The lantern exhibition at the Lantern Festival is an important traditional festival in Taiwan. Visitors play an important role in the promotion and sustainable development of intangible cultural heritage. In recent years, the involvement of digital technology in traditional lantern design and shows has contributed to the protection, inheritance, and promotion of ICH, there remains less research on using augmented reality with ICH tourism. In this study, AR is used for ICH lantern exhibition to discuss the learning experience in lantern (...) tourism and the relationship between technology acceptance and satisfaction from the perspective of visitors, as well as evaluate what AR has on improving visitors’ awareness and learning experience. Then, primary variables of the technology acceptance model are combined with generic learning outcomes to integrate ICH, education, and technology to expand TAM, building a new model to study the ICH learning experience. A questionnaire and observation are used. Respondents are visitors participating in the AR lantern exhibition in Taiwan, which is designed by the author. There is a total of 200 questionnaires collected in the end. The result shows that knowledge and understanding, attitudes and values, activity, behavior, and progression, and enjoyment, inspiration, and creativity from GLOs have a positive effect on technology acceptance and actual use. Therefore, visitors are satisfied with innovative and interesting technology learning experiences, enhancing learning interest and results. Besides, the interaction of the AR system improves visitors’ learning motivation, which shows the combination of AR technology with ICH tourism helps improve cultural awareness. (shrink)

The present “visual world” eye-tracking study examined the time-course of how native and non-native speakers keep track of implied object-state representations during real-time language processing. Fifty-two native speakers of English and 46 non-native speakers with advanced English proficiency joined this study. They heard short stories describing a target object either having undergone a substantial change-of-state or a minimal change-of-state while their eye movements toward competing object-states and two unrelated distractors were tracked. We found that both groups successfully directed their visual (...) attention toward the end-state of the target object that was implied in the linguistic context. However, neither group showed anticipatory eye movements toward the implied object-state when hearing the critical verb. Only native English speakers but not non-native speakers showed a bias in visual attention during the determiner before the noun. Our results suggested that although native and non-native speakers of English largely overlapped in their time-courses of keeping track of object-state representations during real-time language comprehension, non-native speakers showed a short delay in updating the implied object-state representations. (shrink)

In face recognition systems, highly robust facial feature representation and good classification algorithm performance can affect the effect of face recognition under unrestricted conditions. To explore the anti-interference performance of convolutional neural network reconstructed by deep learning framework in face image feature extraction and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the random gradient descent and (...) triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild face database. Then, the portrait pyramid segmentation and local feature point segmentation are applied to extract the features of face images, and the matching of face feature points is achieved using Euclidean distance and joint Bayesian method. Finally, Matlab software is used to simulate the algorithm proposed in this paper and compare it with other algorithms. The results show that the proposed algorithm has the best face recognition effect when the learning rate is 0.0004, the attenuation coefficient is 0.0001, the training method is SGD, and dropout is 0.1, and the algorithm proposed in this paper has the largest mean average precision compared to other CNN algorithms. The correct rate of face feature matching of the algorithm proposed in this paper is 84.72%, which is higher than LetNet-5, VGG-16, and VGG-19 algorithms, the correct rates of which are 6.94%, 2.5%, and 1.11%, respectively, but lower than GoogleNet, AlexNet, and ResNet algorithms. At the same time, the algorithm proposed in this paper has a faster matching time and a higher correct matching rate than the joint Bayesian method, indicating that the deep reconstruction network algorithm proposed in this paper can be used in face image recognition, FE, and matching, and it has strong anti-interference. (shrink)