Abstract: Systems and methods for automated historical risk assessment for risk mitigation in online access control are provided. An entity assessment server can receive a request to assess a risk indicator change from a first risk indicator to a second risk indicator. For each attribute used to generate the first risk indicator and second risk indicator, a first impact can be determined for changing from the first risk indicator to a third risk indicator between the first risk indicator and the second risk indicator. A second impact similarly can be determined for changing from the third risk indicator to the second risk indicator. Aggregating the first impact and the second impact can determine a total impact of each attribute. Assessment results can be generated to include a list of attributes ordered according to the respective total impact and transmitted to a remote computing device for use in improving the risk indicator.

Abstract: A method can be used to predict risk and provide explainable outcomes using machine learning based on wavelet analysis. A risk prediction model can be applied to time-series data for an attribute associated with a target entity to generate a risk indicator for the target entity. The risk prediction model can include a feature learning model and a risk classification model configured to generate the risk indicator as output. Parameters of the feature learning model can be accessed and a plurality of basis functions of a wavelet transformation can be applied on the parameters of the feature learning model to generate a set of parameter wavelet coefficients. Explanatory data can be generated for the risk indicator based on the set of parameter wavelet coefficients. A responsive message can be transmitted to a remote computing device including the risk indicator and the explanatory data for use in controlling access of the target entity to an interactive computing environment.

Abstract: Certain aspects involve building timing-prediction models for predicting timing of events that can impact one or more operations of machine-implemented environments. For instance, a computing system can generate program code executable by a host system for modifying host system operations based on the timing of a target event. The program code, when executed, can cause processing hardware to a compute set of probabilities for the target event by applying a set of trained timing-prediction models to predictor variable data. A time of the target event can be computed from the set of probabilities. To generate the program code, the computing system can build the set of timing-prediction models from training data. Building each timing-prediction model can include training the timing-prediction model to predict one or more target events for a different time bin within the training window. The computing system can generate and output program code implementing the models' functionality.

Abstract: Systems and methods for automated historical risk assessment for risk mitigation in online access control are provided. An entity assessment server can receive a request to assess a risk indicator change from a first risk indicator to a second risk indicator. For each attribute used to generate the first risk indicator and second risk indicator, a first impact can be determined for changing from the first risk indicator to a third risk indicator between the first risk indicator and the second risk indicator. A second impact similarly can be determined for changing from the third risk indicator to the second risk indicator. Aggregating the first impact and the second impact can determine a total impact of each attribute. Assessment results can be generated to include a list of attributes ordered according to the respective total impact and transmitted to a remote computing device for use in improving the risk indicator.

Abstract: Certain aspects involve building timing-prediction models for predicting timing of events that can impact one or more operations of machine-implemented environments. For instance, a computing system can generate program code executable by a host system for modifying host system operations based on the timing of a target event. The program code, when executed, can cause processing hardware to a compute set of probabilities for the target event by applying a set of trained timing-prediction models to predictor variable data. A time of the target event can be computed from the set of probabilities. To generate the program code, the computing system can build the set of timing-prediction models from training data. Building each timing-prediction model can include training the timing-prediction model to predict one or more target events for a different time bin within the training window. The computing system can generate and output program code implementing the models' functionality.

Abstract: Systems and methods for automated historical risk assessment for risk mitigation in online access control are provided. An entity assessment server can receive a request to assess a risk indicator change from a first risk indicator to a second risk indicator. For each attribute used to generate the first risk indicator and second risk indicator, a first impact can be determined for changing from the first risk indicator to a third risk indicator between the first risk indicator and the second risk indicator. A second impact similarly can be determined for changing from the third risk indicator to the second risk indicator. Aggregating the first impact and the second impact can determine a total impact of each attribute. Assessment results can be generated to include a list of attributes ordered according to the respective total impact and transmitted to a remote computing device for use in improving the risk indicator.

Abstract: A host computing system determines a wavelet transform that represents time-series values of predictor data samples. The host computing system applies the wavelet transform to the predictor data samples to generate wavelet predictor variable data comprising a first set and a second set of shift value input data for a first scale and a second scale. The host computing system computes a set of probabilities for a target event by applying a set of timing-prediction models to the first set and the second set of shift value input data. The host computing system determines an event prediction from the set of probabilities and modifies a host system operation based on the determined event prediction.

Abstract: A method can be used to predict risk using machine learning models having efficient feature learning. A risk prediction model can be applied to time-series data associated with a target entity to generate a risk indicator. The risk prediction model can include a feature learning model for generating features from the time-series data. The risk prediction model can also include a risk classification model for generating the risk indicator. The feature learning model can include filters and can be trained. Parameters of the risk prediction model can be adjusted to minimize a loss function associated with risk indicators. An updated risk prediction model can be generated by removing a filter from an original set of filters based on influencing scores of the original filters. The risk indicator can be transmitted to a computing device for use in controlling access of the target entity to a computing environment.

Abstract: Certain aspects involve building timing-prediction models for predicting timing of events that can impact one or more operations of machine-implemented environments. For instance, a computing system can generate program code executable by a host system for modifying host system operations based on the timing of a target event. The program code, when executed, can cause processing hardware to a compute set of probabilities for the target event by applying a set of trained timing-prediction models to predictor variable data. A time of the target event can be computed from the set of probabilities. To generate the program code, the computing system can build the set of timing-prediction models from training data. Building each timing-prediction model can include training the timing-prediction model to predict one or more target events for a different time bin within the training window. The computing system can generate and output program code implementing the models' functionality.

Abstract: A fluid flow meter estimates the velocity of water or another fluid flowing through pipe by comparing measurements of the water velocity to one or more pre-determined templates. The fluid flow meter may collect measurement signals from one or more flow sensors, estimate the fluid velocity or flow rate by comparing the measurement signals to the template, and either store the comparison results in local memory, transmit the results to a remote memory or server, or both. In some embodiments, the fluid flow meter transmits the results to a server via a wireless interface. The transducers and processing system can be powered by a battery, a power line, or, for manifolds installed outdoors, a solar cell. Example transducers and processing systems may also have a passive wake-up feature for power reduction; that is, they may only draw power when water or another fluid flows through the pipe.

Abstract: A fluid flow meter estimates the velocity of water or another fluid flowing through pipe by comparing measurements of the water velocity to one or more pre-determined templates. The fluid flow meter may collect measurement signals from one or more flow sensors, estimate the fluid velocity or flow rate by comparing the measurement signals to the template, and either store the comparison results in local memory, transmit the results to a remote memory or server, or both. In some embodiments, the fluid flow meter transmits the results to a server via a wireless interface. The transducers and processing system can be powered by a battery, a power line, or, for manifolds installed outdoors, a solar cell. Example transducers and processing systems may also have a passive wake-up feature for power reduction; that is, they may only draw power when water or another fluid flows through the pipe.

Abstract: A fluid flow meter estimates the velocity of water or another fluid flowing through pipe by comparing measurements of the water velocity to one or more pre-determined templates. The fluid flow meter may collect measurement signals from one or more flow sensors, estimate the fluid velocity or flow rate by comparing the measurement signals to the template, and either store the comparison results in local memory, transmit the results to a remote memory or server, or both. In some embodiments, the fluid flow meter transmits the results to a server via a wireless interface. The transducers and processing system can be powered by a battery, a power line, or, for manifolds installed outdoors, a solar cell. Example transducers and processing systems may also have a passive wake-up feature for power reduction; that is, they may only draw power when water or another fluid flows through the pipe.

Abstract: A fluid flow meter estimates the velocity of water or another fluid flowing through pipe by comparing measurements of the water velocity to one or more pre-determined templates. The fluid flow meter may collect measurement signals from one or more flow sensors, estimate the fluid velocity or flow rate by comparing the measurement signals to the template, and either store the comparison results in local memory, transmit the results to a remote memory or server, or both. In some embodiments, the fluid flow meter transmits the results to a server via a wireless interface. The transducers and processing system can be powered by a battery, a power line, or, for manifolds installed outdoors, a solar cell. Example transducers and processing systems may also have a passive wake-up feature for power reduction; that is, they may only draw power when water or another fluid flows through the pipe.

Abstract: A fluid flow meter estimates the velocity of water or another fluid flowing through pipe by comparing measurements of the water velocity to one or more pre-determined templates. The fluid flow meter may collect measurement signals from one or more flow sensors, estimate the fluid velocity or flow rate by comparing the measurement signals to the template, and either store the comparison results in local memory, transmit the results to a remote memory or server, or both. In some embodiments, the fluid flow meter transmits the results to a server via a wireless interface. The transducers and processing system can be powered by a battery, a power line, or, for manifolds installed outdoors, a solar cell. Example transducers and processing systems may also have a passive wake-up feature for power reduction; that is, they may only draw power when water or another fluid flows through the pipe.

Abstract: A fluid flow meter estimates the velocity of water or another fluid flowing through pipe by comparing measurements of the water velocity to one or more pre-determined templates. The fluid flow meter may collect measurement signals from one or more flow sensors (e.g., ultrasonic transducers), estimate the fluid velocity or flow rate by comparing the measurement signals to the template(s), and either store the comparison results in local memory, transmit the results to a remote memory or server, or both. In some embodiments, the fluid flow meter transmits the results to a server via a wireless interface (e.g., a Zigbee, Bluetooth, or Wi-fi interface). The transducers and processing system can be powered by a battery, a power line, or, for manifolds installed outdoors, a solar cell. Example transducers and processing systems may also have a passive wake-up feature for power reduction; that is, they may only draw power when water or another fluid flows through the pipe.

Abstract: A response select null steering circuit includes a beamformer, a plurality of separate fixed filters, and a selection circuit. In response to sound signals emitted from a desired speaker and an unwanted interferer, a sum signal containing signal components of the speaker and interferer is generated, and the beamformer generates a difference signal that suppresses signal components of the speaker. Each filter provides a null in a unique direction relative to the desired speaker, and can be individually configured to suppress sound signals from an interferer in a particular direction. The selection circuit selects the filter output signal that has the least amount of signal energy as achieving the best suppression of the unwanted interferer.