Abstract Recently, the global food supply chain has become increasingly complex, and its scalability has grown. From farm to fork, the performance of food‐producing systems is influenced by significant changes in the environment, population and economy. These changes may cause an increase in food fraud and safety hazards and hence, harm human health. Adopting artificial intelligence (AI) technology in the food supply chain is one strategy to reduce these hazards. Although the use of AI has been rising in numerous industries, such as precision nutrition, self‐driving cars, precision agriculture, precision medicine and food safety, much of what AI systems do is a black box due to its poor explainability. This study covers numerous use cases of food fraud risk prediction using explainable artificial intelligence (XAI) techniques, such as LIME, SHAP and WIT. We aimed to interpret the predictions of a machine learning model with the aid of these technologies. The case study was performed on a food fraud dataset using adulteration/fraud notifications retrieved from the Rapid Alert System for Food and Feed system and economically motivated adulteration database. A deep learning model was built based on this dataset and XAI tools have been investigated on the proposed deep learning model. Both features and shortcomings of the current XAI tools in the food fraud area have been presented.

This paper explores the minimal dataset to be collected from vehicles, to efficiently score drivers, using common vehicle sensors. This can lead to important results for insurance companies, advertisements and personalization. Existing work relies on several sensor information that are collected over a drive including acceleration/deceleration patterns or average trip duration. Vehicular companies make vehicular sensor information available to many external services. To explore how to score driving behaviors from such a data, we consider a system that interfaces to vehicle bus and executes supervised learning methods on this data. To facilitate this analysis, we collect invehicle data from 20 drivers on a test route and have less than %10 error in our scoring algorithm.

This paper explores the limits of audio ranging on mobile devices in the context of a keystroke snooping scenario. Acoustic keystroke snooping is challenging because it requires distinguishing and labeling sounds generated by tens of keys in very close proximity. Existing work on acoustic keystroke recognition relies on training with labeled data, linguistic context, or multiple phones placed around a keyboard --- requirements that limit usefulness in an adversarial context. In this work, we show that mobile audio hardware advances can be exploited to discriminate mm-level position differences and that this makes it feasible to locate the origin of keystrokes from only a single phone behind the keyboard. The technique clusters keystrokes using time-difference of arrival measurements as well as acoustic features to identify multiple strokes of the same key. It then computes the origin of these sounds precise enough to identify and label each key. By locating keystrokes this technique avoids the need for labeled training data or linguistic context. Experiments with three types of keyboards and off-the-shelf smartphones demonstrate scenarios where our system can recover $94\%$ of keystrokes, which to our knowledge, is the first single-device technique that enables acoustic snooping of passwords.

The Global Positioning System (GPS) is widely used in critical infrastructures but is vulnerable to radio frequency (RF) interference. A common source of interference are commercial drivers that use GPS jammers to circumvent vehicle tracking systems. Existing mechanisms to detect and identify such interference emitting vehicles on roadways require a large number of specialized detectors or a manual observation process. In this paper, we design a practical, automated system to facilitate enforcement actions. Our system combines information from roadside monitoring points at key locations along the roadway as well as mobile detectors (e.g., smartphones and other mobile GPS systems). Rather than attempting precise localization at a given time, the system exploits the inherent variation in driving speeds and the resulting diverging trajectories of vehicles to uniquely identify the interfering vehicle. Through our experiments on a local highway with a vehicle transmitting interference in the 900MHz ISM band, we found that the vehicle identification rate of our mechanism is 65% for a single-point setup and 100% for a two-point setup. We performed 200 hours of passive monitoring of GPS L1 band on roadways and found two episodes of real interference. We also demonstrate that our mobile detector-based crowdsourced smartphone profiles are sufficiently consistent in time and space to enable reliable interference detection.