Abstract: Protection against prying devices on a computer network is provided. The method comprises identifying a number of prying devices connected to the computer network, wherein the prying devices have the capability to record audio and/or video data, and wherein the prying devices have the potential to eavesdrop on an audio/video conference call. Responsive to detecting initiation of the audio/video conference call, the prying devices are prevented from communicating over the computer network. Responsive to detecting termination of the audio/video conference call, the prying devices are allowed to resume communication over the computer network.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to facilitating a process to compensate a service being provided over a network connection. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a determination component that determines a network connection between a server and a client node, and a predictive component that predicts, employing machine learning, a graphical representation update to a service provided by the server over the network connection. The predictive component can generate the prediction in response to a decrease in bandwidth and/or an increase in latency of a network connection. A training component can train a machine learning model employed by the predictive component based on historical data of the service provided by the server.

Abstract: A computer-implemented method, system and computer program product for verifying the trustworthiness of an aggregation scheme utilized by an aggregator in the federated learning technique. A bit mask is received from each client used for training a machine learning algorithm using the federated learning technique. Such a bit mask contains values of ones and zeros, where a value of one indicates that the updated parameter of the global model corresponds to a parameter used by the local model trained on the client and a value of zero indicates that is not the case. These bit masks, which are encrypted, may then be combined using a homomorphic additive encryption scheme into a mask containing a matrix of values. If the mask contains a matrix of values of only the value of one, then the aggregator is deemed to be trustworthy. Otherwise, the aggregator is deemed to be untrustworthy.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process for privacy-enhanced machine learning and inference. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a processing component that generates an access rule that modifies access to first data of a graph database, wherein the first data comprises first party information identified as private, a sampling component that executes a random walk for sampling a first graph of the graph database while employing the access rule, wherein the first graph comprises the first data, and an inference component that, based on the sampling, generates a prediction in response to a query, wherein the inference component avoids directly exposing the first party information in the prediction.

Abstract: Systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to federated training and inferencing. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a modeling component that trains an inferential model using data from a plurality of parties and comprising horizontally partitioned data and vertically partitioned data, wherein the modeling component employs a random decision tree comprising the data to train the inferential model, and an inference component that responds to a query, employing the inferential model, by generating an inference, wherein first party private data, of the data, originating from a first passive party of the plurality of parties, is not directly shared with other passive parties of the plurality of parties to generate the inference.

Abstract: Protection against prying devices on a computer network is provided. The method comprises identifying a number of prying devices connected to the computer network, wherein the prying devices have the capability to record audio and/or video data, and wherein the prying devices have the potential to eavesdrop on an audio/video conference call. Responsive to detecting initiation of the audio/video conference call, the prying devices are prevented from communicating over the computer network. Responsive to detecting termination of the audio/video conference call, the prying devices are allowed to resume communication over the computer network.

Abstract: Embodiments of the invention include a computer-implemented method that uses a processor system to access a first machine learning (ML) model. The first ML model has been trained using data of a first server. A first performance metric of the first ML model is determined using data of a second server. A benefit analysis is performed to determine a benefit of the first ML server and the second ML server participating in a federated learning system, where the benefit analysis includes using the first performance metric.

Abstract: A golf training aid maintains alignment of a user's vision with an orientation line during a golf swing. The golf training aid has a stand configured to be securely positioned on the ground, a front indicator, and a rear indicator offset from the front indicator. The front indicator may be pivotally adjusted to move in synchronization with the rear indicator. The front indicator may have a first vertical element and a first horizontal element. The rear indicator may have a second vertical element and a second horizontal element. The user aligns the elements of the front indicator and the rear indicator to align the orientation line with the user's line of sight. Alignment of the orientation line with the user's line of sight defines an angle of between 40° and 80° with the ground.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to facilitating a process to compensate a service being provided over a network connection. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a determination component that determines a network connection between a server and a client node, and a predictive component that predicts, employing machine learning, a graphical representation update to a service provided by the server over the network connection. The predictive component can generate the prediction in response to a decrease in bandwidth and/or an increase in latency of a network connection. A training component can train a machine learning model employed by the predictive component based on historical data of the service provided by the server.

Abstract: Embodiments for providing enhanced adversarial robustness of machine learning models using certification for federated learning in a computing environment by a processor. Machine learning model updates, a dataset, and a set of hyperparameters may be received. One or more certification parameters and one or more filtered machine learning model updates for a machine learning model may be generated by certifying each of plurality of data points using one or more abstract representations in a machine learning operation and filtering the plurality of machine learning model updates.

Abstract: Various embodiments are provided for intelligent management of data flows in a computing environment by a processor. One or more data transformation in time-series data applications templates may be created and managed according to concepts, one or more instances of the concepts, relationships between the concepts, and a mapping of the concepts to one or more data sources.

Abstract: A computer-implemented method, a computer program product, and a computer system for federated learning model lineage. A model lineage system receives an initial model, from an aggregator in a federated learning system, where the aggregator starts a round of training the initial model. The model lineage system dispatches the initial model to workers in the federated learning system. The model lineage system records the initial model in a lineage database. The model lineage system receives updates from the workers which train the initial model locally. The model lineage system records the updates in the lineage database.

Abstract: A computer-implemented method, a computer program product, and a computer system for recommending model contributions based on federated learning lineage. The computer system retrieves information of model checkpoints. The computer system trains data analytic models for monitoring activities of training rounds in a federated learning system, based on the information of the model checkpoints. The computer system sends to a user summary statistics of the model checkpoints. The computer system receives from the user natural language instructions of modifying a federated learning plan for future training rounds in the federated learning system. The computer system translates the natural language instructions into updates for the federated learning system. The computer system forwards the updates to the federated learning system.

Abstract: A computer-implemented method, a computer program product, and a computer system for selecting predictions by models. A computer receives a request for a forecast of a dependent variable in a time domain, where the time domain includes first time periods that have normal labels due to normal predictor variable data and second time periods that have anomalous labels due to anomalous predictor variable data. The computer retrieves accuracy scores and robustness scores of models, where the accuracy scores indicate forecasting accuracy in the first time periods and the robustness scores indicate forecasting accuracy in the second time periods. For predictions in the first time period, the computer selects dependent variable values predicted by a first model that has highest values of the accuracy scores. For predictions in the second time periods, the computer selects dependent variable values predicted by a second model that has highest values of the robustness scores.

Abstract: A computer-implemented method, a computer program product, and a computer system for selecting predictions by models. A computer receives a request for a forecast of a dependent variable in a time domain, where the time domain includes first time periods that have normal labels due to normal predictor variable data and second time periods that have anomalous labels due to anomalous predictor variable data. The computer retrieves accuracy scores and robustness scores of models, where the accuracy scores indicate forecasting accuracy in the first time periods and the robustness scores indicate forecasting accuracy in the second time periods. For predictions in the first time period, the computer selects dependent variable values predicted by a first model that has highest values of the accuracy scores. For predictions in the second time periods, the computer selects dependent variable values predicted by a second model that has highest values of the robustness scores.

Abstract: Embodiments for providing optimized machine learning model features using federated learning on distributed data in a computing environment by a processor. Machine learning model features may be learned from one or more data sets extracted from one or more localized machine learning models associated with one or more nodes. The machine learning model features may be aggregated using a centralized machine learning model at a source node. The one or more localized machine learning models may be trained using aggregated machine learning model features provided by the centralized machine learning model.

Abstract: Various embodiments are provided for intelligent management of data flows in a computing environment by a processor. One or more data transformation in time-series data applications templates may be created and managed according to concepts, one or more instances of the concepts, relationships between the concepts, and a mapping of the concepts to one or more data sources.

Abstract: Embodiments for managing aquatic infrastructure by one or more processors are described. A vehicle is detected within a vicinity of an aquatic infrastructure device. A wireless capability level of the vehicle is determined. Based on the determined wireless capability level of the vehicle, at least one of a wireless power transfer between the vehicle and the aquatic infrastructure device or a wireless data transfer between the vehicle and the aquatic infrastructure device is enabled.

Abstract: Embodiments for managing aquatic infrastructure by one or more processors are described. A vehicle is detected within a vicinity of an aquatic infrastructure device. A wireless capability level of the vehicle is determined. Based on the determined wireless capability level of the vehicle, at least one of a wireless power transfer between the vehicle and the aquatic infrastructure device or a wireless data transfer between the vehicle and the aquatic infrastructure device is enabled.

Abstract: Embodiments provide for analyzing interleaving material applied to sheets of material traveling along a conveyor. A system is provided that includes a cross-conveyor support frame and a camera system to capture images of the interleaving material upon the sheets. The camera system may optionally include a movable camera head that travels along the support frame to capture images at multiple positions along substantially the entire width of the conveyor. A control module is provided that includes a processor programmed with instructions for analyzing the interleaving material based on the images. In some cases the analyzing includes receiving the images of the interleaving material from the camera system, assessing coverage of the interleaving material upon the sheets of material from the images, determining adjustments for application of the interleaving material based on the assessed coverage, and/or communicating the adjustments to an interleaving material applicator.