Abstract: A collection device includes a deciding unit that, on the basis of data obtained in advance for each area, decides a collection interval and a collection time for each of the areas, a communication unit that acquires a current position from each of measurement terminals, and an allocating unit that, in a case where there is an area corresponding to the current position acquired regarding each of the measurement terminals, calculates a slot count of slots regarding which the collection interval and the collection time are cyclically allocated, on the basis of the collection interval and the collection time of this area, and allocates slot Nos. of an amount equivalent to the slot count, to any of the measurement terminals present in this area, by a predetermined method. The communication unit transmits, to each of the measurement terminals to which slot Nos.

Abstract: An estimation apparatus according to an embodiment includes: an acquisition unit configured to acquire first information indicating that an event is occurring at a location, along with a type of the event, second information indicating whether the first information is third information determined by a user or fourth information detected by a sensor, and accuracy indicating certainty of the third or fourth information; a first estimation unit configured to estimate a probability that a predetermined event is actually occurring, based on accuracy of the third information determined by the user, a frequency of occurrence of the third information, accuracy of the fourth information detected by the sensor, and a frequency of occurrence of the fourth information, acquired by the acquisition unit; and a second estimation unit configured to estimate whether or not the predetermined event is actually occurring based on the probability estimated by the first estimation unit and a threshold value.

Abstract: Even when there is a plurality of road surface states within one geographical range or when there is a local road surface state, a road surface state is estimated with accuracy. Included are an aggregation unit (13, 15) that aggregates, on a per predetermined geographical unit basis, results of estimating road surface states using sensor data collected in advance for each of geographical ranges and including positional information obtained during travel of a moving object, the aggregation being based on the positional information, and an estimation unit (17) that estimates the road surface state for each of the geographical ranges and based on the estimation results aggregated by the aggregation unit.

Abstract: To make it possible to generate, at low cost, learning data for accurately estimating a state. A first learning unit 103 learns a generation model based on a set of first learning data to which a first correct answer label and a second correct answer label are given, the first correct answer label indicating a correct answer about any one of a plurality of conditions, the second correct answer label indicating a predetermined state, the generation model outputting, when data to which the second correct answer label is given is input, data to which the first correct answer label indicating any one of the plurality of conditions is given.

Abstract: A barrier data collection device includes a unit estimation unit that estimates, based on sensor data with position information including height at a time when a mobile body including a flying mobile body moving in the air moves, the sensor data being collected in advance about each of geographical ranges, with an estimator, about each of sets of the geographical ranges and heights included in the sensor data in a predetermined time unit, a barrier state obtained by estimating a state of which of barrier types the set is, a shape estimation unit that estimates, about a set satisfying a condition among the sets, a barrier shape based on the sensor data and an estimation result of the barrier state estimated about each of the sets in the time unit, and a barrier estimation unit that estimates, based on the estimation result of the barrier state estimated about the each of the sets, the estimated barrier shape, and a correct answer ratio of the estimator calculated in advance, a probability for each of the barri

Abstract: A collection device includes a deciding unit that, on the basis of data obtained in advance for each area, decides a collection interval and a collection time for each of the areas, a communication unit that acquires a current position from each of measurement terminals, and an allocating unit that, in a case where there is an area corresponding to the current position acquired regarding each of the measurement terminals, calculates a slot count of slots regarding which the collection interval and the collection time are cyclically allocated, on the basis of the collection interval and the collection time of this area, and allocates slot Nos. of an amount equivalent to the slot count, to any of the measurement terminals present in this area, by a predetermined method. The communication unit transmits, to each of the measurement terminals to which slot Nos.

Abstract: A generalized data generation device and estimation device, a generalized data generation method and estimation method, and a generalized data generation program and estimation program are provided, which are capable of estimating the state of a target object accurately with the amount of training data being reduced.

Abstract: An information extracting device includes an acquiring unit that acquires, with regard to each information source, a data group made up of data including a content relating to an object, a location where the content was recorded, and a date and time at which the content was recorded, a generating unit that generates a word vector of which the date and time, the location, content using a weight based on the date and time, and a type of the content, are each components, for each of the data of the data group, a distance calculating unit that calculates a distance among the word vectors, a classifying unit that classifies each of data of the data group on the basis of the distance among the word vectors, and an extracting unit that calculates a reliability with regard to the classification, and extracts data from the data group on the basis of the reliability.

Abstract: A training data generation apparatus (10) according to the present invention includes a noise determination unit (11) that determines whether or not training data that is to be used in machine learning includes noise, and a noise addition unit (12) that generates new training data by adding noise to training data that has been determined by the noise determination unit (11) as not including noise.

Abstract: The present invention provides a data analysis system capable of performing analysis appropriately using a CNN model while reducing communication traffic. A data analysis system 90 includes: an instrument 10 that performs a conversion process of outputting compression data obtained as a result of processing observation data received via an input layer of a learned neural network 18A using portions ranging from the input layer to a predetermined intermediate layer; and a device 20 that performs an analysis process of inputting the compression data to a subsequent intermediate layer in a learned neural network 18B, inputting data obtained by decoding the compression data, which is an output of the subsequent intermediate layer, to an output layer configured using a CNN model, and obtaining an analysis result of the observation data as an output of the output layer.

Abstract: It is possible to learn a model for accurately estimating a label indicating the condition of data. A first model for determining, for each label, the likelihood of the label is learned, time-series likelihood data that is the likelihood of each label at each time is output for each piece of learning data, the time-series likelihood data for each piece of the learning data given a correct label is input, and machine learning is performed to learn a second model for outputting one of the labels based on a change in the likelihood of each label.

Abstract: It is possible to learn a model for accurately estimating a label indicating the condition of data. A batch size, which is a unit of learning data used in machine learning, is set to a predetermined size, learning data having the batch size in a learning data set is used to learn a first model for determining, for each label, the likelihood of the label, and time-series likelihood data that is the likelihood of each label at each time is output for each piece of the learning data. The time-series likelihood data for each piece of the learning data given a correct label is input, the batch size is set to a size larger than the predetermined size in a first learning unit 24, and machine learning is performed to learn a second model for outputting one of the labels based on a change in the likelihood of each label.

Abstract: A learning apparatus (10) according to the present invention includes: a normalization/standardization unit (13) that performs normalization or standardization on data detected by a plurality of types of sensors; and a learning unit (14) that builds a model by performing machine learning, using, as training data, the detected data on which normalization or standardization has been performed by the normalization/standardization unit (13). The normalization/standardization unit (13) performs normalization or standardization for each type of sensor, on the data detected by the sensor.

Abstract: To appropriately evaluate an evaluation target based on posts related to an evaluation target from a user. An acquisition unit 24 of an evaluation device 14 acquires, from a group of pieces of post data including a plurality of pieces of post data each including post information indicating a post content related to an evaluation target and poster information indicating a user who has posted the post information, based on a user reliability that indicates the user and becomes greater for the user, indicated by the poster information, with a larger number of pieces of the post information posted in past and with a larger number of pieces of the post information adopted as information having a predetermined reliability or higher, the post data with the user reliability being equal to or greater than a threshold. Then, an evaluation unit 26 evaluates whether the evaluation target exists, based on the post data acquired by the acquisition unit 24.

Abstract: A training data generation device (10) according to the present invention includes a training data correction unit (11) configured to correct training data that is time-series data that indicates states of an object and in which a label corresponding to a state of the object is assigned to each section that corresponds to the state indicated by the label, wherein the training data correction unit (11) corrects a break time that divides a first section from a second section adjacent to the first section, based on time-series data in the first section and time-series data in the second section.

Abstract: To appropriately update evaluation on an evaluation target based on posts related to an evaluation target from a user. When a reliability of an evaluation information drops equal to or below a threshold related to the reliability of the evaluation information, the evaluation information representing an evaluation on an evaluation target, the reliability representing an index provided to evaluation information when the evaluation information is generated, an evaluation unit 26 of an evaluation device 14 generates evaluation information on the evaluation target again based on post information different from post information used for generating the evaluation information on the evaluation target.

Abstract: To provide a non-aqueous electrolyte electricity-storage element including a positive electrode including a positive-electrode active material capable of inserting and releasing anions, a negative electrode including a negative-electrode active material capable of inserting and releasing cations, and a non-aqueous electrolyte, wherein the positive-electrode active material is porous carbon having pores having a three-dimensional network structure, and wherein a changing rate of a cross-sectional thickness of a positive electrode film including the positive-electrode active material defined by Formula (1) below is less than 45%.

Abstract: A non-aqueous electrolyte electricity-storage element which includes a positive electrode including a positive-electrode active material capable of inserting and releasing anions, a negative electrode including a negative-electrode active material, and a non-aqueous electrolyte, wherein the positive-electrode active material is formed of a carbonaceous material, and a surface of the carbonaceous material includes fluorine.

Abstract: To provide: a composition for oxygen detection, which has excellent oxygen detection characteristic (ability), and which is safer than conventional compositions for oxygen detection, thereby being able to be sealed in packages containing food, pharmaceutical products or the like without anxiety; and an oxygen detector onto which this composition for oxygen detection is applied or printed. This composition for oxygen detection contains a redox dye, a food dye, a reducing sugar, an edible reduction promoter and an edible humectant in the composition, and enables quick detection of the presence or absence of oxygen by means of color change. Since this composition for oxygen detection is composed only of food, a pharmaceutical product and edible components allowed to be used for the food or pharmaceutical product, this composition for oxygen detection is safe to the human body.

Abstract: To provide a display medium that is discolored in a state of being exposed in the presence of oxygen. This display medium has a discoloration layer on a substrate, the discoloration layer being formed with a redox dye, a food dye, a reducing sugar, an edible reduction promoter, an edible moisturizer, and an oxygen detection composition that contains an edible thickener. The oxygen detection composition that forms the discoloration layer is reversibly discolored in accordance with the presence of oxygen, so that the discoloration layer changes color upon transition from an oxygen-free state to an oxygen-present state. Therefore, when the discoloration layer is formed with a letter, a drawing pattern, a symbol or the like, information relating to a commodity constituted by the letter, drawing pattern, symbol or the like is highlighted by discoloration.