Difference-based Deep Convolutional Neural Network for Simulation-to-reality UAV Fault Diagnosis

Identifying the fault in propellers is important to keep quadrotors operating safely and efficiently. The simulation-to-reality (sim-to-real) UAV fault diagnosis methods provide a cost-effective and safe approach to detect the propeller faults. However, due to the gap between simulation and reality, classifiers trained with simulated data usually underperform in real flights. In this work, a new deep neural network (DNN) model is presented to address the above issue. It uses the difference features extracted by deep convolutional neural networks (DDCNN) to reduce the sim-to-real gap. Moreover, a new domain adaptation method is presented to further bring the distribution of the real-flight data closer to that of the simulation data. The experimental results show that the proposed approach can achieve an accuracy of 97.9\% in detecting propeller faults in real flight. Feature visualization was performed to help better understand our DDCNN model.Comment: 7 pages, 8 figure

Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making

Today, AI is being increasingly used to help human experts make decisions in high-stakes scenarios. In these scenarios, full automation is often undesirable, not only due to the significance of the outcome, but also because human experts can draw on their domain knowledge complementary to the model's to ensure task success. We refer to these scenarios as AI-assisted decision making, where the individual strengths of the human and the AI come together to optimize the joint decision outcome. A key to their success is to appropriately \textit{calibrate} human trust in the AI on a case-by-case basis; knowing when to trust or distrust the AI allows the human expert to appropriately apply their knowledge, improving decision outcomes in cases where the model is likely to perform poorly. This research conducts a case study of AI-assisted decision making in which humans and AI have comparable performance alone, and explores whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. Specifically, we study the effect of showing confidence score and local explanation for a particular prediction. Through two human experiments, we show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making, which may also depend on whether the human can bring in enough unique knowledge to complement the AI's errors. We also highlight the problems in using local explanation for AI-assisted decision making scenarios and invite the research community to explore new approaches to explainability for calibrating human trust in AI

The status quo of short videos as a source of health information regarding bowel preparation before colonoscopy

BackgroundFor high-quality colonoscopies, adequate bowel preparation is a prerequisite, closely associated with the diagnostic accuracy and therapeutic safety of colonoscopy. Although popular-science short videos can help people quickly access health information, the overall quality of such short videos as a source of health information regarding bowel preparation before colonoscopy is unclear. Therefore, we intend to conduct a cross-sectional study to investigate the quality of bowel preparation information before colonoscopy through short videos taken on TikTok and Bilibili.MethodsThe Chinese phrases “colonoscopy” and “bowel preparation” were used as keywords to search for and screen the top 100 videos in the comprehensive rankings on TikTok and Bilibili. The Global Quality Score (GQS) and the modified DISCERN score were used to assess the quality of the information provided in these short videos.ResultsA total of 186 short videos were included in this study; 56.5% of them were posted by health professionals, whereas 43.5% of them were posted by nonhealth professionals. The overall quality of these videos was unsatisfactory, with a median DISCERN score of 3 (2–4) and a median GQS of 3 (3–4). The radar maps showed that videos posted by gastroenterologists had higher completeness scores regarding outcomes, management, and risk factors, while nongastroenterologists had higher completeness scores concerning adverse effects, symptoms, and definitions of bowel preparation. Additionally, the median DISCERN score and GQS of the videos posted by gastroenterologists were 3 (3–4) and 3 (3–4), respectively, whereas the quality of the videos posted by patients was the worst, with a median DISCERN score of 2 (1–2) and a median GQS of 2 (1.25–3).ConclusionIn conclusion, the overall quality of health information-related videos on bowel preparation before colonoscopy posted on specified short video platforms was not satisfactory. Gastroenterologists provide more information on the outcomes, management, and risk factors for bowel preparation before colonoscopy, while nongastroenterologists focus on adverse effects, symptoms, and definitions of bowel preparation