Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

A method of training an image classification neural network comprises: presenting a first plurality of images to an observer as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of a target by the observer in at least one image of the first plurality of images; training the image classification neural network to identify the target in the image, based on the identification of the neurophysiological event; and storing the trained image classification neural network in a computer-readable storage medium.

The present application provides a method and apparatus for evaluating the health state of a distribution transformer, and a medium and a program. In an embodiment, the method for evaluating the health state of a distribution transformer includes: extracting features from electric power parameters of a plurality of distribution transformers; clustering the plurality of distribution transformers into M subsets based on the extracted features, M being a natural number greater than 1 and less than a number of the distribution transformers; and respectively executing processing for each of the M subsets. In an embodiment, the processing includes determining a cluster center of the subset as a reference transformer for the subset; calculating the similarity between each of the distribution transformers in the subset and the reference transformer therein; and sorting the distribution transformers in the subset according to the calculated similarity.

A variety of systems and methods can include evaluation of human user effort data. Various embodiments apply techniques to identify anomalous effort data for the purpose of detecting the efforts of a single person, as well as to segment and isolate multiple persons from a single collection of data. Additional embodiments describe the methods for using real-time anomaly detection systems that provide indicators for scoring effort data in synthesized risk analysis. Other embodiments include approaches to distinguish anomalous effort data when the abnormalities are known to be produced by a single entity, as might be applied to medical research and enhance sentiment analysis, as well as detecting the presence of a single person's effort data among multiple collections, as might be applied to fraud analysis and insider threat investigations. Embodiments include techniques for analyzing the effects of adding and removing detected anomalies from a given collection on subsequent analysis.

Methods, apparatus, systems and articles of manufacture are disclosed for classification of unknown samples using agglomerative clustering. An apparatus includes an extractor to extract a feature from a sample source code, the feature including at least one of a register, a variable, or a library based on a threshold of occurrence in a corpus of samples, the corpus of samples including malware samples, a dendrogram generator to generate a dendrogram based on features extracted from the sample source code, the dendrogram representing a collection of samples clustered based on similarity among the samples, the samples including sample clusters belonging to known malware families, and an anchor point identifier to traverse the dendrogram to identify similarity of an unknown sample to the sample clusters based on a confidence score, and identify anchor point samples from the sample clusters identified as similar to the unknown sample, the anchor point samples to provide metadata for use in extrapolating information to classify the unknown sample.

A sampling device receives, from a transducer computing device located within a predefined proximity to an equipment in an operating environment, a vibration sample from the operating environment and increments a retrain counter. In response to determining that the incremented retrain counter does not meet or exceed a retrain threshold, the sampling device predicts, using a model, an anomalous or non-anomalous designation for the vibration sample and a cluster assignment, to a particular cluster of a set of clusters, for the vibration sample when the model predicts the non-anomalous designation for the vibration sample. The sampling device receives a subsequent vibration sample and further increments the retrain counter. In response to determining that the further incremented retrain counter exceeds a retrain threshold, the sampling device receives a subsequent set of vibration samples and retrains, using the subsequent vibration sample and the subsequent set of vibration samples, the model.

A systems including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, perform: receiving inventory information from two or more merchants; clustering the two or more merchants into a group of merchants; operating an optimization model for the subset of the inventory information for the group of merchants to determine a first inventory configuration for each of the two or more merchants at a seller location; operating the optimization model for the subset of the inventory information the group of merchants to determine a second inventory configuration for each of the two or more merchants at the seller location; and combining the first inventory configuration and the second inventory configuration to determine third inventory configuration for each of the two or more merchants at the seller location. Other embodiments are described.

A system and method for authenticating a caller may include receiving an incoming call from the caller, determining a gender of the caller, and selecting, based on the determined gender, to search for the caller in one of: a watchlist of untrustworthy female callers, and a watchlist of untrustworthy male callers.

Technologies for generating a graph containing clusters of feature attribute values for training a machine learning model for content item selection and delivery are provided. The disclosed techniques include, for each entity, of a plurality of entities, a system identifies transitions from one geographic location to another geographic location. A graph is generated based on the transitions associated with each entity. The graph comprises nodes representing geographic locations and edges connecting the nodes. Each of the edges connects two nodes, represents a transition from one geographic location to another geographic location, and each edge represents an edge weight value that is based on frequencies of transitions between geographic locations represented by the two connected nodes. The system generates a plurality of clusters from the nodes based upon the edge weight value of each edge. The system includes the plurality of clusters as features in a machine learning model.

A method of recognizing gestures of a person from at least one image from a monocular camera, e.g. a vehicle camera, includes comp the steps: a) detecting key points of the person in the at least one image, b) connecting the key points to form a skeleton-like representation of body parts of the person, wherein the skeleton-like representation represents a relative position and a relative orientation of the respective body parts of the person, c) recognizing a gesture of the person from the skeleton-like representation of the person, and d) outputting a signal indicating the gesture.