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MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

IEEE transactions on cybernetics 2023年第12期53卷 7749-7759页

作者： Tao Chen Richang Hong Yanrong Guo Shijie Hao Bin Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and the School of Computer Science and Information Engineering Hefei University of Technology Hefei China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.

关键词： Task analysis Feature extraction Semantics Depression Graph neural networks Electroencephalography data mining Mental disorders Multisensory integration

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Recent advances in ethanol gas sensors based on metal oxide semiconductor heterojunctions

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Rare Metals 2022年第6期41卷 1818-1842页

作者： Ling-Yun Gai Run-Ping Lai Xian-Hui Dong Xing Wu Qiao-Tong Luan Jue Wang Hao-Feng Lin Wen-Hao Ding Guang-Lei Wu Wan-Feng Xie Big Data and Network Management Center Qingdao Agricultural UniversityQingdao 266109China School of Mechanical Electronic and Information Engineering China University of Mining and Technology-BeijingBeijing 10083China School of Material Science and Engineering State Key Laboratory of Bio-Fibers and Eco-TextilesQingdao UniversityQingdao 266071China School of Electronics and Information State Key Laboratory of Bio-Fibers and Eco-TextilesQingdao UniversityQingdao 266071China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic EngineeringEast China Normal UniversityShanghai 200241China

Metal oxide semiconductor heterojunctions(MOSHs)can enhance the performance of ethanol gas sen-sors *** gas sensors based on MOSHs are cost-effective and have excellent sensing response,good selectivity,fast response and recovery,long-term stability or repeatability,a low operating temperature,a facile fabrica-tion process,and versatile *** paper reviews the recent advances in gas sensors that are based on MOSHs and the advantages of using them to detect ethanol *** to the literature,compared with ethanol gas sen-sors that use single-component sensing materials,the MOSHs exhibit superior performance due to the synergy between the different components,which can amplify the reception and transduction components of the sensor *** the best of our knowledge,heterojunctions can be grouped into four main categories as metal oxide/metal oxide,metal oxide/metal sulfide,metal oxide/noble metal,and metal oxide/other materials,including rare-earth metals,g-C_(3)N_(4),and graphene,*** future trends and challenges that would be faced in the development of ethanol gas sensors based on MOSHs are discussed in ***,critical ideas and thinking regarding the future progress of MOSH-based gas sensors are presented.

关键词： Metal oxides Heterojunction Chemiresistori Ethanol sensor Synergistic behavior

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Regularized Total Least Squares Broad Learning System for Regression

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Journal of Computers (Taiwan) 2025年第1期36卷 89-112页

作者： Xiong, Ke-Jia Yang, Guan-Ci Zhou, Tao Xie, Zhen-Qiang State Key Laboratory of Public Big Data Guizhou University Guizhou Province Guiyang City550025 China Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guizhou Province Guiyang City550025 China National Engineering Research Center of Big Data Application to The Improvement of Governance Capacity Guizhou Province Guiyang City550000 China

The Broad Learning System (BLS) has been extensively developed and applied across various fields due to its significant advantages, including high efficiency, strong generality, and scalability. However, in practical applications where both inputs and outputs are contaminated by noise, traditional BLS demonstrates suboptimal performance in handling sample data. This study introduces a novel regularized total least squares broad learning system (RTLS-BLS) designed to enhance the robustness of BLS when noisy values are present in both the input and the output of the training set. Initially, the k-support norm is integrated into the BLS-based autoencoder (BLS-AE), embedded within BLS, to extract robust features from the original input data. The BLS-AE equipped with the k-support norm effectively addresses or mitigates issues related to overly sparse (based on L1-norm) or overly dense (based on L2-norm) regularization of extracted features. Subsequently, regularized total least squares (RTLS) are employed to assess the output weights of BLS, further enhancing its robustness. Moreover, simultaneous perturbation measures for the coefficient matrix and the output are provided. Experimental results from two function approximation tasks, eight benchmark regression tasks, and two network interface flow datasets demonstrate that the proposed RTLS-BLS achieves significantly robust performance under noisy conditions compared to other methods. © 2025 Codon Publications. All rights reserved.

关键词： Adversarial machine learning

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Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chenyang Bu Jiacheng Liu Jiaxuan Liu Shengwei Ji Hongbin Yang Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China School of Artificial Intelligence and Big Data Hefei University China

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets.

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Service Decision Mechanism Based On Traffic Flow Prediction in the Internet of Vehicles 23

Service Decision Mechanism Based On Traffic Flow Prediction ...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Hu, Yanzhe Feng, Chenyuan Jiang, Wei Shi, Wenzhe Feng, Daquan Shenzhen University Shenzhen Key Laboratory of Digital Creative Technology Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China College of Information Engineering Zhejiang University of Technology Zhejiang Hangzhou China Zte Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology China

ISBN: (纸本)9798350325959

The service time of Road-Side Units (RSUs) for vehicle users will directly affect the Quality of Service (QoS) in the Internet of Vehicles (IoV), and it is related to the traffic flow and service decisions. Consequently, a sophisticated service decision mechanism based on the traffic flow prediction is necessary beforehand in order to optimize QoS. In this work, we propose a service decision based on traffic flow prediction to reduce the service waiting time of vehicles. Firstly, we propose a traffic flow prediction model based on deep learning. Convolutional Neural Network (CNN) is used to capture spatial data in order to handle the complicated road network layout in metropolitan roads. First, to reveal people's travel patterns, temporal features are captured by two different models. While short-term features are captured from historical data by Auto-Encoder, daily period feature as well as weekly period feature are captured from bi-directional data by Bidirectional Long Short-Term Memory (Bi-LSTM). Second, by simulating the process of RSUs serving vehicles using a queuing theory model, we propose a service decision mechanism based on the anticipated traffic flow. The proposed service decision mechanism aims to reduce the average waiting time of vehicles for service. Particularly, the RSUs will ask the Macro Base Station (MBS) for assistance when there are too many vehicles traveling through the RSUs. Finally, we conduct comprehensive experiments using real data to evaluate the model performance, and it is demonstrated that our proposed scheme outperforms other benchmarks. © 2023 IEEE.

关键词： Internet of Vehicles queuing theory service decision mechanism traffic flow prediction

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Semantic-Reconstructed Graph Transformer Network for Event Detection

Semantic-Reconstructed Graph Transformer Network for Event D...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhuochun Miao Xingrui Zhuo Gongqing Wu Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Anhui China

Event detection (ED) is a key subtask of information extraction to extract key events, such as stock rise and fall and social public opinion, from news or social media. Although current GCN-based event detection methods achieve remarkable success via building graphs with dependency trees, they typically suffer from two challenges: 1) They use sequence models to learn contextual information of sentences, ignoring the longterm dependencies problem of sequence models might learn ineffective information and make it propagate in GCN layers. 2) Most methods do not exploit global dependency label information and grammatical structure information that convey rich linguistic knowledge directly, and only consider local dependency label information. To cope with these challenges, we propose a novel event detection model via semantic-reconstructed graph transformer networks (SRGTNED), which incorporates semantic reconstruction and path information collection methods. Using the semantic reconstruction method, we assign a pruned sequence to each word based on the path information to capture contextual information consistent with sentence semantics. Moreover, to better utilize global dependency label information and grammatical structure information, a Graph Transformer Network (GTN)-based heterogeneous graph embedding framework is introduced to automatically learn path information between important words by converting sentences as heterogeneous graphs. We conduct experiments on the ACE2005 dataset and the Commodity News dataset, and the experimental results demonstrate that our method significantly outperforms 11 state-of-the-art baselines in terms of the F1-score.

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Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection

arXiv

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arXiv 2024年

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defence Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2024, CC BY-NC-ND.

关键词： Fake detection

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Acoustic Localization Method for Burst Point of Group Shot Projectile Based on Non-Parametric Estimation

Acoustic Localization Method for Burst Point of Group Shot P...

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Artificial Intelligence and Intelligent Information Processing (AIIIP), International Conference on

作者： Yahui Zhang Song Feng Shuxian Shang Weijie Tan Yulong Wang Chunwei Zhang China Huayin ordnance Test Center Huayin China State Key Laboratory of public big data Guizhou Big Data Academy Guiyang China State Key Laboratory for manufacturing system engineering School of mechanical engineering Xi'an China

In the acoustic localization of mass projectiles, due to the large number of mass projectiles and short firing time, the traditional algorithm has some problems, such as waveform superposition and the order of explosion arrival at the sensor is not easy to distinguish. In order to solve this problem, an acoustic location method for burst point of mass projectile based on nonparametric estimation was proposed. The distribution of explosion probability function was calculated based on the measured acoustic waveform and regional explosion energy function. Then, the actual explosion point coordinates were obtained according to the extreme value. This method does not need to estimate the arrival time and arrival angle parameters, solves the problem that the order of arrival time is not easy to distinguish. Simulation and field tests show that the algorithm can effectively deal with 1∼32 projectiles fired in 2s, and solve the problem of explosion point location in the process of group gun surface attack exercise training and combat test.

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Multivariate Time Series Classification via Hierarchical Graph Embedding

Multivariate Time Series Classification via Hierarchical Gra...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenhao Niu Xingrui Zhuo Gongqing Wu JunWei Lv Zan Zhang Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Anhui China

Multivariate time series classification aims to determine the labels for multivariate time series samples. Although variable interaction relationships and sample similarity relationships exist in multivariate time series, the available related methods usually ignore the rich relationships and are ineffective in exploiting these. To solve this problem, we propose a Hierarchical Graph Embedding for Multivariate Time Series Classification (MTSC-HGE), which consists of a variable-wise attentive graph pooling module and a sample-wise graph convolutional module to obtain the relationships of variables and samples. Specifically, we design an attentive graph pooling module based on self-attention, which can obtain sample features fusing temporal patterns and variable interaction relationships in samples. Furthermore, we propose a graph mapping criterion that converts the MTS dataset into a graph based on dynamic time warping to explicitly reflect the similarity relationships between samples. To capture latent sample relationships, a GCN module is utilized on the sample graph to integrate sample features obtained from the attentive graph pool module. In addition, a classifier takes the rich representation output by the model to get the final predicted class. Extensive experiments on 14 public datasets show that MTSC-HGE significantly outperforms state-of-the-art baselines.

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Enhancing Point Cloud Sampling Quality with Dual-Branch Fusion Networks

Enhancing Point Cloud Sampling Quality with Dual-Branch Fusi...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Jin, Yi Wang, Xu Hu, Mengxia Yu, Hui Li, Yidong Wang, Tao Feng, Songhe Lang, Congyan State Key Laboratory of Intelligent Vehicle Safety Technology China School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing China China Automotive Engineering Research Institute Co. Ltd. China School of Psychology & Neuroscience University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9781665410205

Task-oriented point cloud sampling methods have attracted considerable attention for their ability to adaptively select important point sets based on downstream tasks, achieving an excellent balance between data simplification and task performance. However, existing task-oriented sampling models, primarily based on single-branch designs, struggle to fully extract features from input point clouds that comprehensively reflect multi-dimensional key information, thus limiting their sampling performance. In this paper, we introduce a dual-branch sampling network, named DBS-NET, which conducts crucial point sampling from both the global and local importance perspectives separately before merging them, thereby preserving multi-dimensional key information of the input data during the sampling process. Qualitative and quantitative experimental results demonstrate the competitive performance of DBS-NET on the classification benchmark task. © 2024 IEEE.

关键词： Importance sampling

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