I’m currently a Ph.D. student in Department of Civil and Environmental Engineering at The Hong Kong University of Science and Technology, under the supervision of Prof. Sen Li. I have a wide-ranging interest in any topic related to AI. My current research interests primarily focus on the field of Smart City (Transportation), combined with Ethical Reinforcement Learning.
Prior to this, I received my B.Eng. degree from School of Computer Science and Engineering at Sun Yat-sen University in 2024, under the supervision of Prof. Kai Huang (Robot RL), Prof. Ning Liu and Prof. Chengying Gao (Music AI). Additionally, I had the privilege of visiting the Shenzhen Research Institute of Big Data associated with The Chinese University of Hong Kong (Shenzhen) under the guidance of Dr. Guangxu Zhu for one year, where I also learned from Dr. Xiaoyang Li and Dr. Hang Li (Wireless Sensing & Network Optimization).
Aside from academics, I’m a music enthusiast, having been a singer and guitarist & bassist in bands Frozen Devil (melodic death metal/deathcore), Strike (punk/grunge), NEWS (alternative rock, current my personal band) and Rights of Lethe (melodic death metal, dissolved). I have a strong passion for heavy rock music, particularly alternative punk, melodic death metal, dark metal, and deathcore. If you are interested in playing or collaborating with us, please feel free to reach out to me via mail. For information about our shows and ticket purchases, you can visit xiudong(秀动) and tonggan(同感).
Ph.D. in Civil Engineering (Scientific Computation), 2024~
The Hong Kong University of Science and Technology (Clearwater Bay Campus, Hong Kong)
B.Eng. in Computer Science and Technology (National Basic Subject Talent Training Plan), 2020~2024
Sun Yat-sen University (Guangzhou Campus)
C/C++ (CCF-CSP:320, Top 0.8%), Python, Matlab
Guitar, Bass, Keyboard, Ukulele, and Simple Drum
CCF Student Member (granted for free)TPC Membership: IEEE PIMRC 2024, IEEE WCNC 2024Technical Reviewer: IEEE ICASSP 2024, IEEE ICME 2024, IEEE SMC 2023, IEEE MTAP
In recent years, Wi-Fi sensing has garnered significant attention due to its numerous benefits, such as privacy protection, low cost, and penetration ability. Extensive research has been conducted in this field, focusing on areas such as gesture recognition, people identification, and fall detection. However, many data-driven methods encounter challenges related to domain shift, where the model fails to perform well in environments different from the training data. One major factor contributing to this issue is the limited availability of Wi-Fi sensing datasets, which makes models learn excessive irrelevant information and over-fit to the training set. Unfortunately, collecting large-scale Wi-Fi sensing datasets across diverse scenarios is a challenging task. To address this problem, we propose CrossFi, a siamese network-based approach that excels in both in-domain scenario and cross-domain scenario, including few-shot, zero-shot scenarios, and even works in few-shot new-class scenario where testing set contains new categories. The core component of CrossFi is a sample-similarity calculation network called CSi-Net, which improves the structure of the siamese network by using an attention mechanism to capture similarity information, instead of simply calculating the distance or cosine similarity. Based on it, we develop an extra Weight-Net that can generate a template for each class, so that our CrossFi can work in different scenarios. Experimental results demonstrate that our CrossFi achieves state-of-the-art performance across various scenarios. In gesture recognition task, our CrossFi achieves an accuracy of 98.17% in in-domain scenario, 91.72% in one-shot cross-domain scenario, 64.81% in zero-shot cross-domain scenario, and 84.75% in one-shot new-class scenario. he code for our model is publicly available at https://github.com/RS2002/CrossFi.
As an important part of Music Information Retrieval (MIR), Symbolic Music Understanding (SMU) has gained substantial attention, as it can assist musicians and amateurs in learning and creating music. Recently, pre-trained language models have been widely adopted in SMU because the symbolic music shares a huge similarity with natural language, and the pre-trained manner also helps make full use of limited music data. However, the issue of bias, such as sexism, ageism, and racism, has been observed in pre-trained language models, which is attributed to the imbalanced distribution of training data. It also has a significant influence on the performance of downstream tasks, which also happens in SMU. To address this challenge, we propose Adversarial-MidiBERT, a symbolic music understanding model based on Bidirectional Encoder Representations from Transformers (BERT). We introduce an unbiased pre-training method based on adversarial learning to minimize the participation of tokens that lead to biases during training. Furthermore, we propose a mask fine-tuning method to narrow the data gap between pre-training and fine-tuning, which can help the model converge faster and perform better. We evaluate our method on four music understanding tasks, and our approach demonstrates excellent performance in all of them. The code for our model is publicly available at https://github.com/RS2002/Adversarial-MidiBERT.
Learning musical structures and composition patterns is necessary for both music generation and understanding, but current methods do not make uniform use of learned features to generate and comprehend music simultaneously. In this paper, we propose PianoBART, a pre-trained model that uses BART for both symbolic piano music generation and understanding. We devise a multi-level object selection strategy for different pre-training tasks of PianoBART, which can prevent information leakage or loss and enhance learning ability. The musical semantics captured in pre-training are fine-tuned for music generation and understanding tasks. Experiments demonstrate that PianoBART efficiently learns musical patterns and achieves outstanding performance in generating high-quality coherent pieces and comprehending music.
Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models. To overcome this challenge, we propose a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) for CSI recovery, named CSI-BERT. CSI-BERT can be trained in an self-supervised manner on the target dataset without the need for additional data. Furthermore, unlike traditional interpolation methods that focus on one subcarrier at a time, CSI-BERT captures the sequential relationships across different subcarriers. Experimental results demonstrate that CSIBERT achieves lower error rates and faster speed compared to traditional interpolation methods, even when facing with high loss rates. Moreover, by harnessing the recovered CSI obtained from CSI-BERT, other deep learning models like Residual Network and Recurrent Neural Network can achieve an average increase in accuracy of approximately 15% in Wi-Fi sensing tasks. The collected dataset WiGesture and code for our model are publicly available at https://github.com/RS2002/CSI-BERT.
Small robots encounter considerable difficulties in learning effective motions on complex terrains owing to their underactuated nature and nonlinear dynamics. In this paper, we present a novel framework for robot motion generation that implements reinforcement learning, based on simplified exploration of the robot’s action and time slice conduction. Our framework controls the robot’s actions using normalized signals and hierarchical mappings on mathematical space, which facilitates the learning process. We execute action in the timeslice to make efficient interaction with the environment. We evaluate the efficacy of our approach on a varied set of simulated terrain scenarios, which include various obstacles and terrain undulations. Our results show that our approach effectively achieves efficient motions on complex terrains designed for small-sized robots.