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IET International Radar Conference 2023, IRC 2023

作者： Gao, Silin Wang, Muhan Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839539954

This paper is centered on gridless direction of arrival (DoA) estimation for single-snapshot data collected by non-uniform linear arrays (NLAs) in the context of automotive applications. While recent single-snapshot DoA estimation algorithms rooted in grid-based compressed sensing (CS) offer super-resolution capabilities, they are notably sensitive to discrepancies between the assumed sparsity basis and the actual basis. Furthermore, the existing atomic soft thresholding (AST) algorithm, a promising gridless sparse recovery technique within the Toeplitz model, is constrained to uniform linear arrays (ULAs) exhibiting Vandermonde structure in their array manifolds. However, in automotive scenarios, it becomes imperative to efficiently apply gridless DoA estimation to NLAs featuring non-Vandermonde array manifolds. In this paper, we introduce the non-Vandermonde atomic soft thresholding (NVAST) algorithm and employ it for DoA estimation in NLAs. The novel algorithm establishes a link between Vandermonde and non-Vandermonde atoms by decomposing non-Vandermonde vectors and addressing them using semi-definite programming (SDP). Simulation and measurement experiments conducted with automotive radars underscore the remarkable performance of our proposed method. © The Institution of Engineering & Technology 2023.

关键词： Automotive radar

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AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APPLICATION TO TOMOSAR IMAGING

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APP...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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Enhancing EEG-Based Cross-Subject Emotion Recognition via Adaptive Source Joint Domain Adaptation

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IEEE Transactions on Affective computing 2024年

作者： Liu, Ke Luo, Xin Zhu, Wenrui Yu, Zhuliang Yu, Hong Xiao, Bin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Shanghai Jiao Tong University School of Medicine Department of Neurology Songjjiang Hospital and Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

EEG emotion recognition is crucial in both human-machine interaction and healthcare. However, recognizing emotions across different subjects remains challenging due to individual variability. While existing multi-source domain adaptation methods have been utilized for cross-subject EEG emotion decoding, they often struggle with irrelevant or weakly relevant source domains, leading to negative transfer. Additionally, variations within subdomains are often neglected in these studies. We propose a joint domain adaptation method, Adaptive Source Joint Domain Adaptation (ASJDA) to address these issues. ASJDA utilizes an unsupervised adaptive source selection strategy to select a subset of source domains by evaluating the Jensen-Shannon divergence between the source and target domains, choosing those most relevant to the target. Subsequently, it implements joint domain adaptation with these chosen sources at both the domain and category subdomain levels. Our proposed method outperforms existing state-of-the-art methods, achieving cross-subject accuracies of 96.81% in SEED, 89.69% in SEED-IV, and 69.31% in DEAP. This work significantly advances the state of the art in EEG emotion recognition by effectively addressing the challenges of cross-subject variability. The source code for ASJDA is available at https://***/Pam098/ASJDA. © 2010-2012 IEEE.

关键词： adaptive source selection cross-subject domain adaptation EEG emotion recognition

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Breath of Life: A Biofeedback Game with Diverse Breathing Techniques for Enhanced Real-Life Emotion Regulation

Breath of Life: A Biofeedback Game with Diverse Breathing Te...

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Chen, Qi Du, Jiachen Yao, Zhihao Feng, Yuanling Liu, Guanhong Ni, Ning Wu, Zhenyu Liu, Haoran Chen, Xijing Mi, Haipeng Wuhan Textile University Wuhan China Tsinghua University Beijing China The Future Laboratory Tsinghua University Beijing China College of Design and Innovation Intelligent Big Data Visualization Lab. Tongji University Shanghai China Department of Psychology University of Chinese Academy of Sciences Beijing China Key Laboratory of Cognitive Science and Mental Health Institute of Psychology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400713958

Mental health is a critical public health concern, with emotion regulation (ER) playing a pivotal role in maintaining and improving psychological well-being. While respiratory biofeedback games have emerged as effective tools for ER training, existing research predominantly focuses on single-session interventions with limited techniques, emphasizing short-term skill transfer in simulated environments. This approach has led to a significant gap in understanding the long-term application of these techniques in real-life contexts, which is essential for designing effective ER training games. To address this gap, we developed a biofeedback game incorporating four distinct breathing techniques and conducted a 10-week longitudinal study with 25 participants. After one week of training, the majority of participants demonstrated successful transfer of breathing techniques to real-life scenarios. Our findings provide actionable design insights for promoting long-term skill transfer and highlight the potential of respiratory biofeedback games as sustainable tools for emotion regulation training. © 2025 Copyright held by the owner/author(s).

关键词： Biofeedback

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Learning Counterfactual Explanation of Graph Neural Networks Via Generative Flow Network

SSRN

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SSRN 2023年

作者： He, Kangjia Liu, Li Zhang, Youmin Wang, Ye Liu, Qun Wang, Guoyin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China

Counterfactual subgraphs explain Graph Neural Networks (GNNs) by answering the question: "How would the prediction change if a certain subgraph were absent in the input instance?" The differentiable proxy adjacency matrix is prevalent in current counterfactual subgraph discovery studies due to its ability to avoid exhaustive edge searching. However, a prediction gap exists when feeding the proxy matrix with continuous values and the thresholded discrete adjacency matrix to GNNs, compromising the optimization of the subgraph generator. Furthermore, the end-to-end learning schema adopted in the subgraph generator limits the diversity of counterfactual subgraphs. To this end, we propose CF-GFNExplainer, a flow-based approach for learning counterfactual subgraphs. CF-GFNExplainer employs a policy network with a discrete edge removal schema to construct counterfactual subgraph generation trajectories. Additionally, we introduce a reward and loss function designed to guide CF-GFNExplainer's optimization. The discrete adjacency matrix generated in each trajectory eliminates the prediction gap, enhancing the validity of the learned subgraphs. Furthermore, the multi-trajectories sampling strategy adopted in CF-GFNExplainer results in diverse counterfactual subgraphs. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed method in terms of validity and diversity. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Semi-Supervised Feature Selection Based on Fuzzy Related Family

SSRN

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SSRN 2023年

作者： Yang, Tian Guo, Zhijun Li, Yuan-Jiang Deng, Yanfang Qian, Yuhua Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China

Existing machine learning algorithms face the problems of label-missing and high dimensionality. Feature selection is an effective dimensionality reduction method that can improve the efficiency and accuracy of subsequent machine learning tasks by removing irrelevant and redundant features. Related family is an efficient and rough set-based feature selection method, but is difficult to be used in simisupervised learning tasks. However, due to the difficulty of obtaining all labeled data, partially labeled data has become an important object that machine learning models must deal with. Therefore, this paper proposes a semi-supervised fuzzy related family-based feature selection method for partially labeled data. This method first calculates the fuzzy label values of unlabeled samples through fuzzy similarity relationships to establish a new fuzzy covering system. Then, based on the fuzzy covering system, it constructs a fuzzy related family based on consistent fuzzy set. Finally, based on the constructed feature significance measurement, a semi-supervised feature selection algorithm based on fuzzy related family (SFRF) is established. Compared with existing semi-supervised feature selection algorithms, SFRF can significantly improve the efficiency of feature selection while maintaining classification accuracy, with the average reduction efficiency of nine datasets increased by up to 201 times. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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A Privacy Leak Detection Mechanism based on Service Binding

A Privacy Leak Detection Mechanism based on Service Binding

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International Symposium on System and Software Reliability (ISSSR)

作者： Boyang Wang Jinling He Yuanhan Du Ming Tang Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350362930

ISBN: (纸本)9798350362947

With the development of mobile technology, users gradually store financial information and personal privacy data on mobile terminals, which makes mobile security particularly important. In order to effectively improve the detection speed of application privacy leaks, this paper proposes a concept of service binding, binding a minimum privacy permission set for each service provided by the application. Use the service binding concept to make the static analysis phase track only the sensitive data flow paths between APIs that apply the service's privacy permissions. In order to make up for the lack of static analysis to predict the behavior of application privacy leakage, this paper proposes a static analysis that effectively combines machine learning algorithms, abstracts the path characteristics of the static data streams obtained from static analysis into feature vectors, and further uses machine learning algorithms for learning classification. The experimental demonstration and performance analysis results show that the privacy leak detection mechanism based on service binding has faster detection speed and higher accuracy than FlowDroid.

关键词： Privacy data privacy Machine learning algorithms Accuracy Operating systems Static analysis Vectors

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RELI11D: A Comprehensive Multimodal Human Motion dataset and Method

RELI11D: A Comprehensive Multimodal Human Motion Dataset and...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ming Yan Yan Zhang Shuqiang Cai Shuqi Fan Xincheng Lin Yudi Dai Siqi Shen Chenglu Wen Lan Xu Yuexin Ma Cheng Wang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Comprehensive capturing of human motions requires both accurate captures of complex poses and precise localization of the human within scenes. Most of the HPE datasets and methods primarily rely on RGB, LiDAR, or IMU data. However, solely using these modalities or a combination of them may not be adequate for HPE, particularly for complex and fast movements. For holistic human motion understanding, we present RELI11D, a high-quality multimodal human motion dataset involves LiDAR, IMU system, RGB camera, and Event camera. It records the motions of 10 actors performing 5 sports in 7 scenes, including 3.32 hours of synchronized LiDAR point clouds, IMU measurement data, RGB videos and Event steams. Through extensive experiments, we demonstrate that the RELI 11 D presents considerable challenges and opportunities as it contains many rapid and complex motions that require precise location. To address the challenge of integrating different modalities, we propose LEIR, a multimodal baseline that effectively utilizes LiDAR Point Cloud, Event stream, and RGB through our cross-attention fusion strategy. We show that LEIR exhibits promising results for rapid motions and daily motions and that utilizing the characteristics of multiple modalities can indeed improve HPE performance. Both the dataset and source code release publicly in http://***/reli11d/, fostering collaboration and enabling further exploration in this field.

关键词： Point cloud compression Location awareness Laser radar Source coding Cameras Motion detection Pattern recognition

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Multi-feature Based Memory-Enhanced Knowledge Tracing 6th

Multi-feature Based Memory-Enhanced Knowledge Tracing

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6th International Conference on Computer Science and Educational Informatization, CSEI 2024

作者： Sun, Youwei Liu, Ruyi Miao, Qiguang Lu, Zixiang Zhao, Peipei Li, Ronghan School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Xi’an Key Laboratory of Big Data and Intelligent Vision Shaanxi Xi’an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China

ISBN: (纸本)9789819637379

In the realm of intelligent education, knowledge tracing (KT) has emerged as a crucial tool for enhancing educational efficiency and enabling personalized learning. Traditional KT methods primarily rely on students’ historical response data to evaluate their mastery of specific skills and forecast future performance. However, focusing solely on basic interactions, skills and response outcomes, makes it challenging to capture the complexities of real learning environments and accurately reflect students’ genuine knowledge states. To address this issue, we propose an innovative multi-feature based memory-enhanced knowledge tracing (MMKT) method. The key innovation of the MMKT model lies in the introduction of ‘memory features’ and ‘interaction features’. Memory features account for features such as the interval between repeated responses, the total number of responses, and memory interference during the overall learning process. In contrast, interaction features include response time, number of attempts, and hints. To integrate these features, we employ a self-attention mechanism coupled with a memory enhancement module and a recurrent neural network. This approach effectively uncovers variations in knowledge states while balancing the influence of global and local interactions. Our experimental results from different public datasets emphasize the benefits of the MMKT model in predicting future performance, underscoring the significance of integrating multi-feature and real-world relevant memory improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Short communication:Extreme glacier mass loss triggered by high temperature and drought during hydrological year 2022/2023 in Qilian Mountains

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Research in Cold and Arid Regions 2024年第1期16卷 1-4页

作者： JiZu Chen XingYu Xue WenTao Du State Key Laboratory of Cryospheric Science Northwest Institute of Eco-Environment and ResourcesChinese Academy of Sciences(CAS)LanzhouGansu 730000China Gansu Provincial Industry Technology Center of Intelligent Equipment and Big Data for Disaster Prevention Northwest Institute of Eco-Environment and ResourcesChinese Academy of Science(CAS)LanzhouGansu 730000China Zhangye Meteorological Bureau ZhangyeGansu 734000China College of Resources and Environment University of Chinese Academy of Sciences(UCAS)Beijing 100049China

In the hydrological year 2022/2023,the glaciers in the Qilian Mountains experienced unprecedented mass *** glacier-wide mass balance was-1,188 mm w.e.,in contrast to-350 mm of average mass balance since 1990 over the Bailanghe Glacier No.12 in the middle of Qilian *** temperature during 2022–2023 reached the highest value ever recorded,second only to 2022,while at the same time the precipitation amount was less compared to other year since 2000,which together led to the strongest glacier mass loss during 2022–*** atmospheric circulation analysis shows that the high temperature in the Qilian Mountains in 2023 was jointly caused by the Arctic air mass and East Asian monsoon.

关键词： Bailanghe Glacier No.21 Glacier mass loss Extreme high temperature Atmospheric circulation

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