Abstract: The present invention utilizes computer vision technologies to identify potentially malicious URLs and executable files in a computing device. In one embodiment, a Siamese convolutional neural network is trained to identify the relative similarity between image versions of two strings of text. After the training process, a list of strings that are likely to be utilized in malicious attacks are provided (e.g., legitimate URLs for popular websites). When a new string is received, it is converted to an image and then compared against the image of list of strings. The relative similarity is determined, and if the similarity rating falls below a predetermined threshold, an alert is generated indicating that the string is potentially malicious.

Abstract: The present invention relates to the monitoring of users to prevent pressure ulcers using imaging technologies to detect a user's posture, track the user's poses, detect changes in a user's posture, quantify changes in a user's posture, determine pressure areas from the user's poses, and alert caregivers when a user's current positioning and/or lack of movement may result in the formation of a pressure ulcer. The invention relates to the use of devices that can form images in low to high lighting conditions for monitoring a user to prevent pressure ulcers. These devices include, but are not limited to, thermographic cameras (also known as infrared cameras and thermal imaging camera), image intensifiers (also known as image intensifier tubes), and active illumination.