Abstract: Systems and methods for exchanging data between a set of microservices are disclosed herein. A system may establish an event queue-based persistent connection between a set of microservices, where the set of microservices may include a requesting microservice and a providing microservice. Further, the system may receive a request in a queue of a plurality of requests from the requesting microservice, and retrieve, from the queue, a pre-determined number of requests based on a weighting score associated with each of the pre-determined number of requests. Furthermore, the system may process the pre-determined number of requests in parallel, generate a response to the received request based on the processing, and provide the generated response to the requesting microservice.

Abstract: Systems and methods for providing a digital human in a virtual space are disclosed herein. A system receives, from a user via a digital platform, a selection of a persona, for a digital human, from a plurality of personas. Further, the system receives, in real-time, an input from the user via the digital platform, and identifies a set of parameters associated with the input. Furthermore, the system determines, from a knowledge database, a response information based on the context of the input, and generates, using a repository, a set of attributes for the response information based at least on the set of parameters associated with the input and the persona selected by the user. Additionally, the system aggregates the set of attributes and the response information to generate a personalized response to the input, and renders the personalized response by the digital human on the digital platform.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automatic compliance validation using a dynamically generated set of validation code. The compliance validation process may begin by extracting requirements from a compliance specification. Once extracted, the requirements may be tokenized and vectorized to produce vectorized data. The vectorized data may be labeled using a multi-label classifier to produce a set of labeled data (e.g., labeled vectors representing the requirements extracted from the compliance specification). The set of labeled data may be fed to a machine learning model configured to map the labeled data to pieces of code stored in one or more code libraries. A set of validation code is generated based on the pieces of code mapped to the labeled data and the set of validation code may be applied to a deliverable to evaluate compliance of the deliverable with the requirements.

Abstract: Embodiments herein facilitate a rule-based anonymization of an original dataset. The system may include a processor including a data privacy evaluator and a rules engine. The data privacy evaluator may receive at least one anonymized dataset corresponding to a predefined strategy of anonymization. The at least one anonymized dataset may include a variation from the original dataset by at least one of a privacy metric and a consistency metric. The data privacy evaluator may evaluate the at least one anonymized dataset and may generate a final output value based on a first output and a second output. The processor may assess the final output value with respect to a predefined threshold through the rules engine. If the final output value may be equal or higher than the predefined threshold, the system may permit an access to the anonymized dataset.

Abstract: Systems and methods that offer significant improvements to current virtual agent (VA) conversational experiences are disclosed. The proposed systems and methods are configured to manage conversations in real-time with human customers while accommodating a dynamic goal. The VA includes a goal-driven module with a reinforcement learning-based dialogue manager. The VA is an interactive tool that utilizes both task-specific rewards and sentiment-based rewards to respond to a dynamic goal. The VA is capable of handling dynamic goals with a significantly high success rate. As the system is trained primarily with a user simulator, it can be readily extended for applications across other domains.

Abstract: A method and system for evaluating and enhancing a prompt for use by a generative artificial intelligence processing model are disclosed. The method may include obtaining a prompt representing a natural language text for use by a generative artificial intelligence processing model, obtaining a prompt classifier trained to evaluate a classification of the prompt, and inputting the prompt to the prompt classifier to generate a classification of the prompt. The method may further include identifying an intent underlying the prompt and detecting an implicit constraint for the prompt based on the intent. The method may further include transforming the intent in the prompt into a constraint-enhanced intent based on the implicit constraint, generating an enhanced prompt based on the constraint-enhanced intent, and outputting the enhanced prompt for the generative artificial intelligence processing model.

Abstract: A monitoring system based on a digital converter station includes a first monitoring terminal deployed at a first-level monitoring side, a second monitoring terminal deployed at a second-level monitoring side, and a third monitoring terminal deployed at a third-level monitoring side; the monitoring terminal at each level includes a communication module, a human-machine interaction module, a device status monitoring module, a device alarm management module, a video fusion processing module, and a data transmission adjustment and control module.

Abstract: A system and method for dynamic risk assessment is disclosed. The system may include a data warehouse, an output device and a processor including a data capturer, a process engine and a rules engine. The data capturer may capture information pertaining to a plurality of risk factors associated with an infection risk corresponding to a plurality of data elements in an environment. The plurality of data elements may pertain to at least one of a space and a person in the space. The process engine may include at least one of a space risk profiler and a person risk profiler. The process engine may determine a risk score and a risk profile associated with the person and the space. Based on the risk profile, the processor may perform at least one of an automatic identification of a mitigation to reduce the infection risk and an automated generation of an alert.

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: 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: 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: 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: 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: 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 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.