Abstract: A method and computer program product for obtaining values are run using a neural network according to a machine learning algorithm. One embodiment may comprise accessing one or more datafiles of input data, where the input data is representable in a d-dimensional space, with d>1. The method may explore N distinct paths of the input data in the d-dimensional space, where N?1, and collects data along the N distinct paths explored to respectively form N sequences of M objects each, with M?2. For one or more sequences of the N sequences formed, values obtained from the M objects of each sequence may be coupled into one or more input nodes of a neural network, which is then run according to the machine learning algorithm to obtain L output values from, L?1.

Abstract: Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

Abstract: Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

Abstract: An agricultural data collection framework is provided in a system and method for tracking and managing livestock, and for analyzing animal conditions such as health, growth, nutrition, and behavior. The framework uses ultra-high frequency interrogation of RFID tags to collect individual animal data across multiple geographical locations, and incorporates artificial intelligence techniques to develop machine learning base models for statistical process controls around each animal for evaluating the animal condition. The framework provides a determination of normality at an individual animal basis or for a specific location, and generates alerts, predictions, and a targeted processing or application schedule for prioritizing and delivering resources when intervention is needed.

Abstract: Systems and methods for constructing a layered artificial intelligence (AI) model are provided. The technology determines a set of layers and a set of variables for each layer for the AI model, with each layer relating to a specific domain context of the AI model. Using the layers, the AI model is trained to create layer-specific model logic for each layer using the variables of the layer. By applying the layer-specific model logic to incoming command sets, the model produces detailed layer-specific responses. The trained AI model then generates overall responses to command sets by aggregating the layer-specific responses, along with weights for each layer.

Abstract: Generative AI systems and methods are developed to provide recommendations regarding the control, optimization, and troubleshooting of industrial equipment and manufacturing systems as determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users and/or AI agents (i.e., a form of “agentic AI”) may then subscribe to such information for the use in connection with their own manufacturing systems.

Abstract: Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

Abstract: This disclosure aims to provide a technique for improving the accuracy of prediction. An information processing apparatus performs a process including: acquiring a first data set related to a first machine, the first data set including: combined data, made by combining first measurement data with labels set for the first measurement data; and second measurement data for which no labels are set; generating a first trained model for inferring labels for measurement data of the first machine, based on the first data set; and generating a second trained model for inferring the labels for the measurement data of the first machine, based on the first trained model.

Abstract: An apparatus and a method for segmenting a steel microstructure phase are provided. The apparatus includes a storage configured for storing a machine learning algorithm and a processing device that segments a microstructure phase using the machine learning algorithm. The processing device is configured to receive label data, to learn a machine learning model by use of the label data as learning data for the machine learning model, and to segment a phase of a steel microstructure image by use of the learned machine learning model.

Abstract: A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.

Abstract: A framework for rapidly prototyping federated learning algorithms. Specifically, the disclosed framework proposes a method and system for evaluating different hypotheses for configuring learning model, which may be optimized through federated learning. Through the disclosed framework, these hypotheses may be tested for scalability, hardware and network resource performance, as well as for new learning state compression and/or aggregation technique effectiveness. Further, these hypotheses may be tested through federated learning simulations, which avoid costs associated with deploying these hypotheses to be tested across production systems.

Abstract: Generative AI systems and methods are provided to produce leading indicators of economic activity based on, for example, agricultural, fishing, mining, lumber harvesting, environmental, or ecological attributes and other factors determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users may then subscribe to the date for the use in economic forecasting.

Abstract: Methods, systems, and computer-readable storage media for receiving a project structure representing a regression test file directory for regression inference and including a set of test scenarios, determining that a test scenario of the set of test scenarios is to be executed, transmitting a request for a test inference job to be executed using a second version of the application, the test inference job representing the test scenario, receiving a set of actual results of the test inference job, calculating a prediction score based on the set of actual results and a set of expected results of the test scenario, and selectively indicating regression of the one or more ML models of the test scenario based on the prediction score.

Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for a gated recurrent neural network (RNN). The exemplary embodiments may include providing an element processor, providing a distinct memory array for a respective set of one or more elements of a hidden state vector, storing in the memory array a group of columns of weight matrices that enable a computation of the set of one or more elements, computing one or more elements of each of multiple activation vectors using a set of one or more columns of the group of columns associated with each of the multiple activation vectors, and performing by the element processor an elementwise gating operation on computed elements, resulting in the set of one or more elements.

Abstract: Generative AI systems and methods are developed to provide recommendations regarding the prevention, detection, mitigation, and/or remediation of cybersecurity threats as determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users and/or AI agents (i.e., a form of “agentic AI”) may then subscribe to the data for the use in cybersecurity analytics, protection, mitigation, containment, remediation, and/or counterattacks of cybersecurity threats.

Abstract: Generative AI systems and methods are provided to provide recommendations as to whether a particular security associated with a corporate entity and/or its competitors should be purchased, sold, or held, as determined from a range of available data sources. A consistent, semantic metadata structure is described as well as a hypothesis generating and testing system capable of generating predictive analytics models in a non-supervised or partially supervised mode. Users may then subscribe to the date for the use in economic forecasting.

Abstract: Neural networks and learning algorithms can use a variance of gradients to provide a heuristic understanding of the model. The variance of gradients can be used in active learning techniques to train a neural network. Techniques include receiving a dataset with a vector. The dataset can be annotated and a loss calculated. The loss value can be used to update the neural network through backpropagation. An updated dataset can be used to calculate additional losses. The loss values can be added to a pool of gradients. A variance of gradients can be calculated from the pool of gradient vectors. The variance of gradients can be used to update a neural network.

Abstract: Embodiments provide methods and systems for reducing bias in an artificial intelligence model. A method includes computing, by a processor, a reward value based at least in part on a similarity between model predictions from a pre-trained model and agent predictions from a Reinforcement Learning (RL) agent. The method includes performing each step of one or more steps of a rule of a plurality of rules. The rule is assigned a weight and the rule includes a protected attribute, a cumulative statistic value type, and a comparison threshold. The method includes sending a cumulative reward value generated using the reward value and each weighted punishment value computed based at least in part on applying each rule of the plurality of rules to the RL agent. The RL agent learns to biases from the agent predictions while maintaining similarity with model predictions by maximizing the cumulative reward value.

Abstract: In an approach for forecasting in multivariate irregularly sampled time series, a processor receives time series data having one or more missing values. A processor determines, from the time series data, non-missing values present in the time series data. A processor determines, from the time series data, zero or more mask values for the time series data. A processor determines time interval values. A processor inputs the one or more missing values, the non-missing values, the zero or more mask values, and the time interval values into a recurrent neural network. A processor determines a predicted value for the one or more missing values.

Abstract: Techniques for generating a composite score for data quality are disclosed. Univariate analysis is performed on a plurality of data points corresponding to each of a first feature, a second feature, and a third feature of a data set. The univariate analysis includes at least a first type of analysis generating a first score having a first range of possible values, and a second type of analysis generating a second score having a second range of possible values. A first quality score is computed for the data values for the first, second, and third features based on a normalized first score and a normalized second score. Machine learning is performed on the data points corresponding to one or both of the first feature and the second feature having a first quality score above a threshold value to model the third feature.