Hello, this is the promotion for our work on AI Food Safety, titled, Detecting a Lizard Food Factories from Chemical Declaration Data via Graphware Self-Supervised Contrastive Anomaly Ranking. This work is done by Xingfeng Yang and Qingte Li from National Qing Kong University, Taiwan. As appetites for varied and unique cuisines have increased, we've seen a significant rise in the number of food production facilities. Many maintain high standards and prioritize consumer safety, yet there exists a concerning segment that resorts to illicit activities. Such actions pose risks to public health and erode confidence in the food sector. Regulatory authorities mandate that factories disclose the chemicals used in food processing. Chemical hazards represent a significant portion of food safety issues. Examples include the excessive application of preservatives, drug residues, and the presence of undeclared allergens. Indeed, the FDA has evaluated the safety of chemical exposure within our food supply. This evaluation encompasses ingredients deemed generally recognized as safe, along with food additives, and contaminants. This highlights the urgent need for effective methods to detect illicit food factories based on their use of chemicals. While existing human inspections suffer issues such as limited manpower and funding, we believe AI can help through accurate learning to detect. We plan to use AI to spot likely illicit food factories for targeted physical checks by human inspectors. Working alongside local authorities, we've gained access to a data set of chemical declarations from food factories. Our analysis draws from inspection records of regulatory agencies responsible for food and drug oversight. An intuitive way to AI detection is supervised learning based on declaration tabular data. However, well-known predictors such as tree-based models and MLP produce unsatisfying results. We resort to unsupervised learning because it can operate without labeled examples and has the potential to uncover hidden patterns and anomalies. Given food factories, each is associated with a set of declared chemicals, and labels are absent, our goal is to learn to score illicit factories. We face big hurdles in detection, scarce data on violators, a vast array of reported chemicals, error-filled, and inconsistent data, and subtle signs of illegal activities hidden among legal ones. We've developed graph-car. Graph-car maps food factories and their chemical declarations into a bipartite graph, processed by a graph autoencoder to understand hidden patterns. It uses self-supervised contrastive learning on factory representations to spot unusual patterns, effectively highlighting factories likely engaging in illicit activities by focusing on deviations in chemical usage. The performance of graph-car is promising. It consistently outperforms all soda unsupervised anomaly detection models. Want to learn more about the details? Please visit us at the web conference 2024. Thank you so much.