The traditional Mongolian script poses significant challenges for text rendering due to its vertical orientation, complex glyph variations, and context-dependent shapes, compounded by limited linguistic resources. This paper introduces the first method specifically designed to address the out-of-vocabulary (OOV) word problem in traditional Mongolian script conversion. We present a novel dataset and propose MonTransformer, the first Transformer-based model tailored for converting Unicode phonetic sequences into glyph sequences. MonTransformer utilizes dynamic optimization and contrastive learning to effectively manage OOV cases and improve sequence generation accuracy, transforming Unicode inputs into visually accurate glyph representations. Our approach achieves state-of-the-art performance, maintaining the script’s visual fidelity and advancing efforts in digital preservation for complex scripts.

Prompted by the remarkable progress in cloud computing, more and more game service providers are starting to deploy their gaming applications on infrastructure as a service cloud. To ensure quality of service required by gamers, game service providers need to maintain a large number of cloud servers running game instances requested by players. Therefore, game service providers need not only pay attention to the costs of operating games, but also consider the quality of game services. This paper proposes a quality-of-service aware cost optimization scheme for online gaming services deployed on IaaS clouds which combines dynamic virtual machine provisioning and Long Short-Term Memory (LSTM) based gamer request dispatching to reduce the operating costs while ensuring quality of service. The effectiveness of our proposed scheme is assessed by simulation experiments based on the real-world data sets. The results show that, compared to the state-of-the-art approaches applied to similar problems, our scheme can achieve up to 35% cost savings while providing the just-good-enough quality of service to gamers under rapidly changing workloads.