A hybrid entity recognition system and accompanying method identify composite entities based on machine learning. An input sentence is received and is preprocessed to remove extraneous information, perform spelling correction, and perform grammar correction to generate a cleaned input sentence. A POS tagger tags parts of speech of the cleaned input sentence. A rules based entity recognizer module identifies first level entities in the cleaned input sentence. The cleaned input sentence is converted and translated into numeric vectors. Basic and composite entities are extracted from the cleaned input sentence using the numeric vectors.

A device may receive a hash table that includes lists of protocol detectors, wherein the hash table is generated based on historical process data identifying potential process variables associated with an industrial control system. The device may receive a packet identifying potential process variables associated with the industrial control system, and may extract, from the packet, packet data identifying a source address, a destination address, a port, and a transport protocol. The device may compare the packet data with data in the hash table to identify a set of lists of protocol detectors, and may process the packet data, with the set of lists of protocol detectors, to determine a matching protocol, no matching protocol, or a potential matching protocol for the packet. The device may perform one or more actions based on determining the matching protocol, no matching protocol, or the potential matching protocol for the packet.

Embodiments detect fraud of risk targets that include both customer accounts and cashiers. Embodiments receive historical point of sale (“POS”) data and divide the POS data into store groupings. Embodiments create a first aggregation of the POS data corresponding to the customer accounts and a second aggregation of the POS data corresponding to the cashiers. Embodiments calculate first features corresponding to the customer accounts and second features corresponding to the cashiers. Embodiments filter the risk targets based on rules and separate the filtered risk targets into a plurality of data ranges. For each combination of store groupings and data ranges, embodiments train an unsupervised machine learning model. Embodiments then apply the unsupervised machine learning models after the training to generate first anomaly scores for each of the customer accounts and cashiers.

The present disclosure provides methods and systems for performing non-classical computations. The methods and systems generally use a plurality of spatially distinct optical trapping sites to trap a plurality of atoms, one or more electromagnetic delivery units to apply electromagnetic energy to one or more atoms of the plurality to induce the atoms to adopt one or more superposition states of a first atomic state and a second atomic state, one or more entanglement units to quantum mechanically entangle at least a subset of the one or more atoms in the one or more superposition states with at least another atom of the plurality, and one or more readout optical units to perform measurements of the superposition states to obtain the non-classical computation.

Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.

In order to facilitate electronic meeting scheduling and coordination, systems and methods are disclosed including receiving, by a processor, a plurality of electronic meeting requests to schedule a meeting. The processor determines, for each electronic meeting request, meeting room needs. A meeting scheduling machine learning model is utilized to predict parameters of meeting room objects representing the candidate meeting rooms based at least in part on the meeting room needs, schedule information associated with a respective electronic meeting request and location information associated with the respective electronic meeting request. The processor causes an indication of the candidate meeting rooms to display in response to the electronic meeting request on a screen of computing devices associated with the respective attendees based at least in part on the predicted parameters. The processor receives a selection of the respective candidate meeting rooms from the respective attendees, and dynamically secures each candidate meeting room.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

A method for predicting retrosynthesis of a compound molecule and a related apparatus. The method includes: obtaining a target molecule and determining the target molecule as a root node in a tree structure, then, expanding the first leaf node through a target retrosynthesis model to obtain a plurality of second leaf nodes, further, recursively processing the predicted molecule set corresponding to the second leaf nodes and determining a terminal node that satisfies a preset condition; and then, traversing path information corresponding to the terminal node to determine a retrosynthetic path of the target molecule. In this way, a retrosynthesis prediction process of a multi-step reaction is realized. Leaf nodes are gradually recursively expanded and screened, to ensure the reliability of reactants determined by the retrosynthesis prediction process of the multi-step reaction, thereby improving the accuracy of prediction of retrosynthesis of compound molecules.

A method for generating a predictive model for quantization parameters of a neural network is described. The method comprises accessing a first vector of data values corresponding to input values to a first layer implemented in a neural network, generating a feature vector of one or more features extracted from the data values of the first vector, accessing a second vector of data values corresponding to the input values of a second layer implemented in the neural network, subsequent to the first layer, generating a target vector of data values comprising one or more quantization parameters for the second layer, from the data values of the second vector, evaluating, on the basis of the feature vector and the target vector, a predictive model for predicting the one or more quantization parameters of the second layer and modifying the predictive model on the basis of the evaluation.

A noninvasive method and system for sleep apnea detection is disclosed. The method includes the following steps: acquiring vital sign signals of a sleeping user; performing structured processing on the vital sign signals of the user to remove invalid signals to obtain a set of valid vital sign signals; extracting multi-dimensional morphological features from a sleep respiratory signal and performing feature training on an initial model of a classifier by means of the multi-dimensional morphological features so as to obtain a sleep breathing detection model; and inputting the set of valid vital sign signals into the sleep breathing detection model and performing signal processing to obtain predicted probability of the user suffering from sleep apnea. As a result, data relating to the probability of a user suffering from sleep apnea can be more accurately obtained, thereby facilitating the determination of whether a sleep apnea event occurs during sleep.