Two commands each perform a partial complex multiply and accumulate. By using these two commands together, a full complex multiply and accumulate operation is performed. As compared to traditional implementations, this reduces the number of commands used from eight (four multiplies, a subtraction and three adds) to two. In some example embodiments, a single-instruction/multiple-data (SIMD) architecture is used to enable each command to perform multiple partial complex multiply and accumulate operations simultaneously, further increasing efficiency. One application of a complex multiply and accumulate is in generating images from pulse data of a radar or lidar. For example, an image may be generated from a synthetic aperture radar (SAR) on an autonomous vehicle (e.g., a drone). The image may be provided to a trained machine learning model that generates an output. Based on the output, inputs to control circuits of the autonomous vehicle are generated.

A method, system, and computer program product for correlating dynamic device configurations from multiple sources. The method may include identifying device management settings from a device management system. The method may also include receiving source settings from a second source. The method may also include analyzing individual words from the device management settings and the source settings. The method may also include analyzing strings, integers, and Booleans from the device management settings and the source settings. The method may also include identifying, based on the analyzing individual words and the analyzing strings, integers, and Booleans, corresponding settings from the device management settings and the source settings. The method may also include determining that the corresponding settings are conflicting settings. The method may also include flagging, based on the determining, conflicts of the corresponding settings.

Embodiments generate a demand model for a potential hotel customer of a hotel room. Embodiments, based on features of the potential hotel customer, form a plurality of clusters, each cluster including a corresponding weight and cluster probabilities. Embodiments generate an initial estimated mixture of multinomial logit (“MNL”) models corresponding to each of the plurality of clusters, the mixture of MNL models including a weighted likelihood function based on the features and the weights. Embodiments determine revised cluster probabilities and update the weights. Embodiments estimate an updated estimated mixture of MNL models and maximize the weighted likelihood function based on the revised cluster probabilities and updated weights. Based on the update weights and updated estimated mixture of MNL models, embodiments generate the demand model that is adapted to predict a choice probability of room categories and rate code combinations for the potential hotel customer.

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.

Methods and systems for machine learning inferencing based on directed acyclic graphs are presented. A request for a machine learning application is received from a tenant application. A tenant identifier that identifies one of the tenants is determined from the request. Based on the tenant identifier and a type of the machine learning application, configuration parameters and a graph structure are determined. The graph structure defines a flow of operations for the machine learning application. Nodes of the graph structure are executed based on the configuration parameters to obtain a scoring result. Execution of a node causes a machine learning model generated for the first tenant to be applied to data related to the request. The scoring result is returned in response to the request.

There is provided a method and a system for recommending a given text candidate as a value for a field. A document image is received and a set of text boxes are detected using optical character recognition, each of the set of text boxes comprising a respective character sequence. For each text box, based on at least the respective character sequence, at least one respective text candidate is generated to thereby obtain a set of text candidates. At least one feature extractor is used to generate a respective candidate feature vector based on each respective text candidate. An indication of the field is received, and a respective candidate score indicative of a relevance of the respective text candidate is determined. In response to a given candidate score being above a threshold, the given text candidate associated with the given candidate score is output as a recommendation for the field.

Taste and smell classification from multilanguage descriptions can be performed by extracting, by one or more processors using natural language processing, a text including one or more words associated with taste and smell perceptions from an input received from a plurality of users. The input includes multilanguage information regarding at least one of changes in smell and changes in taste perceived by each of the plurality of users. Feature vectors are generated for the text extracted from the input using global vectors, and a distance between the feature vectors and a plurality of reference descriptors associated with taste and smell is calculated for determining a similarity between the text and the reference descriptors and creating a training dataset based on which a classification model is generated for categorizing the plurality of users according to the at least one of changes in smell and changes in taste.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing text similarity determination. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform text similarity determination by using at least one of Word Mover's Similarity measures, Relaxed Word Mover's Similarity measures, and Related Relaxed Word Mover's Similarity measures.

According to one embodiment, a method, computer system, and computer program product for using mobile devices for anomaly detection in a vehicle. The present invention may include a computer receives sensor data from at least one mobile device associated with the vehicle, where the mobile device having one or more sensors. The computer analyzes data from the one or more sensors to identify an anomaly associated with the vehicle. The computer identifies a message associated with the anomaly. The computer determines an urgency value of the message based on the anomaly. The computer transfers the message with the urgency value to the vehicle and causes the vehicle to notify the message using a vehicle notification device.

Systems and methods for generating one or more comparative features for machine learning based classification are disclosed. A system may be configured to obtain time series data and forecast one or more predicted values based on the time series data. The system may also be configured, for each predicted value of the one or more predicted values, to compare an actual value of the time series data to the predicted value and generate a comparative value of a comparative feature based on the comparison. The comparative feature is to be provided to a machine learning model for a classification task associated with the time series data. The classification task may include determining whether one or more data values in the time series data is fraudulent based on the comparative feature.