Abstract: Various embodiments of apparatuses and methods for a continuous scanning engine with automatic protocol detection are described. In some embodiments, the continuous scanning engine comprises one or more discovery components and one or more protocol detection components. The discovery components, in some embodiments, send initial packets to a plurality of ports of a plurality of network addresses of a network, receive responses to a least some of the initial packets, asynchronously match the received responses to the sent initial packets, and determine that some ports at some network addresses require further analysis.

Abstract: Various embodiments of a scanning engine are described. In some embodiments, the scanning engine comprises discovery components associated with different Internet providers and/or protocol detection components associated with the different Internet providers. When a first discovery component associated with a first Internet provider does not receive a response from a port at an Internet address, then a second discovery component associated with a second Internet provider sends packets to that port at that Internet address to attempt to elicit a response. When a first protocol inspection component associated with a first Internet provider is not able to communicate with a port at an Internet address, then it provides information that can be obtained by a second protocol inspection component associated with a second Internet provider. That second protocol inspection component attempts to communicate with the port at the Internet address through the second Internet provider using various communication protocols.

Abstract: A method for training a machine learning model for generating descriptor images. The method includes recording a plurality of camera images, forming training image pairs from the plurality of camera images, wherein each training image pair includes a first training image and a second training image, ascertaining a loss for the training image pair and at least one key point from a distance between the position of the key point in the first training image or a transformed version of the first training image and an estimated position of the key point, which is ascertained by transitioning from the first training image to the second training image and back to the first training image or its transformed version, and adapting the machine learning model for reducing a total loss that includes the ascertained losses for at least a part of the training image pairs and key points.

Abstract: A method for ascertaining a descriptor image for an image of an object. The method includes training, for each of a plurality of object classes, a respective machine learning model to map images of objects of the object class to descriptor images and storing reference descriptors output by the machine learning model for one or more objects of the object class; receiving an image of an object; generating, for each object class, a respective descriptor image for the object by mapping the received image to a descriptor image using the machine learning model trained for the object class; evaluating, for each object class, the distance between the reference descriptors stored for the object class and the descriptors of the descriptor image generated for the object class; and assigning the descriptor image to the object as the descriptor image of the object generated for an object class based on the distance.

Abstract: A device and a method for training an image generator. The method includes: providing an image sequence that includes an image for each time of a plurality of times; training a first encoder, a second encoder, and a decoder by: for each of a number of times of the plurality of times: for the image assigned to the time, producing a multiplicity of feature maps for the image by a neural network and grouping them into first and second subsets; supplying the first subset to the first encoder to produce first feature vector; supplying the second subset to the second encoder to produce a second feature vector; supplying the first feature vector and the second feature vector to the decoder to produce a predicted target image; producing an error value; and adapting the first encoder, the second encoder, and the decoder to reduce the error value.

Abstract: Various embodiments of apparatuses and methods for a continuous scanning engine with automatic protocol detection are described. In some embodiments, the continuous scanning engine comprises one or more discovery components and one or more protocol detection components. The discovery components, in some embodiments, send initial packets to a plurality of ports of a plurality of network addresses of a network, receive responses to a least some of the initial packets, asynchronously match the received responses to the sent initial packets, and determine that some ports at some network addresses require further analysis.

Abstract: Various embodiments of apparatuses and methods for a continuous scanning engine with automatic protocol detection are described. In some embodiments, the continuous scanning engine comprises one or more discovery components and one or more protocol detection components. The discovery components, in some embodiments, send initial packets to a plurality of ports of a plurality of network addresses of a network, receive responses to a least some of the initial packets, asynchronously match the received responses to the sent initial packets, and determine that some ports at some network addresses require further analysis.

Abstract: A method for training a machine learning model for generating descriptor images for images of one or more objects. The method includes recording multiple camera images, each showing one or more objects, and, for each camera image, generating one or more augmented versions of the camera image by applying a respective augmentation to the camera image for each augmented version of the camera image, wherein the augmentation comprises a change in position of pixel values of the camera image, generating pairs of training images each including the camera image and an augmentation of the camera image or two augmented versions of the camera image; and training the machine learning model with contrastive loss using the pairs of training images.

Abstract: A method for training a machine learning model for generating descriptor images for images of one or of multiple objects. The method includes: formation of pairs of images which show the one or the multiple objects from different perspectives; generation, for each image pair, using the machine learning model, of a first descriptor image for the first image, and of a second descriptor image for the second image, which assigns descriptors to points of the one or multiple objects shown in the second image; sampling, for each image pair, of descriptor pairs, which include in each case a first descriptor from the first descriptor image and a second descriptor from the second descriptor image, which are assigned to the same point, and the adaptation of the machine learning method for reducing a loss.

Abstract: Various embodiments of a scanning engine are described. In some embodiments, the scanning engine comprises discovery components associated with different Internet providers and/or protocol detection components associated with the different Internet providers. When a first discovery component associated with a first Internet provider does not receive a response from a port at an Internet address, then a second discovery component associated with a second Internet provider sends packets to that port at that Internet address to attempt to elicit a response. When a first protocol inspection component associated with a first Internet provider is not able to communicate with a port at an Internet address, then it provides information that can be obtained by a second protocol inspection component associated with a second Internet provider. That second protocol inspection component attempts to communicate with the port at the Internet address through the second Internet provider using various communication protocols.

Abstract: A device and a method for training an image generator. The method includes: providing an image sequence that includes an image for each time of a plurality of times; training a first encoder, a second encoder, and a decoder by: for each of a number of times of the plurality of times: for the image assigned to the time, producing a multiplicity of feature maps for the image by a neural network and grouping them into first and second subsets; supplying the first subset to the first encoder to produce first feature vector; supplying the second subset to the second encoder to produce a second feature vector; supplying the first feature vector and the second feature vector to the decoder to produce a predicted target image; producing an error value; and adapting the first encoder, the second encoder, and the decoder to reduce the error value.