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arXiv 2022年

作者： Zhou, Tao Fu, Huazhu Gong, Chen Shao, Ling Porikli, Fatih Ling, Haibin Shen, Jianbing The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of High Performance Computing Agency for Science Technology and Research Singapore Riyadh Saudi Arabia The Research School of Engineering The Australian National University Australia The Department of Computer Science Stony Brook University Strony Brook NY United States The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University United States

Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion analysis

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Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns 4th

Interpersonal Relationship Analysis with Dyadic EEG Signals...

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4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Ji, Wenqi Liu, Fang Du, Xinxin Liu, Niqi Zhou, Chao Yu, Minjing Guo, Xinyue Zhao, Guozhen Liu, Yong-Jin Department of Computer Science and Technology Tsinghua University Beijing100084 China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China Institute of Software Chinese Academy of Sciences Beijing100190 China College of Intelligence and Computing Tianjin University Tianjin300072 China CAS Key Laboratory of Behavioral Science Institute of Psychology Beijing100049 China

ISBN: (纸本)9789819640003

Interpersonal relationship quality is pivotal in social and occupational contexts. Existing analysis of interpersonal relationships mostly rely on subjective self-reports, whereas objective quantification remains challenging. In this paper, we propose a novel social relationship analysis framework using spatio-temporal patterns derived from dyadic EEG signals, which can be applied to quantitatively measure team cooperation in corporate team building, and evaluate interpersonal dynamics between therapists and patients in psychiatric therapy. First, we constructed a dyadic-EEG dataset from 72 pairs of participants with two relationships (stranger or friend) when watching emotional videos simultaneously. Then we proposed a deep neural network on dyadic-subject EEG signals, in which we combine the dynamic graph convolutional neural network for characterizing the interpersonal relationships among the EEG channels and 1-dimension convolution for extracting the information from the time sequence. To obtain the feature vectors from two EEG recordings that well represent the relationship of two subjects, we integrate deep canonical correlation analysis and triplet loss for training the network. Experimental results show that the social relationship type (stranger or friend) between two individuals can be effectively identified through their EEG data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Safe Online Convex Optimization with Heavy-Tailed Observation Noises 39

Safe Online Convex Optimization with Heavy-Tailed Observatio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Yunhao Xue, Bo Hao, Yunzhi Li, Ying Wan, Yuanyu School of Software Technology Zhejiang University Ningbo China Department of Computer Science City University of Hong Kong China Big Graph Center School of Computer and Computing Science Hangzhou City University Hangzhou China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Bangsheng Technology Co. Ltd. Hangzhou China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)157735897X

We investigate safe online convex optimization (SOCO), where each decision must satisfy a set of unknown linear constraints. Assuming that the unknown constraints can be observed with a sub-Gaussian noise for each chosen decision, previous studies have established a high-probability regret bound of O(T²/³). However, this assumption may not hold in many practical scenarios. To address this limitation, in this paper, we relax the assumption to allow any noise that admits finite (1+ϵ)-th moments for some ϵ ∈ (0, 1], and propose two algorithms that enjoy an O(T^cϵ) regret bound with high probability, where T is the time horizon and cϵ = (1+ϵ)/(1+2ϵ). The key idea of our two algorithms is to respectively utilize the median-of-means and truncation techniques to achieve accurate estimation under heavy-tailed noises. To the best of our knowledge, these are the first algorithms designed to handle SOCO with heavy-tailed observation noises. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Convex optimization

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Defense Against Adversarial Faces at the Source: Strengthened Faces Based on Hidden Disturbances

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Li, Shuangliang Wang, Jinwei Wu, Hao Zhang, Jiawei Cheng, Xin Luo, Xiangyang Ma, Bin School of Software Nanjing Universityof Information Science and Technology Nanjing210044 China College of Cryptology and Cyber Science Nankai University Tianjing300073 China State Key Laboratory of Mathemat-ical Engineering and Advanced Computing Zhengzhou450002 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Cyber Security Qilu University of Technology Shandong Academy of Sciences Shandong250353 China

Face Recognition (FR) systems, while widely used across various sectors, are vulnerable to adversarial attacks, particularly those based on deep neural networks. Despite existing efforts to enhance the robustness of FR models, they still face the risk of secondary adversarial attacks. To address this, we propose a novel approach employing "strengthened face" with preemptive defensive perturbations. Strengthened face ensures original recognition accuracy while safeguarding FR systems against secondary attacks. In the white-box scenario, the strengthened face utilizes gradient-based and optimization-based methods to minimize feature representation differences between face pairs. For the black-box scenario, we propose Shielded Gradient Sign Descent (SGSD) to optimize the gradient update direction of strengthened faces, ensuring the transferability and effectiveness against unknown adversarial attacks. Experimental results demonstrate the efficacy of strengthened faces in defending against adversarial faces without compromising the performance of FR models or face image visual quality. Moreover, SGSD outperforms conventional methods, achieving an average performance improvement of 4% in transferability across different attack intensities. © 2020 IEEE.

关键词： adversarial attacks face recognition facial protection

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Large-scale full-programmable quantum walk and its applications

arXiv

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arXiv 2022年

作者： Wang, Yizhi Liu, Yingwen Zhan, Junwei Xue, Shichuan Zheng, Yuzhen Zeng, Ru Wu, Zhihao Wang, Zihao Zheng, Qilin Wang, Dongyang Shi, Weixu Fu, Xiang Xu, Ping Wang, Yang Liu, Yong Ding, Jiangfang Huang, Guangyao Yu, Chunlin Huang, Anqi Qiang, Xiaogang Deng, Mingtang Xu, Weixia Lu, Kai Yang, Xuejun Wu, Junjie Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology AMS Beijing100071 China China Greatwall Research Institute China Greatwall Technology Group CO. LTD. Shenzhen518057 China

With photonics, the quantum computational advantage has been demonstrated on the task of boson sampling. Next, developing quantum-enhanced approaches for practical problems becomes one of the top priorities for photonic systems. Quantum walks are powerful kernels for developing new and useful quantum algorithms. Here we realize large-scale quantum walks using a fully programmable photonic quantum computing system. The system integrates a silicon quantum photonic chip, enabling the simulation of quantum walk dynamics on graphs with up to 400 vertices and possessing full programmability over quantum walk parameters, including the particle property, initial state, graph structure, and evolution time. In the 400-dimensional Hilbert space, the average fidelity of random entangled quantum states after the whole on-chip circuit evolution reaches as high as 94.29±1.28%. With the system, we demonstrated exponentially faster hitting and quadratically faster mixing performance of quantum walks over classical random walks, achieving more than two orders of magnitude of enhancement in the experimental hitting efficiency and almost half of the reduction in the experimental evolution time for mixing. We utilize the system to implement a series of quantum applications, including measuring the centrality of scale-free networks, searching targets on Erdös-Rényi networks, distinguishing non-isomorphic graph pairs, and simulating the topological phase of higher-order topological insulators. Our work shows one feasible path for quantum photonics to address applications of practical interests in the near future. Copyright © 2022, The Authors. All rights reserved.

关键词： Mixing

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Deep Multi-kernel Clustering Network

Deep Multi-kernel Clustering Network

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IEEE International Conference on Data Mining (ICDM)

作者： Lina Ren Ruizhang Huang Shengwei Ma Yongbin Qin Yanping Chen Chuan Lin State Key Laboratory of Public Big Data Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang China Department of Information Engineering Guizhou Light Industry Technical College Guiyang China

In this paper, a deep multi-kernel clustering network, named DMKCN, is proposed to learn a high-quality and structurally separable kernel representation for the clustering task. Specifically, a multi-kernel learner is proposed to choose a suitable kernel function by learning a suitable combination of kernel functions automatically. A kernel-aid encoder module, consisting of a series of multi-kernel learners, is proposed to learn the structurally separable kernel representation. Besides, a dual self-supervised mechanism, consisting of a kernel self-supervised strategy and a representation self-supervised strategy, is designed to uniformly optimize the kernel representation learning and structural partition. The kernel self-supervised strategy is developed to supervise the multi-kernel learners with the consideration of an objective of clustering task, the representation self-supervised strategy is developed to guide the optimization of kernel representation learning by reconstructing the raw data. Extensive experiments on six real-world datasets demonstrate the outstanding performance of our proposed DMKCN.

关键词：

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ODCP: Optimizing Data Caching and Placement in Distributed File System Using Erasure Coding 17th

ODCP: Optimizing Data Caching and Placement in Distributed F...

引用

17th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2020

作者： Wu, Shuhan Li, Yunchun Yang, Hailong Luan, Zerong Li, Wei School of Computer Science and Engineering Beihang University Beijing100191 China College of Life Sciences and Bioengineering Beijing University of Technology Beijing100083 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214125 China

ISBN: (纸本)9783030794774

Many current distributed file systems use erasure-coding based data redundancy techniques to improve the reliability of data storage. Such techniques can significantly improve the effective storage utilization. However, there are several drawbacks to the above techniques. Firstly, they introduce non-negligible computation overhead for decoding. Secondly, traditional data caching and placement strategies become less effective in such cases. To solve the above drawbacks, this paper proposes a new data cache allocation mechanism based on simulated annealing and a new data placement strategy based on convex optimization, which effectively reduces data block transmission delay and decoding delay. We have implemented the proposed data placement strategy in the real-world distributed file system Alluxio, and evaluated the performance of our strategy. Experiment results show that our strategy can significantly reduce the file read delay compared to traditional data placement strategies. © 2021, IFIP International Federation for Information Processing.

关键词： Convex optimization

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A Resilient Decentralized Learning Approach against Byzantine Attack via Reputation Evaluation

A Resilient Decentralized Learning Approach against Byzantin...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Heng Zhang Wenting Wang Xuguo Jiao Ming Yang Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan P. R. China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan P. R. China College of Control Science and Engineering Zhejiang University Hangzhou China State Grid Shandong Electric Power Research Institute Jinan China School of Information and Control Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Machine learning is a key component of many applications, among which decentralized learning has attracted wide attention because of its cost-effectiveness and high efficiency. However, decentralized learning is vulnerable to various Byzantine attacks. Existing defense models face challenges in defending against complex attacks and are often constrained by strict network topologies. To address this issue, a parameter aggregation rule based on reputation evaluation (RPV) is proposed in this paper. This rule establishes a reputation model for neighboring nodes and continuously updates it based on their distance performance during each iteration. By applying the reputation model of neighboring nodes to the Beta distribution, their reliability is obtained. Based on the reliability of neighboring nodes, the set of reliable nodes for this iteration and select parameter values from them for aggregation are determined. Finally, we gradually eliminate the influence of Byzantine nodes. Our experimental results on the MNIST dataset demonstrate that the proposed algorithm is resilient to attacks from any number of Byzantine nodes and outperforms previous defense models in terms of network topology constraints, training accuracy, and computational costs.

关键词： Training Learning systems Accuracy Network topology Computational modeling Machine learning Classification algorithms

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A COMPREHENSIVE DATASET AND AUTOMATED PIPELINE FOR NAILFOLD CAPILLARY ANALYSIS

arXiv

引用

arXiv 2023年

作者： Zhao, Linxi Tang, Jiankai Chen, Dongyu Liu, Xiaohong Zhou, Yong Shi, Yuanchun Wang, Guangyu Wang, Yuntao Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Beijing China UCL Cancer Institute University College London London United Kingdom Clinical Research Institute Shanghai General Hospital Shanghai China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset—321 images, 219 videos from 68 subjects, with clinic reports and expert annotations—that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotations as supervised labels and integrated them into a novel end-to-end nailfold capillary analysis pipeline. This pipeline excels in automatically detecting and measuring a wide range of size factors, morphological features, and dynamic aspects of nailfold capillaries. We compared our outcomes with clinical reports. Experiment results showed that our automated pipeline achieves an average of sub-pixel level precision in measurements and 89.9% accuracy in identifying morphological abnormalities. These results underscore its potential for advancing quantitative medical research and enabling pervasive computing in healthcare. Our data and code are available at https://***/THU-CS-PI-LAB/ ANFC-Automated-Nailfold-Capillary. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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Self-supervised fast reconstruction method for diffusion MRI based on multi-directional assistance

Self-supervised fast reconstruction method for diffusion MRI...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Yuxin Wu Jian Zhang Lihui Wang Ying Cao Linhai Wu Yunjia Wu Chen Ye Yuemin Zhu State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China INSA Lyon CNRS Inserm CREATIS UMR 5220 U1206 Univ Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Diffusion MRI (dMRI) is widely used for the detection and diagnosis of various pathologies due to its non-invasive nature. However, its signal acquisition process is relatively slow and susceptible to motions. In order to accelerate reconstruction process, many deep learning-based methods have been developed in recent years to achieve promising results, while most of them are supervised based methods and require fully sampled data which are difficult to obtain in practical applications to train the network. Self-supervised based methods were proposed to deal with this problem to a certain extent, however, they are devoted only to conventional MRI data, ignoring the correlation between the directions of dMRI data. To address this issue, we propose a self-supervised fast reconstruction method for dMRI based on multi-directional assistance. The method better utilizes the information between dMRI data directions by Partition-Replace operation, cleverly constructing data pairs and designing effective self-supervised strategy for bi-directional prediction, which can overcome the problem of data incompleteness and guide the model to reconstruct unsampled signals in spite of the difficulty of acquiring fully sampled data. Experimental results show that our method achieves better reconstruction in the absence of fully sampled data, even with an 8-fold acceleration factor.

关键词： Learning systems Pathology Correlation Magnetic resonance imaging Bidirectional control Reconstruction algorithms Signal processing Predictive models Data models

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