Younger generations comprise an essential segment for the mobile payment market to prosper. However, empirical evidence of the drivers/barriers of the young generation’s adoption of mobile payment has been inconclusive. This study intends to advance the body of knowledge on this subject based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT), incorporating the young generation’s risk perception and bonus/rewards provided by the mobile-pay firms. To this end, 295 samples with the majority being more tech-savvy, namely generation Y and generation Z, were collected from an online survey in Taiwan. The empirical results in this study demonstrate the uniquely positive effect of social influence on the young generations’ behavioral intention to adopt mobile payment. While behavioral intention and promotional activities are the drivers of the young generation’s actual usage of mobile payment, perceived risks are found to exert a negative impact, reflecting the risk-averse preferences of the young generation in Taiwan. The ignorable moderation effect of gender, on the other hand, suggests the absence of a gender gap in the use of mobile payment among the young generations. The findings in this research have important implications for the development of promotion programs motivating the young generation’s adoption of mobile payment.

In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis. They are widely adopted in synthesizing facial images which brings potential security concerns to humans as the fakes spread and fuel the misinformation. However, robust detectors of these AI-synthesized fake faces are still in their infancy and are not ready to fully tackle this emerging challenge. In this work, we propose a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces. The studies on neuron coverage and interactions have successfully shown that they can be served as testing criteria for deep learning systems, especially under the settings of being exposed to adversarial attacks. Here, we conjecture that monitoring neuron behavior can also serve as an asset in detecting fake faces since layer-by-layer neuron activation patterns may capture more subtle features that are important for the fake detector. Experimental results on detecting four types of fake faces synthesized with the state-of-the-art GANs and evading four perturbation attacks show the effectiveness and robustness of our approach.

Perioperative anxiety was significantly reduced and overall patient satisfaction increased after viewing a preoperative educational anaesthesia video compared with a standard verbal briefing on anaesthesia.