According to some embodiments, a system and method are provided comprising a vegetation management module to receive image data from an image source; a memory for storing program instructions; a vegetation management processor, coupled to the memory, and in communication with the vegetation module, and operative to execute program instructions to: receive first image data and second image data for an area of interest; overlay the first image data over the second image data to generate an overlaid image; receive feeder attribute data for at least one feeder in the overlaid image; generate a risk score for the at least one feeder based in part on the received feeder attribute data; and generate a visualization based on the at least one feeder and the generated risk score. Numerous other aspects are provided.

Certain aspects of the present disclosure provide techniques for efficiently configuring a machine learning model. An example method generally includes generating a randomly sampled data set from a data set including a larger first set of samples associated with a first classification and a smaller second set of samples associated with a second classification. An analysis plot for the machine learning model is generated based on the randomly sampled data set. A point associated with an accuracy metric for the machine learning model is identified on the analysis plot based on a slope of a line tangential to the identified point and a value identifying a relative importance of precision to recall in the machine learning model. The machine learning model is configured with a threshold value between the first classification and the second classification based at least in part on the identified point on the analysis plot.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting network attacks. One of the methods includes obtaining input data associated with a plurality of accounts associated with a particular entity; extracting features from the input data; performing unsupervised attack ring detection using the extracted features, wherein the unsupervised attack ring detection identifies suspicious clusters of accounts that have strong similarity or correlations in the high dimensional feature space; and generating an output for the detected attack rings.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a clustering neural network. One of the methods includes obtaining unlabeled training data; and training the clustering neural network on the unlabeled training data to determine trained values of the clustering parameters by minimizing a normalized cuts loss function that includes a first term that measures an expected normalized cuts of clustering nodes in a graph representing the data set into the plurality of clusters according to clustering outputs generated by the clustering neural network.

Analysis of genetic disease progression may be provided. Data about a set of molecular status may be received. A dynamic prediction model of molecular interactions may be provided over time. The molecular statuses of the set over time may be determined using the dynamic prediction model. The determined molecular statuses may be clustered by applying an interaction-aware metric for the analysis of the genetic disease progression.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automatically adjusting strategies. One of the methods includes: determining one or more characteristics of a plurality of complaints, wherein each of the complaints corresponds to an order; classifying the plurality of complaints into a plurality of categories based on the one or more characteristics by using a trained classifier; selecting a category from the plurality of categories based on a number of complaints in the selected category; from a group of strategies each associated with one or more conditions and one or more actions, identifying a candidate strategy causing the complaints of the selected category, wherein the one or more actions are executed in response to the one or more conditions being satisfied; and optimizing the candidate strategy using a reinforcement learning model at least based on a plurality of historical orders.

This application relates to an interest recommendation method, a computer device, and a storage medium. The method includes: obtaining feature information of a target user; predicting interest similarities between the target user and a user group according to the feature information by using an interest similarity prediction model, the interest similarity prediction model being implemented according to an interest similarity between each pair of users in a sample set of historical records of users based on a hybrid tree-encoded linear model algorithm, the hybrid tree-encoded linear model algorithm being implemented based on a tree model and a linear model; determining, according to the interest similarities, recommended users in the user group having interests similar to those of the target user; and obtaining an interest list of the recommended users, and creating a recommendation list for the target user according to the interest list.

A computing system includes an anonymizer server. The anonymizer server is communicatively coupled to a data repository configured to store a personal identification information (PII) data. The anonymizer server is configured to perform operations including receiving an anonymized data request, and creating an anonymized data repository based on the anonymized data request. The anonymizer server is also configured to perform operations including anonymizing the PII data to create an anonymized data by applying a cluster-based process, and storing the anonymized data in the anonymized data repository.

One embodiment provides a method, including: receiving a multi-variate time-series dataset comprising a plurality of time-dependent datasets; for each of the plurality of time-dependent datasets, segmenting each of the plurality of time-dependent datasets at a transition point; clustering segments of the plurality of time-dependent datasets into clusters having similar lengths of segments; for each cluster (i) selecting a representative segment length and (ii) identifying a feature subset in that cluster; identifying, across the feature subsets, subset transition points, wherein each of the subset transition points corresponds to a change in value that meets a predetermined threshold within its corresponding feature subset; and determining, by applying a threshold test to the subset transition points, a segment length to be used in segmenting the entire multi-variate time-series dataset.

A method for automated routing of pictures taken on mobile electronic devices to a digital picture frame including a camera integrated with the frame, and a network connection module allowing the frame for direct contact and upload of photos from electronic devices or from photo collections of community members. The integrated camera is used to automatically determine an identity of a frame viewer and can capture gesture-based feedback. The displayed photos are automatically shown and/or changed according to the detected viewers. The photos can be filtered and cropped at the receiver side. Clustering photos by content is used to improve display and to respond to photo viewer desires.