Abstract: Provided are a random number generation device and the like capable of calculating a high precision random number using a memory capacity selected irrespective of the precision of the random number. A random number calculation device is configured to generate first random numbers based on given number and specify, for the given number of second random numbers in a target numeric extent, bin range depending on the first random numbers based on frequency information representing cumulative frequency regarding a frequency of numeric extent including respective second random numbers among given numeric extents, the numeric extent being determined in accordance with a desirable precision.

Abstract: A classification model with a high precision ratio at a high recall ratio is learned. A classification model learning system (100) includes a learning data storage unit (110) and a learning unit (130). The learning data storage unit (110) stores pieces of learning data each of which has been classified as a positive example or a negative example. The learning unit (130) learns, by using the pieces of learning data, a classification model in such a way that a precision ratio of classification by the classification model is made larger under a constraint of a minimum value of a recall ratio of classification by the classification model.

Abstract: A random number generation system 20 generates a random number using a public key, a component of which is the member of a residue class ring modulo of a predetermined natural number excluding natural numbers represented by the power of a prime in composite numbers, the random number generation system including: a factorizing means 21 that computes the prime factorization for a predetermined natural number; and a generation means 22 that generates a random number in accordance with a discrete Gaussian distribution over a lattice wherein a vector having non-zero components of a single prime factor obtained by computing prime factorization and ?1 is a basis vector.

Abstract: The random number generation system 10 includes: a first generation means 11 that generates a random number according to a one-dimensional discrete Gaussian distribution on a first lattice that is a lattice comprising an addition vector obtained by adding the second vector to the first vector and a subtraction vector obtained by subtracting the second vector from the first vector; a second generation means 12 that generates a random number according to a one-dimensional discrete Gaussian distribution on a second lattice that is the first lattice in which a vector obtained by dividing the sum of the addition vector and the subtraction vector by 2 is added; and an instruction means 13 that instructs the first generation means 11 or the second generation means 12 to generate a random number.

Abstract: Provided are a random number generation device and the like capable of calculating a high precision random number using a memory capacity selected irrespective of the precision of the random number. A random number calculation device is configured to generate first random numbers based on given number and specify, for the given number of second random numbers in a target numeric extent, bin range depending on the first random numbers based on frequency information representing cumulative frequency regarding a frequency of numeric extent including respective second random numbers among given numeric extents, the numeric extent being determined in accordance with a desirable precision.

Abstract: An inference system according to the present invention relates to inference from a starting state and a first rule set to an ending state. The inference system includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations includes: receiving a parameter for use in selecting a second rule set from the first rule set; and visualizing the second rule set associated with the parameter.

Abstract: A reasoning system that enables reasoning when there is a shortage of knowledge. An input unit receives a start state and an end state. A rule candidate generation unit identifies a first state, obtained by tracking one or more known rules from the start state, and a second state, obtained by backtracking one or more known rules from the end state, respectively. The generation unit generates a rule candidate relating to the first state and the second state or generates a rule candidate relating to the first state and a rule candidate relating to the second state. A rule selection unit selects, based on feasibility of the generated rule candidate, which is calculated based on one or more known rules, the generated rule candidate as a new rule. A derivation unit derives the end state from the start state, based on one or more known rules and the new rule.

Abstract: A classification model with a high precision ratio at a high recall ratio is learned. A classification model learning system (100) includes a learning data storage unit (110) and a learning unit (130). The learning data storage unit (110) stores pieces of learning data each of which has been classified as a positive example or a negative example. The learning unit (130) learns, by using the pieces of learning data, a classification model in such a way that a precision ratio of classification by the classification model is made larger under a constraint of a minimum value of a recall ratio of classification by the classification model.