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Ruiyuan Li

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3 papers

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ECAI Conference 2025 Conference Paper

SEWLT: Semantic Enhancement for Weak Semantics Low-Resource Languages Translation

Chunming Wu
Yueran Wang
Shanxiong Chen
Xiaoliang Li
Ruiyuan Li

Symbolic scripts carry deep cultural connotations and important historical values. However, due to their unique symbolic structures, linguistic characteristics of weak semantic association and scarce corpus resources, existing neural network machine translation techniques face challenges including insufficient semantic understanding, severe Out-of-Vocabulary issues, poor translation quality, and limited adaptability to semantic noise when handling their translation tasks. To solve this problem, we propose Semantic Enhancement for Weak Semantics Low-Resource Languages Translation method (SEWLT), using the translation task from Naxi Dongba to Chinese as a case study. Experimental results on a self-constructed Naxi Dongba-Chinese small-scale parallel corpus show excellent performance in terms of accuracy, fluency, and semantic fidelity. It not only provides technical support for the digital preservation and research of the Naxi Dongba script, but also provides an important reference for the research of machine translation of similar weak semantic low-resource languages.

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AAAI Conference 2020 Conference Paper

Learning to Generate Maps from Trajectories

Sijie Ruan
Cheng Long
Jie Bao
Chunyang Li
Zisheng Yu
Ruiyuan Li
Yuxuan Liang
Tianfu He

Accurate and updated road network data is vital in many urban applications, such as car-sharing, and logistics. The traditional approach to identifying the road network, i. e. , ﬁeld survey, requires a signiﬁcant amount of time and effort. With the wide usage of GPS embedded devices, a huge amount of trajectory data has been generated by different types of mobile objects, which provides a new opportunity to extract the underlying road network. However, the existing trajectory-based map recovery approaches require many empirical parameters and do not utilize the prior knowledge in existing maps, which over-simpliﬁes or overcomplicates the reconstructed road network. To this end, we propose a deep learning-based map generation framework, i. e. , DeepMG, which learns the structure of the existing road network to overcome the noisy GPS positions. More speciﬁcally, DeepMG extracts features from trajectories in both spatial view and transition view and uses a convolutional deep neural network T2RNet to infer road centerlines. After that, a trajectory-based post-processing algorithm is proposed to re- ﬁne the topological connectivity of the recovered map. Extensive experiments on two real-world trajectory datasets con- ﬁrm that DeepMG signiﬁcantly outperforms the state-of-theart methods.

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AIJ Journal 2018 Journal Article

Predicting citywide crowd flows using deep spatio-temporal residual networks

Junbo Zhang
Yu Zheng
Dekang Qi
Ruiyuan Li
Xiuwen Yi
Tianrui Li

Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), and external conditions (e. g. weather and events). We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i. e. inflow and outflow) in each and every region of a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the residual neural network framework to model the temporal closeness, period, and trend properties of crowd traffic. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We have developed a real-time system based on Microsoft Azure Cloud, called UrbanFlow, providing the crowd flow monitoring and forecasting in Guiyang City of China. In addition, we present an extensive experimental evaluation using two types of crowd flows in Beijing and New York City (NYC), where ST-ResNet outperforms nine well-known baselines.

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