Abstract: Described is a system for human-machine teaching for vehicle operation. The system determines currently enabled status reporting modes on a vehicle interface of a vehicle. The currently enabled status reporting modes are compared to a set of preferred status reporting modes of previous users. Based on the comparison, a status reporting mode is selected. A current operational status of the vehicle is reported to a current user, via the vehicle interface, using the selected status reporting mode. The system then determines preferred solutions of previous users to address the current operational status of the vehicle. Suggestions to address the current operational status of the vehicle based on the preferred solutions are reported to the user via the vehicle interface. A vehicle action corresponding to a solution selected by the current user is implemented via a vehicle component.

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: 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: 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: 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: A method and apparatus for maintaining a vehicle, such as an aircraft. A plurality of maintenance messages generated during operation of the vehicle are stored to form a plurality of stored maintenance messages. The stored maintenance messages are filtered to remove from the stored maintenance messages those maintenance messages that are correlated to minimum equipment list actions to form filtered stored maintenance messages. A predicted maintenance message is generated from the filtered stored maintenance messages by applying a machine learning algorithm to the filtered stored maintenance messages. The predicted maintenance message may be used to perform a maintenance operation on the vehicle.

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: 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: A marine propeller includes a plurality of blades attached to a hub, with a disk area ratio of approximately 50%, a blade area ratio of approximately 61%, a blade rake angle of approximately 26.5 degrees, a blade skew angle of approximately 0 degrees, and wherein the angle between the chord line at any given radius r on said blades with a line that is parallel to the propeller axis of rotation and intersects the chord line, is equal to tan?1(2?Nr/Vo)??, where N is the rotational speed of the propeller at a selected design condition, Vo is the speed of the water entering the propeller (i.e.

Abstract: A method and apparatus for maintaining an aircraft. Real-time event information indicating faults in systems on the aircraft and aircraft condition monitoring system data indicating conditions of the systems on the aircraft are stored during a plurality of legs of flights of the aircraft. A feature table comprising the real-time event information and the aircraft condition monitoring system data is built. Feature vectors are extracted from the feature table. A machine learning algorithm is applied to the extracted feature vectors to generate a predicted maintenance event message that identifies a predicted maintenance event. The predicted maintenance event message is used to perform a maintenance operation on the aircraft.

Abstract: The present invention relates to a surveillance system and, more particularly, to a graphical display and user interface system that provides high-speed triage of potential items of interest in imagery. The system receives at least one image of a scene from a sensor. The image is pre-processed to identify a plurality of potential objects of interest (OI) in the image. The potential OI are presented to the user as a series of chips on a threat chip display (TCD), where each chip is a region extracted from the image that corresponds to a potential OI. Finally, the system allows the user to designate, via the TCD, any one of the chips as an actual OI.

Abstract: Described is a system for identifying communication behavior patterns in communication activity time series. For each pair of variables in the communication activity time series, the system determines a transfer entropy measure, an effective transfer entropy measure from a randomly reordered version of the communication activity time series, and a partial effective transfer entropy measure. A dependency matrix is generated using pair-wised effective transfer entropy measures and partial effective transfer entropy measures, where each element in the matrix represents a total influence of a communication activity time series on another communication activity time series in the future. The dependency matrix is compared with dependency matrices generated from a predefined set of communication patterns to identify the communication behavior pattern.

Abstract: A method and apparatus for maintaining an aircraft. Real-time event information indicating faults in systems on the aircraft and aircraft condition monitoring system data indicating conditions of the systems on the aircraft are stored during a plurality of legs of flights of the aircraft. A feature table comprising the real-time event information and the aircraft condition monitoring system data is built. Feature vectors are extracted from the feature table. A machine learning algorithm is applied to the extracted feature vectors to generate a predicted maintenance event message that identifies a predicted maintenance event. The predicted maintenance event message is used to perform a maintenance operation on the aircraft.

Abstract: A method and apparatus for maintaining a vehicle, such as an aircraft. A plurality of maintenance messages generated during operation of the vehicle are stored to form a plurality of stored maintenance messages. The stored maintenance messages are filtered to remove from the stored maintenance messages those maintenance messages that are correlated to minimum equipment list actions to form filtered stored maintenance messages. A predicted maintenance message is generated from the filtered stored maintenance messages by applying a machine learning algorithm to the filtered stored maintenance messages. The predicted maintenance message may be used to perform a maintenance operation on the vehicle.

Abstract: Described is a system for determining the current state of a drill using downhole sensors. The system includes a sensor suite mounted on a drill string proximate a drill bit and a computer mounted on the drill string proximate the sensor suite. The computer includes a trained classifier and is operable for performing operations of receiving online sensor data from the sensor suite; and classifying the drill bit as being in one of a plurality of pre-trained drill states based on the online sensor data. A drill bit controller can then be used to modify the operation of the drill bit based on the drill state classification.

Abstract: Described is a system for collision detection. The system divides an image in a sequence of images into multiple sub-fields comprising complementary visual sub-fields. For each visual sub-field, motion is detected in a direction corresponding to the visual sub-field using a spiking Reichardt detector with a spiking neural network. Motion in a direction complementary to the visual sub-field is also detected using the spiking Reichardt detector. Outputs of the spiking Reichardt detector, comprising data corresponding to one direction of movement from two complementary visual sub-fields, are processed using a movement detector. Based on the output of the movement detector, an impending collision is signaled.

Abstract: Described is system for surveillance that integrates radar with a panoramic staring sensor. The system captures image frames of a field-of-view of a scene using a multi-camera panoramic staring sensor. The field-of-view is scanned with a radar sensor to detect an object of interest. A radar detection is received when the radar sensor detects the object of interest. A radar message indicating the presence of the object of interest is generated. Each image frame is marked with a timestamp. The image frames are stored in a frame storage database. The set of radar-based coordinates from the radar message is converted into a set of multi-camera panoramic sensor coordinates. A video clip comprising a sequence of image frames corresponding in time to the radar message is created. Finally, the video clip is displayed, showing the object of interest.

Abstract: A marine propeller includes a plurality of blades attached to a hub, with a disk area ratio of approximately 50%, a blade area ratio of approximately 61%, a blade rake angle of approximately 26.5 degrees, a blade skew angle of approximately 0 degrees, and wherein the angle between the chord line at any given radius r on said blades with a line that is parallel to the propeller axis of rotation and intersects the chord line, is equal to tan?1(2?Nr/Vo)??, where N is the rotational speed of the propeller at a selected design condition, Vo is the speed of the water entering the propeller (i.e.

Abstract: Described is a system for detecting objects of interest in imagery. The system is configured to receive an input video and generate an attention map. The attention map represents features found in the input video that represent potential objects-of-interest (OI). An eye-fixation map is generated based on a subject's eye fixations. The eye-fixation map also represents features found in the input video that are potential OI. A brain-enhanced synergistic attention map is generated by fusing the attention map with the eye-fixation map. The potential OI in the brain-enhanced synergistic attention map are scored, with scores that cross a predetermined threshold being used to designate potential OI as actual or final OI.

Abstract: Described is system for optimizing visual inspection of radar targets. The system detects radar hits with a radar system comprising radar and a radar-cued camera. The camera has a current state comprising a current slew position and a current zoom level. The radar hits are stored, and a set of metrics are determined for each radar hit. A track value (TV) metric is determined that combines the set of metrics for each zoom level of the camera. The camera sends a set of commands based on the TV metric, such that the camera slews from the current state to a new state. The new state is a position of a radar hit with the largest TV metric and a corresponding zoom level. Given the new state, an image is captured and processed to generate captured tracks. The current state of the camera is updated to reflect the new state.