Abstract: A chemical product formulation system automatically generates seed formulae from historic experiments data for the synthesis of a chemical product. Independent and dependent features are identified from the historic experiments data and feature importance scores are calculated using a supervised machine learning (ML) model. The feature importance scores are used to build data structures from which analytical rules are extracted. The analytical rules are further processed to derive the seed formulae which are user-editable. The intermediate formulae generated via user edits of the seed formulae are further validated and approved in order to be used as the final formulae which are employed for the synthesis of the chemical product.

Abstract: In some implementations, an application programming interfaces (API) manager may receive, at a set of artificial intelligence (AI) APIs, a set of inputs from a set of on-site devices. Accordingly, the API manager may route the set of inputs to a corresponding set of remote servers and may receive, from at least one server of the corresponding set of remote servers, at least one response based on at least one input, from the set of inputs, routed to the at least one server. The API manager may transmit the at least one response to a corresponding device from the set of on-site devices. Further, the API manager may modify at least one API, of the set of AI APIs, based on a traffic pattern associated with the set of inputs and the at least one response.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, a using a bot management framework to provide bot management solutions for software bots deployed in a robotic process automation (RPA) system. In one aspect, a method includes the actions of receiving, from a RPA system, information regarding a software bot deployed within the RPA system to perform an assigned task, the information including incident data from an anomaly in the RPA system while the software bot performed the assigned task; mapping the received incident data according to a common data model; determining a resolution for the anomaly based an analysis through a trained artificial intelligence model of the mapped incident data; and executing the determined resolution.

Abstract: A system for governing user privacy data may include detecting a dark data source in a data ecosystem including data sources storing user privacy data. The system may further include identifying user information from the data ecosystem according to a lineage of datasets and scanning the dark data source by executing a natural language processing engine to identify existence of and content of user privacy data in the dark data source based on the user information. The system may further include selecting a target user privacy data compliance policy from user privacy data compliance policies based on a geographical region associated with the dark data source and detecting non-compliance in protecting the user privacy data in the dark data source based on the target user privacy data compliance policy. The system may further include, in response to the non-compliance, processing the user privacy data to eliminate the non-compliance.

Abstract: The present disclosure describes a method of controlling a manufacturing system using multivariate time series and includes storing recording data as a plurality of time series, each time series having a first recorded value and a final recorded value, interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, the missing values falling between a first and an end time of the respective time series, storing the interpolated values as prediction data, each interpolated value including an uncertainty, loading recorded data of a second time window, loading prediction data of the second time window, predicting, using the Bayesian model, values for each time series that is absent recorded data and prediction data, storing the predicted values, each prediction value including an uncertainty, and adjusting a device that generates the recorded data based on the prediction values within the second time window.

Abstract: This document describes neuromorphic controllers. In one aspect, a method for controlling one or more joints of a robotic arm includes receiving, by neuromorphic controller comprising a spiking neural network (SNN), a target value of a joint control variable for a joint of the robotic arm. The SNN includes two position proprioceptor neurons, two-speed proprioceptor neurons, a presynaptic inhibitory neuron, an extensor motor neuron, and a flexor motor neuron. The neuromorphic controller updates an actual value of the joint control variable for the joint of the robotic arm based on the target value of the joint control variable. The updating includes generating, by one of the two position proprioceptor neurons, the first spikes to one of the extensors motor neurons or the flexor motor neuron based on a difference between the actual value of the joint control variable and the target value of the joint control variable.

Abstract: Implementations are directed to receiving a first set of images included in a first video captured by a camera that monitors a human performing a task; processing the first set of images using a first machine learning (ML) model to determine whether the first set of images depicts a gesture that is included in a predefined set of gestures; in response to determining that the first set of images depicts a gesture included in a predefined set of gestures, processing a second set of images included in the first video using a second ML model to determine a first gesture type of the gesture; comparing the first gesture type with a first expected gesture type to determine whether performance of the task conforms to a standard operating procedure (SOP) for the task; and providing feedback representative of a comparison result in a user interface.

Abstract: Systems and methods for facilitating an automated detection of an object in a test document are disclosed. A system may include a processor including a dataset generator. The dataset generator may obtain a first input image and a first original document from a data lake. The dataset generator may prune a portion of the first original document to obtain a pruned image. The dataset generator may blend the first input image with the pruned image to generate a modified image. The modified image may include the pruned image bearing the first pre-defined representation. The modified image may be combined with the first original document to generate a training dataset. The training dataset may be utilized to train a neural network based model to obtain a trained model for the automated detection of the object in the test document.

Abstract: A system for providing performance optimization for a software solution may scan multiple predefined levels of the software solution to extract corresponding metadata information from each of the multiple predefined levels. The system may store the extracted corresponding metadata information pertaining to standard parameters associated with performance of the software solution. The system may determine a standard score based on a plurality of attributes of the extracted corresponding metadata information, optimize the determined standard score based on training data received from a learning model, and generate an insight information comprising information related to determined rule violations and of evaluation steps involved in determining the determined standard score.

Abstract: A device may receive, from Internet of Things (IOT) devices, telemetry data in a variety of formats and may convert the telemetry data to converted telemetry data in a first unified format. The device may receive, from one or more of the IoT devices, offline IoT data in a variety of formats and may convert the offline IoT data to converted offline IoT data in a second unified format. The device may receive events from one or more of the IoT devices and may identify anomalies based on the telemetry data and in near-real time relative to receiving the telemetry data. The device may store the converted telemetry data, the converted offline IoT data, the events, and the anomalies, as stored data, in a data structure, and may perform actions based on the stored data.

Abstract: Systems and methods for estimating freight costs are disclosed herein. The system receives, from a user interacting with the system, a first document and a second document, where an actual value is associated with the second document. Further, the system determines a set of variables for each of the first document and the second document based on a statistical analysis of historical data. Furthermore, the system estimates, using a trained artificial neural network model, a cost associated with each of the first document and the second document based on the determined set of variables. The cost associated with the first document includes an estimated freight cost for the first document, and the cost associated with the second document includes an estimated true value for the second document.

Abstract: A device may receive training data for training a first machine learning model, a second machine learning model, and a third machine learning model and may train the first machine learning model, the second machine learning model, and the third machine learning model with the training data. The device may receive input content and may process the input content, with the first machine learning model, the second machine learning model, and the third machine learning model, to generate a first model result, a second model result, and a third model result, respectively. The device may process the first model result, the second model result, and the third model result, with an aggregation model, to generate a detection result indicating whether the particular input content is a deepfake or is real and may perform one or more actions based on the detection result.

Abstract: An autonomous system and method of comprehensively testing serverless applications. The system is configured to automatically generate test scripts and test data based on deployed and modified function code and configuration files. The system is configured to work in multi-cloud environments and is able to accommodate any cloud provider. Implementation of such a system can eliminate manual errors. Furthermore, the proposed embodiments are configured to integrate with already existing devOps pipelines for rapid test execution, and can continue automated testing in real-time as modifications to the app are made.

Abstract: A system may provide blockchain-based storage node control for unified proof-of-storage interaction for off-chain data chunk storage. The system may include verification circuitry that may provide a challenge to a storage node to audit the storage status of a data chunk. The verification circuitry may obtain identities for the storage node and data chunk based on blockchain records. The verification circuitry may obtain a challenge answer from the storage node in response. The verification circuitry may analyze the challenge answer in view of a challenge key to confirm whether the storage node has possession of the data chunk. When the storage node has possession of the data chunk chain circuitry may add a verification record to the blockchain.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated optimization of product management systems. In embodiments, automated optimization of component tools of the product management system is provided by automated evaluation and optimization of associated rules and metadata. In embodiments, metadata and rules may be associated to each other by an assessment engine. A recommendation engine may then identify non-compliant metadata, may determine a condition of the rule, and/or may generate recommendations for the rules based on the non-compliant metadata. Automated optimization of the product management system may include automated creation and mapping of decomposition relationships between commercial products and technical products. In embodiments, input data may be parsed into a set of object with unique attribute fields, which may then be validated.

Abstract: In some implementations, a system may receive, from an analysis system, a data stream associated with monitoring an environment. The system may process, using a first model, the data stream to detect an object associated with a service provided by an application. The system may analyze a monitoring configuration of the analysis system. The system may determine, based on processing the data stream, service data associated with providing the service in association with the object. The system may configure, based on the monitoring configuration, an edge device to access the data stream and host a second model to process the service data according to the data stream. The system may receive, from the edge device, a service output associated with providing the service in association with the object. The system may perform an action associated with the service output and the object.

Abstract: A system of monitoring and optimizing computing resources usage for computing application may include predicting a first performance metric for job load capacity of a computing application for optimal job concurrency and optimal resource utilization. The system may include generating an alerting threshold based on the first performance metric. The system may further include, in response to a difference between the alerting threshold and a job load of the computing application within an interval exceeding a threshold, predicting a second performance metric for job load capacity of the computing application for optimal job concurrency and optimal resource utilization. The system may further include, in response to a difference between the first performance metric and the second performance metric exceeding a difference threshold, updating the alerting threshold with a job load capacity with the optimal resource utilization rate corresponding to the second performance metric.

Abstract: Complex computer system architectures are described for analyzing data elements of a knowledge graph, and predicting new surprising or unforeseen facts from relational learning applied to the knowledge graph. This discovery process takes advantage of the knowledge graph structure to improve the computing capabilities of a device executing a discovery calculation by applying both training and inference analysis techniques on the knowledge graph within an embedding space, and generating a scoring strategy for predicting surprising facts that may be discoverable from the knowledge graph.

Abstract: A method and system for training a federated learning model are disclosed. The method may include receiving the global federated learning model from the server via the client. The client may control remote computing resources. The method may further include identifying a spare computing instance from the remote computing resources and determine a threshold training load for training the global federated learning model based on a training load assigned to the client. The method may further include, in response to a processing capacity of the spare computing instance being sufficient to process the threshold training load, offloading the threshold training load to the spare computing instance and training the global federated learning model on the spare computing instance with the training dataset stored in a data source maintained by the client.

Abstract: A system for providing temporary, transportable cellular communications networks is disclosed. The system includes at least one mobile base station and a control system. The mobile base station can include an aerial vehicular base station with a frame and propellers mounted on the frame that enable flight of the aerial vehicular base station while the control system is configured to control the flight path and functioning of the aerial vehicular base station. The aerial vehicular base station includes hardware and software components for providing cellular network coverage for short ranges. The control system determines the safe flight path for the aerial vehicular base station to reach a service location to provide cellular network coverage. The aerial vehicular base station identifies the closest base stations and available spectrum at the service location to provide communication services for the user equipment at the service location.