Abstract: Techniques are disclosed for detecting an uncovered portion of a body of a person in a frame of video content. In an example, a first machine learning model of a computing system may output a first score for the frame based on a map that identifies a region of the frame associated with an uncovered body part type. Depending on a value of the first score, a second machine learning model that includes a neural network architecture may further analyze the frame to output a second score. The first score and second score may be merged to produce a third score for the frame. A plurality of scores may be determined, respectively, for frames of the video content, and a maximum score may be selected. The video content may be selected for presentation on a display for further evaluation based on the maximum score.

Abstract: In some implementations, an insect trap includes a plurality of individual cells, with each of the cells being configured to trap very few, and in some instances a single flying insect. Each cell defines a cavity and may be provided with an independently operable door to selectively close the cavity. One or more sensors may sense the presence of an insect to be trapped.

Abstract: In some implementations, an insect trap includes a plurality of individual cells, with each of the cells being configured to trap very few, and in some instances a single flying insect. Each cell defines a cavity and may be provided with an independently operable door to selectively close the cavity. One or more sensors may sense the presence of an insect to be trapped.