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Age Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification approaches. According to the speciality of the facial aging effects, this paper proposes the AGES (AGing pattErn Subspace) method for automatic age estimation. The basic idea is to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace. The proper aging pattern for an unseen face image is then determined by the projection in the subspace that can best reconstruct the face image, while the position of the face image in that aging pattern will indicate its age. The AGES method has shown encouraging performance in the comparative experiments either as an age estimator or as an age range estimator.

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Addressing the call for a better understanding of tourist behavior in relation to post-disaster destinations, this study explores the motivations and intentions of potential domestic tourists (from non-hit areas) to visit Sichuan, China in the aftermath of an earthquake. Drawing on dark tourism theories, this study offers a more comprehensive insight into the consumption of destinations recovering from a disaster, aiming to capture the impact of the changes to the destination's attributes on tourist behavior. The findings move beyond the common approach to tourism recovery which solely focuses on reviving the destination's traditional 'non-dark' products. In contrast, this study reveals the importance of newly formed dark attributes emerging from the disaster as another vehicle to destination recovery, reflected in the emergence of new tourist segments.

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Dining is an essential tourism component that attracts significant expenditure from tourists. Tourism practitioners need insights into the dining behaviors of tourists to support their strategic planning and decision making. Traditional surveys and questionnaires are time consuming and inefficient in capturing the complex dining behaviors of tourists at a large scale. Thus far, the understanding about the dining preferences and opinions of different tourist groups is limited. This paper aims to fill the void by presenting a method that utilizes online restaurant reviews and text processing techniques in analyzing the dining behaviors of tourists. The effectiveness of the proposed method is demonstrated in a case study on international tourists visiting Australia using a large-scale data set of more than 40,000 restaurant reviews made by tourists on 2,265 restaurants. The proposed method can help researchers gain comprehensive insights into the dining preferences of tourists.

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