24 results about "Random order" patented technology

The invention discloses a tamper-proof MCU security data writing method and system, and relates to the technical field of data security, and the method comprises the steps: determining a to-be-upgraded MCU, obtaining an associated upgrade data packet, calculating an initial hash value retention, generating a random byte fragment according to a data size, and obtaining a plaintext upgrade data packet fragment sequence; secondly, randomly sorting from the anti-quantum encryption algorithm candidate pool to determine an encryption algorithm set sequence, generating a plaintext encryption algorithm mapping table, encrypting the plaintext encryption algorithm mapping table, and synchronously issuing the plaintext encryption algorithm mapping table along with a ciphertext upgrading data packet fragment; after the to-be-upgraded MCU obtains the related ciphertext, decryption is carried out to determine an encryption algorithm index, ciphertext fragments are decrypted, a hash value is calculated in a combined mode, and upgrading or rollback operation is determined; the MCU is monitored in real time in the process, all keys are cleared after the process is finished, data tampering is effectively prevented, and it is ensured that MCU upgrading is safe and reliable.

This invention relates to the field of chip verification technology, and more particularly to a random verification method, electronic device, and medium based on register arrays. S1: Set m=1, execute S2; S2: Send data to RA in the target register array. m and RB m Input the same random test stimulus; S3, RA m Predict the random write value, and set F m Updated to RA m Predicted random write values; S4, RB m Write random numbers based on the input random test stimulus; S5, read the currently stored data in the RTL instances of all registers in the chip under test design in random order and compare it with the corresponding reference value. If they are all the same, proceed to S6; otherwise, the verification fails and the process ends; S6, compare m and M. If m

The application provides a read cache optimization method and device in an AI model training scene, comprising: during AI model training, setting each Epoch reading training set data process as two rounds of iterations; in the first round of iteration, each Mini-Batch queries S*N items of data to the cache in random order; S items of data are read from the data in the cache after the query for the current Mini-Batch, and the remaining data is marked; after all the training set data is traversed once, the first round of iteration ends, the second round of iteration starts, and the marked data in the first round of iteration is traversed; each Mini-Batch queries S items of data to the cache, the data is directly read in the cache, or the data is read from a remote storage node. The application improves the read cache hit rate when loading the training set, thereby reducing the time delay of reading data, improving the utilization rate of GPU and other scarce computing power devices, saving computing power, and improving the model training efficiency.

This invention discloses a robust watermarking method for medical images based on the Mamba architecture, resistant to multiple attacks. The method includes: generating a watermarked image by processing the original medical image and watermark information through an encoder network, where the encoder includes a hierarchical feature refinement sampler module and a channel-aware Mamba architecture bottleneck layer; recovering the watermark from the potentially attacked watermarked image through a decoder network, where the decoder includes a wavelet domain Mamba module and a sequence context dependency module for dual-path processing; randomly selecting 2-4 attacks from an attack library and applying them to the watermarked image in a random order and intensity using dynamic composite attacks; and end-to-end optimization using a composite loss function containing reconstruction loss, clean decoding loss, robust decoding loss, and perceptual loss. This invention decouples the image frequency band through discrete wavelet transform, embeds the watermark at high frequencies, and utilizes Mamba's selective state-space mechanism to model the long-range dependencies of wavelet coefficients, thereby improving the robustness of the watermark.

A sample randomizer allows a user to have a set of samples from a corresponding set of sources to be physically randomized so that the user does not know which sample came from which source. The sample randomizer keeps track of the samples so that the source of each sample can be revealed after testing the samples. A covered rotating table is used to physically randomize the samples. Each sample is individually loaded into the table in a given sample position. The other sample positions are obscured from view so that the user cannot see if there is a sample in the adjacent sample positions. The order of the sample and their corresponding sources are tracked, and the samples are dispensed from the table in a random order that is also tracked. The user can then test the samples, placing them in ranking positions of a ranking station of the device. When the user is done testing the samples, the sample randomizer can then indicate the source of each sample at each ranking position.

The invention discloses a nonvolatile memory and a control method thereof. The memory comprises a nonvolatile memory, a random number generator, a power supply and a memory access controller. The nonvolatile memory is used for storing at least one piece of data. The random number generator is used for generating a random number. The power supply is used for generating a random power supply according to the random number and providing the random power supply to the nonvolatile memory. The memory access controller is configured to generate a random order according to the random number, obtain random order data from the non-volatile memory according to the random order, and reconstruct the random order data according to the random order to generate at least one datum.

A method is provided for detecting a fault attack when performing a process including a plurality of operations. The method comprises identifying a group of operations selected from the plurality of operations and performing operations from the group of operations in a random order such that each operation is repeated n times where n is less than or equal to a security parameter corresponding to a maximum number of potential fault attacks. The results of the n repetitions of each operation are compared to determine whether the output value of each repetition of the operation is the same. In a case that the output value of each repetition is not the same an action to record or mitigate a fault attack is performed.

In some embodiments, a system includes a processor and a non-transitory computer readable medium coupled to the processor. In some embodiments, the non-transitory computer readable medium includes code that generates a randomized set of keys for authentication purposes, communicates to a reconfigurable braille (RB) board to render raised RB board keys on the RB board according to a randomized order of the randomized set of keys, and utilizes the raised RB board keys to ascertain an authentication code that is utilized to authenticate a user of a mobile device. In some embodiments, the non-transitory computer readable medium further includes code that maps each key in the randomized set of keys to an authentication character in an authentication code.

The invention provides a reentrant complex manufacturing system simulation evaluation method, and relates to the field of manufacturing system simulation. According to the method, the complexity characteristic of a manufacturing system is fully considered, including reentrancy, order random arrival, order splitting and machine type mixing, and the applicability of the simulation method is improved. According to the method, multiple operation priority value calculation rules are provided for two key links of operation release and operation dispatch, a combined strategy is designed by adopting a predicted influence function and constraint planning, the operation property and workshop state influence are comprehensively considered, the number of working products of a manufacturing system is effectively controlled, and meanwhile, the working efficiency of the manufacturing system is improved. And the order on-time delivery rate of an enterprise is improved. A plurality of performance test targets adopted by the invention can effectively describe the state of the production system and measure the effectiveness of the strategy provided by the invention. In a word, the simulation method provided by the invention can assist an enterprise administrator in evaluating the rationality of the production plan on the premise of not changing the actual production state, so that the decision-making cost is reduced.

A computer-implemented method, system, and computer program product are disclosed. According to embodiments, training data can be received, the training data can include speech and pairs of meaning representations associated with the speech as ground truth data. The meaning representations include at least semantic entities associated with the speech, where an order of utterance of the semantic entities is unknown. The semantic entities of the meaning representations in the training data can be reordered into an order of utterance of the associated speech using an alignment technique. A spoken language understanding machine learning model can be trained using the pairs of speech and pairs of meaning representations with the reordered semantic entities. The meaning representations in the received training data can be perturbed, e.g., the semantic entities, to create a random order sequence variant of the semantic entities associated with the speech. The perturbed meaning representations together with the associated speech can augment the training data.

The present disclosure relates to a kind of identification code generation method, device, server and storage medium, involve computer technical field.The method comprises: receiving the identification code generation request of user account;Based on the set self-increment variable, generate the first numerical sequence identification code corresponding to identification code generation request;First numerical sequence is randomly ordered to generate second numerical sequence;Second numerical sequence is compressed in length to generate the target identification code corresponding to identification code generation request.The present disclosure guarantees the uniqueness of the identification code corresponding to different identification code generation request, ensures that the target identification code to be generated finally has no fixed rule, has random order and confidentiality, and reduces the length of the identification code to be generated finally, makes it beneficial to be shown on user terminal, improves user experience.Each step of the present disclosure is completed separately, without using additional storage space, time complexity and space complexity are lower, with high efficiency.

The invention relates to the technical field of big data analysis, in particular to a self-learning intelligent order dispatching method based on cloud big data, and the method comprises the steps: obtaining historical clue data and the clue number of the current month; determining an order sending mode for the clues based on a comparison result of a ratio of the number of clues in the current month to the historical monthly average number of clues in the same period and a preset ratio, wherein the order sending mode comprises random order sending, basic completion duration determined based on a target closeness degree characterization value, order sending based on the determined basic completion duration, and scheduling resource order sending; when it is judged that scheduling resource order dispatching is carried out, a constraint mode is determined based on the historical order completion condition of scheduling resources, and constraint types comprise process node constraints and completion timeliness constraints; according to the method and the system, intelligent order dispatching is realized by improving the accuracy of analyzing the number of the tasks and the closeness degree of the target and the order dispatching platform, so that the task processing efficiency is improved.

The application discloses a virtual digital outbound intelligent customer service system based on generative AI, and relates to the technical field of virtual customer service management.The application solves the technical problems of lack of effective monitoring mechanism for service quality and single encryption processing mode for interactive data.The application monitors the overall state of the intelligent customer service in real time through the calculation of multi-dimensional indexes such as the knowledge anchor point answer rate, semantic ambiguity answer proportion and high-frequency complaint problem correlation rate, so as to facilitate the targeted optimization of the knowledge base, answer template or service process by the management personnel, improve the user service experience and system reliability, generate random modification features for the data packet byte size, dynamically bundle data packets and send them in random order, introduce a random delay transmission strategy, effectively interfere with the analysis of the traffic characteristics by attackers, prevent data eavesdropping and content tampering, and guarantee the safety and privacy of the user interactive information in the transmission process.

The application discloses a hydrological simulation result fine-grained interpolation prediction method based on a graph neural network, relates to the technical field of hydrological simulation, and comprises the following steps: S1, a simulation network data carrier is constructed through a data table; S2, open source data obtained is calculated item by item, column data is added, and a static data table is output; S3, based on the static data table, dynamic simulation results are obtained through modeling simulation; S4, the data table is converted into a graph structure network data set composed of node and edge relationship information; S5, an order division experiment and a random order division experiment are designed, and a training data division mode used is determined; S6, a graph neural network model is constructed and trained; S7, rolling prediction is performed; and S8, the constructed graph neural network model is enhanced in a targeted manner. According to the application, fine-grained interpolation prediction of simulation results in the time dimension is performed, the calculation efficiency of hydrological simulation is accelerated, the prediction period of potential risks is shortened, and the agility of emergency feedback of flood season mountain disaster risks is improved.

The invention provides a fine tuning method of a retrieval reply large model and a retrieval reply method and device, and relates to the technical field of large models, in particular to the technical field of artificial intelligence such as natural language processing and deep learning. Comprising the steps that a candidate retrieval reply large model is obtained, a target sample data set corresponding to the candidate retrieval reply large model is obtained, the target sample data set comprises a target sample question and a target sample text of the target sample question, and the target sample text comprises target sample text blocks spliced based on a random sequence; calling the model capability of the candidate retrieval reply large model, and obtaining an output reply text corresponding to the target sample question based on the target sample text; obtaining a corresponding target dependent text block from each target sample text block, and determining a target correlation score of the target dependent text block; and performing model fine tuning on the candidate retrieval reply large model based on the target correlation score to obtain a fine-tuned target retrieval reply large model.

This invention provides an information processing apparatus, a control method for the information processing apparatus, and a program. Typically, when multiple tags automatically provided for content are arranged and displayed in a random order, the visibility of the tag information deteriorates. The information processing apparatus provided by this invention determines first relevant information about the content and acquires multiple second relevant information about the content. Then, based on the first relevant information, the order of the multiple second relevant information is set, and screen information is generated so that the multiple second relevant information are arranged and displayed on the screen according to the set order.

Some embodiments of the present disclosure provide an associatively indexed circular buffer (ACB). The ACB may be viewed as a dynamically allocatable memory structure that offers in-order data access (say, first-in-first-out, or “FIFO”) or random order data access at a fixed, relatively low latency. The ACB includes a data store of non-contiguous storage. To manage the pushing of data to, and popping data from, the data store, the ACB includes a contiguous pointer generator, a content addressable memory (CAM) and a free pool.

The invention belongs to the technical field of geophysical exploration, and discloses a method and system for matching local anisotropy in seismic inversion, and the method comprises the steps: collecting initial seismic data, and generating a plurality of initial seismic attribute models through random sequence simulation; performing forward modeling based on the initial seismic attribute model to obtain a synthetic seismic section, and comparing the synthetic seismic section with a real seismic trace; and iteratively updating the local anisotropy model to randomly update the seismic attribute model parameters according to the difference between the forward modeling result and the actual data, and optimizing the inversion result until the inversion is converged. According to the method, the local anisotropy model is updated through iteration, the geological medium consistency and credibility of the inversion model are improved, and the inversion result is improved.

A method and a system for performing stochastic sequential assignment of jobs with random arrival times is provided. The method includes receiving a first plurality of jobs in a sequence; and sequentially applying, to each respective job from among the first plurality of jobs, a non-parametric sequential allocation algorithm in order to determine whether to accept the respective job or to decline the respective job. The application of the non-parametric sequential allocation algorithm includes calculating, for each respective job, a corresponding reward value that relates to a reward that is gained when the respective job is accepted; and maximizing an expected cumulative reward value based on the calculated reward values.

The application discloses a privacy protection gene sequence alignment method and system based on a oblivious transfer protocol, and comprises the following steps: setting a shared public random string; adopting one-hot encoding to encode a first gene sequence and a second gene sequence; selecting a first random string and a second random string in valid bits in the first gene sequence base encoding to form a first random ordered pair, wherein the first random string of the valid bits satisfies the public random string, and selecting a second random ordered pair in non-valid bits, so as to obtain a base encoding ordered pair; performing an oblivious transfer protocol according to each bit in the second gene sequence base encoding and the base encoding ordered pair to obtain a matching result of each bit; and judging whether the first gene sequence and the second gene sequence are equal according to the matching result. The method can protect gene sequence information and realize efficient and safe alignment of the gene sequence.

The invention discloses a hydrological simulation result fine-grained interpolation prediction method based on a graph neural network, and relates to the technical field of hydrological simulation, and the method comprises the steps: S1, constructing a simulation network data carrier through a data table; s2, performing item-by-item calculation on the acquired open source data, adding column data, and outputting a static data table; s3, based on the static data table, obtaining a dynamic simulation result through modeling simulation; s4, converting the data table into a graph structure network data set formed by nodes and connection edge relation information; s5, designing a sequence division experiment and an out-of-order division experiment, and determining a used training data division mode; s6, constructing a graph neural network model and performing training; s7, performing rolling prediction; and S8, performing targeted enhancement on the constructed graph neural network model. According to the method, fine-grained interpolation prediction of the time dimension is carried out on the simulation result, the calculation efficiency of hydrological simulation is improved, the prediction period of potential risks is shortened, and the sensitivity of emergency feedback of mountain disaster risks in the flood season is improved.

The invention discloses a digital order-preserving symmetric encryption method and system based on an obfuscation mechanism, and relates to the technical field of information security. The method comprises the following steps: acquiring original data to be encrypted, and constructing a random order-preserving function; the method comprises the following steps: encrypting original data to be encrypted by using a random order-preserving function to obtain an order-preserving symmetric encryption ciphertext, and hiding the order of the order-preserving symmetric encryption ciphertext by using a confusion mechanism to obtain a disordered confused order-preserving encryption result value; range query and size comparison are carried out on disordered confused order-preserving encryption result values, decryption is carried out firstly in the range query process and the size comparison process, and confusion offset brought by a confusion mechanism is removed. According to the method, the problems of insufficient security of sensitive numerical data encryption and improper processing of non-integer data in a data protection scene are solved, and powerful support is provided for data security protection in various fields.

The application discloses a power and computing power cooperative optimization scheduling method and system for a data center microgrid, and relates to the technical field of intelligent scheduling and reinforcement learning. The application firstly constructs a heterogeneous multi-agent cooperative decision system composed of server agents and energy storage agents, uniformly models the computing resources and energy resources of the data center, then introduces a random order policy update mechanism based on a heterogeneous multi-agent proximal policy optimization algorithm, realizes stable cooperative learning and policy optimization among different types of agents, constructs a constraint evaluation network to depict quality of service constraints, and realizes constraint embedded policy optimization by combining a Lagrange multiplier method, so that the scheduling strategy is jointly optimized while meeting the quality of service constraints. The application utilizes the policy optimization mechanism in reinforcement learning to realize efficient cooperative configuration of computing resources and energy resources in a dynamic and uncertain environment, and is suitable for a data center microgrid scene containing renewable energy and energy storage systems.