From facial recognition to autonomous driving, Artificial Intelligence (AI) will transform the way we live and work over the next couple of decades. Existing AI approaches for urban computing suffer from various challenges, including dealing with synchronization and processing of vast amount of data generated from the edge devices, as well as the privacy and security of individual users, including their bio-metrics, locations, and itineraries. Traditional centralized-based approaches require data in each organization be uploaded to the central database, which may be prohibited by data protection acts, such as GDPR and CCPA. To decouple model training from the need to store the data in the cloud, a new training paradigm called Federated Learning (FL) is proposed. FL enables multiple devices to collaboratively learn a shared model while keeping the training data on devices locally, which can significantly mitigate privacy leakage risk. However, under urban computing scenarios, data are often communication-heavy, high-frequent, and asynchronized, posing new challenges to FL implementation. To handle these challenges, we propose a new hybrid federated learning architecture called StarFL. By combining with Trusted Execution Environment (TEE), Secure Multi-Party Computation (MPC), and (Beidou) satellites, StarFL enables safe key distribution, encryption, and decryption, and provides a verification mechanism for each participant to ensure the security of the local data. In addition, StarFL can provide accurate timestamp matching to facilitate synchronization of multiple clients. All these improvements make StarFL more applicable to the security-sensitive scenarios for the next generation of urban computing.
In order to study the eco-environmental carrying capacity of Beijing-TianjinHebei urban agglomeration, this paper constructed the evaluation indexes system of the urban eco-environmental carrying capacity of Beijing-Tianjin-Hebei based on the DPSIR concept model, determined the weights of indexes by entropy method, evaluated the urban eco-environmental carrying capacity combined with TOPSIS method, and analyzed the urban eco-environmental carrying capacity of Beijing-Tianjin-Hebei in 2012-2016 through horizontal comparison and longitudinal analysis. The results show that the spatial differences of Beijing-Tianjin-Hebei city’s eco-environmental carrying capacity are obvious, showing the characteristics of “strong in the middle north and weak in the south”. Since 2014, the eco-environmental carrying capacity of 13 cities in Beijing-Tianjin-Hebei has been significantly improved, and the effect of eco-environmental governance is remarkable.
The popularization of the mobile internet has given rise to demand for flexible and convenient payment methods. For China, it is necessary to keep pace with or even lead the trend of innovation and development in the age of mobile payment. From the perspective of systems engineering, this paper introduces research and implementation of China UnionPay's mobile payment project. The general requirements and core engineering problems of the mobile payment project are summarized based on analyses of the characteristics and engineering difficulties of this project. Integration of innovative technologies is introduced to resolve the contradiction between ease-of-use and security. A rapid iterative development process is adopted to improve the product release efficiency as well as the users' experience. The launch of the mobile payment project also opens the window to coordinating and upgrading the whole payment industry chains.
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