Yang Mo

China University of Petroleum, Beijing

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Lianxin Jiang

Guangxi Research Institute of Chemical Industry

Haiqin Yang

Dingxi City People's Hospital

Jian Ma

Tianjin University

Jianping Shen

Western Michigan University

Shuyi Xie

China National Petroleum Corporation (China)

Mengyuan Zhou

Henan University

Xiaoyi Ruan

RWTH Aachen University

Meizhi Jin

Ping An (China)

Liang Zhao

Dalian University of Technology

Lianxin Jiang

Chongqing University of Posts and Telecommunications

Cooperative Institutions

Ping An (China)

Beihang University

Nova Insurance Services (China)

China University of Petroleum, Beijing

Shanghai Jiao Tong University

Harbin Institute of Technology

Harbin University of Science and Technology

Beijing Institute of Technology

Chinese Academy of Sciences

Southwest Jiaotong University

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Models and Features with Covariate Shift Adaptation for Suspicious Network Event Recognition

2021 IEEE International Conference on Big Data (Big Data) (2019)

Shuyi Xie Jian Ma Yubin Luo Lianxin Jiang Shirly Jin

Detecting a small number of suspicious network events from large amount of network traffic data is a very challenging task. We extract time series features from the network log data and use models such as LightGBM and stacked CNN-LSTM deep neural networks to predict whether the investigated alerts are suspicious. We apply feature alignment, covariate shift adaptation to overcome the covariate shift between training data and test data. In the IEEE Big Data 2019 Cup: Suspicious Network Event Recognition Competition, our model scored the first place on the public board and the fourth place on the final board respectively.

Feature (linguistics)

10.1109/bigdata47090.2019.9006292

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RG PA at SemEval-2021 Task 1: A Contextual Attention-based Model with RoBERTa for Lexical Complexity Prediction

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (2021)

G. Sasibushana Rao Maochang Li Xiaolong Hou Lianxin Jiang Yang Mo

In this paper we propose a contextual attention based model with two-stage fine-tune training using RoBERTa. First, we perform the first-stage fine-tune on corpus with RoBERTa, so that the model can learn some prior domain knowledge. Then we get the contextual embedding of context words based on the token-level embedding with the fine-tuned model. And we use Kfold cross-validation to get K models and ensemble them to get the final result. Finally, we attain the 2nd place in the final evaluation phase of sub-task 2 with pearson correlation of 0.8575.

Ensemble forecasting

Contextual design

10.18653/v1/2021.semeval-1.79

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Design of magnetic flux leakage detection device for three dimensional small diameter pipeline based on MEMS

Highlights in Science Engineering and Technology (2023)

Ziming Luo Zongyang Zhang Yang Mo Xiaoli Jia

Based on the common pipeline detection device faced with small diameter pipeline space, small turning radius is prone to the problem of stuck pipe phenomenon, this paper combined with the actual production needs, for small diameter pipeline leakage detection based on MEMS technology designed a detection device. COMSOL Multiphysics software was used to create a finite element model to simulate the magnetic flux leakage detection of pipeline defects. The influence of the type, size and location of pipeline defects on magnetic flux leakage signals was discussed. Based on MEMS triaxial magnetic force sensor, a magnetic flux leakage detection device with small volume, high precision and strong adaptive ability was designed. Through experiments, the magnetic flux leakage detection data consistent with the simulation results were obtained, which provided ideas for solving the detection problem of small-caliber pipelines.

Magnetic Flux Leakage

Multiphysics

Leakage (economics)

10.54097/hset.v53i.9729

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PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (2021)

Shuyi Xie Jian Ma Haiqin Yang Lianxin Jiang Yang Mo

This paper presents the PALI team's winning system for SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation. We fine-tune XLM-RoBERTa model to solve the task of word in context disambiguation, i.e., to determine whether the target word in the two contexts contains the same meaning or not. In implementation, we first specifically design an input tag to emphasize the target word in the contexts. Second, we construct a new vector on the fine-tuned embeddings from XLM-RoBERTa and feed it to a fully-connected network to output the probability of whether the target word in the context has the same meaning or not. The new vector is attained by concatenating the embedding of the [CLS] token and the embeddings of the target word in the contexts. In training, we explore several tricks, such as the Ranger optimizer, data augmentation, and adversarial training, to improve the model prediction. Consequently, we attain the first place in all four cross-lingual tasks.

SemEval

Word embedding

10.18653/v1/2021.semeval-1.93

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Citations (6)

Multi-modal interactive fusion method for detecting teenagers’ psychological stress

Journal of Biomedical Informatics (2020)

Huijun Zhang Lei Cao Ling Feng Yang Mo

10.1016/j.jbi.2020.103427

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Citations (17)

Dijkstra algorithm based minimum acceleration/snap quadrotor UAV trajectory planning

Journal of Physics Conference Series (2024)

Zizhuo Cai М.С. Селезнева Yang Mo

Abstract This paper focuses on path planning using the Dijkstra/A* algorithm and addresses the trajectory generation problem using the minimum acceleration/snap approach. Firstly, the mathematical model of the optimization problem is formulated, specifically utilizing the Euler-Lagrange equation to derive the necessary conditions for achieving minimum acceleration/snap trajectories, which are represented by either 3rd or 7th order polynomials. The coefficients of these polynomials are determined by imposing appropriate constraints on velocity, acceleration, and higher-order derivatives. Subsequently, a detailed comparison of the performances of the two optimization methods in complex environments is conducted, evaluating differences in position and velocity. Based on the deterministic analysis, conclusive results are obtained.

10.1088/1742-6596/2746/1/012028

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PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation

arXiv (Cornell University) (2021)

Shuyi Xie Jian Ma Haiqin Yang Lianxin Jiang Yang Mo

This paper presents the PALI team's winning system for SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation. We fine-tune XLM-RoBERTa model to solve the task of word in context disambiguation, i.e., to determine whether the target word in the two contexts contains the same meaning or not. In the implementation, we first specifically design an input tag to emphasize the target word in the contexts. Second, we construct a new vector on the fine-tuned embeddings from XLM-RoBERTa and feed it to a fully-connected network to output the probability of whether the target word in the context has the same meaning or not. The new vector is attained by concatenating the embedding of the [CLS] token and the embeddings of the target word in the contexts. In training, we explore several tricks, such as the Ranger optimizer, data augmentation, and adversarial training, to improve the model prediction. Consequently, we attain first place in all four cross-lingual tasks.

SemEval

Word embedding

10.48550/arxiv.2104.10375

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Sattiy at SemEval-2021 Task 9: An Ensemble Solution for Statement Verification and Evidence Finding with Tables

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (2021)

Xiaoyi Ruan Meizhi Jin Jian Ma Haiqin Yang Lianxin Jiang

Question answering from semi-structured tables can be seen as a semantic parsing task and is significant and practical for pushing the boundary of natural language understanding. Existing research mainly focuses on understanding contents from unstructured evidence, e.g., news, natural language sentences and documents. The task of verification from structured evidence, such as tables, charts, and databases, is still less-explored. This paper describes sattiy team’s system in SemEval-2021 task 9: Statement Verification and Evidence Finding with Tables (SEM-TAB-FACT)(CITATION). This competition aims to verify statements and to find evidence from tables for scientific articles and to promote proper interpretation of the surrounding article. In this paper we exploited ensemble models of pre-trained language models over tables, TaPas and TaBERT, for Task A and adjust the result based on some rules extracted for Task B. Finally, in the leadboard, we attain the F1 scores of 0.8496 and 0.7732 in Task A for the 2-way and 3-way evaluation, respectively, and the F1 score of 0.4856 in Task B.

SemEval

Statement (logic)

Coreference

Natural language understanding

10.18653/v1/2021.semeval-1.179

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Citations (1)

RefBERT: Compressing BERT by Referencing to Pre-computed Representations

arXiv (Cornell University) (2021)

Xinyi Wang Haiqin Yang Liang Zhao Yang Mo Jianping Shen

Recently developed large pre-trained language models, e.g., BERT, have achieved remarkable performance in many downstream natural language processing applications. These pre-trained language models often contain hundreds of millions of parameters and suffer from high computation and latency in real-world applications. It is desirable to reduce the computation overhead of the models for fast training and inference while keeping the model performance in downstream applications. Several lines of work utilize knowledge distillation to compress the teacher model to a smaller student model. However, they usually discard the teacher's knowledge when in inference. Differently, in this paper, we propose RefBERT to leverage the knowledge learned from the teacher, i.e., facilitating the pre-computed BERT representation on the reference sample and compressing BERT into a smaller student model. To guarantee our proposal, we provide theoretical justification on the loss function and the usage of reference samples. Significantly, the theoretical result shows that including the pre-computed teacher's representations on the reference samples indeed increases the mutual information in learning the student model. Finally, we conduct the empirical evaluation and show that our RefBERT can beat the vanilla TinyBERT over 8.1\% and achieves more than 94\% of the performance of $\BERTBASE$ on the GLUE benchmark. Meanwhile, RefBERT is 7.4x smaller and 9.5x faster on inference than BERT$_{\rm BASE}$.

Leverage (statistics)

Benchmark (surveying)

Representation

10.48550/arxiv.2106.08898

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MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (2021)

Jian Ma Shuyi Xie Haiqin Yang Lianxin Jiang Mengyuan Zhou

This paper describes MagicPai’s system for SemEval 2021 Task 7, HaHackathon: Detecting and Rating Humor and Offense. This task aims to detect whether the text is humorous and how humorous it is. There are four subtasks in the competition. In this paper, we mainly present our solution, a multi-task learning model based on adversarial examples, for task 1a and 1b. More specifically, we first vectorize the cleaned dataset and add the perturbation to obtain more robust embedding representations. We then correct the loss via the confidence level. Finally, we perform interactive joint learning on multiple tasks to capture the relationship between whether the text is humorous and how humorous it is. The final result shows the effectiveness of our system.

SemEval

10.18653/v1/2021.semeval-1.162

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Citations (2)