Abstract: A method includes obtaining a first dataset including a first feature set, generating a first set of feature values by providing the first dataset to a set of feature primitive stacks, and determining a reduced set of feature values based on the first set of feature values by dimensionally reducing features of the first set of feature values. The method further includes generating an intermediate set of feature values by providing a value of the first dataset and a value of the reduced set of feature values to at least one feature primitive of the set of feature primitive stacks. The method further includes updating the reduced set of feature values by dimensionally reducing features of the intermediate set of feature values and storing a second dataset including features of the intermediate set of feature values in association with the first feature set.

Abstract: A method includes obtaining a first dataset comprising a first set of features and generating a second set of features based on the first set of features set by providing the first dataset to feature primitive stacks that respectively corresponds to features of the second set of features. The method further includes determining a reduced feature set based on the second set of features and a count of correlation values between features of the second set of features, wherein the correlation values satisfy a correlation threshold. The method further includes storing the reduced feature set in a database in association with the first set of features based on a determination that a second dataset comprising the reduced feature set satisfies a set of criteria.

Abstract: Systems and methods for successively imputing missing feature values using machine learning to sequentially fill in missing feature values in partially-filled datasets, and by using the information in populated records of the dataset. The systems and methods disclosed herein may be useful in many machine learning contexts and application where datasets are missing values.

Abstract: Disclosed embodiments may include a system for providing a nearest neighbors classification pipeline with automated dimensionality reduction. The system may receive a dataset. The system may determine whether the dataset has a first dimensionality that exceeds a predetermined threshold. When dataset has a dimensionality that exceeds a predetermined threshold, the system may prompt a user to input an explained variance threshold ratio. The system may receive the explained variance threshold ratio. The system may iteratively perform a binary search on the dataset to determine a reduced dimensionality having a total explained variance ratio closest to but not less than the explained variance threshold ratio. The system may reduce the dataset to the reduced dimensionality to generate a reduced dataset. The system may train a machine learning model using the reduced dataset.

Abstract: A method may be provided for selecting embedding dimension, which can include receiving a trained machine learning (ML) model and a graph neural network (GNN) and extracting, from the received ML model, a count of a number of neurons in a penultimate layer and node embeddings for each input graph node in GNN neurons in the penultimate layer. An importance threshold input for filtering the node embeddings can be received, and a tree-based model may be used to return feature importance values. The extracted node embeddings may be input into the tree-based model and an importance metric of each of the node embedding dimensions may be determined from the penultimate layer neurons. The penultimate layer neuron count of the ML model may be restricted to correspond to a number of the highest importance node embedding dimensions and the ML model may be trained using the restricted penultimate layer.

Abstract: Systems and methods are provided for evaluating and selecting an ensemble of machine language models using extremely randomized bootstrap aggregation (e.g., bagging) with replacement. The method may include the use of a plurality of base models to produce a combined (or aggregated) output. Original data may be randomly sampled with replacement to create N subsets of bootstrapped data for which each of the N-selected base models may produce a prediction based on their subset of data. The individual predictions may be combined and evaluated, and an ensemble having the highest performance may be selected and trained for production. Certain implementations of the disclosed technology can eliminate the need for apriori knowledge about which model (or models) will provide accurate predictions.

Abstract: Systems and methods, as described herein, relate to sequential synthesis and selection for feature engineering. A dataset may be associated with a label defining a machine-learning target attribute and a received operation that can be applied to at least one of the existing features of the dataset. One or more potential features may be generated by applying the operation to one or more existing features. For each of the one or more potential features, a feature importance algorithm may be applied to the respective feature along with the one or more existing features, generating a respective feature importance value. Respective feature importance values may be generated for each of the one or more existing features based on applying the feature importance algorithm and used to sort the potential features. A level of correlation to each of the one or more existing features may be determined to make sure it is under a threshold level to avoid new features heavily correlated to existing ones.

Abstract: The exemplary embodiments may provide a stacked machine learning model ensemble pipeline architecture selector that selects a well-suited stacked machine learning model ensemble pipeline architecture for a specified configuration input and a target data set. The stacked machine learning model ensemble pipeline architecture selector may generate and score possible stacked machine learning model ensemble pipeline architectures to locate one that is well-suited for the target data set and the conforms with the configuration input. The stacked machine learning model ensemble pipeline architecture selector may use genetic programming to generate successive generations of possible stacked ensemble pipeline architectures and to score those architectures to determine how well-suited they are. In this manner, the stacked machine learning model ensemble pipeline architecture selector may converge on an architecture that is well-suited, for example, that meet one or more scores, evaluation metrics, and/or the like.

Abstract: Various embodiments are generally directed to techniques for optimizing hyperparameters, such as optimizing different combinations of hyperparameters, for instance. Some embodiments are particularly directed using a genetic or Bayesian algorithm to identify and optimize different combinations of hyperparameters for a machine learning (ML) model. Many embodiments construct a search using a genetic algorithm that prioritizes the most important hyperparameters in influencing model performance.

Abstract: Disclosed is a motion detection device for monitoring movement of a component comprising a housing removably connectable to the component; an indicator arranged within the housing, the indicator being operable to indicate movement of the component; at least one sensor arranged within the housing, the at least one sensor configured to sense data in response to a gravitational force acting thereon; and a controller operably coupled to the at least one sensor and the indicator, the controller including an algorithm configured to determine when the motion detection device is moving and to operate the indicator when the motion detection device is moving.

Abstract: Examples of the present disclosure can comprise systems and methods for creating and modifying named entity recognition models. The system can use two or more existing named entity recognition models to output responses to natural language queries for which the models have not yet been trained. When the output from the two or more models match, the query and the resulting output can be stored as training data for a new named entity recognition model. If the output from the two or models do not match, the query and the outputs can be stored in an exceptions file for additional review. In some embodiments, the system can comprise one or more processors and a display for providing a user interface (UI).

Abstract: Examples of the present disclosure can comprise systems and methods for creating and modifying named entity recognition models. The system can use two or more existing named entity recognition models to output responses to natural language queries for which the models have not yet been trained. When the output from the two or more models match, the query and the resulting output can be stored as training data for a new named entity recognition model. If the output from the two or models do not match, the query and the outputs can be stored in an exceptions file for additional review. In some embodiments, the system can comprise one or more processors and a display for providing a user interface (UI).

Abstract: I am moved by the Holy Spirit so that customers and banks may prosper. It is to prevent thieves from stealing pin numbers at ATM machines at banks inside and outside of the banks. The curtain hides the pin numbers at the ATM keypad. My invention would be new and would save customers and banks money. Also, it would be new to place a Bible quote and painting on the back of a room darkening curtain at the bank if the banks allow.

Abstract: Examples of the present disclosure can comprise systems and methods for creating and modifying named entity recognition models. The system can use two or more existing named entity recognition models to output responses to natural language queries for which the models have not yet been trained. When the output from the two or more models match, the query and the resulting output can be stored as training data for a new named entity recognition model. If the output from the two or models do not match, the query and the outputs can be stored in an exceptions file for additional review. In some embodiments, the system can comprise one or more processors and a display for providing a user interface (UI).

Abstract: A method and implementing computer system are provided in which a smart power strip is controlled by a master device which is also plugged-into the power strip. In an exemplary implementation, a computer or PC operates as a master device and a direct line of communication from the computer allows the OS to make a decision based on settings whether or not to power on or off individual power outlets on the power strip used in common by the computer and various peripheral devices. Thus, only selected peripheral devices are powered-on along with the computer when the computer or other master device is powered-on at a common power strip.

Abstract: A rollable writing surface is provided that provides a user with a mobile whiteboard surface upon which a dry erase marker can be used to create written signs or to provide a personal writing surface. The writing surface comprises a backside layer of elastomeric material, preferably a rubberized material that is biased to remain flat, while thereover is a whiteboard substrate material that forms the writing surface. Over the substrate layer is a dry erase coating that is disposed along the exposed outer surface of substrate layer. The dry erase coating is substantially non-porous and supports dry erase markings without staining or permanently supporting the ink. The flexibility of the assembly allows the same to be rolled into a compact form and deployed, whereafter the assembly lies substantially flat for use as a writing surface using dry erase markers.

Abstract: A method and implementing computer system are provided in which a smart power strip is controlled by a master device which is also plugged-into the power strip. In an exemplary implementation, a computer or PC operates as a master device and a direct line of communication from the computer allows the OS to make a decision based on settings whether or not to power on or off individual power outlets on the power strip used in common by the computer and various peripheral devices. Thus, only selected peripheral devices are powered-on along with the computer when the computer or other master device is powered-on at a common power strip.

Abstract: A method and implementing computer system are provided in which a smart power strip is controlled by a master device which is also plugged-into the power strip. In an exemplary implementation, a computer or PC operates as a master device and a direct line of communication from the computer allows the OS to make a decision based on settings whether or not to power on or off individual power outlets on the power strip used in common by the computer and various peripheral devices. Thus, only selected peripheral devices are powered-on along with the computer when the computer or other master device is powered-on at a common power strip.

Abstract: A vehicle impact system includes a road accommodating vehicle traffic in a direction along its center line, a support structure and first and second adjacent energy-absorbing fender elements supported by the support structure and extending adjacent to the road. Each of the first and second adjacent energy-absorbing fender elements includes an elastomeric leg extending non-parallel from the support structure and a contact panel extending from the leg. The contact panel has a contact surface extending substantially parallel to the road center line, a forward end and a rearward end. The forward end of the first adjacent fender element faces the rearward end of the second adjacent fender element such that when the contact surface of the first adjacent fender element is engaged by a vehicle, the leg of the first adjacent fender element resiliently flexes to move the forward end of the first adjacent fender element into engagement with the rearward end of the second adjacent fender element.