Aspects of the subject disclosure may include, for example, a method in which a processing system collects information associated with trouble tickets each including a problem abstract and a log text. The method includes analyzing the log text to obtain a problem resolution for that ticket; defining ticket clusters according to the problem abstracts, and labeling the clusters. The processing system creates a library of the labeled clusters, each entry including a cluster label, a problem abstract for that cluster, and a resolution summary for that problem abstract, indicating a mapping of the problem abstract to the resolution summary for that cluster. The method includes training, based on the mapping, machine-learning applications for a predicted resolution summary for each cluster and for classifying a new ticket. The method includes assigning the new ticket to a cluster according to the classifying. Other embodiments are disclosed.

A method for generating a highly distinctive signature of a certain diamond, the method may include generating, based on one or more images of the certain diamond, a certain diamond signature of the certain diamond; finding, out of a group of reference diamonds, other diamonds having other diamond signatures; wherein the finding comprises calculating similarities between the certain diamond signature and reference diamond signatures of the reference diamonds of the group; and generating a new certain diamond signature that significantly differs from signatures of the other diamonds.

An example method of document classification comprises: detecting a set of keypoints in an input image; generating a set of keypoint vectors, wherein each keypoint vector of the set of keypoint vectors is associated with a corresponding keypoint of the set of keypoints; extracting a feature map from the input image; producing a combination of the set of keypoint vectors with the feature map; transforming the combination into a set of keypoint mapping vectors according to a predefined mapping scheme; estimating, based on the set of keypoint mapping vectors, a plurality of importance factors associated with the set of keypoints; and classifying the input image based on the set of keypoints and the plurality of importance factors.

The present disclosure provides a method and apparatus for building a database for retrieval. An implementation of the method comprises: acquiring a data set, and dividing the data set into a first data set and a second data set; clustering the data in the first data set, to obtain at least one first-level cluster center; clustering the data in the first data set based on the first-level cluster center, to obtain corresponding at least one second-level cluster center; obtaining a codebook corresponding to the first data set based on residuals between the data in the first data set and the first-level cluster center and residuals between the data in the first data set and the second-level cluster center; and training the second data set based on the codebook corresponding to the first data set, to obtain a codebook corresponding to the data set.

A warning system and method are provided. The warning system includes a plurality of sensing apparatuses and a server. The sensing apparatuses are used for sensing a driving trajectory of each of a plurality of two-wheeled vehicles. The server compares the driving trajectories with an accident hotspot list to determine whether at least one first driving trajectory matches an accident hotspot location, wherein the accident hotspot list is generated by a plurality of driving behavior events corresponding to each of the two-wheeled vehicles. The server generates a warning message to remind a first driver of a first two-wheeled vehicle corresponding to the at least one first driving trajectory when determining that the at least one first driving trajectory matches the accident hotspot location.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

Performance of devices can be evaluated and enhanced in software-defined systems. For example, a computing device can receive, at a server of a software-defined system, a first plurality of properties describing a client system in the software-defined system. The computing device can compare, by the server, the first plurality of properties to additional properties describing at least one additional client system in the software-defined system. The computing device can determine, by the server, an adjustment for the client system based on the comparison and a similarity of the client system to each of the at least one additional client system. The computing device can output, by the server, an indication of the adjustment to the client system.

Patch partition and image processing techniques are described. In one or more implementations, a system includes one or more modules implemented at least partially in hardware. The one or more modules are configured to perform operations including grouping a plurality of patches taken from a plurality of training samples of images into respective ones of a plurality of partitions, calculating an image processing operator for each of the partitions, determining distances between the plurality of partitions that describe image similarity of patches of the plurality of partitions, one to another, and configuring a database to provide the determined distance and the image processing operator to process an image in response to identification of a respective partition that corresponds to a patch taken from the image.

A computer-implemented clustering method is disclosed for image segmentation, social network analysis, computational biology, market research, search engine and other applications. At the heart of the method is a point-set kernel that measures the similarity between a data point and a set of data points. The method has a procedure that employs the point-set kernel to expand from a seed point to a cluster; and finally identifies all clusters in the given dataset. Applying the method for image segmentation, it identifies several segments in the image, where points in each segment have high similarity: but points in one segment have low similarity with respect to other segments. The method is both effective and efficient that enables it to deal with large scale datasets. In contrast, existing clustering methods are either efficient or effective; and even efficient ones have difficulty dealing with large scale datasets without massive parallelization.

A computer-implemented clustering method is disclosed for image segmentation, social network analysis, computational biology, market research, search engine and other applications. At the heart of the method is a point-set kernel that measures the similarity between a data point and a set of data points. The method has a procedure that employs the point-set kernel to expand from a seed point to a cluster; and finally identifies all clusters in the given dataset. Applying the method for image segmentation, it identifies several segments in the image, where points in each segment have high similarity: but points in one segment have low similarity with respect to other segments. The method is both effective and efficient that enables it to deal with large scale datasets. In contrast, existing clustering methods are either efficient or effective; and even efficient ones have difficulty dealing with large scale datasets without massive parallelization.