Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.

Abstract: A method for generating human-machine hybrid predictions of answers to forecasting problems includes: parsing text of an individual forecasting problem to identify keywords; generating machine models based on the keywords; scraping data sources based on the keywords to collect scraped data relevant to the individual forecasting problem; providing the scraped data to the machine models; receiving machine predictions of answers to the individual forecasting problem from the machine models based on the scraped data; providing, by the computer system via a user interface, the scraped data to human participants; receiving, by the computer system via the user interface, human predictions of answers to the individual forecasting problem from the human participants; aggregating the machine predictions with the human predictions to generate aggregated predictions; and generating and outputting a hybrid prediction based on the aggregated predictions.

Abstract: An apparatus for detecting a fault state of an aircraft is provided. The apparatus accesses a training set of flight data for the aircraft. The training set includes observations of the flight data, each observation of the flight data includes measurements of properties selected and transformed into a set of features. The apparatus builds a generative adversarial network including a generative model and a discriminative model using the training set and the set of features, and builds an anomaly detection model to predict the fault state of the aircraft. The anomaly detection model is trained using the training set of flight data, simulated flight data generated by the generative model, and a subset of features from the set of features. The apparatus deploys the anomaly detection model to predict the fault state of the aircraft using additional observations of the flight data.

Abstract: A method includes obtaining sensor data captured by a sensor of an aircraft during a power up event. The sensor data includes multiple parameter values, each corresponding to a sample period. The method further includes determining a set of delta values, each indicating a difference between parameter values for consecutive sample periods of the sensor data. The method further includes determining a set of quantized delta values by assigning the delta values to quantization bins based on magnitudes of the delta values. The method further includes determining a normalized count of delta values for each quantization bin. The method further includes comparing the normalized counts of delta values to anomaly detection thresholds. The method further includes generating, based on the comparisons, output indicating whether the sensor data is indicative of an operational anomaly.

Abstract: A method for computing a human-machine hybrid ensemble prediction includes: receiving an individual forecasting question (IFP); classifying the IFP into one of a plurality of canonical question topics; identifying machine models associated with the canonical question topic; for each of the machine models: receiving, from one of a plurality of human participants: a first task input including a selection of sets of training data; a second task input including selections of portions of the selected sets of training data; and a third task input including model parameters to configure the machine model; training the machine model in accordance with the first, second, and third task inputs; and computing a machine model forecast based on the trained machine model; computing an aggregated forecast from machine model forecasts computed by the machine models; and sending an alert in response to determining that the aggregated forecast satisfies a threshold condition.

Abstract: Systems and methods for fault prediction through a Bayesian framework are provided. Fault prediction for a valve system may be provided by generating a Bayesian framework by collecting a plurality of historical parameters related to opening and closing of a valve across a plurality of operational legs; generating a plurality of historical feature metrics based on the plurality of historical parameters; in response to detecting a fault, defining a prefault state corresponding to the historical feature metrics; monitoring a plurality of operational parameters related to opening and closing of the valve during a given operational phase of an operational leg; generating a plurality of operational feature metrics based on the plurality of operational parameters monitored during the given operational phase; and in response to determining, using the generated Bayesian framework, that the operational feature metrics indicate the prefault state of the subsystem, generating a notification.

Abstract: A method for remaining useful life prediction includes generating parameter data related to a performance of an electro-mechanical element. The method includes generating simulated behavior data of the electro-mechanical element by executing a digital-twin simulation model based on estimated operating conditions, and generating deviation data that characterizes how the parameter data deviates from the simulated behavior data. The deviation data includes a deterministic component and a stochastic component. The method includes generating extrapolated deviation data by extrapolating the deterministic component and the stochastic component of the deviation data forward in time, calculating a remaining useful life of the electro-mechanical element in response to the extrapolated deviation data, and reporting the remaining useful life to a person associated with the vehicle.

Abstract: A method, apparatus, system, and computer program product for managing a platform. Sensor information for a platform health of the platform is received from a sensor system for the platform. The sensor information for the platform health of the platform is sent by a computer system into a machine learning model trained using historical sensor information indicating a historical platform health and historical context information corresponding to the historical sensor information in which the historical context information is for a set of operating conditions. A remaining useful life of a component in the platform is received by the computer system from the machine learning model.

Abstract: Described is a system for large-scale event prediction and a corresponding response. The system, using an agent-based model, predicts how many users (agent accounts) on a social media platform will become activists related to a large-scale event. This process is accomplished using both Before and During models. Before the large-scale event, the system operates to generate agent attributes and a posting network based on posts on the social media platform. During the large-scale event and based on the agent attributes and posting network, the system determines if a social media user (agent account) will become an activist of the large-scale event and a corresponding magnitude of the large-scale event. Depending on the magnitude, the system can implement a responsive measure and control a device based on the prediction of the activists.

Abstract: A method includes obtaining a plurality of data sets, where each data set of the plurality of data sets includes multivariate time series data for a respective sample period of a plurality of sample periods. The method also includes, for each data set of the plurality of data sets, determining recurrence data indicative of recurrent states in the data set and determining, based on the recurrence data, determinism values of a determinism metric and laminarity values of a laminarity metric. The method further includes determining that a particular data set of the plurality of data sets includes data representing an anomalous state based on a determinism-laminarity curve representing the particular data set, where the determinism-laminarity curve is based on the determinism values of the particular data set and the laminarity values of the particular data set. The method also includes generating output data indicating the anomalous state.

Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.

Abstract: Described is a system for network threat detection. The system identifies a targeted sub-network representing a threat within a multi-layer network having members. The targeted sub-network is identified with differential privacy protection, such that privacy of individuals that are not in the targeted sub-network is protected. The system causes an action to be generated, the action being one of generating an alert of a threat, initiating monitoring of the non-benign persons, or disabling network access of the non-benign persons.

Abstract: A system and method for drift detection is disclosed. The method may comprise training and testing an autoencoder, and using the trained and tested autoencoder to automatically detect data drift. The training may include initializing the autoencoder and training the autoencoder based on a first set of sensor data. The testing of the autoencoder with a second set of sensor data may comprise: for an empirical distribution of the reconstruction errors of the second set of sensor data, determining a value of a reconstruction error at the percentile threshold; determining that data drift is not present when the reconstruction error of the second set of sensor data is less than a threshold; and calculating a deviation output for at least one of the one or more sensors. Using the trained and tested autoencoder to automatically detect data drift in sensor data.

Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.

Abstract: A testing system, a testing method, and a training method for the testing system are disclosed. According to an example, a computing system of the testing system processes a set of data streams of test data for a test subject in combination with a previously trained nominal model by, for each parameter of the test subject: selecting a parameter-specific control band defined by the nominal model for the parameter; comparing a time-based series of measurements of the test data for the parameter to the parameter-specific control band for the parameter, and selectively generating a test result for the parameter responsive to whether a condition is satisfied with respect to any of the time-based series of measurements exceeding the parameter-specific control band for the parameter.

Abstract: An apparatus includes memory to store computer-readable program code for a knowledge-based system including an inference engine and a knowledge base, and processing circuitry configured to access the memory, and execute the code. The apparatus is caused to at least receive a time series of measurements of operating conditions of a machine recorded during an operation of the machine. The apparatus is also caused to cluster the time series of measurements into one or more respective clusters and identify a pattern across the clusters. The apparatus is also caused to define a current state of the machine that includes the pattern across the clusters, access and search a knowledge base for a historical case describing a respective solution to a historical problem state similar to the current state, the respective solution including a repair action, and generate an output display indicating the repair action to address the current state.

Abstract: Described is a system for identification of correlations in customer observables (COs). The system extracts key phrases representing COs from textual inputs from multiple data sources, wherein the COs are related to a consumer product. A unified hypergraph is constructed that models co-occurrences of COs. The unified hypergraph includes nodes and types of hyperedges connecting the nodes, where COs are represented by nodes and data sources are represented by different types of hyperedges. Each node of the unified hypergraph is embedded into a latent feature space. The unified hypergraph is partitioned into clusters within the latent feature space, where each cluster contains correlated CO data. The correlated CO data from a cluster are used to generate and provide targeted messages specific to the consumer product to a display device.

Abstract: A method for collecting and processing user input. In some embodiments the method includes presenting a first user with a prompt for eliciting a first response, the first response including a numerical portion including one or more numbers, and an explanatory portion; receiving, from the first user, the first response; receiving from each of a plurality of other users, a respective response of a plurality of other responses; and displaying, to the first user, an ordered list of other responses. Within the ordered list, a second response, of the plurality of other responses, may be earlier than a third response, of the plurality of other responses, the second response being, according to a measure of distance, more distant, than the third response, from the first response.

Abstract: Described is a system for activity and behavior detection in a target system. Raw data extracted from various heterogeneous sources of the target system is fused across spatial and temporal scales into a multi-graph representation. Information flows of the multi-graph representation are analyzed using a set of multi-layer information dynamic measures. Based on the set of multi-layer information dynamic measures, at least one of an economic and social indicator of emerging activity of interest in the target system is derived. The indicator is then used for prediction of future activity of interest in the target system.

Abstract: Described is a system for selecting measurement nodes in a distributed physical system of agents. In operation, the distributed physical system is represented as a multi-layer network having a communication layer and an agent layer. The communication layer represents the amount of collective communication activities between any pair of areas and the agent layer represents movement of agents within the distributed physical system such that the communication layer and agent layer collectively generate network dynamics. The network dynamics are modeled as hybrid partial differential equations (PDEs) with measurable interconnected states in the communication layer. Notably, placement of a minimum set of measurement nodes is determined within the distributed physical system to provide full-state observability of the distributed physical system. The system can then track the full system state and apply compensation to one or more agents in the distributed physical system based on tracking the full system state.