Abstract: A system described herein may provide a technique for enhanced federated learning in an environment that makes use of one or more centralized models. Different nodes may be associated with different groups. Each node may provide refinement information for a given centralized model. The modifications for particular groups may be aggregated and the model may be modified based on modifications associated with each group, as opposed to modifications associated with each node. Weights for each group may be determined based on attributes of the modifications associated with each group, which may allow for the identification, on a group basis, of bias, maliciously injected data, outliers, and/or other types of modifications which may reduce the quality of the model. As such, embodiments described herein may enhance the quality, accuracy, and predictive ability of federated learning techniques that utilize distributed or federated modifications to a centralized model.

Abstract: A system described herein may receive a set of outputs of a first model, which have been generated by the first model based on a set of inputs, and identify a set of historical values that correspond to the set of inputs and the set of outputs. The inputs and the historical values may be associated with the same time series. The system may train a second model based on the set of inputs to the first model, the set of outputs of the first model, and the set of historical values that correspond to the set of inputs and the set of outputs. The system may determine, based on training the second model, a set of weights associated with the set of historical values; and refine the first model based on the set of weights associated with the set of historical value.

Abstract: A device may receive historical time series data and output data associated with a forecasting model and may process the historical time series data and the output data, with a proxy regression model, to determine inference data. The device may create perturbed data from the historical time series data, the output data, and the inference data, and may process the perturbed data, with the proxy regression model, to generate labelled data and to identify top features of the labelled data. The device may process subsets of the top features of the labelled data, with the proxy regression model, to determine feature data identifying an importance of each of the subsets of the top features, and may evaluate the proxy regression model and the feature data to calculate validation data for validating the forecasting model. The device may validate the forecasting model with the validation data.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a method comprises receiving, by a device, attribute data for a customer, determining, by the device, a cluster label for the customer based on the attribute data, determining, by the device, a first customer persona for the customer using a decision tree classifier model based on the cluster label and the attribute data, and providing, by the device, targeted content based on the first customer persona. Embodiments described herein also include a system comprising a processor configured to execute instructions to facilitate performance of operations comprising receiving attribute data for a customer, determining a cluster label for the customer based on the attribute data, determining a first customer persona for the customer using a decision tree classifier model based on the cluster label and the attribute data, and providing targeted content based on first customer persona.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a method comprises receiving, by a device, attribute data for a region served by a telecommunications service. A cluster label is determined by the device for the region based on the attribute data. A first region persona is determined by the device for the region using a decision tree classifier model based on the cluster label and the attribute data. Recommended service parameters are implemented for the telecommunications service in the region based on the first region persona.

Abstract: According to an embodiment, there is provided an electronic device comprising: a memory storing instructions; and at least one processor configured to execute the instructions to: in response to an input, identify a dataset to be processed for responding to the input; divide the dataset into a plurality of sub-datasets; identify at least one electronic device which processes at least one sub-dataset; assign the at least one sub-dataset to the at least one electronic device to process the at least one sub-dataset; and receive from the at least one electronic device at least one output of the processed at least one sub-dataset to generate a response to the input.

Abstract: Systems and methods described herein provide an online constraint optimizing service that evaluates user requests and inquiries for telecommunications services in the context of a real-time constraint-based analysis. According to an implementation, a network device receives a function for an analytic event and a constraint. The function applies different user attributes for a telecommunications network. The network device generates a training data set using offline constrained optimization of the function. The network device develops a predictive model for utilization of network resources in the telecommunications network using the training data set. The network device receives a user request that corresponds to the analytic event addressed by the predictive model and conducts an online prescriptive analysis using the predictive model. The network device optimizes allocation of the network resources to the user based on the prescriptive analysis.

Abstract: A device may receive device data identifying activation and usage of different types of IoT devices, and may identify a device activation sequence based on the device data. The device may train a model with the device activation sequence, and may generate output data based on training the model with the device activation sequence. The device may calculate weights and a loss function for the output data, and may retrain the model based on the weights and the loss function to generate a retrained model. The device may generate device representations of the different types of IoT devices using the retrained model, and may determine groups of similar IoT devices based on the device representations. The device may generate configuration data for each of the groups of the similar IoT devices, and may cause the different types of IoT devices to be configured based on the configuration data.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a method comprises receiving, by a device, attribute data for a customer, determining, by the device, a cluster label for the customer based on the attribute data, determining, by the device, a first customer persona for the customer using a decision tree classifier model based on the cluster label and the attribute data, and providing, by the device, targeted content based on the first customer persona. Embodiments described herein also include a system comprising a processor configured to execute instructions to facilitate performance of operations comprising receiving attribute data for a customer, determining a cluster label for the customer based on the attribute data, determining a first customer persona for the customer using a decision tree classifier model based on the cluster label and the attribute data, and providing targeted content based on first customer persona.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a method comprises receiving, by a device, attribute data for a region served by a telecommunications service. A cluster label is determined by the device for the region based on the attribute data. A first region persona is determined by the device for the region using a decision tree classifier model based on the cluster label and the attribute data. Recommended service parameters are implemented for the telecommunications service in the region based on the first region persona.

Abstract: A system described herein may receive a set of outputs of a first model, which have been generated by the first model based on a set of inputs, and identify a set of historical values that correspond to the set of inputs and the set of outputs. The inputs and the historical values may be associated with the same time series. The system may train a second model based on the set of inputs to the first model, the set of outputs of the first model, and the set of historical values that correspond to the set of inputs and the set of outputs. The system may determine, based on training the second model, a set of weights associated with the set of historical values; and refine the first model based on the set of weights associated with the set of historical value.

Abstract: A system described herein may provide a technique for enhanced federated learning in an environment that makes use of one or more centralized models. Different nodes may be associated with different groups. Each node may provide refinement information for a given centralized model. The modifications for particular groups may be aggregated and the model may be modified based on modifications associated with each group, as opposed to modifications associated with each node. Weights for each group may be determined based on attributes of the modifications associated with each group, which may allow for the identification, on a group basis, of bias, maliciously injected data, outliers, and/or other types of modifications which may reduce the quality of the model. As such, embodiments described herein may enhance the quality, accuracy, and predictive ability of federated learning techniques that utilize distributed or federated modifications to a centralized model.

Abstract: A device may receive historical time series data and output data associated with a forecasting model and may process the historical time series data and the output data, with a proxy regression model, to determine inference data. The device may create perturbed data from the historical time series data, the output data, and the inference data, and may process the perturbed data, with the proxy regression model, to generate labelled data and to identify top features of the labelled data. The device may process subsets of the top features of the labelled data, with the proxy regression model, to determine feature data identifying an importance of each of the subsets of the top features, and may evaluate the proxy regression model and the feature data to calculate validation data for validating the forecasting model. The device may validate the forecasting model with the validation data.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, streaming and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel machine learning framework that trains classifiers to identify specific features of input data. When implementing these trained classifiers in specific runtime environments, the important features of those specific environments can be identified and leveraged for directing the classifier to the vital information that is relevant to the environment. A customized classifier is thereby dynamically created and deployed which improves how data can be classified, thereby reducing false negatives and positives, and increasing confidence and reliance on how such classifiers can be implemented.

Abstract: Systems and methods described herein provide an online constraint optimizing service that evaluates user requests and inquiries for telecommunications services in the context of a real-time constraint-based analysis. According to an implementation, a network device receives a function for an analytic event and a constraint. The function applies different user attributes for a telecommunications network. The network device generates a training data set using offline constrained optimization of the function. The network device develops a predictive model for utilization of network resources in the telecommunications network using the training data set. The network device receives a user request that corresponds to the analytic event addressed by the predictive model and conducts an online prescriptive analysis using the predictive model. The network device optimizes allocation of the network resources to the user based on the prescriptive analysis.

Abstract: According to an embodiment, there is provided an electronic device comprising: a memory storing instructions; and at least one processor configured to execute the instructions to: in response to an input, identify a dataset to be processed for responding to the input; divide the dataset into a plurality of sub-datasets; identify at least one electronic device which processes at least one sub-dataset; assign the at least one sub-dataset to the at least one electronic device to process the at least one sub-dataset; and receive from the at least one electronic device at least one output of the processed at least one sub-dataset to generate a response to the input.

Abstract: An antimicrobial abrasive article comprising a nonwoven substrate material impregnated with a first and second formulation. The first and second formulations have persistent residual antimicrobial effectiveness against one or more microbial organisms. The first and second formulations include one or more antimicrobial agents and abrasive particles uniformly dispersed in a polymer composition. The first and second antimicrobial agents can be the same or different. The polymer compositions can be the same or different.