Abstract: There is disclosed a computer-implemented method for detecting machine-generated documents in a collection of documents including machine-generated and human-authored documents. The computer-implemented method includes computing a set of long-repeated substrings (such as super-maximal repeats) with respect to the collection of documents and using a subset of the long-repeated substrings to designate documents containing the subset of the repeated substrings as machine-generated. The documents designated as machine-generated serve as positive examples of machine-generated documents and a set of documents including at least one human-authored document serves as negative examples of machine-generated documents. A plurality of classifiers are trained with a dataset including both the positive and negative examples of machine-generated documents. Classified output of the classifiers is then used to detect an extent to which a given document of the dataset is machine-generated.

Abstract: Example embodiments are directed to a robotic painting method performed using a computer system. The computer system comprises at least one processor configured to execute computer-readable instructions included in a memory. The robotic painting method include collecting, by the at least one processor, stroke-level action data for a painting action; and learning, by the at least one processor, a stroke-level robotic painting skill using the stroke-level action data.

Abstract: A method, a computer system, a server, and a non-transitory computer-readable storage medium for providing a search service are provided. The method of providing the search service includes: executing the search service provided through a chatroom associated with a messenger application; and, based on a first search term input for the search service, displaying, on a search entry area, first search results corresponding to the first search term and at least one autocomplete search term of the first search term.

Abstract: Systems and methods are disclosed for determining a policy to recommend transition in a position-representing space for a robotic device using a multi-critic architecture. To learn policy in a multi-critic architecture, a set of critics is defined pertaining to a position-representing space where each critic corresponds to a different objective function such as reach-reward, discovery-reward, and safety-reward. For each one of the critics of the set of critics, a learned value function in position-representing space is determined. The policy is learned based on the weighted feedback of the learned value functions to recommend transitions that are safe in the position-representing space. The multi-critic architecture minimizes interference between multiple reward functions and learns a safe and stable policy for the robotic device.

Abstract: A method for preprocessing a set of feasible transfers within a multimodal transportation network of predetermined stations, comprising, for each trip in the multimodal transportation network hereafter called origin trip: (a) for each station (pti) of the origin trip (t), computing at this station (pti) an earliest arrival/change time associated with all transportation modes (m) of the multimodal transportation network; (b) for at least one transfer of the set of feasible transfers from a station (pti) on the origin trip (t) to a reachable station (puj) on a target trip (u), computing, at each station (puk>j) of the target trip (u) after the reachable station (puj), a value of the earliest arrival/change time specifically associated with the transportation mode (mu) of the multimodal transportation network used by the target trip (u); (c) removing the transfer only if determining that each computed value of the earliest arrival/change time is not improved by the transfer; (d) outputting the set of feasible

Abstract: Provided is a method for providing augmented content, wherein whether a user terminal is located in a preset unit space is determined on the basis of the location of the user terminal, and if the user terminal is determined to be located in the unit space, an image captured by a camera is displayed through an augmented reality (AR) view by being augmented with content associated with the unit space.

Abstract: A method of jointly training of a transferable feature extractor network, an ordinal regressor network, and an order classifier network in an ordinal regression unsupervised domain adaption network by providing a source of labeled source images and unlabeled target images; outputting image representations from a transferable feature extractor network by performing a minimax optimization procedure on the source of labeled source images and unlabeled target images; training a domain discriminator network, using the image representations from the transferable feature extractor network, to distinguish between source images and target images; training an ordinal regressor network using a full set of source images from the transferable feature extractor network; and training an order classifier network using a full set of source images from said transferable feature extractor network.

Abstract: The present invention relates to methods for learning a robot task and robots systems using the same. A robot system may include a robot configured to perform a task, and detect force information related to the task, a haptic controller configured to be manipulatable for teaching the robot, the haptic controller configured to output a haptic feedback based on the force information while teaching of the task to the robot is performed, a sensor configured to sense first information related to a task environment of the robot and second information related to a driving state of the robot, while the teaching is performed by the haptic controller for outputting the haptic feedback, and a computer configured to learn a motion of the robot related to the task, by using the first information and the second information, such that the robot autonomously performs the task.

Abstract: Computer-implemented methods are included for training an autonomous machine to perform a target operation in a target environment. The methods include receiving a natural language description of the target operation and a natural language description of the target environment. The methods further include generating a prompt such as a reward and/or goal position signature by combining the natural language description of a target task or goal and the natural language description of the target environment. The methods then generate a reward or goal position function by prompting a large language model with the generated prompt. The methods further include computing a state description using a model of the target environment, and training a policy for the autonomous machine to perform the target task or goal using the generated function and state description.

Abstract: A method and system for automatically building a three-dimensional pose dataset for use in text to pose retrieval or text to pose generation for a class of poses includes (a) inputting three-dimensional keypoint coordinates of class-centric poses; (b) extracting, from the inputted three-dimensional keypoint coordinates of class-centric poses, posecodes, the posecodes representing a relation between a specific set of joints; (c) selecting extracted posecodes to obtain a discriminative description; (d) aggregating selected posecodes that share semantic information; (e) converting the aggregated posecodes by electronically obtaining individual descriptions by plugging each posecode information into one template sentence, picked at random from a set of possible templates for a given posecode category; (f) concatenating the individual descriptions in random order, using random pre-defined transitions; and (g) mapping the concatenated individual descriptions to class-centric poses to create the three-dimensional po

Abstract: A method and system for text-based pose editing to generate a new pose from an initial pose and user-generated text includes an user input device for inputting the initial pose and the user-generated text; a variational auto-encoder configured to receive the initial pose; a text conditioning pipeline configured to receive the user-generated text; a fusing module configured to produce parameters for a prior Gaussian distribution Np; a pose decoder configured to sample the Gaussian distribution Np and generate, therefrom, the new pose; and an output device to communicate the generated new pose to a user. The variational auto-encoder and the text conditioning pipeline are trained using a PoseFix dataset, wherein the PoseFix dataset includes triplets having a source pose, a target pose, and text modifier.

Abstract: A learning system for a navigating robot includes: a navigation module including: a first policy configured to determine actions for moving the navigating robot and navigating from a starting location to an ending location based on images from a camera of the navigating robot; and a second policy configured to, based on a representation of an environment generated by the navigating robot, determine actions for moving the navigating robot from waypoint locations between the starting location and the ending location to a plurality of subgoal locations without any images from the camera; and a representation module configured to: selectively learn the representation during movement via the first policy based on the representation at previous times, images from the camera, and actions determined by the first policy at previous times; and provide the representation to the second policy.

Abstract: A method for generating a language model for text generation by receiving a pre-trained language model having attributes with existing probability distributions over the pre-trained language model; receiving at least one target constraint; the target constraint specifying an expectation of a target attribute over a language model that approximates the pre-trained language model; computing parameters of an energy based model by applying the target constraint to the pre-trained language model; obtaining samples from a reference policy; updating parameters of a target policy using the obtained samples and the energy based model; updating the reference policy with the target policy if the target policy is superior to the reference policy; and outputting the target policy as a target language model. The target language model is adapted to generate text with the target attribute over a probability distribution that approximates the desired probability distribution specified by the target constraint.

Abstract: A generative adversarial network, a method of training a generator neural network, and a method of generating images using the generator network is provided. The generator neural network is configured to process an input comprising a noise vector and a pair of conditioning variables to generate an image according to the conditioning variables. The generator neural network includes a mixed-conditional batch normalization layer. The mixed-conditional batch normalization layer is configured to normalize a network layer output to generate a normalized network layer output, comprising transforming the network layer output in accordance with mixed-conditional batch normalization layer parameters to generate the normalized network layer output, wherein the mixed-conditional batch normalization layer parameters are computed by applying an affine transformation to the conditioning variables.

Abstract: A method extends the Trip Based Public Transit Routing algorithm to compute personalized itineraries in a multimodal network based on custom transfer speed and maximum transfer duration. The customization is done at query time. The method modifies the pruning phase and the search phase of the Trip Based Public Transit Routing algorithm to obtain Pareto front for minimizing arrival time and number of transfers, along with one optimal solution per value in the Pareto front.

Abstract: In a mechanical system with an object having a set of two or more contacts, methods are disclosed for determining object motion by computing the tangential generalized reaction impulse and force. The methods includes receiving input values that include an initial generalized position and an initial generalized velocity of the object at time t, and computing a generalized force. The methods further includes computing, respectively for the reaction impulse and force: a generalized reaction impulse and force, and a tangential generalized reaction impulse and force. The methods use the tangential generalized reaction impulse and force to, respectively, compute an end-of-interval generalized position and velocity, and a generalized acceleration, which are output for use by an application.

Abstract: A gravity compensation assembly including a main frame having one side connected to a weight body to be supported, and the other side where an inner rotation portion and an outer rotation portion spaced apart from each other and having rotation axis in an x-axis direction are coupled, an auxiliary frame having one side rotatably connected to the inner rotation portion of the main frame, and the other side where an auxiliary rotation portion having a rotation axis in the x-axis direction is coupled, and an elastic force providing having one side rotatably connected to the outer rotation portion of the main frame, and the other side coupled to the auxiliary rotation portion of the auxiliary frame, and configured to perform load compensation by an elastic force when a center of gravity changes as a relative angle of the main frame and the auxiliary frame changes may be provided.

Abstract: There is provided an apparatus for navigating a pedestrian tour including a processor circuit, a memory and a screen. The pedestrian tour includes a starting point, a first pedestrian tour zone and a second pedestrian tour zone, all of which are connected by a series of connecting paths. First and second suggested travel constraints, corresponding respectively with the first and second pedestrian tour zones, are stored in the memory. Responsive to determining that the pedestrian's actual travel within the first pedestrian tour zone will vary from the first suggested travel constraint, the processor circuit dynamically revises the second suggested travel constraint to permit the pedestrian user to execute the pedestrian tour in accordance with a preset target travel constraint for the pedestrian tour.

Abstract: Provided are an electronic device for removing an advertisement thumbnail from a display screen for providing a list of sports and an operating method of the electronic device that may retrieve an image thumbnail extracted from a game video being broadcast live, determine whether the image thumbnail is a game thumbnail or an advertisement thumbnail using a pre-trained deep learning model, and display the game thumbnail on a display screen when the image thumbnail is the game thumbnail and remove the advertisement thumbnail on the display screen when the image thumbnail is the advertisement thumbnail.

Abstract: There is disclosed a method for using a computer to enable correction of misclassified labels in a database. The computer initially applies a dataset (including labels pointing respectively to categories) to a first classifier, which includes a first loss function. Pursuant to the initial application, the computer determines that one or more labels have been misclassified. Responsive to such determination, the computer changes the first loss function to a second loss function to form a second classifier including the second loss function. The computer then applies the dataset to the second classifier for enabling correction of the one or more misclassified labels.