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Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection.

Objective. As the preclinical stage of Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) is characterized by hidden onset, which is difficult to detect early. Traditional neuropsychological scales are main tools used for assessing MCI. However, due to its strong subjectivity and the influence of many factors such as subjects’ educational background, language and hearing ability, and time cost, its accuracy as the standard of early screening is low. Therefore, the purpose of this paper is to propose a new key technology of fast digital early warning for MCI based on eye movement objective data analysis. Methodology. Firstly, four exploratory indexes (test durations, correlation degree, lengths of gaze trajectory, and drift rate) of MCI early warning are determined based on the relevant literature research and semistructured expert interview; secondly, the eye movement state is captured based on the eye tracker to realize the data extraction of four exploratory indexes. On this basis, the human-computer interactive 2.5-minute fast digital early warning paradigm for MCI is designed; thirdly, the rationality of the four early warning indexes proposed in this paper and their early warning effectiveness on MCI are verified. Results. Through the small sample test of human-computer interactive 2.5 fast digital early warning paradigm for MCI conducted by 32 elderly people aged 70–90 in a medical institution in Hangzhou, the two indexes of “correlation degree” and “drift rate” with statistical differences are selected. The experiment results show that AUC of this MCI early warning paradigm is 0.824. Conclusion. The key technology of human-computer interactive 2.5 fast digital early warning for MCI proposed in this paper overcomes the limitations of the existing MCI early warning tools, such as low objectification level, high dependence on professional doctors, long test time, requiring high educational level, and so on. The experiment results show that the early warning technology, as a new generation of objective and effective digital early warning tool, can realize 2.5-minute fast and high-precision preliminary screening and early warning for MCI in the elderly.

Macula fovea detection is a crucial prerequisite towards screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither accuracy nor effectiveness of the diagnose process could be guaranteed. In this project, we proposed a deep learning approach on ultra-widefield fundus (UWF) images for macula fovea detection. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. Methods based on U-shape network (Unet) and Fully Convolutional Networks (FCN) are implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. Results are measured by calculating the Euclidean distance between proposed approaches and the accurate grounded standard, which is detected by Ultra-widefield swept-source optical coherence tomograph (UWF-OCT) approach. Through a comparation of proposed methods, we conclude that, deep learning approach of Unet outperformed other methods on macula fovea detection tasks, by which outcomes obtained are comparable to grounded standard method.

Design for Classroom Units: A Collaborative Multicultural Studio Development with Chinese Students

The manifestation of generating digital visuals through an algorithm is gaining worldwide attention in the graphic design industry. It is a new form of computing that visualizes data input by the designer or collected in the physical environment and turns them into artwork. The generative design of...

Countless historical sites worldwide have become unrecognisable based on their historical context. Many are cultural heritage structures with significant historical and aesthetic importance. The majority have not been well preserved; worse, some were demolished (Stenning, 2015). Furthermore, structures are part of a dynamic and changing environment, and their location within the original landscape is not always clear. People have gradually forgotten cultural traditions as environments where historical stories took place, and the look and feel have been corrupted. Immersive Virtual Reality (V.R.) allows us to relive and explore the past. However, in the Pearl River Delta Region, specifically Macau S.A.R., V.R. is still in its infancy and is not frequently used for reproducing historical sceneries. Our research focuses on reproducing heritage structures and scenery based on scarce historical information. It shows how to incorporate facts and memories into the design and create engaging, immersive experiences in V.R. scenery that takes place, both inside and outside of a cultural heritage site that has lost its original appearance. Following this, a prototype was created with specific parameters relating to past and present sceneries. We partially reproduced an existing building complex currently being used for creative and commercial purposes, but it was a shelter for the poor and a house for old ladies to live in. There were not enough facts or images linked to the inner space in the past. Inadequate information allows audiovisual scene creators to be more imaginative. The prototype focuses on a functional design that integrates cultural traits tied to local industries. The researcher used image processing software, and web 3D tools (A-Frame 1.1.0). Users can navigate by virtually “walking” and starting the visual tour; simultaneously, the story unfolds as the timeline progresses. After entering, the users jump from the present to a specific era in the past. With audio guidance, users enter the private space, shared areas, working space, etc. Users can interact with objects from the virtual scenes while the interface displays relevant audiovisual introductions. Users could utilise the virtual system to learn how the old ladies led their daily lives in the Pearl River Delta Region and grasp the local single ladies’ group lifestyle at a specific time in the past (Kwong, 2020). The interactive experience enhances the users’ interest; additionally, the users become more familiar with the region’s traditional customs. With this approach, we can create old stories using modern technology. A-Frame provides users with great convenience and can be used by any Internet browser without relying on professional V.R. devices. The content from this usage provides a greater understanding of our heritage buildings and their historical context to the wider community. This could be used in other heritage sites worldwide to reproduce and maintain structural qualities over time. This immersive experience could be a means to navigate the past while in the present. This application could benefit exhibition developers, and visitors, notably in exhibition guided tours, virtual tours inside museums, or educational assisted historical storytelling.

In Macau, the effectiveness of traditional classroom learning is questioned as the problem is discovered by the changes in technology advances, social media, and the varieties of learning methods. Learning experiences, interests, discoveries, and creativity development are considered essential to ac...

Creativity and collaboration are crucial to learning development in today's fast-paced educational environment. New technology can bridge humans and their natural needs through immersion in digital environments with physical objects. As knowledge and information evolve, digital interactive experienc...