Abstract: Methods and systems for detecting and predicting anomalies include processing frames of a video stream to determine values of a feature corresponding to each frame. A feature time series is generated that corresponds to values of the identified feature over time. A matrix profile is generated that identifies similarities of sub-sequences of the time series to other sub-sequences of the feature time series. An anomaly is detected by determining that a value of the matrix profile exceeds a threshold value. An automatic action is performed responsive to the detected anomaly.

Abstract: A computer-implemented method executed by at least one processor for detecting tattoos on a human body is presented. The method includes inputting a plurality of images into a tattoo detector, selecting one or more images of the plurality of images including tattoos, extracting, via a feature extractor, tattoo feature vectors from the tattoos found in the one or more images of the plurality of images including tattoos, applying a deep learning tattoo matching model to determine potential matches between the tattoo feature vectors and preexisting tattoo images stored in a tattoo training database, and generating a similarity score between the tattoo feature vectors and one or more of the preexisting tattoo images stored in the tattoo training database.

Abstract: Methods and systems for controlling a user interface include identifying a user at a station based on facial recognition of an image of the user's face in a video stream, to match a profile for the user. At least one preference of the user is determined for the display of content, based on the matched profile. Content for the user is configured in accordance with the at least one preference. The configured content is displayed on a user interface of the station.

Abstract: Systems and methods are disclosed for parsing logs from arbitrary or unknown systems or applications by capturing heterogeneous logs from the arbitrary or unknown systems or applications; generating one pattern for every unique log message; building a pattern hierarchy tree by grouping patterns based on similarity metrics, and for every group it generates one pattern by combing all constituting patterns of that group; and selecting a set of patterns from the pattern hierarchy tree.

Abstract: A computer-implemented method for automatically analyzing log contents received via a network and detecting content-level anomalies is presented. The computer-implemented method includes building a statistical model based on contents of a set of training logs and detecting, based on the set of training logs, content-level anomalies for a set of testing logs. The method further includes maintaining an index and metadata, generating attributes for fields, editing model capability to incorporate user domain knowledge, detecting anomalies using field attributes, and improving anomaly quality by using user feedback.

Abstract: A heterogeneous log pattern editing recommendation system and computer-implemented method are provided. The system has a processor configured to identify, from heterogeneous logs, patterns including variable fields and constant fields. The processor is also configured to extract a category feature, a cardinality feature, and a before-after n-gram feature by tokenizing the variable fields in the identified patterns. The processor is additionally configured to generate target similarity scores between target fields to be potentially edited and other fields from among the variable fields in the heterogeneous logs using pattern editing operations based on the extracted category feature, the extracted cardinality feature, and the extracted before-after n-gram feature. The processor is further configured to recommend, to a user, log pattern edits for at least one of the target fields based on the target similarity scores between the target fields in the heterogeneous logs.

Abstract: A computer-implemented method executed by at least one processor for person identification is presented. The method includes employing one or more cameras to receive a video stream including a plurality of frames to extract features therefrom, detecting, via an object detection model, objects within the plurality of frames, detecting, via a key point detection model, persons within the plurality of frames, detecting, via a color detection model, color of clothing worn by the persons, detecting, via a gender and age detection model, an age and a gender of the persons, establishing a spatial connection between the objects and the persons, storing the features in a feature database, each feature associated with a confidence value, and normalizing, via a ranking component, the confidence values of each of the features.

Abstract: A computer-implemented method for emulating an object recognizer includes receiving testing image data, and emulating, by employing a first object recognizer, a second object recognizer. Emulating the second object recognizer includes using the first object recognizer to perform object recognition on a testing object from the testing image data to generate data, the data including a feature representation for the testing object, and classifying the testing object based on the feature representation and a machine learning model configured to predict whether the testing object would be recognized by a second object recognizer. The method further includes triggering an action to be performed based on the classification.

Abstract: Methods and systems for image clustering include matching a new image to a representative image of a cluster. The new image is set as a representative of the cluster with a first time limit. The new image is set as a representative of the cluster with a second time limit, responsive to a determination that the new image has matched at least one incoming image during the first time limit.

Abstract: A computer-implemented method, computer program product, and computer processing system are provided. The method includes preprocessing, by a processor, a set of heterogeneous logs by splitting each of the logs into tokens to obtain preprocessed logs. Each of the logs in the set is associated with a timestamp and textual content in one or more fields. The method further includes generating, by the processor, a set of regular expressions from the preprocessed logs. The method also includes performing, by the processor, an unsupervised parsing operation by applying the regular expressions to the preprocessed logs to obtain a set of parsed logs and a set of unparsed logs, if any. The method additionally includes storing, by the processor, the set of parsed logs in a log analytics database and the set of unparsed logs in a debugging database.

Abstract: Systems and methods for implementing content-level anomaly detection for devices having limited memory are provided. At least one log content model is generated based on training log content of training logs obtained from one or more sources associated with the computer system. The at least one log content model is transformed into at least one modified log content model to limit memory usage. Anomaly detection is performed for testing log content of testing logs obtained from one or more sources associated with the computer system based on the at least one modified log content model. In response to the anomaly detection identifying one or more anomalies associated with the testing log content, the one or more anomalies are output.

Abstract: A system and method are provided for pattern discovery in input heterogeneous logs having unstructured text content and one or more fields. The system includes a memory. The system further includes a processor in communication with the memory. The processor runs program code to preprocess the input heterogeneous logs to obtain pre-processed logs by splitting the input heterogeneous logs into tokens. The processor runs program code to generate seed patterns from the preprocessed logs. The processor runs program code to generate final patterns by specializing a selected set of fields in each of the seed patterns to generate a final pattern set.

Abstract: A method is provided for classifying objects. The method detects objects in one or more images. The method tags each object with multiple features. Each feature describes a specific object attribute and has a range of values to assist with a determination of an overall quality of the one or more images. The method specifies a set of training examples by classifying the overall quality of at least some of the objects as being of an acceptable quality or an unacceptable quality, based on a user's domain knowledge about an application program that takes the objects as inputs. The method constructs a plurality of first-level classifiers using the set of training examples. The method constructs a second-level classifier from outputs of the first-level automatic classifiers. The second-level classifier is for providing a classification for at least some of the objects of either the acceptable quality or the unacceptable quality.

Abstract: Methods and systems for log management include pre-processing heterogeneous logs and performing a log management action on the pre-processed plurality of heterogeneous logs. Pre-processing the logs includes performing a fixed tokenization of the heterogeneous logs based on a predefined set of symbols, performing a flexible tokenization of the heterogeneous logs based on a user-defined set of rules, converting timestamps in the heterogeneous logs to a single target timestamp format, and performing structural log tokenization of the heterogeneous logs based on user-defined structural information.

Abstract: A method is provided that includes transforming training data into a neural network based learning model using a set of temporal graphs derived from the training data. The method includes performing model learning on the learning model by automatically adjusting learning model parameters based on the set of the temporal graphs to minimize differences between a predetermined ground-truth ranking list and a learning model output ranking list. The method includes transforming testing data into a neural network based inference model using another set of temporal graphs derived from the testing data. The method includes performing model inference by applying the inference and learning models to test data to extract context features for alerts in the test data and calculate a ranking list for the alerts based on the extracted context features. Top-ranked alerts are identified as critical alerts. Each alert represents an anomaly in the test data.

Abstract: A computer-implemented method for generating patterns from a set of heterogeneous log messages is presented. The method includes collecting the set of heterogenous log messages from arbitrary or unknown systems or applications or sensors or instruments, splitting the log messages into tokens based on a set of delimiters, identifying datatypes of the tokens, identifying a log structure of the log messages by generating pattern-signatures of all the tokens and the datatypes based on predefined pattern settings, generating a pattern for each of the log structures and enabling users to edit the pattern for each of the log structures based on user requirements.

Abstract: Systems and methods for implementing content-level anomaly detection for devices having limited memory are provided. At least one log content model is generated based on training log content of training logs obtained from one or more sources associated with the computer system. The at least one log content model is transformed into at least one modified log content model to limit memory usage. Anomaly detection is performed for testing log content of testing logs obtained from one or more sources associated with the computer system based on the at least one modified log content model. In response to the anomaly detection identifying one or more anomalies associated with the testing log content, the one or more anomalies are output.

Abstract: A computer-implemented method for generating patterns from a set of heterogeneous log messages is presented. The method includes collecting the set of heterogenous log messages from arbitrary or unknown systems or applications or sensors or instruments, splitting the log messages into tokens based on a set of delimiters, identifying datatypes of the tokens, identifying a log structure of the log messages by generating pattern-signatures of all the tokens and the datatypes based on predefined pattern settings, generating a pattern for each of the log structures and enabling users to edit the pattern for each of the log structures based on user requirements.

Abstract: A system and method are provided for pattern discovery in input heterogeneous logs having unstructured text content and one or more fields. The system includes a memory. The system further includes a processor in communication with the memory. The processor runs program code to preprocess the input heterogeneous logs to obtain pre-processed logs by splitting the input heterogeneous logs into tokens. The processor runs program code to generate seed patterns from the preprocessed logs. The processor runs program code to generate final patterns by specializing a selected set of fields in each of the seed patterns to generate a final pattern set.

Abstract: Systems and methods for enabling automated log analysis with controllable resource requirements are provided. A training set for log pattern learning is generated based on heterogeneous logs generated by a computer system. An incremental learning process is implemented to generate a set of log patterns from the training set. The heterogeneous logs are parsed using the set of log patterns. A set of applications is applied to the parsed logs.