Author name cluster

Guoxin Wang

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5 papers

2 author rows

JBHI Journal 2024 Journal Article

ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors

Guoxin Wang
Shreejith Shanker
Avishek Nag
Yong Lian
Deepu John

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenience due to passive data acquisition. This paper investigates an electrocardiogram (ECG) based biometric user authentication system using features derived from the Convolutional Neural Network (CNN) and self-supervised contrastive learning. Contrastive learning enables us to use large unlabeled datasets to train the model and establish its generalizability. We propose approaches enabling the CNN encoder to extract appropriate features that distinguish the user from other subjects. When evaluated using the PTB ECG database with 290 subjects, the proposed technique achieved an authentication accuracy of 99. 15%. To test its generalizability, we applied the model to two new datasets, the MIT-BIH Arrhythmia Database and the ECG-ID Database, achieving over 98. 5% accuracy without any modifications. Furthermore, we show that repeating the authentication step three times can increase accuracy to nearly 100% for both PTBDB and ECGIDDB. This paper also presents model optimizations for embedded device deployment, which makes the system more relevant to real-world scenarios. To deploy our model in IoT edge sensors, we optimized the model complexity by applying quantization and pruning. The optimized model achieves 98. 67% accuracy on PTBDB, with 0. 48% accuracy loss and 62. 6% CPU cycles compared to the unoptimized model. An accuracy-vs-time-complexity tradeoff analysis is performed, and results are presented for different optimization levels.

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IJCAI Conference 2024 Conference Paper

TaD: A Plug-and-Play Task-Aware Decoding Method to Better Adapt LLMs on Downstream Tasks

Xinhao Xu
Hui Chen
Zijia Lin
Jungong Han
Lixing Gong
Guoxin Wang
Yongjun Bao
Guiguang Ding

Fine-tuning pre-trained models on downstream tasks is a common practice in leveraging large language models (LLMs) today. A critical issue is how to adapt pre-trained models to downstream tasks better, thereby enhancing their performance. This paper introduces Task-aware Decoding (TaD), a plug-and-play method that exploits the difference in probability distributions before and after fine-tuning to boost the performance of LLMs on downstream tasks. The proposed TaD argues that the difference between the pre-finetuning probability distribution and the post-finetuning one represents the direction from common knowledge towards specific downstream-task knowledge. Aligning the final output probability distribution to that direction can probably result in superior downstream task performance, compared to the original fine-tuned model. Experiments on various datasets across four different task categories well demonstrate TaD's effectiveness on different LLMs, i. e. , GPT, BLOOM, and LLaMA, with different fine-tuning methods. Moreover, further experiments reveal that TaD better enhances model performance in data-scarce scenarios.

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IROS Conference 2023 Conference Paper

An Open-Source Robotic Chinese Chess Player

Shan An
Guangfu Che
Jinghao Guo
Yanjie Xu
Guoxin Wang
Konstantinos A. Tsintotas
Fukai Zhang
Junjie Ye 0004

Consumer robots can accompany children growing up, improving their abilities while playing and entertaining. This paper presents an open-source, practical, low-cost robotic Chinese chess player. The proposed system includes an elaborate mechanical structure, a simple kinematic solution, a novel robot operating system, real-time and accurate chess recognition. Regarding its mechanical design, it combines a magnetism structure and mechanical cam drive, while the overall system has just three servo motors. At the same time, its control strategy is simple and effective. Furthermore, a lightweight robot message communication mechanism, entitled TinyROS, is developed for computing resource-limited embedded chips. Concerning the recognition process, our CNNbased object detector determines chess and achieves accurate identification. As a result, our robotic Chinese chess player is exquisite and easy for large-scale promotion while improving users' chess skills. Aiming to facilitate future consumer robot research and popularize customer robots, the model's mechanical and software design and the TinyROS protocol are open-sourced at https://github.com/Star-Robot/chinese-chess-robot.

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AAAI Conference 2020 Conference Paper

Enhanced Meta-Learning for Cross-Lingual Named Entity Recognition with Minimal Resources

Qianhui Wu
Zijia Lin
Guoxin Wang
Hui Chen
Börje F. Karlsson
Biqing Huang
Chin-Yew Lin

For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing methods directly transfer from source-learned model to a target language, in this paper, we propose to ﬁne-tune the learned model with a few similar examples given a test case, which could beneﬁt the prediction by leveraging the structural and semantic information conveyed in such similar examples. To this end, we present a meta-learning algorithm to ﬁnd a good model parameter initialization that could fast adapt to the given test case and propose to construct multiple pseudo- NER tasks for meta-training by computing sentence similarities. To further improve the model’s generalization ability across different languages, we introduce a masking scheme and augment the loss function with an additional maximum term during meta-training. We conduct extensive experiments on cross-lingual named entity recognition with minimal resources over ﬁve target languages. The results show that our approach signiﬁcantly outperforms existing state-of-the-art methods across the board.

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AAAI Conference 2020 Conference Paper

Multi-Question Learning for Visual Question Answering

Chenyi Lei
Lei Wu
Dong Liu
Zhao Li
Guoxin Wang
Haihong Tang
Houqiang Li

Visual Question Answering (VQA) raises a great challenge for computer vision and natural language processing communities. Most of the existing approaches consider videoquestion pairs individually during training. However, we observe that there are usually multiple (either sequentially generated or not) questions for the target video in a VQA task, and the questions themselves have abundant semantic relations. To explore these relations, we propose a new paradigm for VQA termed Multi-Question Learning (MQL). Inspired by the multi-task learning, MQL learns from multiple questions jointly together with their corresponding answers for a target video sequence. The learned representations of videoquestion pairs are then more general to be transferred for new questions. We further propose an effective VQA framework and design a training procedure for MQL, where the speciﬁcally designed attention network models the relation between input video and corresponding questions, enabling multiple video-question pairs to be co-trained. Experimental results on public datasets show the favorable performance of the proposed MQL-VQA framework compared to state-of-the-arts.

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