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Hypergraph neural network for Emotion Recognition in Conversations

ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2024年第2期23卷 1-16页

作者： Zheng, Cheng Xu, Haojie Sun, Xiao Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230000 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230000 Peoples R China Anhui Univ AHU IAI AI Joint Lab Hefei 230000 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230000 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230000 Peoples R China

Modeling conversational context is an essential step for emotion recognition in conversations. Existing works still suffer from insufficient utilization of local context information and remote context information. This article designs a hypergraph neural network, namely HNN-ERC, to better utilize local and remote contextual information. HNN-ERC combines the recurrent neural network with the conventional hypergraph neural network to strengthen connections between utterances and make each utterance receive information from other utterances better. The proposed model has empirically achieved state-of-the-art results on three benchmark datasets, demonstrating the effectiveness and superiority of the new model.

关键词： Hypergraph convolution neural network graph convolution neural network emotion recognition in conversations

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Reinforcement Learning-Based Online Scheduling of Multiple Workflows in Edge Environment

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IEEE TRANSACTIONS ON network AND SERVICE MANAGEMENT 2024年第5期21卷 5691-5706页

作者： Huang, Binbin Wang, Lingbin Liu, Xiao Huang, Zixin Yin, Yuyu Zhu, Fujin Wang, Shangguang Deng, Shuiguang Hangzhou Dianzi Univ Sch Comp Hangzhou 310018 Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3125 Australia Guangdong OPPO Co Ltd OPPO AI Ctr Shenzhen Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Zhejiang Univ Sch Comp Sci & Technol Hangzhou 310027 Peoples R China

In edge environment, many smart application instances are triggered randomly by resource-constrained Internet of Things (IoT) devices. These application instances usually consist of dependent computation components, which can be modeled as workflows in different shapes and sizes. Due to the limited computing power of IoT devices, a common approach is to schedule partial computation components of multiple workflow instances to the resource-rich edge servers to execute. However, how to schedule the stochastically arrived multiple workflow instances in edge environment with the minimum average completion time is still a challenging issue. To address such an issue, in this paper, we adopt the graph convolution neural network to transform multiple workflow instances with different shapes and sizes into embeddings, and formulate the online multiple workflow scheduling problem as a finite Markov decision process. Furthermore, we propose a policy gradient learning-based online multiple workflow scheduling scheme (PG-OMWS) to optimize the average completion time of all workflow instances. Extensive experiments are conducted on the synthetic workflows with various shapes and sizes. The experimental results demonstrate that the PG-OMWS scheme can effectively schedule the stochastically arrived multiple workflow instances, and achieve the lowest average completion time compared with four baseline algorithms in edge environments with different scales.

关键词： Processor scheduling Scheduling Internet of Things Servers Schedules Heuristic algorithms Feature extraction Edge computing online multiple workflow scheduling graph convolution neural network policy gradient learning

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Enhancing human behavior recognition with dynamic graph convolutional networks and multi-scale position attention

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INTERNATIONAL JOURNAL OF INTELLIGENT COMPUTING AND CYBERNETICS 2025年第1期18卷 236-253页

作者： Huang, Peng Jiang, Hongmei Wang, Shuxian Huang, Jiandeng Guilin Inst Informat Technol Coll Informat Engn Guilin 541004 Peoples R China

Purpose - Human behavior recognition poses a pivotal challenge in intelligent computing and cybernetics, significantly impacting engineering and management systems. With the rapid advancement of autonomous systems and intelligent manufacturing, there is an increasing demand for precise and efficient human behavior recognition technologies. However, traditional methods often suffer from insufficient accuracy and limited generalization ability when dealing with complex and diverse human actions. Therefore, this study aims to enhance the precision of human behavior recognition by proposing an innovative framework, dynamic graph convolutional networks with multi-scale position attention (DGCN-MPA) to sup. Design/methodology/approach - The primary applications are in autonomous systems and intelligent manufacturing. The main objective of this study is to develop an efficient human behavior recognition framework that leverages advanced techniques to improve the prediction and interpretation of human actions. This framework aims to address the shortcomings of existing methods in handling the complexity and variability of human actions, providing more reliable and precise solutions for practical applications. The proposed DGCNMPA framework integrates the strengths of convolutional neural networks and graph-based models. It innovatively incorporates wavelet packet transform to extract time-frequency characteristics and a MPA module to enhance the representation of skeletal node positions. The core innovation lies in the fusion of dynamic graph convolution with hierarchical attention mechanisms, which selectively attend to relevant features and spatial relationships, adjusting their importance across scales to address the variability in human actions. Findings - To validate the effectiveness of the DGCN-MPA framework, rigorous evaluations were conducted on benchmark datasets such as NTU-RGB + D and Kinetics-Skeleton. The results demonstrate that the framework achieves an F1 s

关键词： Big data analytics Decision support Human behavior recognition graph convolution neural network Multi-scale attention Dynamic graph convolution

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Multi-Kernel graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第2期28卷 1110-1121页

作者： Jiao, Cui-Na Zhou, Feng Liu, Bao-Min Zheng, Chun-Hou Liu, Jin-Xing Gao, Ying-Lian Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China Qufu Normal Univ Sch Engn Rizhao 276826 Peoples R China

Accumulating evidence indicates that microRNAs (miRNAs) can control and coordinate various biological processes. Consequently, abnormal expressions of miRNAs have been linked to various complex diseases. Recognizable proof of miRNA-disease associations (MDAs) will contribute to the diagnosis and treatment of human diseases. Nevertheless, traditional experimental verification of MDAs is laborious and limited to small-scale. Therefore, it is necessary to develop reliable and effective computational methods to predict novel MDAs. In this work, a multi-kernel graph attention deep autoencoder (MGADAE) method is proposed to predict potential MDAs. In detail, MGADAE first employs the multiple kernel learning (MKL) algorithm to construct an integrated miRNA similarity and disease similarity, providing more biological information for further feature learning. Second, MGADAE combines the known MDAs, disease similarity, and miRNA similarity into a heterogeneous network, then learns the representations of miRNAs and diseases through graph convolution operation. After that, an attention mechanism is introduced into MGADAE to integrate the representations from multiple graph convolutional network (GCN) layers. Lastly, the integrated representations of miRNAs and diseases are input into the bilinear decoder to obtain the final predicted association scores. Corresponding experiments prove that the proposed method outperforms existing advanced approaches in MDA prediction. Furthermore, case studies related to two human cancers provide further confirmation of the reliability of MGADAE in practice.

关键词： Attention mechanism graph convolution neural network miRNA-disease associations multiple kernel learning

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Fall Detection Based on graph neural networks with Variable Time Windows

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2024年第4期28卷 974-982页

作者： Wei, Jiawei Li, Junjie Liu, Yuqing Ma, Hongbin Shanghai Ocean Univ Dept Engn Pudong New Area 999 Hucheng Huan Rd Shanghai 201306 Peoples R China Lanzhou Univ Dept Informat Sci & Engn 456 Jiayuguan Rd Lanzhou 730020 Gansu Peoples R China Beijing Inst Technol Sch Automat 5 South St Beijing 100081 Peoples R China

The precise detection of falls is essential for promptly providing first aid to individuals who are at risk of accidental injury. Presently, the predominant approach for detecting falls is through inertial measurement unit (IMU) sensors, which can capture the real-time motion of an object. However, it is difficult for the current approach to face the challenges in attaining the anticipated performance in real-world applications, owing to the diverse nature of human behavior. To tackle this concern, a fall detection approach that uses a graph convolutional neural network (GCN) with variable time windows (T-GCN) is introduced. The proposed method uses well-designed graph topologies to effectively mitigate the impact of inconsistent data dimensions. Meanwhile, variable time windows are designed to capture keyframe data and to enhance their validity. To evaluate the effectiveness of the T-GCN method, a dataset D-hard containing 12 suspected falls and four real falls is built. The experimental results show that the T-GCN method achieves an accuracy of 91.3% and a precision of 92.5%, surpassing the average accuracy and precision of conventional fall detection methods.

关键词： fall detection graph topologists wrist-worn IMU sensors data variable time windows graph convolution neural network

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Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads

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BMC MEDICAL RESEARCH METHODOLOGY 2024年第1期24卷 1页

作者： Andayeshgar, Bahare Abdali-Mohammadi, Fardin Sepahvand, Majid Almasi, Afshin Salari, Nader Kermanshah Univ Med Sci Sch Hlth Dept Biostat Kermanshah *** Iran Razi Univ Dept Comp Engn & Informat Technol Kermanshah *** Iran Kermanshah Univ Med Sci Mohammad Kermanshahi Hosp Clin Res Dev Ctr Kermanshah Iran Kermanshah Univ Med Sci Farabi Hosp Clin Res Dev Ctr Kermanshah Iran Kermanshah Univ Med Sci Sleep Disorders Res Ctr Kermanshah *** Iran

One of the most common causes of death worldwide is heart disease, including arrhythmia. Today, sciences such as artificial intelligence and medical statistics are looking for methods and models for correct and automatic diagnosis of cardiac arrhythmia. In pursuit of increasing the accuracy of automated methods, many studies have been conducted. However, in none of the previous articles, the relationship and structure between the heart leads have not been included in the model. It seems that the structure of ECG data can help develop the accuracy of arrhythmia detection. Therefore, in this study, a new structure of Electrocardiogram (ECG) data was introduced, and the graph convolution network (GCN), which has the possibility of learning the structure, was used to develop the accuracy of cardiac arrhythmia diagnosis. Considering the relationship between the heart leads and clusters based on different ECG poles, a new structure was introduced. In this structure, the Mutual Information(MI) index was used to evaluate the relationship between the leads, and weight was given based on the poles of the leads. Weighted Mutual Information (WMI) matrices (new structure) were formed by R software. Finally, the 15-layer GCN network was adjusted by this structure and the arrhythmia of people was detected and classified by it. To evaluate the performance of the proposed new network, sensitivity, precision, specificity, accuracy, and confusion matrix indices were used. Also, the accuracy of GCN networks was compared by three different structures, including WMI, MI, and Identity. Chapman's 12-lead ECG Dataset was used in this study. The results showed that the values of sensitivity, precision, specificity, and accuracy of the GCN-WMI network with 15 intermediate layers were equal to 98.74%, 99.08%, 99.97% & 99.82%, respectively. This new proposed network was more accurate than the graph convolution network-Mutual Information (GCN-MI) with an accuracy equal to 99.71% and GCN-Id with

关键词： New structure Arrhythmic Electrocardiogram graph convolution neural network Weighted mutual information

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Group Vehicle Trajectory Prediction With Global Spatio-Temporal graph

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第2期8卷 1219-1229页

作者： Xu, Dongwei Shang, Xuetian Liu, Yewanze Peng, Hang Li, Haijian Zhejiang Univ Technol Dept Inst Cyberspace Secur Hangzhou 311121 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 311121 Peoples R China Zhejiang Univ Technol Dept Coll Informat Engn Hangzhou 311121 Peoples R China Zhejiang Univ Technol Inst Cyberspace Secur Hangzhou 311121 Peoples R China Beijing Univ Technol Dept Beijing Key Lab Traff Engn Beijing 100000 Peoples R China

Vehicle trajectory prediction is a challenging problem in the field of autonomous driving, which is of great significance to the safety of autonomous driving and traffic roads. In view of the interaction between surrounding vehicles and target vehicle and its own trajectory, we propose a new graph network model to predict future vehicle trajectory. First, the correlation network of vehicles at each time is constructed based on the complex network method. In order to make up for the lack of real spatial relevance caused by the fixed graph, we propose an adaptive parameter matrix to coordinate and optimize the global spatio-temporal graph. Second, the global spatio-temporal features of vehicle historical trajectory data are extracted by stacked graph convolution module. Finally, the obtained graph features are coded based on seq2seq network, and the trajectory prediction of road vehicles at different times in the future is realized. Our model has been trained and verified on the published NGSIM US-101 and I-80 data sets. Compared with other advanced schemes, our model has more accurate results in the future time of 5 seconds. In predicting the future group trajectory of vehicles on the road, the accuracy of long-term prediction is 16.6% higher than that of the most advanced scheme.

关键词： Trajectory Predictive models Roads Topology Feature extraction Data models Correlation Automatic driving graph convolution neural network Index Terms intelligent transportation spatial topology graph temporal logic network trajectory prediction

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Atrial Fibrillation Detection Algorithm Based on graph convolution network

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IEEE ACCESS 2023年 11卷 67191-67200页

作者： Ma, Hua Xia, Lingnan Zhengzhou Railway Vocat & Tech Coll Henan High Speed Railway Operat & Maintenance Engn Zhengzhou 451460 Peoples R China

Atrial fibrillation (AF) is a common type of arrhythmia with a high incidence and risk, and it is difficult to monitor. Deep learning-based algorithms for AF detection have made preliminary progress, with Recurrent convolution neural networks (RCNN) being widely used for AF detection. The RCNN algorithm combines electrocardiogram (ECG) morphology and rhythm information, where convolution neural network (CNN) mainly processes morphological features and Recurrent neural network (RNN) mainly deals with rhythm information. However, a problem with this method is that direct splicing and fusion of ECG rhythm features and ECG morphological features cannot fully exploit the information of both features for AF detection because they have different representative spaces. Therefore, this paper proposes a novel AF detection algorithm based on graph convolution network, named AF-GCN. First, we transform heartbeats into a graph structure, called heartbeat diagrams. The node features of the heartbeat graph are the morphological information of each heartbeat, and the edge of the heartbeat is linked according to the time interval between heartbeats. Moreover, we calculate the edge weight according to the morphological similarity between heartbeats. Therefore, the morphological information and rhythm information of the ECG signal can be unifiedly expressed in one heartbeat. Then, using the graph convolution network (GCN), the morphological and rhythmic features of the ECG signal can be effectively extracted, modeled, and fused in one network modal. We evaluated our AF-GCN method on two popular and widely recognized AF detection benchmarks, MIT-BIH AFDB and Physionet Challenge 2017 AFDB datasets. The results show that AF-GCN outperforms other existing methods.

关键词： Atrial fibrillation ECG-based graph convolution neural network

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Progressively Helical Multi-Omics Data Fusion GCN and Its Application in Lung Adenocarcinoma

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IEEE ACCESS 2023年 11卷 73568-73582页

作者： Zhu, Junxuan Zhang, Jinhan Wang, Liyan Huang, Hao Zhang, Zhibo Song, Kai Zhang, Xiaofei Tianjin Univ Sch Chem Engn & Technol Tianjin 300072 Peoples R China Tianjin Hosp Tianjin 300211 Peoples R China Tianjin Univ Frontiers Sci Ctr Synthet Biol Tianjin 300072 Peoples R China Tianjin Univ Key Lab Syst Bioengn Minist Educ Tianjin 300072 Peoples R China

Compared to single-omics data, utilizing multi-omics data helps to gain a more comprehensive understanding of the occurrence and development of cancer, which emphasizes the necessity of developing efficient multi-omics data fusion approaches. In this study, a novel framework based on graph convolution neural networks with a progressively helical multi-omics data fusion strategy, named phMFGCN, is proposed to effectively integrate multiple omics data. To demonstrate the effectiveness of our framework in addressing the challenges of multi omics data fusion, phMFGCN and other widely-used machine learning methods conducted comparative experiments on predicting gene-gene interactions in lung adenocarcinoma. The results illustrated that phMFGCN outperforms other models with an accuracy of 97.94%. Additionally, 506 new gene-gene interactions predicted by this framework have been validated in databases such as BioGrid. Finally, it was used to perform gene function prediction, and the results were inconsistent with other existing research, for examples: Sam68, DHX9, and HNRNPK were involved in regulating multiple lung adenocarcinoma related pathways simultaneously. All these results demonstrate the universality of phMFGCN for different clinical tasks and it can provide reference target genes or gene-gene interactions for cancer mechanism research and treatment research in clinical practice.

关键词： Multi-omics data fusion graph convolution neural network lung adenocarcinoma gene-gene interaction

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Learning Distinct Relationship in Package Recommendation With graph Attention networks

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第6期10卷 3308-3320页

作者： Lu, Wei Jiang, Nan Jin, Di Chen, Honglong Liu, Ximeng East China Jiaotong Univ Coll Informat Engn Nanchang 330013 Jiangxi Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300000 Peoples R China China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China

Recommendation systems have been widely developed and extensively used in various websites and platforms to promote products or services to interested users. However, in quite a few sale scenarios, the platform has the necessity to display users a series of items, which is called package recommendation. There is very little research in this area. This article develops a novel and realistic package recommendation system named package graph attention network (PGAT) based on graph neural network. PGAT integrates users, items, and packages to build a unified heterogeneous graph and treat them as a whole. PGAT incorporates an attention mechanism in the first-order neighborhood aggregation operation, which can differentiate the weight of different neighbor nodes to the center node. By performing graph attention and graph convolution operations on the tripartite graph, PGAT can learn node embeddings more expressively and address the problem of data sparse to a large extent. Extensive experiment results on two real-world datasets validate the outstanding performance of PGAT, which is superior to the state-of-the-art baselines by 0.77%-10.12%.

关键词： graph attention neural network graph convolution neural network information systems package recommendation

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