Abstract: A learning device includes processing circuitry configured to acquire a plurality of pieces of communication data for learning, extract feature amounts of the communication data, train a generative model with the feature amounts of the communication data, extract first representative points of the feature amounts of the communication data using kernel herding, and output the first representative points extracted.

Abstract: A detection apparatus includes processing circuitry configured to acquire, from a storage having stored therein normal communication models for determining, for each function of a communication device, whether or not communication of the communication device having the function is normal, at least one normal communication model that corresponds to a function of a monitoring target communication device, and monitor communication of the monitoring target communication device using the acquired normal communication model, and detect an abnormality in the communication.

Abstract: An information processing apparatus includes a processor configured to acquire image data from preprocessing, calculate information related to accuracy of a result of processing for each partial region of an image corresponding to the acquired image data, and notify the preprocessing of the calculated information related to the accuracy and information for specifying a corresponding partial region.

Abstract: An information processing apparatus includes a processor configured to acquire, from a read image, a predetermined item, and a value corresponding to the item, the read image being obtained by reading a document and being subjected, prior to acquisition of the item and the value, to preprocessing and character recognition. Further, the processor is configured to, in response to not successfully acquiring at least one of the item and the value, change a setting on the preprocessing or a setting on the character recognition in accordance with the acquisition or non-acquisition state of the item and the value, and then perform the preprocessing or the character recognition.

Abstract: A detection apparatus includes processing circuitry configured to acquire, from a storage having stored therein normal communication models for determining, for each function of a communication device, whether or not communication of the communication device having the function is normal, at least one normal communication model that corresponds to a function of a monitoring target communication device, and monitor communication of the monitoring target communication device using the acquired normal communication model, and detect an abnormality in the communication.

Abstract: An information processing apparatus includes a processor configured to acquire, from a read image, a predetermined item, and a value corresponding to the item, the read image being obtained by reading a document and being subjected, prior to acquisition of the item and the value, to preprocessing and character recognition. Further, the processor is configured to, in response to not successfully acquiring at least one of the item and the value, change a setting on the preprocessing or a setting on the character recognition in accordance with the acquisition or non-acquisition state of the item and the value, and then perform the preprocessing or the character recognition.

Abstract: A learning apparatus includes an input data reading unit configured to input data and a label indicating whether the data is abnormal, an objective function calculation unit configured to calculate a value of an objective function based on the label and a predetermined function for calculating an anomaly score of the data by applying a parameter relating to the anomaly score, by using the data and a value of the parameter, and a parameter update unit configured to calculate a value of the parameter that maximizes the value of the objective function by repeatedly executing a process by the objective function calculation unit while updating the value of the parameter.

Abstract: An information processing apparatus includes a processor configured to execute first preprocessing on acquired image data, and execute second preprocessing on a specified partial region of the image data as a target in a case where information for specifying at least one partial region in an image corresponding to the image data is received from post processing on which the image data after the first preprocessing is processed.

Abstract: An information processing apparatus includes a processor configured to acquire, from a read image, a predetermined item, and a value corresponding to the item, the read image being obtained by reading a document and being subjected, prior to acquisition of the item and the value, to preprocessing and character recognition. Further, the processor is configured to, in response to not successfully acquiring at least one of the item and the value, change a setting on the preprocessing or a setting on the character recognition in accordance with the acquisition or non-acquisition state of the item and the value, and then perform the preprocessing or the character recognition.

Abstract: A detecting device (10) acquires information related to communication by an IoT device. The detecting device (10) inputs data representing a feature of the information related to the communication to a generative model which generates output data on the basis of a latent variable which is a random variable according to a mixed Gaussian distribution and input data, and calculates the output data. The detecting device (10) calculates an anomaly score on the basis of the output data and detects an anomaly in the IoT device when the anomaly score exceeds a threshold value.

Abstract: An acquisition unit (15a) acquires data output by sensors. A learning unit (15b) substitutes a prior distribution of an encoder in a generative model including the encoder and a decoder and representing a probability distribution of the data with a marginalized posterior distribution that marginalizes the encoder, approximates a Kullback-Leibler information quantity using a density ratio between a standard Gaussian distribution and the marginalized posterior distribution, and learns the generative model using data. A detection unit (15c) estimates a probability distribution of the data using the learned generative model and detects an event in that an estimated occurrence probability of the data newly acquired is lower than a prescribed threshold as abnormality.

Abstract: An evaluation apparatus includes a memory, and processing circuitry coupled to the memory and configured to accept an input of communication data of an evaluation target, and estimate a probability density of the communication data of the evaluation target by using a first model in which a feature of a probability density of normal initial learning data is learned and a second model in which a feature of a probability density of normal excess detection data detected as abnormal in a course of evaluation processing is learned, and evaluate presence or absence of an anomaly of the communication data of the evaluation target based on the estimated probability density.

Abstract: An information processing apparatus includes a processor configured to acquire image data from preprocessing, calculate information related to accuracy of a result of processing for each partial region of an image corresponding to the acquired image data, and notify the preprocessing of the calculated information related to the accuracy and information for specifying a corresponding partial region.

Abstract: An information processing apparatus includes a processor configured to execute first preprocessing on acquired image data, and execute second preprocessing on a specified partial region of the image data as a target in a case where information for specifying at least one partial region in an image corresponding to the image data is received from post processing on which the image data after the first preprocessing is processed.

Abstract: A detecting device obtains a network log and a host log of a device. The detecting device converts the network log into a network feature value in a form which is inputtable to a multimodal generative model which generates output data on the basis of multiple latent variables represented by random variables. The detecting device converts the host log into a host feature value in a form which is inputtable to the generative model. The detecting device also inputs at least one of the network feature value and the host feature value to the generative model and calculates output data. The detecting device detects an abnormality about the device using an anomaly score calculated on the basis of the output data.

Abstract: The present invention provides a method for evaluating comfort evoking performance of sheets, with which whether or not a sheet has an ability to evoke comfort as a result of the sheet being touched, or a level of the ability is evaluated. The method includes the following steps (A) to (D): (A) bringing a test sheet, which is an evaluation target, into contact with the skin or hairs of an animal and applying a tactile stimulation to the animal; (B) extracting a biological sample from the animal after the tactile stimulation has been applied; (C) measuring an amount of oxytocin in the biological sample; and (D) comparing the amount of oxytocin measured with an amount of oxytocin in a biological sample extracted from the animal under a condition where there is no effect of the tactile stimulation.

Abstract: A piezoelectric stack includes a porous resin sheet, and a surface coating layer disposed on an exterior surface of the porous resin sheet, the exterior surface including at least one of a front surface and a back surface of the porous resin sheet, the surface coating layer having a volume resistivity of not less than 1×1013 ?·cm, the porous resin sheet and the surface coating layer having different elastic moduli.

Abstract: A calculation unit (121) calculates, for an output signal of an output layer in a neural network, an output function obtained by replacing an exponential function included in softmax with a product of the exponential function and a predetermined function having no parameter, the output function having a non-linear log likelihood function. An update unit (122) updates a parameter of the neural network on the basis of the output signal such that the log likelihood function of the output function is optimized.

Abstract: An information processing device includes: a line of sight detection unit that detects a position of a user's line of sight on a display to display an image; and a display controller that displays a pop-up screen about an item to be displayed on the display and controls a display position of the pop-up screen in response to the position of line of sight detected by the line of sight detection unit.

Abstract: An image processing apparatus includes an image data acquiring unit that acquires image data, and a transmitting unit that, if a first destination specified as a destination of the image data by a user is not safe, transmits the image data to a second destination different from the first destination, and transmits information indicating the second destination to the first destination.