Abstract: Provided are an analysis device, an analysis method, and a program capable of easily identifying a factor of a prediction error in prediction using a prediction model on the basis of various viewpoints. An analysis device (1) includes: a metric evaluation unit (2) that calculates and evaluates a plurality of types of metrics with respect to a prediction model, data of explanatory variables used in the prediction model, or data of target variables used in the prediction model; and a factor identification unit (3) that identifies a factor of an error in prediction by the prediction model according to a combination of evaluation results of the plurality of types of metrics.

Abstract: A position indicator that indicates a position is disclosed. The position indicator includes a casing of an electronic pen, a position indicating part, a tracker, and a grip part configured to be gripped by a user. The position indicating part is disposed at one end of the casing. The position indicating part, in operation, indicates the position. The tracker, in operation, causes an external device to detect the position in a three-dimensional space. The grip part is orthogonal to the casing of the electronic pen.

Abstract: In the strange feeling prediction device, the prediction result acquisition means inputs diagnosis data to a target prediction model which is a trained prediction model serving as a target, and acquires a prediction result by the target prediction model. The label acquisition means acquires a strange feeling label indicating a strange feeling of an expert with respect to the prediction result. The strange feeling prediction model training means trains a strange feeling prediction model using the prediction result and the strange feeling label. The strange feeling prediction means outputs a strange feeling index indicating the strange feeling with respect to the prediction result outputted by the target prediction model, using the trained strange feeling prediction model.

Abstract: A rendering device that renders an object in a virtual reality space includes a processor and a memory storing instructions that, when executed by the processor, cause the processor to perform a first rendering process that renders in the virtual reality space an image of an input surface on which a position is indicated by a stylus, perform a second rendering process that renders in the virtual reality space an image of a display surface that displays the object, the image of the display surface being different from the image of the input surface, perform an acquisition process that acquires the position on the input surface indicated by the stylus, and perform a display process that displays the object in the image of the display surface based on the position on the input surface acquired by performing the acquisition process.

Abstract: A rendering device that renders an object in a virtual reality space includes a processor and a memory storing instructions that, when executed by the processor, cause the processor to perform a first rendering process that renders in the virtual reality space an image of an input surface on which a position is indicated by a stylus, perform a second rendering process that renders in the virtual reality space an image of a display surface that displays the object, the image of the display surface being different from the image of the input surface, perform an acquisition process that acquires the position on the input surface indicated by the stylus, and perform a display process that displays the object in the image of the display surface based on the position on the input surface acquired by performing the acquisition process.

Abstract: The learning device 80 includes a first learning means 81 and a second learning means 82. The first learning means 81 generates a skill state sequence representing time-series changes in a learner's skill state by machine learning using learner's learning results. The second learning means 82 learns a model in which problem characteristics that represent characteristics of problems used by a learner for learning, user characteristics that represent characteristics of the learner, and time information that represents time the learner solved the problem are explanatory variables, and the learner's skill state represented by the skill state sequence is an objective variable.

Abstract: A system 80 for personalized e-leaming includes a Hierarchical Knowledge Tracing (HKT) model unit 81 which includes two sequential models comprising of a lower level model and a higher level model, wherein the lower level model estimates and updates the estimate of knowledge state of a learner from a question-response data of the learner while the learner is active (in-session) on the e-leaming application and predicts the probability of answering a question within the domain using the estimate of knowledge state, and the higher level model updates the knowledge state estimate of the lower level model when a new session starts.

Abstract: A method of training an image recognition model includes masking a first region of a first image with a first portion of a second image to define a mixed image, wherein the first image is different from the second image, and a location of the first region in the first image corresponds to a location of the first portion in the second image. The method further includes performing masked global average pooling (GAP) on both the mixed image. The method further includes generating a first classification score for the first image and a second classification score for the second image based on the masked GAP of the mixed image.

Abstract: A spatial position indication system includes an electronic pen, an indicated position detecting circuit, a spatial position detecting circuit, and a selecting circuit. The indicated position detecting circuit detects a position of the electronic pen at an input surface or a hover region over the input surface. The spatial position detecting circuit detects the position of the electronic pen in a spatial region including at least a part of the hover region separately from and independently of the indicated position detecting device circuit. Based on output from the indicated position detecting circuit and the spatial position detecting circuit, the selecting circuit performs selection control so as to use output from the indicated position detecting circuit or the spatial position detecting circuit according to a separation distance of the electronic pen from the input surface when a drawing image to be displayed on a display device is generated.

Abstract: A rendering device that renders an object in a virtual reality space includes a processor and a memory storing instructions that, when executed by the processor, cause the processor to perform a first rendering process that renders in the virtual reality space an image of an input surface on which a position is indicated by a stylus, perform a second rendering process that renders in the virtual reality space an image of a display surface that displays the object, the image of the display surface being different from the image of the input surface, perform an acquisition process that acquires the position on the input surface indicated by the stylus, and perform a display process that displays the object in the image of the display surface based on the position on the input surface acquired by performing the acquisition process.

Abstract: A position indicating device includes a housing in which an electronic pen including a pen tip is mountable, a force sense generator configured to generate a force sense, and a processor configured to control the force sense generator to generate the force sense when, with the electronic pen mounted in the housing, a distance between a position of the pen tip of the electronic pen in a virtual reality space and an object in the virtual reality space is equal to or less than a predetermined value.

Abstract: The skill visualization device 90 includes a learning plan input unit 91, a state estimation unit 92, and a state visualization unit 93. The learning plan input unit 91 accepts input of a learning plan, which is information that lists problems a learner plans to solve in time series. The state estimation unit 92 estimates a learner's skill state at each future point in time when each problem scheduled in the learning plan is solved in time series. The state visualization unit 93 visualizes the learner's skill state at each estimated time point.

Abstract: A spatial position indication system includes an electronic pen, an indicated position detecting circuit, a spatial position detecting circuit, and a selecting circuit. The indicated position detecting circuit detects a position of the electronic pen at an input surface or a hover region over the input surface. The spatial position detecting circuit detects the position of the electronic pen in a spatial region including at least a part of the hover region separately from and independently of the indicated position detecting device circuit. Based on output from the indicated position detecting circuit and the spatial position detecting circuit, the selecting circuit performs selection control so as to use output from the indicated position detecting circuit or the spatial position detecting circuit according to a separation distance of the electronic pen from the input surface when a drawing image to be displayed on a display device is generated.

Abstract: The output means 81 outputs a threshold value indicating a proficiency level of a skill required to answer a target question in association with the proficiency level of the skill that the learner is assumed to have.

Abstract: A position indicating device includes a housing in which an electronic pen including a pen tip is mountable, a force sense generator configured to generate a force sense, and a processor configured to control the force sense generator to generate the force sense when, with the electronic pen mounted in the housing, a distance between a position of the pen tip of the electronic pen in a virtual reality space and an object in the virtual reality space is equal to or less than a predetermined value.

Abstract: An object is to provide an information processing apparatus capable of analyzing operation log data that can be acquired through a digital device and effectively using the operation log data. An information processing apparatus (10) according to the present disclosure includes an information collection unit (11) configured to collect an operation log generated when a digital device is operated from a plurality of the digital devices, an execution unit (12) configured to co-cluster operator information and content information. The operator information is for identifying an operator of the digital device included in a plurality of the operation logs, and the content information is about content viewed by the operator using the digital device, and a post-processing unit (13) configured to create confirmation data using an execution result of the co-clustering.

Abstract: The variational parameter determination unit 81 determines a variational parameter that specifies a position where a likelihood function and a lower bound of the likelihood function to be approximated by Gaussian are in contact. The gradient direction lower bound calculation unit 82 generates a likelihood function made one-dimensional in a gradient direction at the center of a prior distribution and calculates the lower bound of the generated likelihood function. The full dimensional lower bound calculation unit 83 sets covariances in directions other than the gradient direction to an arbitrary covariance and calculates the lower bounds of the set covariances.

Abstract: A position indicator that indicates a position is disclosed. The position indicator includes a casing of an electronic pen, a position indicating part that is disposed at one end of the casing and, in operation, indicates the position, a tracker which, in operation, causes an external device to detect the position in a three-dimensional space, and a holder to which the casing of the electronic pen is mounted.

Abstract: A rendering device that renders an object in a virtual reality space includes a processor and a memory storing instructions that, when executed by the processor, cause the processor to perform a first rendering process that renders in the virtual reality space an image of an input surface on which a position is indicated by a stylus, perform a second rendering process that renders in the virtual reality space an image of a display surface that displays the object, the image of the display surface being different from the image of the input surface, perform an acquisition process that acquires the position on the input surface indicated by the stylus, and perform a display process that displays the object in the image of the display surface based on the position on the input surface acquired by performing the acquisition process.

Abstract: An input unit 81 inputs pieces of training data indicating products as targets of a behavior in accordance with preferences of users. An estimation unit 82 estimates a preference distribution indicating the preferences of each user corresponding to product attributes and a preference degree of the user between the products for the product attributes indicating characteristics of the products based on the product attributes and the training data. The estimation unit 82 estimates the preference distribution and the preference degree between the products by using the training data when a behavior probability for the product based on the training data is calculated by a product of a probability of the preference of the user selected based on the preference distribution and a probability of selecting a specific product based on the preference of the user selected with the probability and the preference degree between the products.