amongst the sensors.

Systems and methods relating to fault detection and diagnosis. Signals received from sensors are first filtered to remove noise and are then analyzed using wavelet packet transform (WPT) based PCA. The results of the PCA analysis are then automatically classified to thereby quickly and easily determine what issues there may be in a finished product or in a machine being monitored.

The technology disclosed herein enables performing data correlation to optimize continuous integration environments. An example method comprises receiving, by a processor of a client device, input data identifying a plurality of execution environment parameters associated with an execution environment; retrieving, during execution of a software build job, parameter data associated with each of the execution environment parameters of the plurality of execution environment parameters; generating, in view of the retrieved parameter data, correlation data indicative of a relationship between a first execution environment parameter of the plurality of execution environment parameters and a second execution environment parameter of the plurality of execution environment parameters, wherein the first execution environment parameter reflects an observable aspect of a state of the execution environment, and the second execution environment parameter reflects an aspect associated with a performance of the execution environment; and displaying, on a graphical user interface, the correlation data.

The technology disclosed herein enables performing data correlation to optimize continuous integration environments. An example method comprises receiving, by a processor of a client device, input data identifying a plurality of execution environment parameters associated with an execution environment; retrieving, during execution of a software build job, parameter data associated with each of the execution environment parameters of the plurality of execution environment parameters; generating, in view of the retrieved parameter data, correlation data indicative of a relationship between a first execution environment parameter of the plurality of execution environment parameters and a second execution environment parameter of the plurality of execution environment parameters, wherein the first execution environment parameter reflects an observable aspect of a state of the execution environment, and the second execution environment parameter reflects an aspect associated with a performance of the execution environment; and displaying, on a graphical user interface, the correlation data.

A feature extraction method of fruit spectrum includes taking a vector of each wavelength point in spectrum of samples as source data, and acquiring a sorting of all vectors by processing the source data by SPA; according to the sorting of the vectors, acquiring distribution points of each sample on a coordinate system; acquiring classification results of the samples by destructive analysis, and acquiring a number of first sample categories; acquiring a first Euclidean distance between the first sample categories; according to a sorting of the wavelength points, acquiring distribution points of each sample on the coordinate system; acquiring a number of second sample categories; acquiring a second Euclidean distance between the second sample categories; determining whether the first Euclidean distance is less than the second Euclidean distance; determine a (M+2)-th vector to be valid or invalid based on a comparison result.

The present disclosure provides a sample classification method and apparatus, an electronic device and a storage medium, and relate to the technical field of data mining, in particular to the field of machine learning. The method includes that: a sample to be classified is acquired, and a sample feature dimension of the sample to be classified is greater than a preset threshold; feature encoding is performed on a sample feature of the sample to be classified according to various feature encoding modes to obtain multiple feature vectors; and clustering analysis is performed on the multiple feature vectors to determine a target class of the sample to be classified.