Abstract: A method of monitoring behavior of a device includes obtaining, at a computing device, first data based on first sensor data from a first sensor device coupled to the device. The method includes processing, at the computing device, the first data at a first anomaly detection model and at a second anomaly detection model of multiple anomaly detection models trained to detect anomalous behavior of the device. The method also includes determining, based on outputs of the multiple anomaly detection models, whether to generate an alert.

Abstract: A method includes accessing aircraft maintenance log data including maintenance entries associated with a plurality of aircraft. Each of the maintenance entries describes a respective maintenance task performed on a respective aircraft of the plurality of aircraft. The method also includes providing the maintenance entries as input to a trained classifier. In response to input of a maintenance entry, the trained classifier outputs a task identifier for the maintenance entry based on one or more fields of the maintenance entry. The method further includes determining task type labels for the maintenance entries. A task type label for a particular maintenance entry is determined based on the task identifier for the particular maintenance entry, and the task type labels indicate whether a respective maintenance entry describes a substantive task. The method also includes storing supplemental log data including the task identifiers and the task type labels.

Abstract: A method includes performing, by a computing device, a clustering operation to group documents of a document corpus into clusters in a feature vector space. The document corpus includes one or more labeled documents and one or more unlabeled documents. Each of the one or more labeled documents is assigned to a corresponding class in classification data associated with the document corpus, and each of the one or more unlabeled document is not assigned to any class in the classification data. The method also includes generating, by the computing device, a prompt requesting classification of a particular document of the document corpus, where the particular document is selected based on a distance between the particular document and a labeled document of the one or more labeled documents.

Abstract: A method includes receiving, via a graphical user interface including a plurality of document elements and a plurality of class elements, user input associating a first document element of the plurality of document elements with a first class element of the plurality of class elements. Each document element represents a corresponding document of a plurality of documents, and each class element represents a corresponding class of a plurality of classes. The method also includes generating a document classifier using supervised training data, where the supervised training data indicates, based on the user input, that a first document represented by the first document element is assigned to a first class associated with the first class element.

Abstract: A method includes performing, by a computing device, a clustering operation to group documents of a document corpus into clusters in a feature vector space. The document corpus includes one or more labeled documents and one or more unlabeled documents. Each of the one or more labeled documents is assigned to a corresponding class in classification data associated with the document corpus, and each of the one or more unlabeled document is not assigned to any class in the classification data. The method also includes generating, by the computing device, a prompt requesting classification of a particular document of the document corpus, where the particular document is selected based on a distance between the particular document and a labeled document of the one or more labeled documents.

Abstract: A method includes receiving, via a graphical user interface including a plurality of document elements and a plurality of class element, user input associating a first document element of the plurality of document elements with a first class element of the plurality of class elements. Each document element represents a corresponding document of a plurality of documents, and each class element represents a corresponding class of a plurality of classes. The method also includes generating a document classifier using supervised training data, where the supervised training data indicates, based on the user input, that a first document represented by the first document element is assigned to a first class associated with the first class element.