Abstract: The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.

Abstract: This disclosure involves detecting biases in predictive models and the root cause of those biases. For example, a processing device receives test data and training data from a client device. The processing device identifies feature groups from the training data and the test data generates performance metrics and baseline metrics for a feature group. The processing device detects biases through a comparison of the performance metrics and the baseline metrics the feature group. The processing device then isolates a portion of the training data that corresponds to the detected bias. The processing device generates a model correction usable to remove the bias from the predictive model.

Abstract: The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.

Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for generating lookalike segments corresponding to a target segment using decision trees and providing a graphical user interface comprising nodes representing such lookalike segments. Upon receiving an indication of a target segment, for instance, the disclosed systems can generate a lookalike segment from a set of users by partitioning the set of users according to one or more dimensions based on probabilities of subsets of users matching the target segment. By partitioning subsets of users within a node tree, the disclosed systems can identify different subsets of users partitioned according to different dimensions from the set of users. The disclosed systems can further provide a node tree interface comprising a node for the set of users and nodes for subsets of users within one or more lookalike segments.

Abstract: Disclosed systems and methods predict and visualize outcomes based on past events. For example, an analysis application encodes a sequence of events into a feature vector that includes, for each event, a numerical representation of a respective category and a respective timestamp. The application applies a time-aware recurrent neural network to the feature vector, resulting in one or more of (i) a set of future events in which each event is associated with a probability and a predicted duration and (ii) a sequence embedding that contains information about predicted outcomes and temporal patterns observed in the sequence of events. The application applies a support vector model classifier to the sequence embedding. The support vector model classifier computes a likelihood of a categorical outcome for each of the events in the probability distribution. The application modifies interactive content according to the categorical outcomes and probability distribution.

Abstract: The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.

Abstract: A method of managing digital rights in a network without a central license server where each instance of the program broadcasts its identification into the network, and any instance of the program detecting its own license code from another node closes down. A closing program may also generate a close down message to the conflicting program.

Abstract: Nodes in a mesh network are configured to access centralized Internet Protocol (IP) services whenever the Internet infrastructure is accessible and then dynamically switch to providing the IP services locally in the mesh network when the Internet infrastructure is not accessible and operate through collaborative cooperation. In one embodiment, a Reported Node Time Server (RNTS) is elected when the mesh network is disconnected. In another embodiment, a Mesh Network Name Cache (MNNC) protocol pre-provisions each mesh node with the names and addresses of all other mesh nodes. In another embodiment, a Mesh Address Allocation Protocol (MAAP) provides DHCP services for DHCP clients in the mesh network.

Abstract: In one embodiment, a dynamic rate control scheme controls transmission rates and adaptively filters out video packets when a packet queue is full. This allows video streams to be more efficiently transmitted through low bandwidth and dynamically changing links.

Abstract: In one embodiment, a dynamic rate control scheme controls transmission rates and adaptively filters out video packets when a packet queue is full. This allows video streams to be more efficiently transmitted through low bandwidth and dynamically changing links.

Abstract: A method of managing digital rights in a network without a central license server where each instance of the program broadcasts its identification into the network, and any instance of the program detecting its own license code from another node closes down. A closing program may also generate a close down message to the conflicting program.

Abstract: Nodes in a mesh network are configured to access centralized Internet Protocol (IP) services whenever the Internet infrastructure is accessible and then dynamically switch to providing the IP services locally in the mesh network when the Internet infrastructure is not accessible and operate through collaborative cooperation. In one embodiment, a Reported Node Time Server (RNTS) is elected when the mesh network is disconnected. In another embodiment, a Mesh Network Name Cache (MNNC) protocol pre-provisions each mesh node with the names and addresses of all other mesh nodes. In another embodiment, a Mesh Address Allocation Protocol (MAAP) provides DHCP services for DHCP clients in the mesh network.