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The novel coronavirus, COVID-19, has caused a crisis that affects all segments of the population. As the knowledge and understanding of COVID-19 evolve, an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the virus. Recent studies indicate that a city Digital Twin (DT) is beneficial for tackling this health crisis, because it can construct a virtual replica to simulate factors, such as climate conditions, response policies, and people's trajectories, to help plan efficient and inclusive decisions. However, a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions, limiting its advantages when facing urgent crises, such as the COVID-19 pandemic. Federated Learning (FL), in which all clients can learn a shared model while retaining all training data locally, emerges as a promising solution for accumulating the insights from multiple data sources efficiently Furthermore, the enhanced privacy protection settings removing the privacy barriers lie in this collaboration. In this work, we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status quickly. In particular, an FL central server manages the local updates of multiple collaborators (city DTs), providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection trends. This approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a "global view" of city crisis management. Meanwhile, it also helps improve each city's DT by consolidating other DT's data without violating privacy rules. In this paper, we use the COVID-19 pandemic as the use case of the proposed framework. The experimental results on a real dataset with various response plans validate our proposed solution and demonstrate its superior performance.

A hybrid collaborative filtering algorithm based on the user preferences and item features is proposed. A thorough investigation of Collaborative Filtering (CF) techniques preceded the development of this algorithm. The proposed algorithm improved the user-item similarity approach by extracting the item feature and applying various item features' weight to the item to confirm different item features. User preferences for different item features were obtained by employing user evaluations of the items. It is expected that providing better recommendations according to preferences and features would improve the accuracy and efficiency of recommendations and also make it easier to deal with the data sparsity. In addition, it is expected that the potential semantics of the user evaluation model would be revealed. This would explain the recommendation results and increase accuracy. A portion of the MovieLens database was used to conduct a comparative experiment among the proposed algorithms, i.e., the collaborative filtering algorithm based on the item and the collaborative filtering algorithm based on the item feature. The Mean Absolute Error (MAE) was utilized to conduct performance testing. The experimental results show that employing the proposed personalized recommendation algorithm based on the preference-feature would significantly improve the accuracy of evaluation predictions compared to two previous approaches.

The Kepler General Purpose GPU (GPGPU) architecture was developed to directly support GPU virtualization and make GPGPU cloud computing more broadly applicable by providing general purpose computing capability in the form of on-demand virtual resources. This paper describes a baseline GPGPU cloud system built on Kepler GPUs, for the purpose of exploring hardware potential while improving task performance. This paper elaborates a general scheme which defines the whole cloud system into a cloud layer, a server layer, and a GPGPU layer. This paper also illustrates the hardware features, task features, scheduling mechanism, and execution mechanism of each layer. Thus, this paper provides a better understanding of general-purpose computing on a GPGPU cloud.