Abstract: Information processing system S acquires sensing data obtained through sensing downward from the sky by an UAV 1 flying in a specific zone, and identifies flood-damaged area where flood damage has occurred in the specific zone on the basis of the acquired sensing data.

Abstract: An information processing apparatus (100) includes: an acquisition unit configured to acquire a content-to-content similarity that is a similarity between a target content and one or more pieces of other content, and a user-to-user similarity that is a similarity between a target user and one or more other users; an estimation unit configured to estimate a prior distribution of expected rewards obtained as a result of execution processing performed by the target user on the target content, based on the content-to-content similarity and the user-to-user similarity; and a derivation unit configured to derive a posterior distribution of the expected rewards, using the prior distribution.

Abstract: Provided is a ranking determination system including at least one processor configured to: acquire a plurality of second classifications relating to second data based on a first model which has learned a relationship between first data and a first classification relating to the first data; acquire, for each of the second classifications, a second score relating to a validity of a combination of the second data and the second classification based on a second model which has learned a validity of a combination of third data and a third classification relating to the third data; and determine a second ranking for each of the plurality of second classifications based on the second score of each second classification.

Abstract: An input data generation unit is configured to generate, based on log data indicating a log of a behavior of a user with respect to a given computer resource for each period, input data for the period, which is associated with the log data. A user probability data generation unit is configured to generate user probability data based on output obtained from a trained machine learning model when the input data is input to the trained machine learning model. An anomaly determination unit is configured to determine whether an anomaly has occurred in the behavior of the user during a latest period based on the user probability data generated based on the input data for the latest period and the user probability data generated based on the input data for a period before the latest period.

Abstract: An operation guidance corresponding to a user is output. A server (10) of a logistics management system (1) sets, based on user setting information associated with users, an output mode of an operation guidance that is output by an unmanned ground vehicle (30) that carries a package, generates a notification code for a package subject to carrying, associates the generated notification code with a user who picks up or deposits the package, sends the generated notification code to a user device used by the user, obtains an authentication code which includes the notification code and which the user has entered into the unmanned vehicle (30), specifies, based on the notification code included in the obtained authentication code, the output mode of the operation guidance corresponding to the user who has entered the authentication code, causes the unmanned vehicle (30) to output the operation guidance using the specified output mode.

Abstract: Authentication means (101) of an authentication system (S) performs authentication based on input authentication information. Determination means (102) determines, based on the input authentication information, whether there are a plurality of users for which there is a possibility of successful authentication. Input request means (104) requests, when it is determined that there are the plurality of users, input of additional authentication information that is based on a difference in user information on each of the plurality of users. Additional authentication means (105) performs additional authentication based on the input additional authentication information.

Abstract: An information processing apparatus acquires a plurality of images related to a specified article, decides suitability of each of the plurality of images for estimating a state of the article, and outputs state information indicating an estimated state of the specified article based on a result of the decision and at least a part of the plurality of images.

Abstract: An information processing system configured to provide an electronic commerce platform. An information processing system is configured to acquire a descriptive text related to an item and at least one image related to the item; identify an object from the descriptive text; detect an object from each of the at least one image; and generate correspondence information based on correspondence between the object identified from the descriptive text and the object detected from the image.

Abstract: A logistics management system includes a memory that stores operation record information indicating a record of each of users operating an unmanned vehicle and a circuitry. The circuitry is configured to specify, when the unmanned vehicle moves on a carrying route that includes carrying points, a user who operates the unmanned vehicle at each of the carrying points, obtain the operation record information corresponding to the specified user, and specify, based on the operation record information, an estimated operation time for each of the users to operate the unmanned vehicle and determine a carrying sequence using the estimated operation time. The carrying sequence is a sequence in which the unmanned vehicle moves between the carrying points on the carrying route.

Abstract: An information processing apparatus includes an acquiring unit configured to acquire an object image that includes an object, and a predicting unit configured to predict a class of the object by applying the object image acquired by the acquiring unit to a learning model. The learning model includes a main network (201) that extracts feature amounts regarding hierarchical classes of the object based on the object image, and a sub network (202) that outputs hierarchical classes of the object based on the feature amounts. The main network (201) includes extractors that extract the feature amounts. The sub network (202) includes classifiers that output classes of the object based on the feature amounts, and in the sub network, a higher-level classifier has a connection to one or more lower-level classifiers.

Abstract: A control device (100) includes an acquirer (110) that acquires transport article information expressing a characteristic of an article to be transported, and a generator (130) that generates, on the basis of the characteristic expressed in the acquired transport article information, transport control information that causes an unmanned vehicle to transport the article.

Abstract: An authentication system, comprising at least one processor configured to: execute first authentication for a first user when the first user is present at a first place or arrives at a first place; predict a predicted period in which a second user who has a possibility of being authenticated as the first user by the first authentication is not expected to be present at the first place or arrive at the first place, based on at least one type of information out of second-time information about a second time at which the second user has been present at a second place or has arrived at a second place and second-place information about the second place; and execute first processing for the first user based on the first authentication executed in the predicted period.

Abstract: To analyze images of more diverse graphs at higher speed and accuracy, disclosed herein is an information processing apparatus, comprising: a graph image acquisition unit configured to acquire a graph image; a graph classification unit configured to classify the graph image acquired by graph type; a probability map generation unit configured to generate, from the graph image, a probability map that is of a different type by the graph type; a component extraction unit configured to extract a plurality of components in the graph image based on the probability map generated by the probability map generation unit, and trace a graph with the plurality of components extracted; a value extraction unit configured to extract values of the components of the graph image extracted by the component extraction unit; and an output unit configured to output the values of the components extracted.

Abstract: An information processing apparatus (1) includes: an acquisition unit (101) configured to acquire an object image that contains a target object; a generation unit (102;103;104;105) configured to generate a plurality of feature vectors for the object by applying the object image to a plurality of learning models; a concatenation unit (106) configured to concatenate and embed the plurality of feature vectors into a common feature space to generate a compounded feature vector in the feature space; and a search unit (107) configured to search for a similar image that is similar to the object image, using the compounded feature vector.

Abstract: An information processing apparatus (10): sets a plurality of styles that each indicate a moving-image composition of a moving image which relates to a predetermined item and in which a plurality of pieces of image content corresponding to the predetermined item are used; acquires an attribute of a target user; estimates the most-suitable style for the target user among the plurality of styles based on the plurality of pieces of image content and the attribute of the target user; and provides a moving image that is in accordance with the estimated style to the user.

Abstract: A first attitude estimation unit 16 estimates an attitude of an UAV 1 by performing a Visual-SLAM on the basis of detection values detected by an imaging unit 14, and a second attitude estimation unit 17 estimates an attitude of the UAV 1 on the basis of detection values detected by a rotation angle detection unit 15. And then a control unit 18 controls the attitude of the UAV 1 by utilizing a first attitude estimation result from the first attitude estimation unit 16 and a second attitude estimation result from the second attitude estimation unit 17 at a ratio based on positional information of the UAV 1.

Abstract: An alert device includes a data communication circuit configured to receive, from an alert control device, time information representing a vehicle-related time that is a time relating to a vehicle. The alert device also includes a storer that is a memory or a storage device, the storer being configured to store the received time information, and an alertor configured to execute an alert relating to the vehicle, based on the vehicle-related time represented by the stored time information.

Abstract: An authentication system, comprising at least one processor configured to: execute first authentication for a first user when the first user is present at or arrives at a first place; determine, based on schedule information about scheduled visits to the first place, whether a second user who has a possibility of being authenticated as the first user by the first authentication is scheduled to be present at the first place, or whether the second user is scheduled to arrive at the first place at a first scheduled date or a first scheduled date/time, which is when the first user is scheduled to be present at or arrive at the first place; and execute first processing for the first user based on the first authentication.

Abstract: An information processing device 1 includes: a feature area detection unit 28 that acquires, by inputting a target image showing a fastening part that is an inspected target to a machine learning model, a feature area based on an estimated coordinate that includes a feature point relating to reference indication added to the fastening part and relates to a position of the feature point and a distribution of the estimated coordinate; an uncertainty evaluation unit 29 that determines whether the feature area satisfies a prescribed reference; and a fastening state determination unit 30 that determines a fastening state of the fastening part based on the estimated coordinate when the feature area satisfies the prescribed reference.

Abstract: To enable graph partitioning using a graph convolutional neural network without preparing training data, an information processing apparatus is configured to classify a plurality of delivery destinations into a plurality of groups, and includes: a memory storing a program; and at least one processor that, by executing the program stored in the memory, is configured to: perform unsupervised learning to train a graph convolutional neural network, which is determined using an adjacency matrix indicating a connection relationship of the plurality of delivery destinations, and receives as input a feature matrix indicating a feature of the plurality of delivery destinations, the learning unit performing unsupervised learning using a first loss function defined such that the smaller a value for distance between delivery destinations belonging to a same group and the smaller a difference in features between delivery destinations belonging to a same group, the less a loss; and output information about a group to which