Abstract: A security service utilizes a machine learning model to detect unused open ports. A security agent on client machines tracks the operating executables and the open ports on a machine. A machine learning model is trained for a specific port number using the more commonly-used executables that run on machines having the port opened from a large and diverse population of machines. The model is then used to determine the ports that an executable is likely to be associated with which is then used to determine if a particular machine has an unused open port.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.

Abstract: Malicious computer behavior is detected automatically based on a user session. A user session comprising a sequence of process events is identified and a text-based representation is generated, wherein process events correspond to words and a sequence of words corresponds to a sentence. Subsequently, a text-based classifier classifies the session as malicious or non-malicious based on the sequence of events within the session in the text representation.

Abstract: A technique for 3D registration of three or more 3D models using parallel computing. The technique treats the pairwise 3D registration problem as an atomic sub-problem, and solves in parallel a plurality of pairwise 3D registration. The initial guess for the pairwise 3D registration is calculated based on the, possibly incomplete, information available at the moment the calculation is made. At each point the available pairwise transformations are examined based on current available information. Transformations that are identified as outliers or as inaccurate are marked for repeated pairwise 3D registration when additional information relevant for the calculation of the initial guess becomes available.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.

Abstract: A technique for 3D registration of three or more 3D models using parallel computing. The technique treats the pairwise 3D registration problem as an atomic sub-problem, and solves in parallel a plurality of pairwise 3D registration. The initial guess for the pairwise 3D registration is calculated based on the, possibly incomplete, information available at the moment the calculation is made. At each point the available pairwise transformations are examined based on current available information. Transformations that are identified as outliers or as inaccurate are marked for repeated pairwise 3D registration when additional information relevant for the calculation of the initial guess becomes available.

Abstract: The present disclosure describes a target tracker that evaluates frames of data of one or more targets, such as a body part, body, and/or object, acquired by a depth camera. Positions of the joints of the target(s) in the previous frame and the data from a current frame are used to determine the positions of the joints of the target(s) in the current frame. To perform this task, the tracker proposes several hypotheses and then evaluates the data to validate the respective hypotheses. The hypothesis that best fits the data generated by the depth camera is selected, and the joints of the target(s) are mapped accordingly.

Abstract: The present disclosure describes a target tracker that evaluates frames of data of one or more targets, such as a body part, body, and/or object, acquired by a depth camera. Positions of the joints of the target(s) in the previous frame and the data from a current frame are used to determine the positions of the joints of the target(s) in the current frame. To perform this task, the tracker proposes several hypotheses and then evaluates the data to validate the respective hypotheses. The hypothesis that best fits the data generated by the depth camera is selected, and the joints of the target(s) are mapped accordingly.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.