Abstract: Feature engineering, for example, in automated machine learning, can include receiving streaming data representing at least one attribute detected by a sensor over time. Long term point statistics associated with the streaming data can be computed. The streaming data can be quantized into intervals of time windows and short term point statistics based on the intervals can be computed. The long term point statistics and the short term point statistics can be normalized. Dynamic time warping can be applied across the normalized long term point statistics and short term point statistics. A pair of probability distributions can be generated associated with the dynamic time warped normalized long term point statistics and short term point statistics. Based on distance between the mean values of the probability distributions, machine learning input features can be produced. The machine learning input features can be fed to train a machine learning model for detecting anomaly.

Abstract: The present invention may include a computer receives multimedia data. The computer parses the multimedia data into an audio stream. The computer analyzes the audio stream to identify recognized patterns. The computer calculates a probability of an undesired content based on the recognized patterns and taking an action based on determining the probability is above a threshold.

Abstract: A method and system of generating inferential text are provided. The method includes ingesting a data set that includes at least one structured hierarchical or multidimensional table for a particular domain. The method includes processing the ingested data set that includes the at least one structured hierarchical or multidimensional table for the particular domain by applying a generated machine learning model. The method includes generating inferential natural language text based on applying the machine learning model. The method includes outputting the generated inferential natural language text in a sequence format.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods provided herein relate to determining veracity of answers generated by machine comprehension question and answer models. According to an embodiment, a machine comprehension component can generate a first answer to a query by extracting the first answer from a passage of text corpus. The text corpus alteration component can alter the text corpus one or more times to produce one or more altered text corpora. The machine comprehension component can further extract one or more additional answers to the query from the altered text corpora. A comparison component can determine a veracity score for the first answer based on one or more comparisons of the first answer with the one or more additional answers.

Abstract: One or more computer processors facilitate compatibility between one or more multivariate regression models and a multidimensional dataset, wherein the program instructions. The one or more computer processors extract a plurality of unidimensional chains from the multidimensional dataset. The one or more computer processors double index the plurality of extracted unidimensional chains. The one or more computer processors construct a plurality of partial fit regression trees from the double indexed unidimensional chains. The one or more computer processors, responsive to a stop criterion, calculate one or more predictions utilizing the plurality of constructed partial fit regression trees. The one or more computer processors repopulate the multidimensional dataset with the one or more calculated predictions.

Abstract: A mechanism is provided in a data processing system for preventing data leakage in automated machine learning. The mechanism receives a data set comprising a label for a target variable for a classifier machine learning model and a set of features. For each given feature in the set of features, the mechanism trains a subprime classifier model using the given feature as a target variable and remaining features as independent input features, tests the subprime classifier model, and records results of the subprime classifier model. The mechanism performs statistical analysis on the recorded results to identify an outlier result corresponding to an outlier subprime classifier model.

Abstract: One or more computer processors facilitate compatibility between one or more multivariate regression models and a multidimensional dataset, wherein the program instructions. The one or more computer processors extract a plurality of unidimensional chains from the multidimensional dataset. The one or more computer processors double index the plurality of extracted unidimensional chains. The one or more computer processors construct a plurality of partial fit regression trees from the double indexed unidimensional chains. The one or more computer processors, responsive to a stop criterion, calculate one or more predictions utilizing the plurality of constructed partial fit regression trees. The one or more computer processors repopulate the multidimensional dataset with the one or more calculated predictions.

Abstract: Detecting spatial positioning between a user's hands and an object, using a wearable device, to provide feedback to a user. Motion data of a person's body motion while performing an action can be received at a computer. The motion data can include sensor data from sensors at a location where the sensors detect the person's body motion. The sensors can include a wearable device on the person's body. The computer can be used to model the person's body motion using the motion data. A set of parameters for acceptable motions is determined based on an action risk assessment of the action. Feedback can be initiated to the person, using the wearable device, based on the person's body motion exceeding a body motion threshold based on the set of parameters for the acceptable motions and the model.

Abstract: The present invention may include a computer receives multimedia data. The computer parses the multimedia data into an audio stream. The computer analyzes the audio stream to identify recognized patterns. The computer calculates a probability of an undesired content based on the recognized patterns and taking an action based on determining the probability is above a threshold.

Abstract: A network computing apparatus configured to perform an automated resource allocation method including obtaining price-demand data for a product, macro-clustering the price-demand data to identify a plurality of product categories, building a plurality of demand curves corresponding to the product categories, micro-clustering the demand curves to find a refined set of demand curves for each of the product categories, selecting one of the refined set of demand curves based on a difference between a predicted demand and an observed demand, selecting a price for the product according to the selected one of the demand curves, and allocating a resource according to the selected one of the demand curves corresponding to the pricing data generated, wherein the macro-clustering is performed using a first hyperparameter and the micro-clustering is performed using a second hyperparameter.

Abstract: A mechanism is provided in a data processing system for preventing data leakage in automated machine learning. The mechanism receives a data set comprising a label for a target variable for a classifier machine learning model and a set of features. For each given feature in the set of features, the mechanism trains a subprime classifier model using the given feature as a target variable and remaining features as independent input features, tests the subprime classifier model, and records results of the subprime classifier model. The mechanism performs statistical analysis on the recorded results to identify an outlier result corresponding to an outlier subprime classifier model.

Abstract: Aspects of the present invention provide devices that generate a job description by generating at least one job description according to a plurality of job areas and a linguistic model trained on a plurality of cataloged job descriptions, each job area including one or more assigned job skills, and displaying the generated at least one job description on a display device.

Abstract: Aspects of the present invention provide devices that process cancelling an automatic payroll payment for an employee pay by, in response to a termination request terminating employment of an employee from an entity, determining a recommendation to cancel the automatic payroll pay according to a trained classification model and a feature vector for the employee termination. The devices train the trained classification model on prior decisions for cancelling automatic payroll payments for terminated employees and corresponding feature vectors, and display the recommendation to cancel the automatic payroll pay on a display device.

Abstract: Aspects of the present invention provide devices that generate a job description by generating at least one job description according to a plurality of job areas and a linguistic model trained on a plurality of cataloged job descriptions, each job area including one or more assigned job skills, and displaying the generated at least one job description on a display device.