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arXiv 2024年

作者： Jiao, Xianhe Lv, Chenlei Zhao, Junli Yi, Ran Wen, Yu-Hui Pan, Zhenkuan Wu, Zhongke Liu, Yong-Jin College of Computer Science and Technology Qingdao University China College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Computer Science & Technology Beijing Jiaotong University China School of Artificial Intelligence Beijing Normal University China Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China

For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency. However, current methods cannot balance such different requirements. Particularly with large-scale point clouds, classical methods often struggle with decreased efficiency and accuracy. To address such issues, we propose a weighted Poisson-disk (WPD) resampling method to improve the usability and efficiency for the processing. We first design an initial Poisson resampling with a voxel-based estimation strategy. It is able to estimate a more accurate radius of the Poisson-disk while maintaining high efficiency. Then, we design a weighted tangent smoothing step to further optimize the Voronoi diagram for each point. At the same time, sharp features are detected and kept in the optimized results with isotropic property. Finally, we achieve a resampling copy from the original point cloud with the specified point number, uniform density, and high-quality geometric consistency. Experiments show that our method significantly improves the performance of large-scale point cloud resampling for different applications, and provides a highly practical solution. Copyright © 2024, The Authors. All rights reserved.

关键词： Poisson distribution

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Risk Contagion in Interbank Lending Networks: A Multi-Agent-Based Modeling and Simulation Perspective

SSRN

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SSRN 2024年

作者： Ding, Zhijun Yan, Huanlan Chen, Yijun The Key Laboratory of Embedded System and Service Computing Tongji University No.4800 Caoan Gonglu Road Shanghai201804 China The Department of Computer Science and Technology Tongji University No.4800 Caoan Gonglu Road Shanghai201804 China Shanghai Artificial Intelligence Laboratory No.701 Yunjin Road Shanghai200232 China

The interbank lending network, while facilitating transactions, poses risk contagion in the banking system. To address challenges in existing research, this paper models the interbank lending network using a multi-agent approach and generates a dynamic network through a bidirectional matching mechanism(BiMM). Additionally, We divide banks into three categories based on realistic bank balance sheet data. Given the fact that banks of different categories exhibit different degrees of contagion, we propose the weighted DebtRank (W-DebtRank). Then, comprehensive simulation analysis is conducted on the risk contagion of heterogeneous banks in the dynamic network using W-DebtRank. The proposed method addresses four issues: the heterogeneity of banks, the dynamic nature of networks, the practicality of lending strategies, and the comprehensiveness of risk analysis. The model is validated using the SRISK metric and the Monte Carlo-based SYMBOL simulation model. Through multiple perspectives in simulation experiments, the risk contagion process among banks is deeply investigated in our paper. © 2024, The Authors. All rights reserved.

关键词： Computational methods

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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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Detecting Major Depression Disorder with Multiview Eye Movement Features in a Novel Oil Painting Paradigm

Detecting Major Depression Disorder with Multiview Eye Movem...

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International Joint Conference on Neural Networks (IJCNN)

作者： Tian-Fang Ma Lu-Yu Liu Li-Ming Zhao Dan Peng Yong Lu Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Emotion Helper Shanghai China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Major Depressive Disorder (MDD) is a debilitating condition marked by persistent low mood, reduced interest, cognitive impairments, and vegetative neurological symptoms such as sleep and appetite disturbances. In this paper, we collected eye movement signals from 40 patients diagnosed with MDD and 40 healthy controls to study the relation between eye movements and cognitive processes for depression detection. The eye movement data were captured during a novel emotional cognition task using oil paintings. Subsequently, the data were transformed into multiview eye movement features, including heatmaps, trajectories, and statistical vectors. Rigorous statistical analyses were then conducted on these features to identify significant patterns and correlations between eye movements and depressive symptoms. A multiview invariant & specific eye movement model (MISEYE) was proposed to fuse different eye movement features. The proposed achieved an accuracy rate of 79.88% in depression detection. This performance surpassed not only the outcomes of single-mode approaches and combinations of any two features but also outperformed other fusion methodologies. These findings not only shed light on the intricate relationship between eye movement patterns and MDD but also underscore the potential of eye-tracking technology in psychiatric research.

关键词： Heating systems Accuracy Statistical analysis Oils Depression Feature extraction Vectors

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Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation

Memory-Based Contrastive Learning with Optimized Sampling fo...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration

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A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

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Microsystems & Nanoengineering 2022年第3期8卷 277-290页

作者： Hao Wang Yue Wu Quchao Zou Wenjian Yang Zhongyuan Xu Hao Dong Zhjing Zhu Depeng Wang Tanxing Wang Ning Hu Diming Zhang State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Research Center for Itelligent Sensing Systems Zhejiang LabHangzhou 311121China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemistryThe Second Affiliated Hospital Zhejiang University School of MedicineDepartment of Clinical Medical EngineeringZhejiang UniversityHangzhou 310058China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-LinkCare Meditech Co LtdHangzhou 310011China State Key Laboratory of Transducer Technology Chinese Academy of SciencesShanghai 200050China

Cardiovascular disease is the number one cause of death in ***,cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug ***,cell-based techniques developed for arhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large *** carried out the frst report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm(MNDA)analysis for druginduced arrhythmia recognition and *** biosensing system cultured cardiomyocytes as physiologically relevant models,used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes,and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating *** best performing MNDA parameter,approximate entropy,enabled the system to recognize the appearance of sertindoleand norepinephrine-induced arrhythmia in the *** MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of *** new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

关键词： arrhythmia cardio analysis

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Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch

arXiv

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arXiv 2025年

作者： Liu, Yijie Shang, Xinyi Zhang, Yiqun Lu, Yang Gong, Chen Xue, Jing-Hao Wang, Hanzi Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China Department of Statistical Science University College London United Kingdom School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Automation Shanghai Jiao Tong University China

Federated Semi-Supervised Learning (FSSL) aims to leverage unlabeled data across clients with limited labeled data to train a global model with strong generalization ability. Most FSSL methods rely on consistency regularization with pseudo-labels, converting predictions from local or global models into hard pseudo-labels as supervisory signals. However, we discover that the quality of pseudo-label is largely deteriorated by data heterogeneity, an intrinsic facet of federated learning. In this paper, we study the problem of FSSL in-depth and show that (1) heterogeneity exacerbates pseudo-label mismatches, further degrading model performance and convergence, and (2) local and global models’ predictive tendencies diverge as heterogeneity increases. Motivated by these findings, we propose a simple and effective method called Semi-supervised Aggregation for Globally-Enhanced Ensemble (SAGE), that can flexibly correct pseudo-labels based on confidence discrepancies. This strategy effectively mitigates performance degradation caused by incorrect pseudo-labels and enhances consensus between local and global models. Experimental results demonstrate that SAGE outperforms existing FSSL methods in both performance and convergence. Our code is available at https://***/Jay-Codeman/SAGE. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Resilient Collaborative Caching for Multi-Edge systems with Robust Federated Deep Learning

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IEEE/ACM Transactions on Networking 2024年

作者： Chen, Zheyi Liang, Jie Yu, Zhengxin Cheng, Hongju Min, Geyong Li, Jie Fuzhou University College of Computer and Data Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom University of Exeter Faculty of Environment Science and Economy Department of Computer Science ExeterEX4 4QF United Kingdom Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

As a key technique for future networks, the performance of emerging multi-edge caching is often limited by inefficient collaboration among edge nodes and improper resource configuration. Meanwhile, achieving optimal cache hit rates poses substantive challenges without effectively capturing the potential relations between discrete user features and diverse content libraries. These challenges become further sophisticated when caching schemes are exposed to adversarial attacks that seriously impair cache performance. To address these challenges, we introduce RoCoCache, a resilient collaborative caching framework that uniquely integrates robust federated deep learning with proactive caching strategies, enhancing performance under adversarial conditions. First, we design a novel partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a new Discrete-Categorical Variational Auto-Encoder (DC-VAE) to accurately predict content popularity by overcoming posterior collapse. Finally, we create an original training mode and proactive cache replacement strategy based on robust federated deep learning. Notably, the residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using the real-world testbed and datasets, extensive experiments verify that the RoCoCache achieves higher cache hit rates and efficiency than state-of-the-art methods while ensuring better robustness. Moreover, we validate the effectiveness of the components designed in RoCoCache for improving cache performance via ablation studies. © 1993-2012 IEEE.

关键词： cache space partitioning content popularity prediction Multi-edge collaborative caching proactive cache replacement robust federated deep learning

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From Head to Tail: Efficient Black-box Model Inversion Attack via Long-tailed Learning

arXiv

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arXiv 2025年

作者： Li, Ziang Zhang, Hongguang Wang, Juan Chen, Meihui Hu, Hongxin Yi, Wenzhe Xu, Xiaoyang Yang, Mengda Ma, Chenjun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Shanghai Innovation Institute China Ant Group China Department of Computer Science and Engineering University at Buffalo SUNY United States

Model Inversion Attacks (MIAs) aim to reconstruct private training data from models, leading to privacy leakage, particularly in facial recognition systems. Although many studies have enhanced the effectiveness of white-box MIAs, less attention has been paid to improving efficiency and utility under limited attacker capabilities. Existing black-box MIAs necessitate an impractical number of queries, incurring significant overhead. Therefore, we analyze the limitations of existing MIAs and introduce Surrogate Model-based Inversion with Long-tailed Enhancement (SMILE), a high-resolution oriented and query-efficient MIA for the black-box setting. We begin by analyzing the initialization of MIAs from a data distribution perspective and propose a long-tailed surrogate training method to obtain high-quality initial points. We then enhance the attack’s effectiveness by employing the gradient-free black-box optimization algorithm selected by NGOpt. Our experiments show that SMILE outperforms existing state-of-the-art black-box MIAs while requiring only about 5% of the query overhead. Our code is available at https://***/L1ziang/SMILE. Copyright © 2025, The Authors. All rights reserved.

关键词： Face recognition

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Multi-modal Large Language Model Enhanced Pseudo 3D Perception Framework for Visual Commonsense Reasoning

arXiv

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arXiv 2023年

作者： Zhu, Jian Wang, Hanli Shi, Miaojing The Department of Computer Science & Technology The Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai200092 China The College of Electronic and Information Engineering Tongji University Shanghai201804 China

The visual commonsense reasoning (VCR) task is to choose an answer and provide a justifying rationale based on the given image and textural question. Representative works first recognize objects in images and then associate them with key words in texts. However, existing approaches do not consider exact positions of objects in a human-like three-dimensional (3D) manner, making them incompetent to accurately distinguish objects and understand visual relation. Recently, multi-modal large language models (MLLMs) have been used as powerful tools for several multi-modal tasks but not for VCR yet, which requires elaborate reasoning on specific visual objects referred by texts. In light of the above, an MLLM enhanced pseudo 3D perception framework is designed for VCR. Specifically, we first demonstrate that the relation between objects is relevant to object depths in images, and hence introduce object depth into VCR frameworks to infer 3D positions of objects in images. Then, a depth-aware Transformer is proposed to encode depth differences between objects into the attention mechanism of Transformer to discriminatively associate objects with visual scenes guided by depth. To further associate the answer with the depth of visual scene, each word in the answer is tagged with a pseudo depth to realize depth-aware association between answer words and objects. On the other hand, BLIP-2 as an MLLM is employed to process images and texts, and the referring expressions in texts involving specific visual objects are modified with linguistic object labels to serve as comprehensible MLLM inputs. Finally, a parameter optimization technique is devised to fully consider the quality of data batches based on multi-level reasoning confidence. Experiments on the VCR dataset demonstrate the superiority of the proposed framework over state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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