Abstract: The disclosure is directed to an automated disinfection system for a CT patient table, where the automated disinfection system for a CT patient table includes: a disinfection mechanism disposed above the CT patient table; a detection mechanism, disposed on the disinfection mechanism and configured to detect a position of the CT patient table; and a control mechanism, configured to communicate with the disinfection mechanism by using a communication interface, so as to control start/stop and operation of the disinfection mechanism, where the disinfection mechanism is configured to perform horizontal movement and vertical movement during scanning gaps of a plurality of patients, so as to disinfect a use surface of the CT patient table. The disclosure enables rapid and automated disinfection on the use surface of the CT patient table during scanning gaps of a plurality of patients, thereby reducing a risk of cross-infection and saving manpower.

Abstract: A training data set for training a machine learning module is prepared by dividing normal files and malicious files into sections. Each section of a normal file is labeled as normal. Each section of a malicious file is labeled as malicious regardless of whether or not the section is malicious. The sections of the normal files and malicious files are used to train the machine learning module. The trained machine learning module is packaged as a machine learning model, which is provided to an endpoint computer. In the endpoint computer, an unknown file is divided into sections, which are input to the machine learning model to identify a malicious section of the unknown file, if any is present in the unknown file.

Abstract: A training data set for training a machine learning module is prepared by dividing normal files and malicious files into sections. Each section of a normal file is labeled as normal. Each section of a malicious file is labeled as malicious regardless of whether or not the section is malicious. The sections of the normal files and malicious files are used to train the machine learning module. The trained machine learning module is packaged as a machine learning model, which is provided to an endpoint computer. In the endpoint computer, an unknown file is divided into sections, which are input to the machine learning model to identify a malicious section of the unknown file, if any is present in the unknown file.