39 results about "Heuristics" patented technology

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

This disclosure describes techniques and architecture provide automated contribution analysis for “why question” style NLQ answering, e.g., “why is revenue down in North America Q1 2022.” In particular, the techniques described herein combine multiple signals together including, for example, frequency of use of combinations of dimensions in previous NLQs (warm-start), statistical information about columns (e.g., entropy), correlation / co-occurrence between pairs of dimension columns, and correlation between dimensions and dates. This information is used with a set of heuristics and rules to pick the best set of dimensions as contributing factors for a particular metric over a particular time period and present an automatic contribution analysis to the users to give them insights into their data.

There is provided a system and methods to determine an improved solution to a Mixed Integer Problem (MIP) using a quantum-assisted MIP solver. The methods are performed by a digital processor in communication with a quantum processor. Methods include: selecting at least one feasible solution determined by an MIP solver, determining a first sub-problem of the MIP based on the at least one feasible solution; casting the first sub-problem as Binary Quadratic Models (BQMs); solving the BQMs using the quantum processor to generate sample solutions; determining a second sub-problem based on at least the sample solutions, and obtaining a current solution to the MIP by evaluating the second sub-problem; and updating an incumbent solution if the current solution improves over the current incumbent solution. The quantum-assisted MIP solver uses hybrid crossover and mutation heuristics to improve the convergence time and accuracy of solutions obtained using Branch-and-Cut solvers.

Systems, computer program products, and methods are described herein for synthetic intrusion data generation and remediation via machine learning. The present disclosure includes training a first machine learning model using a plurality of malicious code segments from a code repository, generating, using the first machine learning model, a predetermined number of generated code segments, training a second machine learning model using the plurality of malicious code segments, generating, by using the second machine learning model, at least one generated heuristic mitigation resource for the generated code segments, analyzing, via a static heuristics analysis, stored code on an endpoint device, and applying the at least one generated heuristic mitigation resource upon a first condition wherein the static heuristics analysis identifies a malicious activity.

A system method for performing distributed training of models. The method includes co-optimizing memory optimizations with parallelism to increase model training throughput under a memory constraint by orchestrating a plurality of optimizations to utilize system resources in consideration of computation, communication and memory footprint. The system can include an overlap-centric schedule template that determines granularity and order of how techniques utilized by the plurality of optimizations are applied to a model. The overlap-centric schedule template mitigates tuning complexity by applying heuristics to orchestrate optimizations in an overlapped manner.

The invention discloses a two-stage reinforcement learning SAT solving method for structure perception and a related device, and belongs to the field of chip design. Chip signal polarity and logic consistency information is introduced in a variable initialization stage, an initial feature vector for structure perception is constructed, and the limitation that traditional graph modeling can only represent a local logic relation is broken through; a two-stage reinforcement learning framework composed of a main path and a sampling path is designed, the sampling path is used for exploring a chip logic solution space, the main path is used for stably utilizing an optimal verification strategy, and the problems of unstable training convergence, single strategy and the like are effectively relieved. Based on a reward-guided feature updating mechanism, dynamic evolution of chip signal features and enhanced feedback are explicitly bound, the interpretability of a solver is improved, and the difference between neural reasoning and chip logic symbol reasoning is reduced. A signal assignment decision is executed based on GNN multi-round message passing results, and a verification track is continuously optimized, so that efficient chip design verification capability can be obtained without depending on an artificial heuristic mode.

The present application relates to the technical field of GPU computing optimization, and in particular to a sparse attention computing method, device and medium for GPU, wherein the method realizes the high efficiency of long context reasoning through the geometric perception sparse attention framework of ball hashing, and combines a large-scale parallel hashing optimization algorithm and a load adaptive computing kernel. Compared with the existing sparse attention methods based on heuristics or gradient learning, the present application realizes higher retrieval recall rate, lower preprocessing overhead and efficient hardware adaptation to irregular sparse patterns.

The application relates to the technical field of binary program analysis and rewriting, and discloses an immediate number symbolization judgment method and system of a binary rewriting program, which comprises a preprocessing step, a basic evidence analysis step, an intermediate evidence analysis step, a high-level evidence analysis step and a fusion decision step. The preprocessing layer carries out structural analysis and semantic modeling on a binary file, extracts candidate immediate numbers and meta information, the basic evidence layer carries out preliminary weighting based on explicit rules, the intermediate evidence layer carries out quantitative evaluation through heuristics and statistical patterns, the high-level evidence layer carries out high-precision analysis on the immediate numbers with semantic ambiguity, the fusion decision layer summarizes the evidences at all levels, forms a comprehensive tendency value through weighted calculation, classifies the immediate numbers, and generates a final symbolization strategy. The application significantly improves the accuracy and interpretability of symbolization judgment, balances the analysis precision and processing efficiency, and is suitable for binary files of different compilers, platforms and file stripping states.

This invention discloses a single-arm combined device scheduling method based on hierarchical attention graph deep reinforcement learning. This method aims to address the problems of existing rule-based methods relying on artificial heuristics, and the complexity of Petri net models while neglecting topological properties. This invention proposes a State Extraction Graph (SEG) to simplify the modeling process for multi-variety wafer manufacturing, and its graph structure is simpler than Petri nets. Based on SEG, a graph deep reinforcement learning framework is developed, integrating a graph neural network based on a hierarchical attention mechanism (called A-GIN) as a feature extractor to better extract the topological properties of the graph. The single-arm robot agent interacts with the environment, autonomously learns and optimizes wafer release and task sequences, achieving end-to-end policy optimization.

Aspects of this technical solution can generate, according to one or more first environment metrics, a three-dimensional (3D) model including a first surface corresponding to one or more physical ways through a physical environment, the one or more first environment metrics indicative of boundaries of the one or more physical ways, generate, according to one or more second environment metrics, one or more geometric two-dimensional (2D) objects on the first surface, the second environment metrics indicative of the one or more physical ways, identify, according to one or more viewpoint metrics indicative of cameras of a physical object configured to move along the one or more physical ways, one or more viewpoints oriented to capture corresponding portions of the 3D model, and render, from the one or more corresponding portions of the 3D model, one or more 2D images each corresponding to respective ones of the viewpoints.

Systems and methods for enterprise production scheduling using a self-learning, resilient Reinforcement Learning (RL) agent. The RL agent interacts with a simulated production environment modeled as a dynamic graph, enabling efficient handling of complex multi-stage scheduling dependencies. Through iterative training, inference, and continuous learning modes, the agent autonomously learns optimal scheduling policies, adapts to evolving production conditions, and incorporates user preferences. The system includes components such as a data profiler for historical analysis, a synthesizer for training data generation, and an initializer for environment setup. The RL agent generates multiple feasible schedules, refines its policy based on feedback, and significantly reduces computational overhead compared to traditional heuristics and genetic algorithms.

Methods, systems, and computer-readable storage media for providing a set of heuristics representative of training data that is to be used to process a ML model through a training pipeline, the training pipeline including multiple phases, determining a set of time estimates by providing the set of heuristics as input to a training heuristics model that provides the set of time estimates as output, each time estimate in the set of time estimates indicating an estimated duration of a respective phase of the training pipeline, receiving, during processing of the ML model through the training pipeline, progress data representative of a progress of processing of the ML model, determining a set of status estimates including a status estimate for each phase of the training pipeline based on the progress data, and transmitting the set of time estimates and the set of status estimates for display.

A system method for performing distributed training of models. The method includes co- optimizing memory optimizations with parallelism to increase model training throughput under a memory constraint by orchestrating a plurality of optimizations to utilize system resources in consideration of computation, communication and memory footprint. The system can include an overlap-centric schedule template that determines granularity and order of how techniques utilized by the plurality of optimizations are applied to a model. The overlap-centric schedule template mitigates tuning complexity by applying heuristics to orchestrate optimizations in an overlapped manner.

A training optimization system implements algorithmic solutions to solve the conjoined problem of accelerator architecture search and model partitioning for distributed training. The system makes the multi-dimensional optimization space of architecture search and device placement tractable by reducing the number of accelerator architectures explored through area-based heuristics and employing a novel integer linear program (ILP), the size of which is dependent only on the number of operators. The ILP scheduling optimization also explores the partitioning of operators across cores, known as intra-operator parallelism. Despite the vast space, the ILP described herein requires significantly less time to perform the optimizations across all explored accelerator configurations. Based on the optimal backward and forward pass latencies, the system leverages a novel dynamic programming (DP) approach to determine the device placement and model partitioning scheme.

The approaches presented here involve clustering geometric primitives, such as triangular areas, within a mesh representation, which is advantageous for downstream processing. In at least one embodiment, clustering can be partially based on the spatial positions of the geometric primitives, for example, to minimize the surface area of ​​the bounding boxes around clustered primitives or to minimize the overlap between bounding boxes. Other factors, such as connectivity within or between clusters, can also be considered. A cost function can be used that comprises a weighted combination of cost terms, where these cost terms can be selected, in part, based on the type of downstream processing to be performed or on hardware requirements / optimizations for performing the processing.

The invention relates to the technical field of on-machine measurement, and discloses a heuristic and TS-based OMI path optimization method, system, medium and equipment, and the method comprises the steps: obtaining on-machine measurement points to obtain an initial measurement point set, and constructing an initial flag bit set; combining the two initial sets to construct an initial measurement path, and updating a measurement point set according to the initial measurement path; iteratively optimizing the measurement path and the measurement point set, in each iteration optimization, selecting points from the measurement point set according to the distance between the measurement path and the points in the measurement point set, adding the points into the measurement path, and updating the flag bit set; obtaining a candidate path until the measurement point set is empty; repeatedly generating candidate paths by using the same method until the flag bit set contains all the measurement points, and selecting the shortest path from all the candidate paths as an initial path; and optimizing the initial path by using a tabu search algorithm to obtain a final measurement path. According to the method, the optimal measurement path can be ensured under different measured characteristics, and the solving efficiency is improved.

A system and method of source and type-independent organization, presentation, and management of security detection artifacts, with heuristic and ML-based (machine learning) prioritization algorithms, providing for unified threat detection management and accurate prioritization without requiring an expert user. This is accomplished using an alternative presentation paradigm underpinned by a combination of heuristics and machine learning. The benefits include greater information gain; improved speed of decisioning cybersecurity detection data; elimination of the split-brain problem where different classes of detection artifacts are stored and managed in different systems; and reduced need for expert users. In cases where machine learning and / or anomaly detections are not available for clustering.

Prior methods of state estimation rely on penalty-based heuristics to enforce inequality constraints, which can produce very large weight values, resulting in ill-conditioning of the gain matrix. Disclosed embodiments of state estimation convert the inequality-constrained optimization problem into an unconstrained optimization problem in which violated inequality constraints are represented as parameterized potential functions, each comprising a center-of-attraction parameter. This unconstrained convex optimization problem can be iteratively prepared, using successively updated values for the center-of-attraction parameters, and solved, until no inequality constraints are violated, to produce a final estimated state. This final estimated state may then be used to control the system being monitored, such as a power system.

The invention relates to the technical field of binary program analysis and rewriting, and discloses an immediate operand symbolization judgment method and system for a binary rewriting program, and the method comprises a preprocessing step, a basic evidence analysis step, an intermediate evidence analysis step, an advanced evidence analysis step and a fusion decision-making step. The preprocessing layer is used for carrying out structure analysis and semantic modeling on the binary file and extracting candidate immediate operands and meta-information; performing preliminary weighting on the basic evidence layer based on an explicit rule; the intermediate evidence layer carries out quantitative evaluation through a heuristic mode and a statistical mode; the advanced evidence layer performs high-precision analysis on the immediate data of semantic ambiguity; and the fusion decision-making layer summarizes the evidences of all levels, forms a comprehensive tendency value through weighted calculation, classifies immediate operands, and generates a final symbolization strategy. According to the method, the accuracy and interpretability of symbolization judgment are remarkably improved, the analysis precision and the processing efficiency are both considered, and the method is suitable for binary files in different compilers, platforms and file stripping states.

The invention provides a terminal strategy optimization heuristic MPC control method and system considering interference, and relates to the technical field of control, and the method comprises the steps: constructing a discrete time nonlinear kinetic model containing time-varying interference; a disturbance predictor is introduced, and disturbance model parameters are obtained through historical disturbance data fitting; using a disturbance predictor to predict disturbance of a future step; obtaining an optimal parameter of the parameterized terminal cost function through supervised learning, and obtaining an optimal terminal cost function; constructing a terminal model predictive control MPC framework, forming a corresponding terminal model predictive control MPC optimization problem, and embedding the optimal terminal cost function into the terminal MPC framework to replace the traditional terminal cost; and solving a constrained terminal MPC optimization problem, and outputting a control law to complete online control. The method can adapt to a time-varying interference scene, reduces interference and control errors caused by various uncertain factors, and improves the stability, accuracy and reliability of online control of the nonlinear unmanned system.

Mechanisms are provided for automated rule set generation for identifying relations in knowledge graph data structures. An input knowledge graph is processed to extract tuples representing relations between entities present in the input knowledge graph. A set of rules is generated based on one or more heuristics applied to tuples, and candidate rule(s) are identified that are candidates for adding to the set of rules. A linear programming computer model is evaluated for a modified set of rules comprising the set of rules and the candidate rule(s) to determine whether or not adding the candidate rule(s) improves an objective function of the linear programming model. The set of rules is expanded to include the candidate rule(s) in response to the evaluation of the linear programming computer model indicating that the addition of the candidate rule(s) improves the objective function of the linear programming computer model.

Flow Shop Scheduling Problems (FSSP) solved using combination of Reinforcement Learning (RL), Genetic Algorithm (GA) and Heuristics is effective if can provide makespan as minimum as possible. Embodiments herein provide a method and system for evolved State-Action-Reward-State-Action (evolved SARSA) RL for flow shop scheduling, which is a hybrid framework of hierarchical RL with evolutionary techniques and heuristics method to solve FSSP. An optimum job sequence is estimated that minimizes the makespan thereby achieving maximum utilization of the resources. The evolutionary and heuristics strategy is applied in a reinforced way of learning for estimating the optimal schedule. The framework refines FSSP solution provided by Reinforced-SARSA (R-SARSA) using the evolutionary Genetic Algorithms (GAs), which is further guided by heuristic in moving towards the optimal solutions and prevents from being stuck at a local optimum.

The application discloses a structure-aware two-stage reinforcement learning SAT solving method and related device, and belongs to the field of chip design. In the variable initialization stage, chip signal polarity and logic consistency information are introduced to construct a structure-aware initial feature vector, breaking through the limitation of traditional graph modeling which can only represent local logical relationship. A two-stage reinforcement learning framework composed of a main path and a sampling path is designed. The sampling path is used to explore the chip logic solution space, and the main path is used to stabilize the optimal verification strategy, effectively alleviating problems such as unstable training convergence and single strategy. Based on the reward-guided feature update mechanism, the dynamic evolution of the chip signal feature is explicitly bound with the reinforcement feedback, improving the solver interpretability and narrowing the gap between neural inference and chip logic symbolic inference. Based on the GNN multi-round message passing result, signal assignment decisions are executed and verification trajectories are continuously optimized, without relying on artificial heuristics to obtain efficient chip design verification capability.

A method for operating a quantum error correction (QEC) code on a quantum computing system (QCS) is disclosed. The QCS includes a set of qubits and a set of couplers. An indication of a set of dropouts is received. Each dropout corresponds to a qubit that is non-functional or a coupler that is non-functional. The dropouts define a set of non-functional qubits, a set of functional qubits, a set of non-functional couplers, and a set of functional couplers. The QEC code is generated based on a set of heuristics, the set of functional qubits, and the set of functional couplers. The QEC code operates on the functional qubits. Each functional coupler provides a coupling between a pair of functional qubits. A quantum algorithm is executed that includes employing the QEC code to protect a set of logical qubits formed by the first subset of functional qubits from logical errors.

This invention discloses a key node detection method based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This method primarily aims to optimize and improve the accuracy of key node detection in complex networks, enabling it to find an optimal set of non-dominated solutions suitable for various application scenarios. First, for the key node detection problem in complex networks, this invention designs a non-uniform attack cost, using PWC in non-cascade scenarios and a cascade failure simulation framework based on node load and capacity constraints in cascade scenarios, designing attack effects and constructing a dual-objective optimization function. Second, a bisection method is used to obtain the minimum number of nodes required for total failure, compressing the search space and improving the evolution speed. Prior knowledge heuristics and random generation of the initial population are used to improve population quality. Next, based on the GA algorithm framework, the crossover, mutation, local search, and selection strategies are improved to adapt to the current discretized space, achieving comprehensive exploration of the candidate key node set within the search space. Finally, based on the PSO framework, the update strategies for particle velocity and position are improved to adapt to the discretized space, achieving directed guided local optimization and improving solution quality. Finally, based on the Knee Point technique and non-dominated sorting technique, the Pareto front is selected to obtain the optimal set of non-dominated solutions, thereby obtaining the key node set with the minimum attack cost and the maximum attack effect.

This invention discloses a stable prediction method for a large structured data model based on causal heuristics, belonging to the fields of industrial equipment fault prediction, data mining, and artificial intelligence. This method addresses the problem that prior-fitted networks are susceptible to spurious correlation features and struggle to explicitly utilize causal structural information during cross-environment prediction. It constructs a large structured data model comprising a shared encoder, decoupling branches, a discovery branch, and a prediction branch. Then, a multi-task joint training approach is employed to enable the model to learn the decoupling weights of samples, the Markov boundary of the target variable, and the posterior prediction distribution. During the inference phase, a Markov boundary mask is constructed based on the prediction probability and applied to the feature attention map of the prediction branch to constrain the model to predict only based on a stable subset of features. This invention effectively suppresses spurious correlation interference caused by environmental changes, improving the prediction stability, generalization ability, and robustness of tabular data in cross-environment scenarios.

A system and method of minimum turn coverage of arbitrary non-convex regions. Coverage planning is the task of generating a path that ensures the tool carried by the robot covers all regions of interest. The number of turns in the path can affect the time to cover the region and the quality of coverage (tools like cameras and cleaning attachments commonly have poor performance around turns). In recent turn-minimizing coverage methods, the region is partitioned to be covered by the least number of rectangles of width equal to the tool's width. The partitioning problem is typically solved using heuristics that have no optimality guarantees. A linear programming (LP) approach is disclosed to generate an axis-parallel coverage plan that minimizes the number of turns taken by the robot. The LP method solves this problem optimally in polynomial time. Coverage plans are generated for real regions using the LP method.

A method and system provide the ability to determine a fire hydrant fire flow. Input is obtained. And identifying key elements. And based on a physics-based heuristic method, determining a new fire-fighting anti-plug fire-fighting flow guess. Search directions are evaluated and used to maintain / cover the fire flow guess (using a heuristic approach). A new guess is designated as a fire hydrant demand. And updating the pipe network pressure and flow value. The constraints are evaluated. The guess decreases if at least one constraint has been violated, and increases if all constraints have been met. It is evaluated whether the new guess converges, and if not, the process is repeated.

Systems, computer program products, and methods are described herein for synthetic intrusion data generation and remediation via machine learning. The present disclosure includes training a first machine learning model using a plurality of malicious code segments from a code repository, generating, using the first machine learning model, a predetermined number of generated code segments, training a second machine learning model using the plurality of malicious code segments, generating, by using the second machine learning model, at least one generated heuristic mitigation resource for the generated code segments, analyzing, via a static heuristics analysis, stored code on an endpoint device, and applying the at least one generated heuristic mitigation resource upon a first condition wherein the static heuristics analysis identifies a malicious activity.

The present application discloses a method, system, and computer system for detecting stockpiled domains. The method includes (i) determining that a candidate domain is a malicious stockpiled domain using one or more of (a) a fingerprinting classification, (b) a heuristics-based classification, and (c) a machine learning classification, and (ii) applying a security policy based on a classification of the candidate domain as the malicious stockpiled domain.