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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

SFF-DA: Sptialtemporal Feature Fusion for Detecting Anxiety Nonintrusively

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arXiv 2022年

作者： Mo, Haimiao Li, Yuchen Yang, Shanlin Zhang, Wei Ding, Shuai The School of Management Hefei University of Technology Anhui Hefei23009 China The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China The National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200135 China The West China Hospital of Sichuan University Sichuan Chengdu610041 China

Early detection of anxiety is crucial for reducing the suffering of individuals with mental disorders and improving treatment outcomes. Utilizing an mHealth platform for anxiety screening can be particularly practical in improving screening efficiency and reducing costs. However, the effectiveness of existing methods has been hindered by differences in mobile devices used to capture subjects' physical and mental evaluations, as well as by the variability in data quality and small sample size problems encountered in real-world settings. To address these issues, we propose a framework with spatiotemporal feature fusion for detecting anxiety nonintrusively. We use a feature extraction network based on a 3D convolutional network and long short-term memory ("3DCNN+LSTM") to fuse the spatiotemporal features of facial behavior and noncontact physiology, which reduces the impact of uneven data quality. Additionally, we design a similarity assessment strategy to address the issue of deteriorating model accuracy due to small sample sizes. Our framework is validated with a crew dataset from the real world and two public datasets: the University of Burgundy Franche-Comté Psychophysiological (UBFC-Phys) dataset and the Smart Reasoning for Well-being at Home and at Work for Knowledge Work (SWELL-KW) dataset. The experimental results indicate that our framework outperforms the comparison methods. © 2022, CC BY-NC-SA.

关键词： mHealth

Dynamic Viscosity of the ABC-stacked Multilayer Graphene in the Collisionless Regime

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arXiv 2024年

作者： Chen, Weiwei Shen, Yedi Zhan, Tianle Zhu, W. Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Science China Jiliang University Hangzhou310018 China School of Science Westlake University 18 Shilongshan Road Hangzhou310024 China Hefei National Laboratory for Physical Sciences at the Microscale and Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Anhui Hefei230026 China Institute of Natural Sciences Westlake Institute for Advanced Study 18 Shilongshan Road Hangzhou310024 China

We explore the dynamic shear viscosity of the undoped ABC-stacked multilayer graphene based on the chiral-N effective Hamiltonian, where the chirality N is equivalent to the layer number. We investigate the dependence of the dynamic shear viscosity on the frequency in the collisionless regime and calculate Coulomb interaction corrections by three leading order Feynman diagrams: self-energy diagram, vertex diagram, and honey diagram. We propose that the dynamic shear viscosity is generated by the relaxation of momentum flux polarization through electron-hole excitations, and that the interaction can amplify this effect. Furthermore, our research indicates that the dynamic shear viscosity exhibits a robust linear positive dependence on N. This finding suggests that by making modifications to the number of layers in graphene, it is possible to finely tune the electron viscous effects. © 2024, CC0.

关键词： Graphene

Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection

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arXiv 2024年

作者： Zhang, Yaning Li, Qiufu Yu, Zitong Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China

Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible to capturing local forgery patterns generated by various manipulation methods. Though transformer-based detectors exhibit improvements in modeling global dependencies, they are not good at exploring local forgery artifacts. Hybrid transformer-based networks are designed to capture local and global manipulated traces, but they tend to suffer from the attention collapse issue as the transformer block goes deeper. Besides, soft labels are rarely available. In this paper, we propose a distilled transformer network (DTN) to capture both rich local and global forgery traces and learn general and common representations for different forgery faces. Specifically, we design a mixture of expert (MoE) module to mine various robust forgery embeddings. Moreover, a locally-enhanced vision transformer (LEVT) module is proposed to learn locally-enhanced global representations. We design a lightweight multi-attention scaling (MAS) module to avoid attention collapse, which can be plugged and played in any transformer-based models with only a slight increase in computational costs. In addition, we propose a deepfake self-distillation (DSD) scheme to provide the model with abundant soft label information. Extensive experiments show that the proposed method surpasses the state of the arts on five deepfake datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

Research on STF-DBSCAN based personal semantic location acquisition

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Research on STF-DBSCAN based personal semantic location acqu...

第33届中国控制与决策会议

作者： Zixuan Yao Dong Wei Yibing Ran School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission

With the development of technology and economy,the planning and construction of smart cities have generated new demands and *** intelligence-led technologies and applications are changing people's lives where location-based services are one of the most important technical *** recent years,the popularity of portable devices with GPS(Global Positioning System) function and the development of positioning technology make a large amount of individual track data ***,collecting personal semantic location by mining these tracklogs would be the application direction and research focus of location-based *** semantic location is a practical significant location that users will visit frequently,stay for a long time such as a home address,workplace,etc.).The acquisition of personal semantic location involves two steps,which are acquiring location and matching ***,most of the relevant research use density clustering-based algorithms to collect location *** the original DBSCAN algorithm is used to acquire location,all the trajectory points in the raw dataset need to be included in the clustering samples,which increases calculation and reduces the program *** paper addresses this problem and proposes an advanced density-based clustering algorithm ***,the trajectory points in the raw dataset are filtered to obtain the dwell ***,the dwell points are clustered and analyzed to achieve the purpose of improving the running *** the semantic recognition issue,this paper pre-defines the daily behavior pattern of individual and analyzes the temporal features of the trajectory points in each *** using the same trajectory data samples for location acquisition,the running time of the proposed algorithm in this paper is one-fifteenth of the original algorithm.

关键词： Personal Semantic Location DBSCAN Spatio-temporal Features

Deep learning radiomics model related with genomics phenotypes for pathological complete response prediction in locally advanced rectal cancer under neoadjuvant chemoradiotherapy

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Deep learning radiomics model related with genomics phenotyp...

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Ye Wang Zhen Pan Lanyan Lin Huajun Cai Feng Ding Chaojun Tan Dingyuan Zheng Yajun Xie Guoxian Guan Department of Colorectal Surgery The First Affiliated Hospital of Fujian Medical University Fuzhou China Department of Geriatric Medicine Fujian Key Laboratory of Geriatrics Diseases Fujian Provincial Center for Geriatrics Fuzhou University Affiliated Provincial Hospital Shengli Clinical Medical College of Fujian Medical University Fuzhou China Fujian Huaye Information Technology Co. Ltd China School of Big Data \& Key Laboratory of Data Science and Intelligent Computing Fuzhou University of International Studies and Trade Fuzhou China Department of Colorectal Surgery The First Affiliated Hospital of Fujian Medical University National Regional Medical Center Binhai Campus of the First Affiliated Hospital Fuzhou China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Background: Developing a treatment strategy after neoadjuvant chemoradiotherapy (nCRT) based on accurate pathological complete response (pCR) predictions is crucial for locally advanced rectal cancer (LARC).Objective: To determine the effectiveness of a radiomics model using diffusion-weighted imaging (DWI) and T2-weighted in predicting post-nCRT pCR efficacy after ***: This study enrolled 237 LARC patients who had radical surgery under nCRT between February 2020 and August 2023. Tumor tissues from 54 patients were RNA sequenced. Radiomics features from preoperative MRI data were used to develop a model for deep learning via the LASSO algorithm. ROC curves were used to assess the model performance. Following Spearman's correlation, analyses of transcriptomic data were conducted to identify the genes correlated with the DPL ***: The DPL model, incorporating 7 features and 5 machine-learning algorithms, was used to create risk assessment models. The RF model excelled in predicting pCR, achieving an AUC of 0.91, accuracy of 0.88, and F1 score of 0.89. Furthermore, catabolic processes and pathways related to immunity were associated with selected DPL ***: The modified MRI-based radiomics model shows good predictive efficacy in predicting pCR after nCRT in LARC patients. Additionally, we investigated the genomic phenotypes associated with DPL.

关键词： Deep learning Support vector machines Pathology Analytical models Transcriptomics Predictive models data models Random forests Radiomics Cancer

Story Ending Generation with Multi-Level Graph Convolutional Networks over Dependency Trees 35

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Story Ending Generation with Multi-Level Graph Convolutional...

35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence

作者： Huang, Qingbao Mo, Linzhang Li, Pijian Cai, Yi Liu, Qingguang Wei, Jielong Li, Qing Leung, Ho-fung South China Univ Technol Sch Software Engn Guangzhou Peoples R China Guangxi Univ Sch Elect Engn Nanning Peoples R China MOE China Key Lab Big Data & Intelligent Robot SCUT Beijing Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781577358664

As an interesting and challenging task, story ending generation aims at generating a reasonable and coherent ending for a given story context. The key challenge of the task is to comprehend the context sufficiently and capture the hidden logic information effectively, which has not been well explored by most existing generative models. To tackle this issue, we propose a context-aware Multi-level Graph Convolutional Networks over Dependency Parse (MGCN-DP) trees to capture dependency relations and context clues more effectively. We utilize dependency parse trees to facilitate capturing relations and events in the context implicitly, and Multilevel Graph Convolutional Networks to update and deliver the representation crossing levels to obtain richer contextual information. Both automatic and manual evaluations show that our MGCN-DP can achieve comparable performance with state-of-the-art models. Our source code is available at https://***/VIS LANG-Lab/MLGCN-DP.

关键词： Forestry

A Tutorial on Movable Antennas for Wireless Networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore National Key Laboratory of Wireless Communications Chengdu611731 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Beihang University School of Electronic and Information Engineering Beijing100191 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Institute of Technology State Key Laboratory of CNS/ATM Miit Key Laboratory of Complex-field Intelligent Sensing Beijing100081 China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

Entity Guided Question Generation with Contextual Structure and Sequence Information Capturing 35

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Entity Guided Question Generation with Contextual Structure ...

35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence

作者： Huang, Qingbao Fu, Mingyi Mo, Linzhang Cai, Yi Xu, Jingyun Li, Pijian Li, Qing Leung, Ho-fung South China Univ Technol Sch Software Engn Guangzhou Peoples R China Guangxi Univ Sch Elect Engn Nanning Peoples R China MOE China Key Lab Big Data & Intelligent Robot SCUT Beijing Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781577358664

Question generation is a challenging task and has attracted widespread attention in recent years. Although previous studies have made great progress, there are still two main shortcomings: First, previous work did not simultaneously capture the sequence information and structure information hidden in the context, which results in poor results of the generated questions. Second, the generated questions cannot be answered by the given context. To tackle these issues, we propose an entity guided question generation model with contextual structure information and sequence information capturing. We use a Graph Convolutional Network and a Bidirectional Long Short Term Memory Network to capture the structure information and sequence information of the context, simultaneously. In addition, to improve the answerability of the generated questions, we use an entity-guided approach to obtain question type from the answer, and jointly encode the answer and question type. Both automatic and manual metrics show that our model can generate comparable questions with state-of-the-art models. Our code is available at https://***/VISLANG-Lab/EGSS.

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