Abstract: A non-transitory computer-readable recording medium stores a data management program for performing a zero knowledge proof of a credential owned by a user causing a computer to execute: disclosing a commitment of a digital signature of the credential to a verifier server; requesting the verifier server to verify a knowledge proof of the digital signature using the commitment; transmitting, to the verifier server, a plurality of digital signatures including the digital signature of the credential; and requesting the verifier server to verify a set membership proof in which one of the plurality of digital signatures is owned by the user.

Abstract: A control method including: obtaining an image of a given range captured by an image capturing device and a first invisible light image of the range captured by an invisible light image capturing device having a resolution lower than a resolution of the image capturing device; generating a second invisible light image at a resolution higher than a resolution of the first invisible light image by a machine learning model using the image and the first invisible light image as an input; identifying a target area from the range, based on an indicator indicating an uncertainty of each pixel in the second invisible light image; and obtaining, by an optical magnification control of the invisible light image capturing device, a third invisible light image of the target area at a resolution higher than a resolution of the target area in the first invisible light image.

Abstract: A medium storing a program for causing a computer to perform processing including: executing optimization including iteratively performing a search and update for each variable in a target problem, the search including searching for a candidate for a solution to the variable, the update including updating a provisional value of the solution with the candidate when a difference between evaluation values of the provisional value and the candidate is equal to or smaller than a first threshold, and updating the provisional value with the candidate stochastically when the difference is greater than the first threshold; in the optimization, counting the number of times that the difference becomes equal to or greater than a second threshold before the last step in which the provisional value is updated with the candidate; and after the optimization, evaluating an appropriateness of the optimization for the target problem based on the counted number of times.

Abstract: A method of optical signal adjustment may include obtaining a number of bits per second of data transmission in a frequency channel of an optical network. The method may also include directing a reduction in a number of bits per symbol transmitted in the frequency channel and directing an increase in a number of symbols transmitted per second in the frequency channel to maintain the number of bits per second of data transmission in the frequency channel.

Abstract: A computation processing apparatus includes: a memory; and a processor coupled to the memory and configured to: decode instructions; execute the instructions which is decoded and operate as a plurality of sub-computation processing apparatuses in accordance with a bit width of data to be computed; and observe an operation state of the computation processing apparatus, wherein, when observing that a subset of the plurality of sub-computation processing apparatuses does not execute an instruction or instructions, the processor parallelizes the instructions and outputs the parallelized instructions.

Abstract: A control method implemented by a computer operable as one of a plurality of processing nodes arranged in a redundant configuration in a first blockchain, the control method including: detecting generation of transaction data related to a first transaction in the first blockchain stored in each of the processing nodes; and controlling whether or not to transmit a second transaction to be executed after the first transaction to a second blockchain on a basis of the detected transaction data.

Abstract: A recording medium stores a program for causing a computer to execute a process including: classifying data into classes based on a density of the data; performing data augmentation on first data that is positioned in a region where data which is positioned in a region of a first class and which belongs to the first class exists at a higher density than a predetermined density and on second data that is positioned in a region where the data which is positioned in the region of the first class and which belongs to the first class exists at a lower density than the predetermined density; and setting, when the first data after the data augmentation and the second data after the data augmentation overlap each other, a label that corresponds to the first class to first augmentation data, the second data, or second augmentation data.

Abstract: A non-transitory computer-readable storage medium stores a program that causes a computer to execute a process, the process includes: obtaining a video that includes a person; detecting an object included in the video by analyzing the obtained video; determining, on a basis of a positional relation between the detected object and any one of a plurality of parts of the person, whether movement of the object and movement of the any one of the plurality of parts have a correlation; when determining that the movement of the object and the movement of the any one of the plurality of parts have the correlation, specifying, among a plurality of frames constituting the video, a first frame which has the correlation; and performing authentication processing of the person by using a second frame that is among the plurality of frames and excludes the first frame.

Abstract: A method of machine learning algorithm selection may include obtaining a dataset that includes multiple data entries. In some embodiments, each of the data entries may include multiple features and one of the multiple features may be designated as a target variable. The method may further include selecting a subset of the data entries. In some embodiments, selecting the subset of the data entries may include binning the data entries into multiple data bins based on values in the target variable and selecting a subset of the binned data entries from each of the multiple data bins as the subset of the data entries. The method may further include constructing multiple machine learning models using the subset of the data entries and selecting one of the multiple machine learning models based on an evaluation of the multiple machine learning models.

Abstract: A non-transitory computer-readable storage medium storing therein a position prediction program that causes a computer to execute a process comprising: first calculating, from each of a second positions of a positioning object at a plurality of prediction timings in the past, a third position of the positioning object at a prediction timing; second calculating, on the basis of the second position at a last prediction timing out of the plurality of prediction timings and a first position of the positioning object calculated from image data received from a shooting device after the last prediction timing, a fourth position of the positioning object at the prediction timing; and predicting the second position at the prediction timing on the basis of the third position and the fourth position.

Abstract: A non-transitory computer-readable storage medium stores a program that causes a computer to execute a process, the process includes: detecting a position of an object different from a target person on a target video in which the target person is captured; specifying, among a plurality of parts of the target person on the target video, one or more parts excluding a part having a correlation in movement with the object, the one or more parts being specified on a basis of a positional relation between the detected position of the object and a position of at least one of the plurality of parts; and performing comparison processing of the target person on a basis of characteristics related to the specified one or more parts on the target video.

Abstract: An information processing apparatus calculates a parameter for each of a plurality of layers included in a first neural network through machine learning using a plurality of image datasets each containing a human biometric image. The information processing apparatus generates a determination model of determining the authenticity of a human biometric image included in a received image dataset, by setting a parameter calculated for a first layer of the first neural network in the first layer included in a second neural network that includes the first layer and does not include a second layer.

Abstract: A communication device including, a subgroup management unit allocates each of N resources in the frequency axis direction at a first position in the time axis direction to each communication device as a first resource, allocates each of second to NTth resources in the time axis direction at a first position in the frequency axis direction to each of the communication devices as the second resource, allocates each of the second to Nth resources in the frequency axis direction at a second position in the time axis direction to each of other communication devices as the first resource, and allocates each of the third to NTth resources in the time axis direction at the second position in the frequency axis direction to each of the other communication devices as the second resource, and repeat this operation to allocate the first and second resources to a plurality of communication devices.

Abstract: According to an aspect of an embodiment, operations include receiving an input comprising a node-based graph associated with a real-world optimization problem and generating a sparse graph by removing a subset of edges from the node-based graph, The operations further include formulating operators of a quantum circuit on a quantum computer based on the sparse graph and formulating a cost function for the real-world optimization problem. The operations further include executing a set of operations which includes operating the quantum circuit on the quantum computer to generate a result, estimating a value of the cost function using the result, and updating parameters of the operators based on the value. The operations further include generating a final solution of the real-world optimization problem by repeating the execution of the set of operations using the updated parameters, until the estimated value of the cost function approaches a predefined threshold value.

Abstract: A process includes receiving, by a machine learning support system, an instruction to generate a machine learning model from a plurality of candidate-programs, specifying, for each of the plurality of candidate-programs generated using a program-component included in any of a plurality of program-component sets, a first proficiency-level of a user for a first program-component set which includes a first program-component used in the candidate-program, the first proficiency-level is based on proficiency-level information which indicates a proficiency-level of the user related to use of each of the plurality of program-component sets and is determined based on a use record of the plurality of program-component sets in an editing process of the candidate-program by the user and a change in performance of the candidate-program by the editing process, and determining, for each of the plurality of candidate-programs, a priority to present the candidate-program to the user, based on the specified first proficiency-l

Abstract: A non-transitory computer-readable storage medium storing an optimization program that causes at least one computer to execute a process, the process includes setting, for a region of a design target, a plurality of Gaussian functions as basis functions of a shape function that corresponds to a shape of a design target item in the region; and identifying the shape of the design target item indicated by the shape function obtained by combining the plurality of Gaussian functions identified to be disposed by identifying whether to dispose the plurality of Gaussian functions.

Abstract: A non-transitory computer-readable storage medium storing a merchandise purchase support program that causes at least one computer to execute a process, the process includes obtaining pieces of position information of each of a plurality of users; when there is an inquiry about an arrangement position of certain merchandise from a user of the plurality of users, specifying a first position where the user has performed a certain action indicating a feature of searching for the arrangement position of the certain merchandise, based on the piece of position information obtained in certain time before execution of the inquiry among the pieces of position information of the user; and determining a second position near the specified first position as a candidate for a new arrangement position of the certain merchandise.

Abstract: A process for machine learning processing of a machine learning model in which a natural language processing model and a classification model are combined, the process includes obtaining a first projection matrix that is obtained in an n-th iteration of the machine learning processing and that indicates a correspondence between input data input from the natural language processing model to the classification model and output data output from the classification model, updating a parameter of the natural language processing model, updating a parameter of the classification model by using the first projection matrix, and obtaining, in an n+1-th iteration of the machine learning processing, a second projection matrix that indicates a correspondence between input data input from the updated natural language processing model to the updated classification model and output data output from the updated classification model, wherein the n is a natural number.

Abstract: A data processing method including: upon switching an object to a first group among groups of an Ising model when a search for a solution to a problem represented by the Ising model is performed by switching to each of the groups obtained by dividing the state variables, reading first weighting factors corresponding to pairs of the state variables whose values have changed and each first state variable belonging to the first group; updating a local field of the first state variable; executing the search on the first group by using second weighting factors and the local field of the first state variable; and after ending the search on the first group, updating the change information according to presence or absence of a change in the values for the first state variable by the search at a current time, to switch the object to be searched to a next group.

Abstract: A computer acquires cluster data and performance data. The cluster data represents classification results obtained by classifying multiple sample programs into two or more clusters and arranging the clusters in multiple levels in such a manner that each level contains a different number of clusters. The performance data represents an execution performance of each sample program. The computer calculates, for each of two or more clusters in each level, a first evaluation value based on an index value for reusability of two or more sample programs belonging to the cluster and the execution performances of those sample programs. The computer also calculates, for each level, a second evaluation value based on the first evaluation values corresponding to the two or more clusters of the level. The computer selects, based on the second evaluation values corresponding to the multiple levels, the classification results of a level amongst the multiple levels.