Fujitsu Patente

In an embodiment, workflow for timeseries forecasting may be performed based on automated machine learning. Sensor data for measurement parameter is received from plurality of sensors installed in built environment and the received sensor data is stored in table of relational database. Cut-off record associated with previous training checkpoint is determined of the forecasting model for the measurement parameter. Records including new records are determined for which respective timestamps occur after the measurement timestamp of cut-off record. Size of the determined records are compared with threshold size and training dataset is prepared. The forecasting model is trained on the training dataset based on the comparison.

An information processing program that causes a computer to perform a process including: classifying a plurality of items to be determined into one or more categories based on a plurality of appearance images of the plurality of items, the plurality of appearance images being stored in a storage area (41a) in association with corresponding prices of the plurality of items; calculating, for each of the one or more categories into which the items are classified, a statistical value of prices of a plurality of items in the category, based on the plurality of prices stored in the storage area (41a); changing a pattern for the classifying and performing the classifying and the calculating so that one or more of the statistical values satisfy a given condition; and outputting category information (41b) indicating a category of each of the plurality of items when the given condition is satisfied.

A processing unit determines, from a plurality of solutions to a combinatorial optimization problem stored in a storage unit, a plurality of higher-ranked solutions based on evaluation function values of the plurality of solutions, determines, for each of the plurality of higher-ranked solutions, a selection probability such that higher-ranked solutions having evaluation function values closer to that of the highest-ranked solution among the plurality of higher-ranked solutions are more likely to be selected, selects a first solution from the plurality of higher-ranked solutions according to the selection probabilities, selects a second solution different from the first solution, from the plurality of solutions, generates a third solution with a path relinking method using the selected first solution and second solution, and performs a solution search on the combinatorial optimization problem using the third solution as an initial solution.

An answer generation program causes a computer to execute a process including acquiring a question input to a first agent that generates information based on input information, the question being related to a video for monitoring a specific task, identifying a specific agent that has a function of either video recognition for the specific task or domain knowledge of the specific task, from among a plurality of second agents capable of cooperating with the first agent, based on the acquired question, and causing the first agent to output, as an answer to the question, an answer result based on generation information that is generated by the identified specific agent in accordance with an instruction from the first agent.

According to an aspect of at least one embodiment, a method to improve a user interface may include obtaining transcript data include one or more words from a transcription of speech in the audio data. The transcript data may be generated by automated speech recognition technology from the audio data. The transcript data and criteria may be provided to a large language model configured to analyze the transcript data based on the criteria to select a word from the transcript data. A definition of the selected word may also be generated by the large language model. The selected word and the definition of the selected word may be obtained. The audio data may be broadcasted by a device and the selected word and the definition of the selected word may be presented on a display of the device with the broadcasting of the audio data.

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A method of training a neural network as a natural language processing, NLP, model, comprises: inputting annotated training data to first architecture portions of the neural network, the first architecture portions being executed respectively in a plurality of distributed client computing devices in communication with a server computing device, the training data being derived from text data private to the client computing device in which the first architecture portion is executed, the server computing device having no access to any of the private text data; deriving from the training data, using the first architecture portions, weight matrices of numeric weights which are decoupled from the private text data; concatenating the weight matrices, in a second architecture portion of the neural network executed in the server computing device, to obtain a single concatenated weight matrix; and training, on the second architecture portion, the NLP model using the concatenated weight matrix.

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A quantum calculation support program for causing a computer to execute processing including: expanding an imaginary-time evolution expression into multiple expressions for multiple orders; generating sets of orders obtained by extracting an order twice from the multiple orders; performing, for each set, generating a quantum circuit indicating quantum calculation of the physical quantity obtained by partial imaginary-time evolution using expressions of orders in the set, and causing a quantum computer to repeatedly execute the quantum calculation using the quantum circuit for the set until the physical quantity obtained from the quantum calculation converges such that an output state obtained by the quantum calculation using the quantum circuit for the set is used as an input state in the subsequent quantum calculation; and calculating the thermal equilibrium expectation value at the finite temperature of the physical quantity using a value of the physical quantity after convergence for each set.

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