A problem of supervised learning is overcome by using patches to discover objects in unlabeled training images. The discovered objects are embedded in a pattern space. An AI machine replaces manual entry steps of training with a machine-centric process including clustering in a pixel space, clustering in latent space and building the pattern space based on different losses derived from pixel space clustering and latent space clustering. A distance structure in the pattern space captures the co-occurrence of patterns due to frequently appearing objects in training image data. Embodiments provide image representation based on local image patch naturally handles the position and scale invariance property that is important to effective object detection. Embodiments successfully identifies frequent objects such as human faces, human bodies, animals, or vehicles from unorganized data images based on a small quantity of training images.

A method may include obtaining a dataset including one or more data points. The method may include separating the dataset into one or more partitions based on a target number of subjects and a dimensionality of the data points included in the dataset. The method may include obtaining one or more weight vectors, each respective weight vector corresponding to a respective subject. The method may include selecting a first partition of the plurality of partitions to remove from the dataset based on respective relationships between a first weighted centroid of the dataset and first partition weights corresponding to each of the partitions. The method may include obtaining a first subset of the dataset by removing the data points associated with the selected first partition from the dataset. The method may include training a machine learning model based on the first subset of the dataset.

A system, method, and computer-readable storage medium are disclosed that execute machine vision operations to categorize a locality. At least one embodiment accesses a map image of a locality, where the map image includes geographical artefacts corresponding to entities within the locality; analyzes the map image to detect the entities in the locality using the geographical artefacts; assigns entity classes to detected entities in the locality; assigns a locality score to the locality based on entity classes included in the locality; retrieves street view images for one or more of the detected entities in the locality; and analyzes street view images of the detected entities to assign one or more further classifications to the detected entities. Other embodiments include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

Described is a system and method that provides a data protection risk assessment for the overall functioning of a backup and recovery system. Accordingly, the system may provide a single overall risk assessment score that provide an operator with an “at-a-glance” overview of the entire system. Moreover, the system may account for changes that occur over time based on leveraging statistical methods to automatically generate assessment scores for various components (e.g. application, server, network, load, etc.). In order to determine a risk assessment score, the system may utilize a predictive model based on historical data. Accordingly, residual values for newly observed data may be determined using the predictive model and the system may identify potentially anomalous or high risk indicators.

A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.

A natural language processor extracts features from batches of unstructured traffic. A feature weighted distance engine computes a distance matrix between pairs of feature vectors for sessions of unstructured traffic using a weight vector that assigns importance to relative placement of features in feature vectors. The distance function used to compute the distance matrix with the weight vector is conducive to generating high-quality clusters and patterns in unstructured traffic. The sessions of unstructured traffic are clustered according to the pairwise distance matrix. Generated clusters are merged with clusters for previously analyzed sessions of unstructured traffic. A pattern identification engine extracts patterns from the merged clusters that correspond to behavior of applications generating the unstructured traffic.

Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

A system for identifying redundant road lane detections in map data and subsequently updating the map data to remove the redundant road lane detections is provided. The system may be configured to determine, based on sensor data, a plurality of road lane detections associated with a road link represented by the map data. The system is further configured to determine a cluster for the road link based on a clustering criterion. The system is further configured to establish a plurality of road lane detection groups based on connectivity of the road lane detections in the cluster. The plurality of road lane detection groups is evaluated to identify one or more redundant road lane detections based on one or more of a parallel-detection criterion or a heading difference criterion. The identified redundant road lane detections are used to update the map data by a computer-implemented update process.

A system for identifying redundant road lane detections in map data and subsequently updating the map data to remove the redundant road lane detections is provided. The system may be configured to determine, based on sensor data, a plurality of road lane detections associated with a road link represented by the map data. The system is further configured to determine a cluster for the road link based on a clustering criterion. The system is further configured to establish a plurality of road lane detection groups based on connectivity of the road lane detections in the cluster. The plurality of road lane detection groups is evaluated to identify one or more redundant road lane detections based on one or more of a parallel-detection criterion or a heading difference criterion. The identified redundant road lane detections are used to update the map data by a computer-implemented update process.

Various embodiments provide systems and methods for discovering APIs for use in relation to network application security.