Abstract: A machine-learning-based cyber-attack susceptibility detection and/or monitoring system and method is disclosed for providing quantitative measures for a system's cyber-attack susceptibility, in particular a machine-learning-based cyber risk method and system for technical measuring a quantitative cyber risk score measure indicating a potential damage or loss related to a cyber risk event impacting a computer application. The machine-learning-based cyber risk system comprises a status assessment module and a risk allocation module. A weighing module weighs one or more risk factors associated to a detected type parameters of the set of type identification data based on the weighing factors. An aggregation module aggregates the weighted risk factors as an aggregated cyber risk score for the computer application, and an output signal generator providing the aggregated cyber risk score as an output signal of the machine-learning-based cyber risk system.

Abstract: A data processing system is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory and executable by the one or more processors. The instructions may be executed to convert an input video file to a commonly used video format, and compare a resultant file size to a threshold size. In response to the file size being greater than the threshold size, the instructions may be executed to reduce a video bit rate of the input video file, and produce a Portable Document Format file that includes contents of an input Portable Document File and in which the input video file is embedded.

Abstract: A data processing system is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory and executable by the one or more processors. The instructions may be executed to convert an input video file to a commonly used video format, and compare a resultant file size to a threshold size. In response to the file size being greater than the threshold size, the instructions may be executed to reduce a video bit rate of the input video file, and produce a Portable Document Format file that includes contents of an input Portable Document File and in which the input video file is embedded.

Abstract: A data processing system is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory and executable by the one or more processors. The instructions may be executed to convert an input video file to a commonly used video format, and compare a resultant file size to a threshold size. In response to the file size being greater than the threshold size, the instructions may be executed to reduce a video bit rate of the input video file, and produce a Portable Document Format file that includes contents of an input Portable Document File and in which the input video file is embedded.

Abstract: A data processing system is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory and executable by the one or more processors. The instructions may be executed to convert an input video file to a commonly used video format, and compare a resultant file size to a threshold size. In response to the file size being greater than the threshold size, the instructions may be executed to reduce a video bit rate of the input video file, and produce a Portable Document Format file that includes contents of an input Portable Document File and in which the input video file is embedded.