Abstract: Provided are techniques for a Generative Adversarial Networks (GANs) based identification of an edge server. At a first edge server, a global discriminator that has been trained with common data is received. It is determined that area data is imbalanced using the global discriminator. A local discriminator is trained with the area data to generate a first result. An exchanged local discriminator from a second edge server is trained with the area data to generate a second result. The first result and the second result indicate that the first edge server and the second edge server are proximate. The first edge server and the second edge server are added to an edge server group list. At least one of an application model and a configuration of an application is updated from one of the first edge server and the second edge server, and the application is executed.

Abstract: A computer system trains a federated learning model. A federated learning model is distributed to a plurality of computing nodes, each having a set of local training data comprising labeled data samples. Statistical data is received from each computing node that indicates the node's count of data samples for each label, and is analyzed to identify one or more computing nodes having local training data in which a label category is underrepresented beyond a threshold value with respect to data samples. Additional data samples labeled with the underrepresented labels are provided, and the computing nodes perform training. Results of training are received and are processed to generate a trained global model. Embodiments of the present invention further include a method and program product for training a federated learning model in substantially the same manner described above.

Abstract: A processor may generate, based on a predicted route of a user, a timeslot for presenting information to the user. The predicted route may be associated with a route segment, and the timeslot may be associated with the route segment. The processor may match one or more proposals to the timeslot associated with the route segment. The one or more proposals may be matched, at least in part, based on proposal criteria set by a proposal criteria user associated with the proposal. The processor may predict an acceptance potentiality of the user. The processor may select a subset of the one or more proposals. The processor may provide the selected subset of the one or more proposals. The selected subset may be provided, at least in part, based on the acceptance potentiality.

Abstract: A computer-implemented method, a computer program product, and a computer system for parallel cross validation in collaborative machine learning. A server groups local models into groups. In each group, each local device uses its local data to validate accuracies of the local models and sends a validation result to a group leader or the server. The group leader or the server selects groups whose variances of the accuracies are not below a predetermined variance threshold. In each selected group, the group leader or the server compares an accuracy of each local model with an average value of the accuracies and randomly selects one or more local models whose accuracies do not exceed a predetermined accuracy threshold. The server obtains weight parameters of selected local models and updates the global model based on the weight parameters.

Abstract: Utilizing a trained generative adversarial network (GAN) model to cause a computer to output multivariate forecasted time-series data by providing a trained GAN model, the GAN model comprising dilated convolutional layers for receiving time-series multivariate data, receiving time-series multivariable biometric data, generating, using the GAN model, successive time series multivariate biometric data according to the time-series multivariate biometric data, determining an outcome according to the successive time-series multivariate biometric data, and providing an output associated with the outcome.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may manage, by a first computing system, a plurality of mobile objects moving within a geographic space. Managing the plurality of mobile objects may assisting with movement of the plurality of mobile objects. The embodiment may determine whether a first mobile object among the plurality of mobile objects is a real mobile object based on a first sensor information received from the first mobile object. The embodiment may use information received from the first mobile object in managing the plurality of mobile objects moving within the geographic space based on determining that the first mobile object is the real mobile object.

Abstract: Utilizing a trained generative adversarial network (GAN) model to cause a computer to output multivariate forecasted time-series data by providing a trained GAN model, the GAN model comprising dilated convolutional layers for receiving time-series multivariate data, receiving time-series multivariable biometric data, generating, using the GAN model, successive time series multivariate biometric data according to the time-series multivariate biometric data, determining an outcome according to the successive time-series multivariate biometric data, and providing an output associated with the outcome.

Abstract: A processor may generate, based on a predicted route of a user, a timeslot for presenting information to the user. The predicted route may be associated with a route segment, and the timeslot may be associated with the route segment. The processor may match one or more proposals to the timeslot associated with the route segment. The one or more proposals may be matched, at least in part, based on proposal criteria set by a proposal criteria user associated with the proposal. The processor may predict an acceptance potentiality of the user. The processor may select a subset of the one or more proposals. The processor may provide the selected subset of the one or more proposals. The selected subset may be provided, at least in part, based on the acceptance potentiality.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may identify, by an event agent (EA), an event occurring in a geographic space in which a plurality of mobile objects move. The embodiment may include determining the event is an expected event based on predicting time-series changes of the event handled by the EA. The embodiment may manage the one mobile object based on the expected event.

Abstract: Position data may gradually pseudonymized by a method, comprising: generating a sequence of relative positions from a sequence of absolute positions of a moving object; randomizing the sequence of relative positions using at least a sequence of random numbers generated from at least one seed; in response to receiving an analytical job comprising the at least one seed, restoring the sequence of relative positions from the randomized sequence of relative positions; and in response to receiving an analytical job comprising both the at least one seed and at least one absolute position derived from the sequence of absolute positions, restoring the sequence of absolute positions from the randomized sequence of relative positions.

Abstract: Provided is a method, computer program product, and system for generating extended rules from common rules for vehicles. A processor may receive real-time data from one or more internet of things (IoT) devices that are communicatively coupled to a vehicle. The processor may analyze the real-time data by applying one or more event detection rules. The processor may determine that the one or more event detection rules have not been met. The processor may extract contextual data from the real-time data. The processor may correlate the contextual data with the one or more event detection rules. The processor may generate, in response to the correlating, one or more extended rules incorporating the contextual data. The processor may apply the one or more generated extended rules to the real-time data.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for detecting anomalous vehicle behavior. It is determined whether a plurality of received car probes include one or more indicators of unusual behavior. An object agent corresponding to a vehicle having a car probe that includes an indicator of unusual behavior is selected. A search is performed to select one or more vehicles surrounding the vehicle having a car probe that includes an indicator of unusual behavior. An information entropy is determined from received car probes, for the vehicle having a car probe that includes an indicator of unusual behavior and for each of the selected surrounding vehicles. An anomalous point value is calculated for each vehicle, based on the determined information entropies. In response to determining that the anomalous point value for a car exceeds a pre-determined threshold, the vehicle is flagged as a vehicle exhibiting anomalous behavior.

Abstract: Position data may gradually pseudonymized by a method, comprising: generating a sequence of relative positions from a sequence of absolute positions of a moving object; randomizing the sequence of relative positions using at least a sequence of random numbers generated from at least one seed; in response to receiving an analytical job comprising the at least one seed, restoring the sequence of relative positions from the randomized sequence of relative positions; and in response to receiving an analytical job comprising both the at least one seed and at least one absolute position derived from the sequence of absolute positions, restoring the sequence of absolute positions from the randomized sequence of relative positions.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may identify, by an event agent (EA), an event occurring in a geographic space in which a plurality of mobile objects move. The embodiment may include determining the event is an expected event based on predicting time-series changes of the event handled by the EA. The embodiment may manage the one mobile object based on the expected event.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may manage, by a first computing system, a plurality of mobile objects moving within a geographic space. Managing the plurality of mobile objects may assisting with movement of the plurality of mobile objects. The embodiment may determine whether a first mobile object among the plurality of mobile objects is a real mobile object based on a first sensor information received from the first mobile object. The embodiment may use information received from the first mobile object in managing the plurality of mobile objects moving within the geographic space based on determining that the first mobile object is the real mobile object.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may identify, by an event agent (EA), an event occurring in a geographic space in which a plurality of mobile objects move. The embodiment may include determining the event is an expected event based on predicting time-series changes of the event handled by the EA. The embodiment may manage the one mobile object based on the expected event.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may identify, by an event agent (EA), an event occurring in a geographic space in which a plurality of mobile objects move. The embodiment may determine the event is an expected event based on predicting time-series changes of the event handled by the EA. The embodiment may manage, by a predictive environment agent (PEA), the expected event.

Abstract: Position data may gradually pseudonymized by a method, comprising: generating a sequence of relative positions from a sequence of absolute positions of a moving object; randomizing the sequence of relative positions using at least a sequence of random numbers generated from at least one seed; in response to receiving an analytical job comprising the at least one seed, restoring the sequence of relative positions from the randomized sequence of relative positions; and in response to receiving an analytical job comprising both the at least one seed and at least one absolute position derived from the sequence of absolute positions, restoring the sequence of absolute positions from the randomized sequence of relative positions.

Abstract: Position data may gradually pseudonymized by a method, comprising: generating a sequence of relative positions from a sequence of absolute positions of a moving object; randomizing the sequence of relative positions using at least a sequence of random numbers generated from at least one seed; in response to receiving an analytical job comprising the at least one seed, restoring the sequence of relative positions from the randomized sequence of relative positions; and in response to receiving an analytical job comprising both the at least one seed and at least one absolute position derived from the sequence of absolute positions, restoring the sequence of absolute positions from the randomized sequence of relative positions.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for managing mobile objects. The embodiment may manage, by a first computing system, a plurality of mobile objects moving within a geographic space. Managing the plurality of mobile objects may assisting with movement of the plurality of mobile objects. The embodiment may determine whether a first mobile object among the plurality of mobile objects is a real mobile object based on a first sensor information received from the first mobile object. The embodiment may use information received from the first mobile object in managing the plurality of mobile objects moving within the geographic space based on determining that the first mobile object is the real mobile object.