A method for classifying an object in a point cloud includes computing first and second classification statistics for one or more points in the point cloud. Closest matches are determined between the first and second classification statistics and a respective one of a set of first and second classification statistics corresponding to a set of N classes of a respective first and second classifier, to estimate the object is in a respective first and second class. If the first class does not correspond to the second class, a closest fit is performed between the point cloud and model point clouds for only the first and second classes of a third classifier. The object is assigned to the first or second class, based on the closest fit within near real time of receiving the 3D point cloud. A device is operated based on the assigned object class.

The present invention provides a data collection system comprising: a camera; a location module; a plurality of sensors; and a first processor communicatively coupled to the camera and the location module, the first processor programmed to: obtain a plurality of frames from the camera; obtain a plurality of locations from the location module; obtain a plurality of data measurements from the plurality of sensors; apply a previously trained first neural network model for identifying problematic road segments to frames captured by the camera; and if the first neural network model indicates that a frame is a problematic road segment, save the frame in association with a location provided by the location module.

Systems, devices, and methods of the present invention detect rhetoric agreement between texts. In an example, a rhetoric agreement application accesses a multi-part initial query and generates a question communicative discourse tree that represents rhetorical relationships between fragments of the query. The application identifies a sub-discourse tree from the question communicative discourse tree. The application generates a candidate answer communicative discourse tree for each candidate answer of a set of candidate answers. The application computes a level of complementarity between the sub-discourse tree and each candidate answer discourse tree by applying a classification model to the sub-discourse tree and candidate answer communicative discourse trees. The application selects an answer from the candidate answers based on the computed complementarity, thereby building a dialogue structure of an interactive session.

In an approach for optimally grouping containers, a processor passively monitors a set of parameters for a set of containers within a network. A processor records the set of parameters for each container of the set of containers. A processor deploys a k-nearest neighbor neural network (KNN) to determine a first set of groupings of the set of containers based on the set of parameters. A processor simulates the network having grouped containers based on the first set of groupings of the set of containers output by the KNN. A processor simulates an action on the set of containers. A processor updates a reward function based on the action. A processor determines whether a maximum value of the reward function is reached.

A method and system for determining an access score is disclosed. The method includes receiving an access request to access a resource by a user device. Next, a user embedding is retrieved from an embedding table, the user embedding associated with a user identifier of the user device and providing a multidimensional data point that represents a context of a user identifier. The context may correspond to the user identifier appearing in previous access requests within temporal proximity to other access requests from a subset of other user devices among a plurality of user devices. The method then inputs the user embedding into a first machine learning model that is trained based at least in part on the embedding table. The first machine learning model subsequently outputs an access score that corresponds to a level of authenticity of authorizing the user device to access the resource.

A method and system of graph feature extraction and graph classification based on adjacency matrix is provided. The invention first concentrates the connection information elements in the adjacency matrix into a specific diagonal region of the adjacency matrix which reduces the non-connection information elements in advance. Then the subgraph structure of the graph is further extracted along the diagonal direction using the filter matrix. Further, it uses a stacked convolutional neural network to extract a larger subgraph structure. On one hand, it greatly reduces the amount of computation and complexity, getting rid of the limitations caused by computational complexity and window size. On the other hand, it can capture large subgraph structure through a small window, as well as deep features from the implicit correlation structures at both vertex and edge level, which improves speed and accuracy of graph classification.

The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. A plurality of combined neighborhoods are received from a plurality of local join executors. Each combined neighborhood represents a neighborhood of a source data point and has one or more pairs of neighbors, each of which includes the source data point, a neighbor of the source point, and a distance in-between. A plurality of KNN lists corresponding to a plurality of source data points are obtained. Each KNN list includes K neighbors to a corresponding source data point, each of which is represented by an index of the neighbor and a distance between the source data point and the neighbor. The plurality of KNN lists are updated based on the plurality of combined neighborhoods to generate updated KNN lists.

A method of handling outliers is provided. The method includes determining a set of residuals, wherein each residual represents a difference between a measurement included in a set of measurements and a predetermined estimate; clustering the residuals into a plurality of clusters; calculating a consistency value for each of the plurality of clusters based on a number of measurements included in the set of measurements and a standard deviation of the measurements; identifying a cluster having a maximum consistency value among the plurality of clusters as inliers by applying the consistency function to the plurality of clusters; and handling the outliers based on an approximation of one or more parameters as a function of a statistical relationship of the inliers included in the cluster having the maximum consistency value among the plurality of clusters and an initial estimation of the one or more parameters.

A computer identifies, within a hierarchical database, data elements associated with a selected function associated with the database, comprising. The computer identifies at least one function associated with a hierarchical database containing data elements. The computer, in response to identifying the function, identifies within a list of indica, at least one reference indicia corresponding to the at least one function. The computer identifies within a monolithic application relevant code elements associated with the reference indicia. The computer generates an activity log associated with execution of the relevant code elements. The computer identifies, within the activity log, a group of data elements associated with the execution of the relevant code elements. The computer generates a group data element clusters using a Machine Learning algorithm. The computer identifies at least one of the group of data element clusters as relevant to the at least one function.

Described herein is a technique for mapping the raw text of a job title of an online job posting to an entity embedding, associated with an entity or entry of a title taxonomy. The raw text of the job title is first encoded to generate a multilingual word embedding in a multilingual word embedding space. Then, the vector representation of the job title, as represented in the multilingual word embedding space is translated, using a neural network, to a vector representation of the job title in the entity embedding space. Finally, a nearest neighbor search is performed to identify an entity embedding associated with an entity or entry in the title taxonomy that has a vector representation that is closest in distance to the vector output by the neural network.