Systems, devices, computer-implemented methods, and/or computer program products that facilitate event schema induction from unstructured or semi-structured data. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a schema component and a retrieval component. The schema component can derive an event schema for a document corpus using parsing results obtained from the document corpus. The retrieval component can populate a response to a query with a document of the document corpus using events extracted from the query and the document using the event schema.

A method for reducing parking violations includes: searching for an empty parking spot in an area surrounding a vehicle; receiving, by a controller of the vehicle, parking restriction information in the area surrounding the vehicle, wherein the controller receives the parking restriction information from sensors of the vehicle; determining, by the controller of the vehicle, that the empty parking spot is invalid; and activating, by the controller of the vehicle, an alarm to alert a vehicle operator of the vehicle that the empty parking spot is invalid.

A domain-general artificial intelligence platform or system and methods that enable data-informed decision making for anyone without the need for any coding ability are disclosed. This artificial intelligence platform has domain-generality, interoperability across heterogeneous sources of data, and controllability by tracking provenance. The artificial intelligence platform works by receiving a natural language query, converts the natural language query into executable code grounded in the deep semantic understanding of the underlying data, using a natural language artificial intelligence engine, runs the executable code on a distributed runtime engine to generate data output, and augments the data with a generated natural language report which becomes the ultimate output to the user.

Embodiments of the present disclosure relate to a method, an electronic device, a storage medium and a program product for sample analysis. The method comprises: obtaining a sample set, the sample set being associated with annotation data; processing the sample set with a target model to determine prediction data for the sample set and confidence of the prediction data; determining accuracy of the target model based on a comparison between the prediction data and the annotation data; and determining a candidate sample which is potentially inaccurately annotated from the sample set based on the accuracy and the confidence. In this way, a potential inaccurately annotated sample may be efficiently screened out.

A system for navigating a vehicle may include a processor programmed to receive an output provided by a vehicle sensor, and determine a navigational maneuver for the vehicle along a road segment based on the output provided by the vehicle sensor. The processor may also be programmed to determine a yaw rate command and a speed command for implementing the navigational maneuver. The processor may also be programmed to determine a first vehicle steering angle based on the yaw rate and speed commands using a first control subsystem, and determine a second vehicle steering angle based on the yaw rate and speed commands using a second control subsystem. The processor may further be programmed to determine an overall steering command for the vehicle based on a combination of the first and second steering angles, and cause an actuator associated with the vehicle to implement the overall steering command.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

A controller for controlling a motion of at least one device subject to constraints on the motion, is disclosed. The controller comprises a processor and a memory, where the controller inputs parameters of the task including the state of the at least one device to a neural network trained to output an estimated motion trajectory for performing the task. Further, the controller extracts at least some of the integer values of a solution to a mixed-integer optimization problem for planning an execution of the task that results in the estimated motion trajectory. Further, the controller solves the mixed-integer optimization problem for the parameters of the task with corresponding integer values fixed to the extracted integer values to produce an optimized motion trajectory subject to the constraint and changes the state of the at least one device to track the optimized motion trajectory.

Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can, in response to receiving parameters associated with a problem, train at least one generated data model to evaluate an estimation of a solution for the problem. Embodiments of the present invention can then generate an uncertainty quantification measure associated with an estimation of error for the at least one generated data model. Embodiments of the present invention can then filter data based on the generated uncertainty quantification measure associated with the at least one generated data model and automatically retrain the at least one generated data model using the remaining data from the filtered data

A data processing method includes obtaining first data and second data, where the first data and the second data are adjacent sequence data, and a sequence of the first data is prior to a sequence of the second data; padding third data between the first data and the second data according to a preset rule to obtain fourth data, where the third data isolates the first data from the second data; and completing data processing on the fourth data using a convolutional neural network.

A data processing method includes obtaining first data and second data, where the first data and the second data are adjacent sequence data, and a sequence of the first data is prior to a sequence of the second data; padding third data between the first data and the second data according to a preset rule to obtain fourth data, where the third data isolates the first data from the second data; and completing data processing on the fourth data using a convolutional neural network.