This study introduces a pioneering Time Series-based Transaction Pattern Analysis model for scrutinizing profit dynamics within the pre-owned luxury goods domain via network motifs. By employing a model that integrates network motif analysis with time series, this study aims to elucidate the transactional patterns that govern market efficiency and profitability. Utilizing data from a Japanese enterprise specializing in pre-owned luxury goods, this investigation highlights the critical role of specific transaction patterns, identified as network motifs, in enhancing our understanding of market dynamics. The findings demonstrate the model’s capability in revealing insights into the temporal and structural aspects of transactions, thus offering a comprehensive tool for optimizing sales strategies and market operations. Beyond contributing to the theoretical understanding of network motifs in economic contexts, this study provides actionable insights for market practitioners.

The luxury goods market has long captivated the attention of economists and sociologists due to its intricate dynamics and high-value products. Despite this, the burgeoning pre-owned sector within this market has often been overlooked. This paper introduces a novel analytical model, the Pre-owned Luxury Goods Analysis (PLGA) model, to decode complex buyer-seller interactions in this sector. Utilizing network motifs as a methodological innovation, the PLGA model offers a structured approach for market analysis, including a specialized focus on ROI (Return on Investment) analysis. We validate the model using real-world transaction data and rigorously verify the analytical results, including ROI implications, through statistical methods. Our findings provide actionable insights for market stakeholders and pave the way for future research in this area.

Attention mechanisms are prominent in neural networks for EEG sentiment classification. However, many overlook EEG's inherent domain-specific knowledge, especially its temporal and spatial dependencies. Recognizing the underutilization of past temporal sequences, we introduce the Regulator-Feedback based attention (RFA) method. This approach incorporates a Band-Channel Fusion Mechanism and Feedback Attention. By infusing a "Regulator"context into the attention process, it assigns adaptive weights to specific bands and channels, optimizing the model's focus. Furthermore, a tailored Gated Recurrent Unit (GRU) enriches the model with feedback linkage. By altering GRU's gate calculations, attention weights get integrated, balancing present input, past state, and attention-based information flow. This strategy aptly connects attention mechanisms with EEG's temporal nuances. When tested on the DEAP dataset, RFA outperforms, achieving an impressive 97.6%(96.6%) accuracy in a ten-category sentiment classification task.

Community improvement is about enhancing the physical infrastructure and promoting social, economic, and health outcomes. For comprehensive community enhancement, this paper proposed an analysis model to identify multiple clusters using the Cartesian product of network motifs and local-determined keywords. These clusters have the potential to intersect various domains and disciplines, fostering a more holistic understanding of community phe-nomena. Furthermore, we demonstrate the practical application of the model through two case studies: tourism and second-hand luxury goods transactions. Our findings in case studies reveal the potential of network motifs in identifying clusters that have the possibility of contributing to community improvement to some degree. These results have significant potential implications for both theoretical research and practical applications in community improvement, providing a new approach to identifying multiple clusters across diverse activities.

Traditional cultural experiences are valuable for tourist destinations. Previous studies have developed systems for experiencing traditional culture using augmented reality (AR). However, few works have considered the effectiveness of different types of AR experiences of traditional cultural elements. In this article, we focus on equipment elements and environment elements, and propose design recommendations for an AR traditional cultural experience system that considers differences in the environmental presence of traditional cultural experiences. We conducted a comparative study with 49 users to understand the impact of these differences. We compared three options for handling environmental presence, including nonpresence, AR-presence, and real-presence. The results show that presenting environmental elements in a state of real-presence improved an AR experience of traditional culture. Additionally, we found that this setting worked positively for Japanese participants, whereas Chinese participants preferred environmental elements in the state of nonpresence.

The development of artificial intelligence (AI) technologies has led to significant impacts on society. Simultaneously, the ethical issues involved in AI have become increasingly serious, and AI ethics has attracted attention as a topic of active research and discussion. Education in ethics is widely understood to play a crucial role in solving these emerging issues. In this study, we propose a model developed to address some ethical issues of AI based on the case method using network motifs to analyze educational consequences. We evaluated the method in terms of a network analysis of textual data of opinions to understand the changes in participants’ perspectives before and after their experience with the case method. The experimental results validated the method we proposed, and the results of the analysis show that the participants’ opinions on the ethics of AI changed to some extent after participating in the case.

While the tourism industry has grown rapidly in recent years, overtourism has become a major problem at tourist destinations. One way of dealing with overtourism is to discover novel potential tourism resources. There are problems with the existing methods for doing so. The first is that it is difficult to discover such resources in areas where there are no resources attractive enough to be potential tourism resources. The second is that direct changes in tourist destinations are required to create new potential resources, but direct changes would entail great human and financial costs. Therefore, in our study, we aimed to construct a framework for creating new value in local communities by designing social support systems that support the communities. To construct the framework, we propose the designable evaluation network (DEN), which is a network that expresses the evaluation relationship between evaluation subjects (people or artifacts) and evaluation objects (people or artifacts) in a local community. In addition, the relationships in DEN are formed by the designable evaluation model (DEM), which is a mathematical model with designability. To verify the effectiveness of the proposed framework, we conducted a case study to discover novel potential tourism resources in Kyoto, Japan. As a result, two potential resources could be created by designing the order in which resources are visited. That is, new value was created by designing a social support system in a tourist destination community without directly designing the tourist destination. Furthermore, it can be seen that social network analysis with DEN can lead to novel ideas that contribute to new ways of managing tourism.

In this study, we proved that augmented reality (AR) technology increases the motivation to experience traditional culture. We developed an AR Tosenkyo application to simulate "Tosenkyo,"a traditional Japanese game, and conducted experiments with 47 participants, including foreigners. Because Tosenkyo AR application components can be categorized into play equipment and backgrounds, we prepared three applications with different degrees of AR environmental conditions to investigate their effect on motivation. The results of the experiment revealed that, before the AR experience, Japanese people were less motivated to experience traditional culture compared with Chinese people; however, after the AR experience, their motivation increased considerably. This tendency was pronounced in a harmonic AR environment, which is considered a moderate fusion of reality and AR. Furthermore, the path analysis of the questionnaire results revealed that higher quality of AR application considerably improves motivation.

The used luxury goods market has great economic benefits and research significance. Many precedence studies investigate the second-hand luxury goods market, but few studies focus on how to help second-hand luxury goods suppliers improve their profit. Therefore, based on network analysis, this study proposes an analysis model that can discover multiple clusters of second-hand luxury goods. And these multiple clusters of second-hand luxury goods have the potential to be used to increase the profitability of second-hand luxury goods suppliers. We successfully discovered multiple clusters of second-hand luxury goods using data provided by a Japanese second-hand luxury goods supplier. Then, we statistically validated these discovered multiple clusters of second-hand luxury goods. Finally, we suggest some sales strategies for the provider based on discovered multiple clusters of second-hand luxury goods.

In this study, we proposed the use of case methods in AI ethics education in emerging technologies. To solve ethical problems related to the use of emerging technologies, it is indispensable to form a consensus on social implementation of new technologies, along with, understanding the technical characteristics and sharing moral and ethical views of the people who use the technologies. Case education provides an opportunity to touch on examples of concrete and practical ethical issues caused by the technologies especially in the recent times. It is also possible to understand the importance of building a social consensus from the perspectives of various problems and interactions with people with different ethical standards. In this study, we investigated how students understood cases and how they were influenced by the existence of diverse opinions by conducting questionnaire surveys before and after the case method education program on the ethical aspects of emerging technologies with AI. The result of the surveys showed the transformation of students’ way of thinking about AI ethical issues in emerging technologies and indicated the educational effects through the case method educational program.

Tourist satisfaction plays a very important role in the development of local community tourism. For the development of tourist destinations in local communities, it is important to measure, maintain, and improve tourist destination royalties over the medium to long term. It has been proven that improving tourist satisfaction is a major factor in improving tourist destination royalties. Therefore, to improve tourist satisfaction in local communities, we identified multiple clusters of sightseeing spots and determined that the satisfaction of tourists can be increased based on these clusters of sightseeing spots. Our discovery flow can be summarized as follows. First, we extracted tourism keywords from guidebooks on sightseeing spots. We then constructed a complex network of tourists and sightseeing spots based on the data collected from experiments conducted in Kyoto. Next, we added the corresponding tourism keywords to each sightseeing spot. Finally, by analyzing network motifs, we successfully discovered multiple clusters of sightseeing spots that could be used to improve tourist satisfaction.

Social innovation is an attempt to solve various social problems. It requires the creation of new value in a local community. However, a concrete method for creating new value in a local community is yet to be established. Therefore, in our study, we aim to construct a framework for creating new value in the local community by designing a social support system of the local community. To construct such a framework, we propose a designable evaluation network (DEN), which is a social network that expresses the evaluation relationship between people/artifacts and people/artifacts in a local community as a network structure. Moreover, the evaluation relationship in a DEN is formed through a designable evaluation model, which is a mathematical model with designability. It is possible to construct a framework by designing a social support system on a computer using a DEN and conducting operations to create new value for a local community.