ISSN 2512-6660 (Online) ISSN 1478-9906 (Print)
CN 10-1192/N
CN 10-1192/N
Data element circulation trading platforms are pivotal in unlocking the value of data, optimizing resource allocation, and enabling cross-industry data sharing. However, existing platforms face significant limitations in addressing the evolving demands of the data market and the diverse requirements of users, with their architectural frameworks remaining underdeveloped. This study proposes a comprehensive theoretical framework that integrates advanced technologies such as blockchain, non-fungible tokens, and federated learning to enhance the functionality and adaptability of these platforms. A comparative analysis with the Ocean Protocol platform is conducted to elucidate the theoretical contributions and practical implications of the proposed framework. Based on this analysis, the study offers strategic recommendations for advancing the design and implementation of data element circulation trading platforms, providing valuable insights to guide future research and practice in this critical domain.
In this paper, we discuss two cases of two-player repeated games in an environment of incomplete information with randomness and cognitive uncertainty, where incomplete information refers to the situation in which the transition probabilities and player payoffs are uncertain. We use robust optimization techniques to handle data uncertainty and determine the optimal solution within this framework. Our contributions are threefold: Firstly, we apply Markov processes to the repeated game model. Secondly, we propose an effective robust optimization method that can handle uncertain data in the context of incomplete information and solve the uncertainty problem in different types of repeated games. Finally, our method is more general than previous methods and can adapt to various types of data, providing decision support for risk-averse players.
This research introduces a novel framework that integrates graph convolutional networks (GCNs) with clustering techniques to examine the intricate spatial structure of contemporary service industries. Utilizing 2023 point-of-interest (POI) data from Xi’an, the study extends beyond analyzing single industries to uncover 18 unique multi-industry composite clusters, highlighting significant patterns such as the blending of education with real estate and the merging of business and financial services. Additionally, by employing DBSCAN, the research identifies the high-density core regions within these clusters and their spatial coexistence patterns, pinpointing multifunctional areas. These results contribute to the theory of urban polycentricity and offer data-driven guidance for planners to promote evidence-based zoning, encourage mixed-use development, and enhance functional integration in service-focused urban economies.
Personalized recommendation services have recently been widely provided in online social networks (OSNs). OSNs have large number of users, and users’ information and browsing data are saved in online services due to frequent communication between users. To achievebetter performance, personalized recommendation services need users’ characteristics and behavior information, which brings privacy issues into concern. Therefore, balancing privacy preservation and recommendation results has become the main focus in this field. In this paper, we combine a privacy preserving method with the social network model and make full use of the user’s attribute information in the social network to improve personalized recommendation results based on the privacy-preserving link prediction(PPLP) framework. Additionally, a link prediction algorithm with attribute classification is proposed in this paper, which considers the connections between user attributes and the similarity between users. The improved PPLP was evaluated on Google+ datasets and the results show that it can improve the accuracy of recommendation results while protecting users’ information.
The research purpose is to contribute to the field of forecasting foreign exchange. This is due to the ever-changing economic conditions under which analysts can observe the significant volatility of exchange rate forecasts, as exchange rate forecasting has been challenging for analysts for many years. Stakeholders (central banks, governments, and investors) will seek to maximize asset returns and minimize risk in their decision-making using exchange rate forecasting. Therefore, this study proposes a new approach from the Black-Scholes model to forecast daily, weekly, monthly, and yearly exchange rates for the domestic currency pair the indonesian rupiah (IDR) to the United States dollar (USD) traded. The Black-Scholes model, originally referred to as a partial differential equation, was changed to an ordinary differential equation, which is used to approximate the numerical solution of the new Black-Scholes equation. The numerical solution used in this research is the fourth-order Runge-Kutta method. This study uses actual exchange rate data for more than one year, and the prediction results show that the proposed methodology can be an effective method for forecasting the exchange rate (IDR/USD). It is indicated by a small error, of less than 5%, and a mean absolute percentage error of less than 2%.
The study aims to analyse transformational changes in banking risk management and marketing policy of banks caused by digitalisation. The study addressed examples of successful digital technology implementation in the banking sector around the world and conducted a comparative analysis of the current situation in Kyrgyz banking. The study addresses the process of digital transformation of the banking sector, with a focus on Kyrgyzstan. Digital transformation involves the integration of technologies such as artificial intelligence, big data and blockchain into all aspects of banking operations, leading to significant changes in their functioning and customer experience. The study analysed automating processes, improvement of customer service, enhancement of transaction security and creation of new business models. The study included examples of successful digitalisation in banks around the world, such as the use of artificial intelligence to automate and analyse data, which helps to predict risks and improve customer experience. Based on the analysis, recommendations for banks in Kyrgyzstan were proposed, including investments in IT infrastructure, literacy programmes and enhanced cybersecurity. The results show that digitalisation can significantly increase the accessibility and quality of banking services, improving the overall standard of living of the population.
Smart manufacturing is a key aspect of current urban sustainability concerns, with urban skills impacting the growth of smart manufacturing. This raises questions for sustainable urban development regarding the polarization of skills between cities. This study investigates the influence of inter-city skills polarization on the wages and employment of workers in smart manufacturing in China by examining social-cognitive skill score data. The regression results show that the social cognitive skill score of the city has a significant positive effect on local manufacturing wages. However, it reduces the number of local manufacturing jobs and the proportion of manufacturing in the industrial structure. Smart manufacturing development policies have a significant impact on local manufacturing employment but do not influence the wage levels of local manufacturing workers. In addition, productivity in the secondary industry may reduce local manufacturing wages and employment. Nevertheless, it has a negative skew when mediating the connection between intercity skill polarization and local manufacturing wages. The study reveals the reasons for workers participating in production during the smart manufacturing era, predicts future wage changes for these workers, and examines the differences in industrial layouts across cities.
This paper investigates an
