Abstract: An exemplary system and method are disclosed for identifying anomalies relating to distribution power line disturbances and faults indicative of foliage impingement and potential equipment failure. The exemplary system and method employ neural network-based models such as generative adversarial networks models that can continuously monitor for electrical-signal anomalies to locate faults, predict power outages and safety hazards, thereby reducing the likelihood of wildfires. The exemplary system and method can beneficially learn and update its neural network models in a continuous and unsupervised manner using a live stream of sensor inputs.

Abstract: A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

Abstract: One or more computer processors group a plurality of predictors contained in training data into a plurality of predictor groups. The one or more computer processors create a plurality of sample sets, wherein each sample set in the plurality of sample sets contains one or more predictors selected from a respective predictor group in the plurality of predictor groups. The one or more computer processors create a cluster model for each created sample set in the plurality of created sample sets. The one or more computer processors generate a score for a record with one or more missing values utilizing at least one created cluster model of the created cluster models and at least one created sample set of the created sample sets.

Abstract: Presented herein are embodiments that use a language model to embed or encode configuration elements (e.g., commands, prompts, etc.) into dense, latent representations that incorporate semantic and contextual information. Using a trained language model, a configuration for a network device may be converted into a set of configuration path sentences. Given a first set of encoded configuration path sentences for a first configuration and a second set of encoded configuration path sentences for a second configuration, these two sets may be compared to gauge a degree of difference between the two sets. In one or more embodiments, an Optimal Transport method with Wasserstein distance metric may be used to obtain a comparison value that gauges difference between the two configurations. In one or more embodiments, the comparison valuation may be labeled or classified by comparing the comparison value to one or more pre-defined thresholds.

Abstract: Methods, systems, and techniques for producing and using enhanced machine learning models and computer-implemented tools to investigate cybersecurity related data and threat intelligence data are provided. Example embodiments provide an Enhanced Predictive Security System, for building, deploying, and managing applications for evaluating threat intelligence data that can predict malicious domains associated with bad actors before the domains are known to be malicious. In one example, the EPSS comprises one or more components that work together to provide an architecture and a framework for building and deploying cybersecurity threat analysis application, including machine learning algorithms, feature class engines, tuning systems, ensemble classifier engines, and validation and testing engines.

Abstract: A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

Abstract: A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

Abstract: This disclosure relates to method and system for training of deep learning based time-series models based on self-supervised learning. The problem of missing data is taken care of by introducing missing-ness masks. The deep learning model for univariate and multivariate time series data is trained with the distorted input data using the self-supervised learning to reconstruct the masked input data. Herein, the one or more distortion techniques include quantization, insertion, deletion, and combination of the one or more such distortion techniques with random subsequence shuffling. Different distortion techniques in the form of reconstruction of masked input data are provided to solve. The deep learning model performs these different distortion techniques, which force the deep learning model to learn better features. It is to be noted that the system uses a lot of unlabeled data available cheaply as compared to the label or annotated data which is very hard to get.

Abstract: Provided are a training method for an air quality prediction model, a prediction method and apparatus, a device, a program, and a medium. The method includes the steps described below. A target monitoring range is divided into a plurality of regions; the air quality prediction model is pre-trained by adopting a pre-training sample and a pre-training objective function, where the pre-training sample includes measurement values; and the pre-trained air quality prediction model is trained by adopting a formal training sample and a formal training objective function, where the formal training sample includes the measurement values. The air quality prediction model is configured to predict air quality of the plurality of regions according to spatial information, historical information and environmental information.

Abstract: A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

Abstract: Disclosed is a finger kneading rating method based on intelligent model processing. The method includes the following steps: acquiring and sending finger kneading piezoelectric data meeting a preset pressure value; receiving the piezoelectric data meeting the preset pressure value, calculating and processing the piezoelectric data by using a data model, and outputting finger effective data; receiving the finger effective data, and inputting a preset training model for training to obtain a rating model; outputting the finger effective data and displaying the finger kneading evaluation result. Through the steps above, the effective times of finger kneading could be easily obtained, and the results of inaccurate counting and scoring in a short time could be avoided, thus greatly ensuring good accuracy and reliability of the test results of this project; meanwhile, the application provides powerful evidence for early identification, early treatment and treatment detection of Parkinson's disease.

Abstract: The prediction system accesses a flowchart of questions relating to surgical cases and receives, for each of set of surgical case identifiers, surgical case information and an actual surgical case length. The prediction system trains a machine learning model to predict surgical case lengths using the surgical case information and prunes the flowchart by removing questions associated with a uniform set of answers. The prediction system receives, from a client device, a request to reserve an operating room for a surgical case, and transmits, for display via a user interface of the client device, questions from the flowchart. The prediction system receives a feature vector of answers to the transmitted questions from the client device and inputs a type surgical case and the feature vector to the machine learning model, which outputs a predicted surgical case length. The prediction system reserves an operating room for the predicted surgical case length.

Abstract: An information processing system that carries out a specified processing based on a learning model, comprises: a processor; a programmable logic device that rewrites logic data and reconstitutes a circuit; a machine learning processing unit that carries out machine learning and generates a new learning model for the specified processing; a convertor that converts the new learning model into the logic data that is operable in the programmable logic device; and a controller that enables the processor to carry out the specified processing based on the new learning model while the time the new learning model is converted into the logic data by the convertor.

Abstract: Techniques are disclosed relating to the execution of machine learning models on client devices, particularly in the context of transaction risk evaluation. This reduces computational burden on server systems. In various embodiments, a server system may receive, from a client device, a request to perform a first operation and select a first machine learning model, from a set of machine learning models, to send to the client device. In some embodiments the first machine learning model is executable, by the client device, to generate model output data for the first operation based on one or more encrypted input data values that are encrypted with a cryptographic key inaccessible to the client device. The server system may send the first machine learning model to the client device and then receive, from the client device, a response message that indicates whether the first operation is authorized based on the model output data.

Abstract: A method and system that allows on-the-fly synthesis and evaluation of services a scale has been provided. The method and system provide a mechanism where the service offering and their price points are accessible in a machine format and allow themselves to be lent to synthesize new service offerings with budgetary constraints, within the parameters of the predefined time windows. The system allows both a service provider and a service consumer to use a shared screen environment for service synthesis and valuation with the service provider playing the role of navigator. The system comprises of re-routing and navigating a plurality of nodes in the service composition graphs based on specified optimization parameters as chosen by the service consumer and tuned by the service provider. The method comprises of generation of the graph and graph traversal algorithms for along with service composition nodes and their specifications.

Abstract: Embodiments of various systems, methods, and devices are disclosed for generating artificial intelligence or machine learning models for predicting denials of medical claims, predicting approvals of resubmitted medical claims, as well as automatic workflow clustering processes for automatically assigning medical claims to workflow queues using predictive segmentation and smart resource allocation.

Abstract: Provided is a label inference system including a data generator configured to generate a training set and a test set, each including a plurality of images labeled with experts' annotations, a data trainer configured to perform training for a base model based on the generated training set and test set, a determiner configured to identify whether an evaluation metric f1 of the training model satisfies a base evaluation metric f1base, and a data inference unit configured to perform inference using the training set, the test set, and an unlabeled data set with the training model satisfying the base evaluation metric f1base.

Abstract: An embodiment method comprises applying domain transformation processing to a time-series of signal samples, received from a sensor coupled to a dynamical system, to produce a dataset of transformed signal samples therefrom, buffering the transformed signal samples, obtaining a data buffer having transformed signal samples as entries, computing statistical parameters of the data buffer, producing a drift signal indicative of the evolution of the dynamical system as a function of the computed statistical parameters, selecting transformed signal samples buffered in the data buffer as a function of the drift signal, applying normalization processing to the buffered transformed signal samples, applying auto-encoder artificial neural network processing to a dataset of resealed signal samples, and producing a dataset of reconstructed signal samples and calculating an error of reconstruction.

Abstract: A type estimation model generation system is a system that generates a type estimation model used for type estimation of estimating one of a plurality of types to which a user belongs, the system including: a learning data acquiring unit configured to acquire learning time series information that is information of a time series about a plurality of used musical pieces and learning type information representing types to which users who have used the plurality of musical pieces belong that are learning data used for machine learning; and a model generating unit configured to generate the type estimation model by performing machine learning using information based on the learning time series information as an input for the type estimation model in units of musical pieces in order of the time series and information based on the learning type information as an output of the type estimation model.

Abstract: A computer-implemented method according to one embodiment includes causing an environment generator of a Generative Adversarial Network (GAN) to generate realistic training environments, and causing a first discriminator of the GAN to determine whether the realistic training environments are real or fake. In response to a determination that an accuracy of the first discriminator at determining whether the realistic training environments are real or fake is within a predetermined range, the environment generator is caused to generate a first realistic environment. The method further includes causing the first realistic environment to be shared with an agent of a reinforcement learning (RL) algorithm and a second discriminator, and receiving, from the agent of the RL algorithm and the second discriminator, feedback associated with the first realistic environment. The environment generator is caused to generate a second realistic environment based on the feedback associated with the first realistic environment.