Abstract: A concealment device includes at least one memory configured to store instructions and at least one processor configured to execute the instructions to generate a second string in which a substring of a first string detected as a concealment target has been replaced with another substring, and input the second string to a device that outputs a response to the second string.

Abstract: A learning apparatus according to the present example embodiment includes: a data dividing unit that generates n sets of divided data by dividing first learning data into n (n is an integer of 2 or more); an inference device generation unit that generates n inference devices for learning data generation by machine learning using data excluding one set of divided data from the first learning data; a learning data generation unit that generates second learning data by inputting the one set of the divided data excluded from the machine learning into each of the n inference devices for learning data generation; and a learning unit that generates a second inference device by machine learning using the second learning data.

Abstract: A learning apparatus according to the present disclosure includes: a first training data generating unit configured to generate first training data in which a vector including values of a plurality of elements output by inputting unlabeled training data to a pre-learned machine learning model is a target variable; a second training data generating unit configured to generate second training data in which the values of the elements of the vector that is the target variable of the first training data are set so that a difference in magnitude of value between at least some of the elements becomes larger; and a learning unit configured to generate a machine learning model by machine learning using the first training data and the second training data.

Abstract: A server device includes an acquisition unit that acquires, from each of a plurality of client devices, information representing a value of a node constituting a decision tree, the information being determined based on the learning data held by the own device of each client device, and a determination unit that determines the value of the node constituting the decision tree by integrating the acquired results. The decision tree is learned by determination of the value of each node constituting the decision tree by the determination unit.

Abstract: A learning device includes an acquisition unit that acquires a local model corresponding to a feature value held by the own device, a residual calculation unit that calculates a difference between an output of a vertical federated learning model having been learned previously and an output of the local model acquired by the acquisition unit, and an additional tree learning unit that learns an additional tree to be added to the local model acquired by the acquisition unit, on the basis of the result of calculation by the residual calculation unit and the feature value held by the own device.

Abstract: A server apparatus includes: a receiving unit configured to receive, from each of a plurality of client apparatuses performing federated learning of a neural network model having multiplex branches capable of performing different operations on a common input, a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches; a calculating unit configured to calculate a parameter of a global model based on the local model parameter and the weight received by the receiving unit; and a transmitting unit configured to transmit the parameter calculated by the calculating unit to the client apparatuses.

Abstract: A server apparatus has: a receiving unit that receives, from a plurality of client apparatuses that perform federated learning of a neural network model having multiplex branches capable of performing different operations on a common input and learn a local model parameter of each of the multiplex branches and a weight for each branch used in superposing outputs from the respective multiplex branches, the local model parameters corresponding to each of the branches; a similarity degree calculating unit that calculates a degree of similarity between the local model parameters corresponding to each of the branches, received from different client apparatuses; a parameter calculating unit that calculates a parameter of a global model based on the local model parameter selected based on a result of calculation by the similarity degree calculating unit; and a parameter transmitting unit that transmits the parameter calculated by the parameter calculating unit to the client apparatus.

Abstract: A semiconductor processing sheet contains a base layer having a plastic sheet containing pigment as a core layer, wherein a non-pigment-containing layer is arranged in the outermost layer on front and back main surfaces of the core layer. According to the present invention, in a process for manufacturing semiconductor devices, it is possible to minimize contamination of the inner surface for the film manufacturing die that is due to the pigment contained in the film while maintaining the visibility of the semiconductor processing sheet. Consequently, partial occlusion of the die lip aperture due to pigment contamination is reduced, with the result that effective prevention of the deterioration of the sheet thickness precision due to adhering pigment is possible.