Abstract: A method of equality verification using relational encryption including receiving a relational key that includes a first relational key component and a registration ciphertext that includes an encryption of a first plaintext data set. The method includes storing the registration ciphertext without decrypting the registration ciphertext. After the storing of the registration ciphertext, the method includes receiving an authentication request and communicating a safeguard data set that includes a random challenge in response to the authentication request. The method includes receiving an encrypted response that is generated based on the safeguard data set and a second plaintext data set. The method includes verifying a relationship between the encrypted response and the registration ciphertext using the relational key without decrypting the encrypted response and without decrypting the registration ciphertext. The relationship indicates that equality exists between the first and the second plaintext data sets.

Abstract: A method includes receiving biometric data, the biometric data non-uniformly distributed and processing the biometric data to a level of randomness as a plaintext vector, the level of randomness associated with a security level. The method also includes encrypting the plaintext vector using a relational linearity encryption scheme to generate a linearity ciphertext representative of the plaintext vector, encrypting the plaintext vector using a relational proximity encryption scheme to generate a proximity ciphertext representative of the plaintext vector, and communicating the linearity ciphertext and the proximity ciphertext to an authentication server.

Abstract: A method includes receiving biometric data, the biometric data non-uniformly distributed and processing the biometric data to a level of randomness as a plaintext vector, the level of randomness associated with a security level. The method also includes encrypting the plaintext vector using a relational linearity encryption scheme to generate a linearity ciphertext representative of the plaintext vector, encrypting the plaintext vector using a relational proximity encryption scheme to generate a proximity ciphertext representative of the plaintext vector, and communicating the linearity ciphertext and the proximity ciphertext to an authentication server.

Abstract: A method of equality verification using relational encryption including receiving a relational key that includes a first relational key component and a registration ciphertext that includes an encryption of a first plaintext data set. The method includes storing the registration ciphertext without decrypting the registration ciphertext. After the storing of the registration ciphertext, the method includes receiving an authentication request and communicating a safeguard data set that includes a random challenge in response to the authentication request. The method includes receiving an encrypted response that is generated based on the safeguard data set and a second plaintext data set. The method includes verifying a relationship between the encrypted response and the registration ciphertext using the relational key without decrypting the encrypted response and without decrypting the registration ciphertext. The relationship indicates that equality exists between the first and the second plaintext data sets.

Abstract: A method executed by a computer includes applying a correction to an acceleration value of an object at an interval; calculating an initial speed of the object by assigning the corrected acceleration value in a period to a third relational expression that is obtained by transforming a first relational expression and a second relational expression so as to obtain an initial speed from the acceleration in the period and the interval, where the first relational expression is to obtain an object position from acceleration and an initial speed, and the second relational expression represents force applied to the object due to fluctuation of the center of gravity of the object as a motion equation of a spring model; and calculating coordinate values over a horizontal plane that indicates a center of gravity of the object by assigning the corrected acceleration value and the initial speed to the first relational expression.

Abstract: A farming support method includes accepting input of information indicating a state of a crop or information indicating a state of an environment, and extracting, from a first storage unit that stores restriction information including a condition related to the state of the crop or the state of the environment and a work instruction for a case in which the condition is satisfied, the restriction information having the condition matching the information indicating the state of the crop or the information indicating the state of the environment that is input. The method further includes judging, using a second storage unit that stores a judgement result on whether to adopt each restriction information for each cultivation example of the crop, whether to adopt the restriction information that is extracted.

Abstract: A method executed by a computer includes applying a correction to an acceleration value of an object at an interval; calculating an initial speed of the object by assigning the corrected acceleration value in a period to a third relational expression that is obtained by transforming a first relational expression and a second relational expression so as to obtain an initial speed from the acceleration in the period and the interval, where the first relational expression is to obtain an object position from acceleration and an initial speed, and the second relational expression represents force applied to the object due to fluctuation of the center of gravity of the object as a motion equation of a spring model; and calculating coordinate values over a horizontal plane that indicates a center of gravity of the object by assigning the corrected acceleration value and the initial speed to the first relational expression.

Abstract: A rare pattern that may be difficult to extract is extracted by extracting data where a rare pattern is likely to exist, and then generating the rare pattern using a degree of influence.

Abstract: A computer-readable recording medium stores a case generation program that allows a computer to execute a process of generating a case having one or more design variable values and one or more object variable values corresponding to the design variable values, the process including: acquiring, from a storage section, a plurality of past cases which are obtained through a past evaluation process; predicting the object variable value of the past cases from the design variable values of the acquired past cases; calculating a prediction error and confidence as the prediction result of the past cases; selecting a reference case being a past case used as a reference of the generation based on the calculated prediction error and the confidence; determining a new design variable value based on the design variable value of the selected reference case; and determining a new case based on the new design variable value.

Abstract: A rare pattern that may be difficult to extract is extracted by extracting data where a rare pattern is likely to exist, and then generating the rare pattern using a degree of influence.

Abstract: A prediction analysis apparatus includes a prediction unit for predicting a result value corresponding to one or more attribute values of unknown data using known data indicating the correspondence between one or more attribute values and corresponding result values, and an analysis unit for outputting analysis information indicating how at least one attribute value of the unknown data is to be amended to change a result value predicted by the prediction unit into a desired prediction value. The prediction analysis apparatus with the configuration can be realized by, for example, a computer executing a program for realizing the function of each of the above mentioned units.

Abstract: A data display apparatus and method for displaying the result of a data mining process as multi-dimensional data. In one embodiment, the multi-dimensional data is displayed on a parallel coordinate axis. An engine unit executes the data mining process on displayed multi-dimensional data according to an instruction from a visual data mining tool and transfers the result to the visual data mining tool. The user interface unit of the visual data mining tool generates an axis corresponding to the result of the data mining process, adds the axis to the parallel coordinate axis and displays the result of the data mining process on the added axis.

Abstract: A data display apparatus and method for displaying the result of a data mining process as multi-dimensional data. In one embodiment, the multi-dimensional data is displayed on a parallel coordinate axis. An engine unit executes the data mining process on displayed multi-dimensional data according to an instruction from a visual data mining tool and transfers the result to the visual data mining tool. The user interface unit of the visual data mining tool generates an axis corresponding to the result of the data mining process, adds the axis to the parallel coordinate axis and displays the result of the data mining process on the added axis.

Abstract: An optimum similar case extracting condition is automatically determined using a known case aggregate, and the unknown field of an unknown case aggregate is predicted from the known case aggregate using the condition. At this point, the degree of similarity depending on the distribution of the values of the unknown field in the known case aggregate is calculated and a similar case aggregate is extracted based on the degree of similarity. When it is confirmed that the degree of similarity does not satisfy a predetermined condition, the similarity calculation is stopped.

Abstract: A prediction analysis apparatus includes a prediction unit for predicting a result value corresponding to one or more attribute values of unknown data using known data indicating the correspondence between one or more attribute values and corresponding result values, and an analysis unit for outputting analysis information indicating how at least one attribute value of the unknown data is to be amended to change a result value predicted by the prediction unit into a desired prediction value. The prediction analysis apparatus with the configuration can be realized by, for example, a computer executing a program for realizing the function of each of the above mentioned units.

Abstract: The present invention displays multi-dimensional data on a parallel coordinate axis. An engine unit executes the data mining process on displayed multi-dimensional data according to an instruction from a visual data mining tool and transfers the result to the visual data mining tool. The user interface unit of the visual data mining tool generates an axis corresponding to the result of the data mining process, adds the axis to the parallel coordinate axis and displays the result of the data mining process on the added axis.

Abstract: The present invention displays multi-dimensional data on a parallel coordinate axis. An engine unit executes the data mining process on displayed multi-dimensional data according to an instruction from a visual data mining tool and transfers the result to the visual data mining tool. The user interface unit of the visual data mining tool generates an axis corresponding to the result of the data mining process, adds the axis to the parallel coordinate axis and displays the result of the data mining process on the added axis.

Abstract: An adaptive equalizer detects characteristics of a transmission line in radio and cable communications and equalizes received signals by using the detected results. Equalization is for not only linear distortion but also nonlinear distortion in accordance with variation of characteristics of the transmission line. The adaptive equalizer includes a characteristic detection unit 11 for detecting real part and imaginary parts of a transfer function by using an output signal corresponding to an input signal of an object to be equalized to compensate for deterioration, for example a transmission line; and an equalizing unit 12 for equalizing an output signal of the object to be equalized 10 in accordance with an unknown input signal by using the detected result of the characteristic detection unit 11.