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318 results on '"Huang, Jiajin"'

4. Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition

Author

Kan, Haoning, Yu, Jiale, Huang, Jiajin, Liu, Zihe, and Zhou, Haiyan

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, I.2.6
Abstract

The progress of EEG-based emotion recognition has received widespread attention from the fields of human-machine interactions and cognitive science in recent years. However, how to recognize emotions with limited labels has become a new research and application bottleneck. To address the issue, this paper proposes a Self-supervised Group Meiosis Contrastive learning framework (SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC, a novel genetics-inspired data augmentation method, named Meiosis, is developed. It takes advantage of the alignment of stimuli among the EEG samples in a group for generating augmented groups by pairing, cross exchanging, and separating. And the model adopts a group projector to extract group-level feature representations from group EEG samples triggered by the same emotion video stimuli. Then contrastive learning is employed to maximize the similarity of group-level representations of augmented groups with the same stimuli. The SGMC achieves the state-of-the-art emotion recognition results on the publicly available DEAP dataset with an accuracy of 94.72% and 95.68% in valence and arousal dimensions, and also reaches competitive performance on the public SEED dataset with an accuracy of 94.04%. It is worthy of noting that the SGMC shows significant performance even when using limited labels. Moreover, the results of feature visualization suggest that the model might have learned video-level emotion-related feature representations to improve emotion recognition. And the effects of group size are further evaluated in the hyper parametric analysis. Finally, a control experiment and ablation study are carried out to examine the rationality of architecture. The code is provided publicly online., Comment: 14 pages, 11 figures

Published
2022

24. Box4Rec: Box Embedding for Sequential Recommendation

Author

Deng, Kai, Huang, Jiajin, Qin, Jin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Karlapalem, Kamal, editor, Cheng, Hong, editor, Ramakrishnan, Naren, editor, Agrawal, R. K., editor, Reddy, P. Krishna, editor, Srivastava, Jaideep, editor, and Chakraborty, Tanmoy, editor

Published
2021
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31. Estimating the Heterogeneous Causal Effects of Parent–Child Relationships among Chinese Children with Oppositional Defiant Symptoms: A Machine Learning Approach.

Author

Zhou, Haiyan, Han, Fengkai, Chen, Ruoxi, Huang, Jiajin, Chen, Jianhui, and Lin, Xiuyun

Subjects
PARENT-child relationships, CHINESE people, MACHINE learning, SCHOOL children, SYMPTOMS
Abstract

Oppositional defiant symptoms are some of the most common developmental symptoms in children and adolescents with and without oppositional defiant disorder. Research has addressed the close association of the parent–child relationship (PCR) with oppositional defiant symptoms. However, it is necessary to further investigate the underlying mechanism for forming targeted intervention strategies. By using a machine learning-based causal forest (CF) model, we investigated the heterogeneous causal effects of the PCR on oppositional defiant symptoms in children in Chinese elementary schools. Based on the PCR improvement in two consecutive years, 423 children were divided into improved and control groups. The assessment of oppositional defiant symptoms (AODS) in the second year was set as the dependent variable. Additionally, several factors based on the multilevel family model and the baseline AODS in the first year were included as covariates. Consistent with expectations, the CF model showed a significant causal effect between the PCR and oppositional defiant symptoms in the samples. Moreover, the causality exhibited heterogeneity. The causal effect was greater in those children with higher baseline AODS, a worse family atmosphere, and lower emotion regulation abilities in themselves or their parents. Conversely, the parenting style played a positive role in causality. These findings enhance our understanding of how the PCR contributes to the development of oppositional defiant symptoms conditioned by factors from a multilevel family system. The heterogeneous causality in the observation data, established using the machine learning approach, could be helpful in forming personalized family-oriented intervention strategies for children with oppositional defiant symptoms. [ABSTRACT FROM AUTHOR]

Published
2024
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32. A Multi-domain Named Entity Recognition Method Based on Part-of-Speech Attention Mechanism

Author

Zhang, Shun, Sheng, Ying, Gao, Jiangfan, Chen, Jianhui, Huang, Jiajin, Lin, Shaofu, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Founding Editor, Sun, Yuqing, editor, Lu, Tun, editor, Yu, Zhengtao, editor, Fan, Hongfei, editor, and Gao, Liping, editor

Published
2019
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33. Classification of Mental Arithmetic Cognitive States Based on CNN and FBN

Author

Liu, Ruohao, Zhong, Ning, Zhang, Xiaofei, Yang, Yang, Huang, Jiajin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liang, Peipeng, editor, Goel, Vinod, editor, and Shan, Chunlei, editor

Published
2019
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44. Effective adsorptive removal of trace CO2 from olefin using alkanolamine‐polyethylenimine‐functionalized fumed silica.

Author

Huang, Jiajin, Yu, Liang, Fan, Qichen, Yao, Jinze, Huang, Yongsheng, Luo, Haoyuan, Liu, Baoyu, Wang, Hao, and Xia, Qibin

Subjects
POLYETHYLENEIMINE, ALKENES, ADSORPTION capacity, SILICA, HUMIDITY, SORBENTS
Abstract

The purification of gaseous olefins, such as C2H4 and C3H6, requires the efficient removal of trace CO2 impurities. However, the development of ideal adsorbents with high CO2/olefin selectivity has remained a challenge. In this study, we synthesized a novel adsorbent, denoted as PEI/DEA@FS, by modifying fumed silica (FS) with a combination of polyethylenimine (PEI) and diethanolamine (DEA). At 298 K and 0.0004 bar, PEI/DEA@FS presented the highest CO2 adsorption capacity among previously studied adsorbents, with a value of 2.12 mmol/g. Notably, PEI/DEA@FS demonstrated very high ideal adsorption solution theory selectivity for CO2/C2H4 (>1010) and CO2/C3H6 (>107) at 298 K and 0.01 bar. Breakthrough experiments revealed its excellent separation performance, producing olefins (C2H4 and C3H6) with ultra‐high purity (>99.9%) from a CO2/olefin (1/99) mixture. Moreover, PEI/DEA@FS exhibited outstanding stability over 20 adsorption and desorption cycles, and its CO2 adsorption performance remained almost unchanged under 75% relative humidity in the feed stream. [ABSTRACT FROM AUTHOR]

Published
2024
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