A method includes identifying, using at least one processor, a first point associated with an uncertain location of an object in a space and a polynomial curve associated with an uncertain location of a feature in the space. The method also includes determining, using the at least one processor, a probabilistic proximity of the object and the feature. The probabilistic proximity is determined by identifying a second point on the polynomial curve, transforming an uncertainty associated with the polynomial curve into an uncertainty associated with the second point, and identifying the probabilistic proximity of the object and the feature using the first and second points and the uncertainty associated with the second point.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images; identifying a foreground of the one or more digital images; analyzing the foreground of the one or more digital images to identify a skin region in the foreground of the one or more digital images; when the skin region is identified, clustering a non-skin remainder of the foreground of the one or more digital images into one or more clusters; extracting one or more patches of the one or more digital images from the one or more clusters of the foreground of the one or more digital images; determining one or more scores for the one or more patches of the one or more digital images; and coordinating displaying a patch of the one or more patches on an electronic display based on the one or more scores for the one or more patches. Other embodiments are disclosed herein.

A method of training a machine learning model to predict seafloor ripple geometry that includes receiving one or more input values, each input value based on an observation associated with ocean wave and seafloor conditions, and preprocessing the one or more input values. The method includes generating a training data set based on the preprocessed data set, splitting the training data set into a plurality of folds, and training via stacked generalization the machine learning model by performing a cross validation of each fold of training data based on at least one deterministic equilibrium ripple predictor model and on at least one machine learning algorithm. The method may include generating via the trained machine learning model, a set of one or more seafloor ripple geometry predictions, and performing Bayesian regression on the set of one or more seafloor ripple predictions to generate a probabilistic distribution of predicted seafloor ripple geometry.

A delta debugging method and system based on a probability model which includes: acquiring an initial probability model and historical test data; optimizing and iterating the initial probability model through the historical test data to obtain an optimized and iterated probability model; and performing delta debugging on a target program containing multiple lines of code based on the optimized and iterated probability model to obtain a debugging result that meets a first preset condition. Therefore, by adopting the embodiment of the present application, the initial probability model can be continuously optimized and iterated through the historical test data, and the target program can be debugged based on the optimized and iterated probability model, so that the debugging result can be improved, or the compression amount of the size of the target program can reach a target compression amount, or the debugging time can be greatly reduced.

In some implementations there may be provided a system. The system may include a processor and a memory. The memory may include program code which causes operations when executed by the processor. The operations may include analyzing a series of events contained in received data. The series of events may include events that occur during the execution of a data object. The series of events may be analyzed to at least extract, from the series of events, subsequences of events. A machine learning model may determine a classification for the received data. The machine learning model may classify the received data based at least on whether the subsequences of events are malicious. The classification indicative of whether the received data is malicious may be provided. Related methods and articles of manufacture, including computer program products, are also disclosed.

A system optimal control technique with accuracy guarantee that enables high-speed calculations is provided. One aspect of the present invention is related to a system optimal control device including a graph converting unit configured to convert, based on an upper bound of an probability of arrival from an initial state to a final state of a stochastic game representing system information, the stochastic game into a flow analysis graph, a path selecting unit configured to select a path having a maximum width among paths from each state node to a final state node in the converted flow analysis graph, a width of each of the paths being defined as a minimum weight of edges forming the path, and a convergence determining unit configured to determine convergence of the upper bound and a lower bound of the probability of arrival of the stochastic game based on information about the selected path.

The present disclosure relates to a system, a method, and a product for optimizing hyper-parameters for generation and execution of a machine-learning model under constraints. The system includes a memory storing instructions and a processor in communication with the memory. When executed by the processor, the instructions cause the processor to obtain input data and an initial hyper-parameter set; for an iteration, to build a machine learning model based on the hyper-parameter set, evaluate the machine learning model based on the target data to obtain a performance metrics set, and determine whether the performance metrics set satisfies the stopping criteria set. If yes, the instructions cause the processor to perform an exploitation process to obtain an optimal hyper-parameter set, and exit the iteration; if no, perform an exploration process to obtain a next hyper-parameter set, and perform a next iteration with using the next hyper-parameter set as the hyper-parameter set.

The present disclosure relates to methods for estimating an accuracy and robustness of a model and devices thereof. According to an embodiment of the present disclosure, the method comprises calculating a parameter representing a possibility that a sample in the first dataset appears in the second dataset; calculating an accuracy score of the model with respect to the sample in the first dataset; calculating a weighted accuracy score of the model with respect to the sample in the first dataset, based on the accuracy score, by taking the parameter as a weight; and calculating, as the estimation accuracy of the model with respect to the second dataset, an adjusted accuracy of the model with respect to the first dataset according to the weighted accuracy score.

The present disclosure relates to methods for estimating an accuracy and robustness of a model and devices thereof. According to an embodiment of the present disclosure, the method comprises calculating a parameter representing a possibility that a sample in the first dataset appears in the second dataset; calculating an accuracy score of the model with respect to the sample in the first dataset; calculating a weighted accuracy score of the model with respect to the sample in the first dataset, based on the accuracy score, by taking the parameter as a weight; and calculating, as the estimation accuracy of the model with respect to the second dataset, an adjusted accuracy of the model with respect to the first dataset according to the weighted accuracy score.

Systems and methods that address an optimized method in the area of optimization by showing how to generate Ising Hamiltonians automatically for a large class of optimization problems specially handling the constraints. The innovation facilitates qubit reduction in connection with an optimization problem by representing respective integer variables as linear sums of binary variables, wherein depending on the representation, additional equality constraints are provided. Additional slack variables are introduced to change inequality constraints to equality constraints. Based on the equality constraints, an unconstrained pseudo-boolean optimization problem is created. The pseudo-boolean optimization problem is quadratized to generate a quadratic pseudo-boolean function (QPBF) and the number of variables in the QPBF is reduced to facilitate qubit reduction. This results in an automated, problem instance dependent qubit reduction procedure. Thus, this innovation provides an effective method to solve such class of optimization problems by formulating efficient Ising Hamiltonians for integer optimization problems followed by an automated qubit reduction procedure to get the final Ising Hamiltonian, which can be solved using a quantum optimization algorithm.