A face liveness detection method is provided, and includes: receiving an image transmitted by a terminal, the image including a face of an object; performing data augmentation on the image, to obtain an extended image corresponding to the image, a number of extended images corresponding to the image being more than one; performing liveness detection on the extended images corresponding to the image, to obtain intermediate detection results of the extended images, a liveness detection model used in liveness detection being obtained by performing model training on an initial neural network model according to a sample image and extended sample images corresponding to the sample image; and obtaining a liveness detection result of the object in the image after fusing the intermediate detection results of the extended images.

An input unit 81 receives input of a decision-making history of a subject. A learning unit 82 learns hierarchical mixtures of experts by inverse reinforcement learning based on the decision-making history. An output unit 83 outputs the learned hierarchical mixtures of experts. The learning unit 82 learns the hierarchical mixtures of experts using an EM algorithm, and when a learning result using the EM algorithm satisfies a predetermined condition, learns the hierarchical mixtures of experts by factorized asymptotic Bayesian inference.

An information processing system includes an objective data acquisition unit and a numerical analysis processing unit (or structure inference unit). The objective data acquisition unit acquires data of the object of numerical analysis (or a design simulation of a construction) expressed as a mesh shape. The numerical analysis processing unit (or structure inference unit) uses a machine learning model obtained by performing machine learning on the result of numerical analysis of physical properties (or a design simulation of a construction) in units of relationships between two adjacent nodes in graph data corresponding to a mesh shape to acquire an inference result inferring a result of numerical analysis (or a result of a design simulation of a construction) for the object of numerical analysis (or a design simulation of a construction).

Artificial intelligence-based systems and methods for learning management are described.

A method and system for developing a predictive model for diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) in a human are disclosed. The method comprises receiving immune system data for each member of a population comprising healthy humans and humans with ME/CFS; extracting a set of features from the immune system data; and training a machine learning algorithm using the set of features to classify a human as healthy or having ME/CFS to obtain a predictive model. The system comprises a processor; and a memory storing computer executable instructions, which when executed by the processor cause the processor to perform operations of said method.

A classification unit causes each of a plurality of classifiers trained to classify data of a corresponding class into one of two values through qSVM to classify data for prediction. Further, the calculation unit calculates the energy of the classification result of the data for prediction for each of the plurality of classifiers. Further, the determination unit determines a class of the data for prediction based on the classification result of the classification unit and the energy calculated by the calculation unit.

Techniques relating to delivery plan generation are improved. A delivery plan generation method comprises: generating a plurality of delivery patterns (S110); narrowing the plurality of delivery patterns down to a designated number of delivery patterns, based on a plurality of solutions calculated as a result of inputting the plurality of delivery patterns to an annealing machine (30) (S120); and generating a delivery plan based on delivery patterns selected from the designated number of delivery patterns (S130).

A data labeling processing method and apparatus, an electronic device, and a medium are provided. A method includes: determining an item feature of an item to be labeled and a resource feature of a labeling end to be matched; determining a co-occurrence feature for the item to be labeled and the labeling end to be matched; obtaining a classification result based on the item feature, the resource feature, and the co-occurrence feature, wherein the classification result indicates whether the labeling end to be matched is matched with the item to be labeled; and sending the item to be labeled to the labeling end to be matched based on the classification result.

A non-transitory computer-readable storage medium storing a program that causes a processor to execute a process, the process includes reading a plurality of first solutions of an optimization problem represented by a combination of values of a plurality of state variables, converting the plurality of first solutions into a plurality of second solutions by executing principal component analysis on the plurality of first solutions, determining a region that indicates a spread of the plurality of second solutions in a solution space, generating a third solution located at a second position outside the region and away from a first position within the region by a first distance, converting the third solution into a fourth solution that consists of the plurality of state variables, and searching for a solution of the optimization problem by using the fourth solution as an initial value of the plurality of state variables.

Techniques are described for implementing an automated control system to control operations of a target physical system, such as production of electrical power in an electrical grid. The techniques may include determining how much electrical power for each of multiple producers to supply for each of a series of time periods, such as to satisfy projected demand for that time period while maximizing one or more indicated goals, and initiating corresponding control actions. The techniques may further include repeatedly performing automated modifications to the control system's ongoing operations to improve the target system's functionality, by using reinforcement learning to iteratively optimize particles generated for a time period that represent different state information within the target system, to learn one or more possible solutions for satisfying projected electrical power load during that time period while best meeting the one or more defined goals.