Abstract: Example methods are provided to improve placement of an adaptor (210,220) to a mobile computing device (100) to measure a test strip (221) coupled to the adaptor (220) with a camera (104) and a screen (108) on a face of the mobile computing device (100). The method may include displaying a light area on a first portion of the screen (108). The first portion may be adjacent to the camera (104). The light area and the camera (104) may be aligned with a key area of the test strip (221) so that the camera (104) is configured to capture an image of the key area. The method may further include providing first guiding information for a user to place the adaptor (210,220) to the mobile computing device (100) according to a position of the light area on the screen (108).

Abstract: A smart home includes Internet of things (IOT) devices that are paired with an IOT gateway. A backend system is in communication with the IOT gateway to receive IOT operating data of the IOT devices. The backend system generates a machine learning model for an IOT device. The machine learning model is consulted with IOT operating data of the IOT device to detect anomalous operating behavior of the IOT device. The machine learning model is updated as more and newer IOT operating data of the IOT device are received by the backend system.

Abstract: An email attempting to perpetrate a business email compromise (BEC) attack is detected based on similarity of the email to a known BEC email and on similarity of the email to a user email that would have been sent by the purported sender of the email. Metadata of the email is extracted and input to a BEC machine learning model to find the known BEC email among BEC email samples. The extracted metadata are also input to a personal user machine learning model of the purported sender to generate the user email.