Abstract: A server computer is connected to a plurality of client computers through a network, and controls objects in a Metaverse accessed by the client computers. The server computer includes a storage unit for storing an object ID specifying an object accessible in the Metaverse by the plurality of client computers and authenticity information associated with the object ID. The authenticity information indicates that the object is genuine. The server computer also includes a communication unit for communicating with each of the client computers. The server computer also includes an enquiry unit for causing the communication unit to transmit the authenticity information corresponding to the object ID to at least one of the plurality of client computers upon receipt of an enquiry request to enquire about the object ID of the object from one of the plurality of client computers.

Abstract: Aspects of the invention include systems and methods configured to extract enriched target-oriented common sense from grounded graphs to support efficient next step decision making of an autonomous agent. A non-limiting example computer-implemented method includes extracting common sense from a source. The extracted common sense can include a first knowledge graph. An environment state can be extracted from an observation. The extracted environment state can include a second knowledge graph. The second knowledge graph can include an interactive object and a state of the interactive object. A difference graph including the extracted common sense and the extracted environment state can be generated. A next action is selected based on the difference graph and the next action is taken by an autonomous agent.

Abstract: Embodiments of the invention describe a computer-implemented method of detecting anomalous data associated with a system-under-analysis. The computer-implemented method includes using a first encoder stage of a neural network to generate content-irrelevant latent code from input data. A second encoder stage of the neural network is used to generate domain-irrelevant latent code from the input data. A decoder stage of the neural network is used to generate reconstructed input data. The reconstructed input data includes a reconstruction of the input data based at least in part on the content-irrelevant latent code and the domain-irrelevant latent code. A reconstruction loss is generated based at least in part on the reconstructed input data. The reconstruction loss is used to determine that the input data includes an anomalous data candidate.

Abstract: Aspects of the present invention disclose a method for a distance-based vector classification in anomaly detection. The method includes one or more processors identifying one or more audio communications from a first user to a second user, wherein the one or more audio communications is transmitted utilizing a first computing device. The method further includes determining an objective of the first user based at least in part on the audio communication of the first user. The method further includes determining a set of conditions corresponding to the one or more audio communications and the objective, wherein the set of conditions indicate a vulnerability of personal data of the first user. The method further includes prohibiting the first computing device from transmitting audio data that includes the personal data of the first user.

Abstract: A computer-implemented method is provided for redundancy reduction for driving test scenarios. The method includes receiving an original test set of driving scenarios and a driving model which simulates a vehicle behavior under a driving scenario inputted to the driving model. The method includes, for each driving scenario of the original test set, obtaining vehicle dynamics timeseries data as an output of the driving model. The method includes determining similar driving scenarios by comparing driving model outputs. The method additionally includes creating a new test set of driving scenarios by discarding duplicated ones of the similar driving scenarios from the original test set.

Abstract: In a method for training temporal precipitation interpolation models, the method may include receiving an initial image, a first intermediate image, and a final image, computing a first preliminary forward optical flow vector field from the initial image, and a first preliminary backward optical flow vector field, computing a first refined forward optical flow vector field and a first refined backward optical flow vector field using a terrain factor, among other things, and computing backpropagation losses to train neural networks by comparing the first intermediate image to an interpolated frame calculated using the first refined forward optical flow vector field and the first refined backward optical flow vector field.

Abstract: A computer-implemented method for reinforcement learning with Logical Neural Networks (LNNs) is provided including receiving a plurality of observation text sentences from a target environment, extracting one or more propositional logic values from the plurality of observation text sentences, finding a class for each propositional logic value by using external knowledge, converting each propositional logic value into a first-order logic by replacing a part in the propositional logic value with a variable word, the part indicating the class, selecting a LNN based on the class among LNNs prepared in advance for each class, each LNN receiving the one or more propositional logic values as a status input and outputting an action with a score indicating a degree of preference for taking the action, and performing a highest score action to the target environment to obtain a next state of the target environment and a reward for the highest score action.

Abstract: A method is provided for driving model calibration. The method clusters a plurality of vehicle trajectories into a plurality of datasets for different driving styles based on a score. The score is calculated for each vehicle trajectory by an objective entropy weight method. The method trains, for each of the plurality of datasets for the different driving styles relative to an existing target driving model, a respective neural network which inputs a respective one of the plurality of datasets and outputs a respective parameter for the existing target driver model to obtain a plurality of trained neural networks. The existing target driver model is for simulating human driving behaviors. The method performs, for each trained neural network, an online adaptation of the existing target driving model based on a respective output of each of the plurality of trained neural networks to obtain a plurality of adapted driver models.

Abstract: A computer-implemented method is provided for redundancy reduction for driving test scenarios. The method includes receiving an original test set of driving scenarios and a driving model which simulates a vehicle behavior under a driving scenario inputted to the driving model. The method includes, for each driving scenario of the original test set, obtaining vehicle dynamics timeseries data as an output of the driving model. The method includes determining similar driving scenarios by comparing driving model outputs. The method additionally includes creating a new test set of driving scenarios by discarding duplicated ones of the similar driving scenarios from the original test set.

Abstract: In an approach, a processor trains a model, via a reinforcement learning process, to produce a first action function for relating states of a natural language based response environment to actions applicable to the natural language based response environment.

Abstract: A method is provided for detecting a higher-level action from one or more trajectories of real states. The trajectories are based on an experts' action demonstration. The method trains predictors to predict future states. Each predictor has a different duration of the higher-level action to be detected. The method predicts, using the predictors, the future states using past ones of the real states in the one or more trajectories as inputs for the predictors. The method determines if a match exists between any of the future states relative to a real future state with a corresponding same duration from the one or more trajectories. The method outputs a pair that includes the matching one of the future states as a prediction input and the real future state with the corresponding same duration from the one or more trajectories as the higher-level action corresponding thereto, responsive to the match existing.

Abstract: Methods and computer program products for training a neural network perform multiple forms of data augmentation on sample waveforms of a training dataset that includes both normal and abnormal samples to generate normal data augmentation samples and abnormal data augmentation samples. The normal data augmentation samples are labeled according to a type of data augmentation that was performed on each respective normal data augmentation sample. The abnormal data augmentation samples are labeled according to a type of data augmentation other than that which was performed on each respective abnormal data augmentation sample. A neural network model is trained to identify a form of data augmentation that has been performed on a waveform using the normal data augmentation samples and the abnormal data augmentation samples.

Abstract: A method for inferring an operator including a precondition and an effect of the operator for a planning problem is disclosed. In the method, a set of examples, each of which includes a base state, an action and a next state after performing the action in the base state is prepared. In the method, variable lifting is performed in relation to the set of examples. In the method, a validity label is computed for each example in the set of examples. In the method, a model is trained by using the set of examples with the validity label so that the model is configured to receive an input state and a representation of an input action and output at least validity of the input action for the input state. In the method, the precondition of the operator based on the model and the effect of the operator are outputted.

Abstract: Methods, systems, and computer program products for detecting anomalous behavior include reconstructing a waveform of a target device from an input waveform using a target autoencoder. A waveform of unrelated sound events is reconstructed from the input waveform using an environmental autoencoder. The input waveform is classified to determine that the input waveform is produced by anomalous behavior of the target device using a classifier, based on the reconstructed waveform of the target device and the reconstructed waveform of the unrelated sound events. An automatic response to the anomalous behavior is generated.

Abstract: Methods, systems, and computer program products for detecting anomalous behavior include reconstructing a waveform of a target device from an input waveform using a target autoencoder. A waveform of unrelated sound events is reconstructed from the input waveform using an environmental autoencoder. The input waveform is classified to determine that the input waveform is produced by anomalous behavior of the target device using a classifier, based on the reconstructed waveform of the target device and the reconstructed waveform of the unrelated sound events. An automatic response to the anomalous behavior is generated.

Abstract: A visual localization support system is provided. The visual localization support system includes one or more guidance indicators place on a road surface of a roadway, wherein the one or more guidance indicators each include a matrix barcode that uniquely identifies a location by latitude, longitude, and altitude, and describes an affine shape of the guidance indicator.

Abstract: Methods and systems for training a semantic parser includes performing an automated intervention action in a text-based environment. An inverse action is performed in the text-based environment to reverse the intervention action. States of the text-based environment are recorded before and after the intervention action and the inverse action. The recorded states are evaluated to generate training data. A semantic parser neural network model is trained using the training data.

Abstract: A method for action pruning in Reinforcement Learning receives a current state of an environment. The method evaluates, using a Logical Neural Network (LNN) structure, a logical inference based on the current state. The method outputs upper and lower bounds on each action from a set of possible actions of an agent in the environment, responsive to an evaluation of the logical inference. The method calculates, for each pair of a possible action of the agent in the environment and the current state, a probability by using the upper and lower bounds. Each of calculated probabilities indicates a respective priority ratio for the each action. The method obtains a policy in Reinforcement Learning for the current state by using the calculated probabilities. The method prunes one or more actions from the set of actions as being in violation of the policy such that the one or more actions are ignored.

Abstract: A method for safe reinforcement learning receives an action and a current state of an environment. The method evaluates, using a Logical Neural Network (LNN) structure, an action safetyness logical inference based on the current state of an environment and a current action candidate from an agent. The method outputs upper and lower bounds on the action, responsive to an evaluation of the action safetyness logical inference. The method calculates a contradiction value for the action by using the upper and lower bounds. The contradiction value indicates a level of contradiction for each of a plurality of logic rules implemented by the LNN structure. The method evaluates the action L with respect to safetyness based on the contradiction value. The method selectively performs the action responsive to an evaluation of the action indicating that the action is safe to perform based on the contradiction value exceeding a safetyness threshold.

Abstract: Methods and systems for anomaly detection include training a neural network model to identify a form of data augmentation that has been performed on a waveform. Multiple forms of data augmentation are performed on a sample waveform to generate data augmentation samples. The data augmentation samples are classified with the neural network model. An anomaly score is determined based on the classification of the data augmentation samples.