A system for selection and configuration of an automated robotic process includes a media input module structured to receive at least one functional media, a media analysis module structured to analyze the at least one functional media and identify an action parameter; and a solution selection module structured to select at least one component of an AI solution for use in an automated robotic process, wherein the selection is based, at least in part, on the action parameter.

The present invention provides a method, a system, and a device for a hash generation and network traffic detection. It uses a method of storing intermediate calculation results to perform hash calculation for streaming data, and uses a matrix multiplication operation as a strong hash algorithm to reduce memory occupation. The present invention can generate hash in real time in the case of streaming data comprising defects, unordered, and overlapping, which is suitable for detecting files from network traffic, and is applicable to virus detection, intrusion detection, data anti-leakage, network content review, digital forensics, digital rights protection, and other fields.

The present invention provides a method, a system, and a device for a hash generation and network traffic detection. It uses a method of storing intermediate calculation results to perform hash calculation for streaming data, and uses a matrix multiplication operation as a strong hash algorithm to reduce memory occupation. The present invention can generate hash in real time in the case of streaming data comprising defects, unordered, and overlapping, which is suitable for detecting files from network traffic, and is applicable to virus detection, intrusion detection, data anti-leakage, network content review, digital forensics, digital rights protection, and other fields.

In some embodiments, a self-monitoring intravenous catheter system is provided. An alarm controller is provided that receives signals representing a pH value, an oxygen saturation value, and a pressure value in proximity to the distal end of the catheter. By performing a fuzzy logic analysis of the values, the alarm controller is able to detect that the catheter is about to fail or has failed, and can cause alerts to be presented. In some embodiments, an intravenous catheter is provided that has a pH sensor and an oximeter disposed at a distal end of the catheter to obtain the pH value and oxygen saturation values analyzed by the alarm controller. Embodiments of the catheter and self-monitoring intravenous catheter system may be particularly useful in treating neonates, who are sensitive to catheter failure and are not capable of detecting the signs of failure themselves.

Provided is a remote sensing image feature discretization method based on rough-fuzzy model, comprising the following steps: listing the digital number in each band and category of a selected sample in remote sensing images, and building an image information decision table based on the digital number and category; initializing the class center of each category and the membership degree of a sample example relative to the class center; updating the class center of each category and the membership degree of the sample example relative to the class center iteratively, and obtaining the final value of the class center of each category and the final value of the membership degree; building a rough-fuzzy set, computing the mean approximation accuracy of the rough-fuzzy set, discretizing the image information decision table, evaluating the discretization results based on the mean approximation accuracy and a genetic algorithm, and selecting an optimal discretization solution.

In an approach for reasoning with real-valued propositional logic, a processor receives a set of propositional logic formulae, a set of intervals representing upper and lower bounds on truth values of a set of atomic propositions in the set of propositional logic formulae, and a query. A processor generates a logical neural network based on the set of propositional logic formulae and the set of intervals representing upper and lower bounds on truth values. A processor generates a credal network with a same structure of the logical neural network. A processor runs probabilistic inference on the credal network to compute a conditional probability based on the query. A processor outputs the conditional probability as an answer to the query.

An example method for identifying a reading location in a text source as a user reads the text source aloud includes determining phoneme data of the text source, the text source comprising a sequence of words; receiving audio data comprising a spoken word associated with the text source; comparing, by a processing device, the phoneme data of the text source and phoneme data of the audio data; and identifying a location in the sequence of words based on the comparing phoneme data.

An example method for identifying a reading location in a text source as a user reads the text source aloud includes determining phoneme data of the text source, the text source comprising a sequence of words; receiving audio data comprising a spoken word associated with the text source; comparing, by a processing device, the phoneme data of the text source and phoneme data of the audio data; and identifying a location in the sequence of words based on the comparing phoneme data.

The present disclosure provides a leakage measurement error compensation method based on cloud-edge collaborative computing, implemented on a communication network formed by interconnection between a leakage current edge monitoring terminal and a power consumption management cloud platform, and including the following steps: monitoring, by the leakage current edge monitoring terminal, leakage current data, and sending the leakage current data to the power consumption management cloud platform; iteratively training, by the power consumption management cloud platform, a pseudo-leakage compensation model by using the received leakage current data, continuously updating pseudo-leakage model parameters, and feeding the pseudo-leakage model parameters back to the leakage current edge monitoring terminal; and processing, by the leakage current edge monitoring terminal, the leakage current data according to the pseudo-leakage compensation model parameters, so as to eliminate the influence of a pseudo-leakage phenomenon in the leakage current data.

A method for large-scale distributed machine learning using input data comprising formal knowledge and/or training data. The method consisting of independently calculating discrete algebraic models of the input data in one or many computing devices, and in sharing indecomposable components of the algebraic models among the computing devices without constraints on when or on how many times the sharing needs to happen. The method uses an asynchronous communication among machines or computing threads, each working in the same or related learning tasks. Each computing device improves its algebraic model every time it receives new input data or the sharing from other computing devices, thereby providing a solution to the scaling-up problem of machine learning systems.