Abstract: Systems and methods are described for automated threat detection. For example, the system receives various types of unlabeled data and determines, through an unsupervised machine learning model, a label for the data. The labels are provided to a supervised machine learning model during a first training process. When new data is received, the supervised machine learning model is executed during an inference process to cluster the new data in accordance with the labels that were determined by the unsupervised machine learning model. In some examples, a label audit process may be implemented to update the cluster/output of the supervised machine learning model. The updated labels from the label audit process may be provided back to the supervised machine learning model during a second training process. In other words, the system may combine the unsupervised machine learning model with a supervised machine learning model to perform automated threat detection.

Abstract: Systems and methods are described for automated threat detection. For example, the system receives various types of unlabeled data and determines, through an unsupervised machine learning model, a label for the data. The labels are provided to a supervised machine learning model during a first training process. When new data is received, the supervised machine learning model is executed during an inference process to cluster the new data in accordance with the labels that were determined by the unsupervised machine learning model. In some examples, a label audit process may be implemented to update the cluster/output of the supervised machine learning model. The updated labels from the label audit process may be provided back to the supervised machine learning model during a second training process. In other words, the system may combine the unsupervised machine learning model with a supervised machine learning model to perform automated threat detection.

Abstract: Systems and methods are described for automated threat detection. For example, the system receives labels that are generated by an unsupervised machine learning model. Using the labels, the system initiates a training process of a supervised machine learning model using the set of labels from the unsupervised machine learning model. The supervised machine learning model can generate a set of clustered data during an inference process. The supervised machine learning model can be updated and stored in a model data store for future inference processes on new data.

Abstract: Systems and methods are described for automated threat detection. For example, the system receives various types of unlabeled data and determines, through an unsupervised machine learning model, a label for the data. The labels are provided to a supervised machine learning model during a first training process. When new data is received, the supervised machine learning model is executed during an inference process to cluster the new data in accordance with the labels that were determined by the unsupervised machine learning model. In some examples, a label audit process may be implemented to update the cluster/output of the supervised machine learning model. The updated labels from the label audit process may be provided back to the supervised machine learning model during a second training process. In other words, the system may combine the unsupervised machine learning model with a supervised machine learning model to perform automated threat detection.

Abstract: A chainsaw system includes a chainsaw and a saw blade module, which has a cutting chain and at least one guide unit for forming a guide for at least one part of the cutting chain. The chainsaw system further includes at least one coupling unit for coupling the saw blade module to the chainsaw and at least one release unit that has at least one fastening element and is provided in order to provide tool-free decoupling of the at least one saw blade module which is coupled by means of the coupling unit.

Abstract: A chainsaw system includes a chainsaw and a saw blade module, which has a cutting chain and at least one guide unit for forming a guide for at least one part of the cutting chain. The chainsaw system further includes at least one coupling unit for coupling the saw blade module to the chainsaw and at least one release unit that has at least one fastening element and is provided in order to provide tool-free decoupling of the at least one saw blade module which is coupled by means of the coupling unit.

Abstract: An oscillating gardening device includes a drive unit and at least one cam gear that is configured to be driven by the drive unit and has at least one guide section configured to guide at least one element. The guide section is rotatable about an axis of rotation and closed relative to the rotation axis in the circumferential direction. The guide section is arranged eccentrically with respect to the axis of rotation and deviates at least substantially from one or more of a circular shape and a circular ring shape in the circumferential direction. The oscillating gardening device in at least one embodiment is configured as one or more of hedge shears, grass shears, and branch shears.

Abstract: An oscillating gardening device includes a drive unit and at least one cam gear that is configured to be driven by the drive unit and has at least one guide section configured to guide at least one element. The guide section is rotatable about an axis of rotation and closed relative to the rotation axis in the circumferential direction. The guide section is arranged eccentrically with respect to the axis of rotation and deviates at least substantially from one or more of a circular shape and a circular ring shape in the circumferential direction. The oscillating gardening device in at least one embodiment is configured as one or more of hedge shears, grass shears, and branch shears.

Abstract: A line trimmer or mower head includes an automatic and/or manual line feed system with reduced tendency for cut and worn line to retract fully back into the spool housing. The line trimmer includes a rotatable spool assembly of at least one feedable line, at least one primary wall member adjacent a periphery of said spool assembly and a guide passage extending generally radially away from said primary wall member. The guide passage is partly defined by a substantially radially extending first formation arranged to bear upon line extending through said guide passage, such that in use line located within said guide passage is restricted to a substantially radial orientation.