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arXiv 2023年

作者： Xing, Zhibo Zhang, Zijian Zhang, Ziang Li, Zhen Li, Meng Liu, Jiamou Zhang, Zongyang Zhao, Yi Sun, Qi Zhu, Liehuang Russello, Giovanni School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science The University of Auckland Auckland1010 New Zealand Southeast Institute of Information Technology Beijing Institute of Technology Fujian351100 China Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Anhui230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province [Hefei University of Technology China School of Cyber Science and Technology Beihang University Beijing100191 China Department of Bioinformatics Hangzhou Nuowei Information Technology Co.Ltd Zhejiang 310053 China

Over recent decades, machine learning has significantly advanced network communication, enabling improved decision-making, user behavior analysis, and fault detection. Simultaneously, the growth of communication networks has facilitated the efficient collection of large-scale training data. Traditional centralized machine learning, however, requires data collection from users, raising significant concerns about privacy and security. Decentralized approaches, where participants exchange computation results instead of raw private data, mitigate these risks but introduce challenges related to trust and verifiability. A critical issue arises: How can one ensure the integrity and validity of computation results shared by other participants? Existing survey articles predominantly address security and privacy concerns in decentralized machine learning, whereas this survey uniquely highlights the emerging issue of verifiability. Recognizing the critical role of zero-knowledge proofs in ensuring verifiability, we present a comprehensive review of Zero-Knowledge Proof-based Verifiable Machine Learning (ZKP-VML). To clarify the research problem, we present a definition of ZKP-VML consisting of four algorithms, along with several corresponding key security properties. Besides, we provide an overview of the current research landscape by systematically organizing the research timeline and categorizing existing schemes based on their security properties. Furthermore, through an in-depth analysis of each existing scheme, we summarize their technical contributions and optimization strategies, aiming to uncover common design principles underlying ZKP-VML schemes. Building on the reviews and analysis presented, we identify current research challenges and suggest future research directions. To the best of our knowledge, this is the most comprehensive survey to date on verifiable decentralized machine learning and ZKP-VML. © 2023, CC BY.

关键词： Adversarial machine learning

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Asynchronous Brain-computer Interface Shared Control of Robotic Grasping

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Tsinghua science and Technology 2019年第3期24卷 360-370页

作者： Wenchang Zhang Fuchun Sun Hang Wu Chuanqi Tan Yuzhen Ma the Department of Computer Science and Technology Tsinghua University State Key Lab.of Intelligent Technology and Systems Institute of Medical Support Technology Academy of Military Sciences Drugs Control of PAP

The control of a high Degree of Freedom(DoF) robot to grasp a target in three-dimensional space using Brain-computer Interface(BCI) remains a very difficult problem to solve. Design of synchronous BCI requires the user perform the brain activity task all the time according to the predefined paradigm; such a process is boring and fatiguing. Furthermore, the strategy of switching between robotic auto-control and BCI control is not very reliable because the accuracy of Motor Imagery(MI) pattern recognition rarely reaches 100%. In this paper, an asynchronous BCI shared control method is proposed for the high DoF robotic grasping task. The proposed method combines BCI control and automatic robotic control to simultaneously consider the robotic vision feedback and revise the unreasonable control commands. The user can easily mentally control the system and is only required to intervene and send brain commands to the automatic control system at the appropriate time according to the experience of the user. Two experiments are designed to validate our method: one aims to illustrate the accuracy of MI pattern recognition of our asynchronous BCI system; the other is the online practical experiment that controls the robot to grasp a target while avoiding an obstacle using the asynchronous BCI shared control method that can improve the safety and robustness of our system.

关键词： asynchronous Brain-computer Interface (BCI) shared control motor imagery robotic grasping

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Consistency and Diversity induced Human Motion Segmentation

arXiv

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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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Towards Efficient Dynamic Uplink Scheduling over Multiple Unknown Channels

arXiv

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arXiv 2022年

作者： Wu, Shuang Ren, Xiaoqiang Jia, Qing-Shan Johansson, Karl Henrik Shi, Ling Huawei Noah’s Ark Lab Hong Kong The School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China The Center for Intelligent and Networked Systems Department of Automation BNRist Tsinghua University Beijing100084 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

Age-of-Information (AoI) is a critical metric for network applications. Existing works mostly address optimization with homogeneous AoI requirements, which is different from practice. In this work, we optimize uplink scheduling for an access point (AP) over multiple unknown channels with heterogeneous AoI requirements defined by AoI-dependent costs. The AP serves N users by using M channels without the channel state information. Each channel serves only one user in each decision epoch. The optimization objective is to minimize the time-averaged AoI-dependent costs plus additional communication transmission costs over an infinite horizon. This decision-making problem can be formulated as a Markov decision process, but it is computationally intractable because the size of the state space grows exponentially with respect to the number of users. To alleviate the challenge, we reformulate the problem as a variant of the restless multi-armed bandit (RMAB) problem and leverage Whittle’s index theory to design an index-based scheduling policy algorithm. We derive an analytic formula for the indices, which reduces the computational overhead and facilitates online adaptation. Our numerical examples show that our index-based scheduling policy achieves comparable performance to the optimal policy and outperforms several other heuristics. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimization

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Comparative analyses of multi-criteria methods in supplier selection problem

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Procedia computer science 2022年 207卷 4593-4602页

作者： Bartosz Paradowski Zdzisław Szyjewski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland Institute of Management University of Szczecin ul. Cukrowa 8 71-004 Szczecin Poland

The ever-growing interest in multi-criteria decision-making methods requires research that would compare them with each other as the decision-makers would need help choosing the appropriate method. It is important to highlight the differences between them, because using different methods may result in discrepancies in the obtained rankings. Additionally, it is worthwhile for the research to take into account the problems whose solution is extremely valuable. To this end, we present a comparison of five multi-criteria decision-making methods: TOPSIS, VIKOR, COMET, SPOTIS, and MARCOS in the supplier selection problem. The conducted research showed significant differences in the evaluation of the preferences of the alternatives and some discrepancies in the obtained rankings.

关键词： MCDM MCDA supplier selection comparison rank similarity

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3d-Swim: 3d Vision Based Seam Width Measurement for Industrial Composite Fiber Layup In-Situ Inspection

SSRN

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SSRN 2022年

作者： Zhang, Yinlong Yuan, Libiao Liang, Wei Xia, Xiaofang Pang, Zhibo State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Shenyang Pharmaceutical University China School of Computer Science and Technology Xidian University Xian710071 China Department of Automation Technology ABB Corporate Research Sweden Vasteras72178 Sweden Stockholm11428 Sweden

Composite fiber layup in-situ inspection plays an important role in the advanced manufacturing. An accurate seam width measurement is an indispensable procedure during the layup inspection. However, the traditional seam measurements may fail in presence of surface curvature variance, curved edges between sheets, to name a few. This paper intentionally designs a novel 3D vision based Seam Width Measurement for Industrial Composite Fiber Layup In-situ inspection (called 3D-SWiM). It aims to address three major challenges in measuring the seam width between the composite fiber sheets, namely, seam region over-segmentation, failures on region of interests (ROI) seam detection, seam width measurement inconsistency. Firstly, the region growing with semantic refinement is designed for 3D point cloud clustering, to overcome the over-segmentation on seam regions. Afterwards, the gridding model with edge discrimination is applied to extract the 3D potential candidates in the edge areas. Eventually, we use the cubic B-spline fitting model to describe the seam region curves and the improved particle swarm optimization model to estimate the minimum and maximum distance between the seam edges. Our method has been evaluated extensively on the developed composite fiber layup platform. The experimental results prove the competitive accuracy against the state-of-the-art methods in seam width measurement. © 2022, The Authors. All rights reserved.

关键词： Inspection

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Practical Study of Selected Multi-Criteria Methods Comparison

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Procedia computer science 2022年 207卷 4565-4573页

作者： Jakub Więckowski Zdzisław Szyjewski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland Institute of Management University of Szczecin ul. Cukrowa 8 71-004 Szczecin Poland

Multi-Criteria Decision Analysis (MCDA) methods enable a comprehensive analysis of decision options to identify the most preferred values. However, the abundance of these techniques, using different transformations in the calculations, can cause a divergence of results. However, the resulting rankings should be as consistent and reliable as possible. In this paper, we used the TOPSIS, COPRAS, SPOTIS, and COMET methods to examine the consistency of their results in the practical problem of laptop evaluation. The rankings were then compared with the reference ranking. The research showed that among the methods listed, the COPRAS method's results differed the most from the others. In turn, this technique ensured a ranking in line with the reference ranking. Based on the results, it can be established that the methods used are characterized by high consistency of results.

关键词： MCDA TOPSIS COPRAS SPOTIS COMET Comparison Laptops assessment

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Efficient factored gradient descent algorithm for quantum state tomography

arXiv

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arXiv 2022年

作者： Wang, Yong Liu, Lijun Cheng, Shuming Li, Li Chen, Jie College of Electronics and Information Engineering Tongji University Shanghai201804 China Department of Mathematics and Computer Science Shanxi Normal University Taiyuan030006 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201203 China Institute for Advanced Study Tongji University Shanghai200092 China

Reconstructing the state of quantum many-body systems is of fundamental importance in quantum information tasks, but extremely challenging due to the curse of dimensionality. In this work, we present an efficient quantum tomography protocol that combines the state-factored with eigenvalue mapping to address the rank-deficient issue and incorporates a momentum-accelerated gradient descent algorithm to speed up the optimization process. We implement extensive numerical experiments to demonstrate that our factored gradient descent algorithm efficiently mitigates the rank-deficient problem and admits orders of magnitude better tomography accuracy and faster convergence. We also find that our method can accomplish the full-state tomography of random 11-qubit mixed states within one minute. © 2022, CC BY.

关键词： Eigenvalues and eigenfunctions

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Unsupervised Multilayer Fuzzy Neural Networks for Image Clustering

SSRN

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SSRN 2022年

作者： Wang, Yifan Ishibuchi, Hisao Er, Meng Joo Zhu, Jihua School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation China College of Marine Electrical Engineering Dalian Maritime University Dalian116026 China Institute of Artificial Intelligence Marine Robotics China

Nowadays, labeling a huge number of images is still a very challenging task. To tackle the problem of unlabeled data, unsupervised learning has been proposed. Among many unsupervised learning algorithms, K-means is the most popular algorithm. However, in a low-dimensional space, fuzzy c-means, which is more robust and less sensitive to initialization, has several advantages over K-means clustering. On the other hand, stacked convolutional pooling structures and manifold representation play pivotal roles in image clustering. In this paper, we proposed an unsupervised multilayer fuzzy neural network for image clustering, which unifies fuzzy systems, multilayer convolutional structures and manifold representation. The main contributions of this work are: First, we extend fuzzy systems to unsupervised tasks by introducing the manifold representation. Next, we proposed the idea of using only a small number of attributes with the largest variances to compute firing strengths. This is to prevent the firing strengths from falling to zero in clustering high-dimensional data. Then, randomly generated convolutional weights are used to extract features, which is a good choice for data without labels. It is demonstrated on a wide range of image datasets that the proposed approach is superior to existing fuzzy and non-fuzzy clustering algorithms. © 2022, The Authors. All rights reserved.

关键词： Fuzzy neural networks

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Role of substrate clamping on anisotropy and domain structure in the canted antiferromagnet α−Fe2O3

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Physical Review B 2022年第22期106卷 224419-224419页

作者： Angela Wittmann Olena Gomonay Kai Litzius Allison Kaczmarek Alexander E. Kossak Daniel Wolf Axel Lubk Tyler N. Johnson Elizaveta A. Tremsina Alexandra Churikova Felix Büttner Sebastian Wintz Mohamad-Assaad Mawass Markus Weigand Florian Kronast Larry Scipioni Adam Shepard Ty Newhouse-Illige James A. Greer Gisela Schütz Norman O. Birge Geoffrey S. D. Beach Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Institute of Physics Johannes Gutenberg-University Mainz 55128 Mainz Germany Max Planck Institute for Intelligent Systems 70569 Stuttgart Germany Leibniz Institute for Solid State and Materials Research IFW Dresden 01069 Dresden Germany Department of Chemical Engineering and Materials Science Michigan State University East Lansing Michigan 48824 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Helmholtz-Zentrum Berlin für Materialien und Energie GmbH 14109 Berlin Germany PVD Products Wilmington Massachusetts 01887 USA Department of Physics and Astronomy Michigan State University East Lansing Michigan 48824 USA

Antiferromagnets have recently been propelled to the forefront of spintronics by their high potential for revolutionizing memory technologies. For this, understanding the formation and driving mechanisms of the domain structure is paramount. In this work, we investigate the domain structure in a thin-film canted antiferromagnet α−Fe2O3. We find that the internal destressing fields driving the formation of domains do not follow the crystal symmetry of α−Fe2O3, but fluctuate due to substrate clamping. This leads to an overall isotropic distribution of the Néel order with locally varying effective anisotropy in antiferromagnetic thin films. Furthermore, we show that the weak ferromagnetic nature of α−Fe2O3 leads to a qualitatively different dependence on the magnetic field compared to collinear antiferromagnets such as NiO. The insights gained from our work serve as a foundation for further studies of electrical and optical manipulation of the domain structure of antiferromagnetic thin films.

关键词： Antiferromagnetism Magnetic domains Magnetic texture Spin Hall magnetoresistance

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