Aggressor rows may be detected by comparing access count values of word lines to a threshold value. Based on the comparison, a word line may be determined to be an aggressor row. The threshold value may be dynamically generated, such as a random number generated by a random number generator. In some examples, a random number may be generated each time an activation command is received. Responsive to detecting an aggressor row, a targeted refresh operation may be performed.

An electronic device is disclosed that comprises a substrate and an electronic circuit with a layer between them. The layer comprises an electrically insulating medium containing a spatial distribution of conductive elements. The electronic circuit comprises memory contacts arranged for electrical connection to a corresponding contact on the substrate when at least one of the conductive element forms a connection between a memory contact and the corresponding contact but for electrical insulation from the corresponding contact when no conductive elements forms such a connection. A selection of the memory contacts, that is at least partially random, is thus electrically connected to the corresponding contact on the substrate. Memory circuitry is configured to store a representation of a respective electrical connection status of the memory contacts.

A methods comprises: receiving, by a pseudo random number generator module, an instruction to generate pseudo random numbers from a security application; determining, by the pseudo random number generator module, at least one algebraic input parameter value for a transcendental equation from a randomness library in memory of the device, wherein the transcendental equation comprises a transcendental function that is capable of generating transcendental number outputs from algebraic number inputs; calculating, by the pseudo random number generator module, a solution to the transcendental equation based on the at least one algebraic input parameter value; determining, by the pseudo random number generator module, pseudo random number(s) based on the solution; and storing, by the pseudo random number generator module, the pseudo random number(s) in a randomness library for use as seeds for keys by the security application and as subsequent input parameter values for the pseudo random number generator module.

A semiconductor device includes a first control unit, a second control unit, a random number generator, a first memory in which random numbers generated by the random number generator are stored, an encryption engine configured to perform encryption and decryption processes by using the random numbers stored in the first memory, and a second memory in which information related to random number generation is stored. The second control unit is configured to generate the random numbers by the random number generator based on the information related to random number generation.

An electronic element includes: a module for storing reference data; a module for receiving data from a processor; a module for verifying the received data by comparison by way of reference data; and a module for transmitting an instruction to cut off supply of the processor, the supply cutoff instruction being transmitted after occurrence of a failure event, the failure event being an absence of reception of data or a failure in verifying the data. A system including such an electronic element and a method for monitoring a processor by the electronic element are also described.

A search method for a neural network model structure, includes: generating an initial generation population of network model structure based on multi-objective optimization hyper parameters, as a current generation population of network model structure; performing selection and crossover on the current generation population of network model structure; generating a part of network model structure based on reinforcement learning mutation, and generating a remaining part of network model structure based on random mutation on the selected and crossed network model structure; generating a new population of network model structure based on the part of network model structure generated by reinforcement learning mutation and the remaining part of network model structure generated by random mutation; and searching a next generation population of network model structure based on the current generation population of network model structure and the new population of network model structure.

A search method for a neural network model structure, includes: generating an initial generation population of network model structure based on multi-objective optimization hyper parameters, as a current generation population of network model structure; performing selection and crossover on the current generation population of network model structure; generating a part of network model structure based on reinforcement learning mutation, and generating a remaining part of network model structure based on random mutation on the selected and crossed network model structure; generating a new population of network model structure based on the part of network model structure generated by reinforcement learning mutation and the remaining part of network model structure generated by random mutation; and searching a next generation population of network model structure based on the current generation population of network model structure and the new population of network model structure.

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.

Techniques are disclosed for securely distributing entropy in a distributed environment. The entropy that is distributed may be quantum entropy that is generated by a quantum entropy generator or source. The true random entropy generated by a trusted entropy generator can be communicated securely among computer systems or hosts using secure communication channels that are set up using a portion of the entropy. The distribution techniques enable computer systems and hosts, which would otherwise not have access to such entropy generated by the trusted entropy source, to have access to the entropy.

A system and method for sharing user preferences pertaining to one or more products, without having the user reveal their identity, is described herein. The system is configured for registering a user by receiving a set of biometric samples of the user, processing the set of biometric samples to compute a Secret-Key (S1) corresponding to the user, generating a Unique-Number (N1) using a random number generation algorithm, applying a Function (F1) to the Secret-Key (S1) and the Unique-Number (N1), to compute a Public-Key (P1). Once the user is registered, the system is configured to receive a biometric sample from the user in real-time and compute the Secret-Key (S2) for authenticating the user. Once the user is authenticated, the system may recommend to the user, a candidate product from a product catalog, based on the user's preferences.