yearbook photo
Florida Christian school teacher accused of using AI to produce erotic content from yearbook photos
A Florida Christian school teacher was arrested this week after allegedly creating child sexual abuse materials using photos from the school yearbook and artificial intelligence (AI), according to authorities. The Pasco County Sheriff'sOffice said 67-year-old Steven Houser of New Port Richey faces charges for possession of child pornography. Deputies initiated an investigation after receiving an unspecified tip about Houser. Steven Guy Houser, a third-grade science teacher at a Christian school in New Port Richey, Florida, was allegedly found to be in possession of child pornography he created using yearbook photos and artificial intelligence. The investigation discovered that Beacon, a third-grade science teacher at Beacon Christian Academy, allegedly possessed two photos and three videos depicting child pornography.
- Law > Criminal Law (1.00)
- Law Enforcement & Public Safety > Crime Prevention & Enforcement (1.00)
- Education > Educational Setting > Religious School (0.86)
ANOVA exemplars for understanding data drift
The distributions underlying complex datasets, such as images, text or tabular data, are often difficult to visualize in terms of summary statistics such as the mean or the marginal standard deviations. Instead, a small set of exemplars or prototypes---real or synthetic data points that are in some sense representative of the entire distribution---can be used to provide a human-interpretable summary of the distribution. In many situations, we are interested in understanding the \textit{difference} between two distributions. For example, we may be interested in identifying and characterizing data drift over time, or the difference between two related datasets. While exemplars are often more easily understood than high-dimensional summary statistics, they are harder to compare. To solve this problem, we introduce ANOVA exemplars. Rather than independently find exemplars $S_X$ and $S_Y$ for two datasets $X$ and $Y$, we aim to find exemplars that are both representative of $X$ and $Y$, and that maximize the overlap $|S_X\cap S_Y|$ between the two sets of exemplars. We can then use the differences between the two sets of exemplars to describe the difference between the distributions of $X$ and $Y$, in a concise, interpretable manner.
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