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arXiv 2022年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Bob Zhang, Yanning Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Department of Computer and Information Science University of Macau 999078 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which may cause training difficulty. In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB). DCB uses a dynamic convolution to dynamically adjust parameters of several convolutions for making a tradeoff between denoising performance and computational costs. WEB uses a combination of signal processing technique (i.e., wavelet transformation) and discriminative learning to suppress noise for recovering more detailed information in image denoising. To further remove redundant features, RB is used to refine obtained features for improving denoising effects and reconstruct clean images via improved residual dense architectures. Experimental results show that the proposed MWDCNN outperforms some popular denoising methods in terms of quantitative and qualitative analysis. Codes are available at https://***/hellloxiaotian/MWDCNN. © 2022, CC BY-NC-ND.

关键词： Wavelet transforms

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Dual Consistent Constraint via Disentangled Consistency and Complementarity for Multi-view Clustering

arXiv

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arXiv 2025年

作者： Li, Bo Yun, Jing College of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Huhhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data-Based Software Service Inner Mongolia Huhhot010080 China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Inner Mongolia Huhhot010080 China

Multi-view clustering can explore common semantics from multiple views and has received increasing attention in recent years. However, current methods focus on learning consistency in representation, neglecting the contribution of each view’s complementarity aspect in representation learning. This limit poses a significant challenge in multi-view representation learning. This paper proposes a novel multi-view clustering framework that introduces a disentangled variational autoencoder that separates multi-view into shared and private information, i.e., consistency and complementarity information. We first learn informative and consistent representations by maximizing mutual information across different views through contrastive learning. This process will ignore complementary information. Then, we employ consistency inference constraints to explicitly utilize complementary information when attempting to seek the consistency of shared information across all views. Specifically, we perform a within-reconstruction using the private and shared information of each view and a cross-reconstruction using the shared information of all views. The dual consistency constraints are not only effective in improving the representation quality of data but also easy to extend to other scenarios, especially in complex multi-view scenes. This could be the first attempt to employ dual consistent constraint in a unified MVC theoretical framework. During the training procedure, the consistency and complementarity features are jointly optimized. Extensive experiments show that our method outperforms baseline methods. © 2025, CC BY.

关键词： Contrastive Learning

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Arrhythmia Classification with Attention-Based Res-BiLSTM-Net 1st

Arrhythmia Classification with Attention-Based Res-BiLSTM-Ne...

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1st International Workshop on Machine Learning and Medical engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Huang, Chengbin Zhao, Renjie Chen, Weiting Li, Huazheng MOE Engineering Research Center for Hardware/Software Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9783030333263

In the modern clinical diagnosis, the 12-lead electrocardiogram (ECG) signal has proved effective in cardiac arrhythmias classification. However, the manual diagnosis for cardiac arrhythmias is tedious and error-prone through ECG signals. In this work, we propose an end-to-end deep neural network called attention-based Res-BiLSTM-Net for automatic diagnosis of cardiac arrhythmias. Our model is capable of classifying ECG signals with different lengths. The proposed network consists of two parts: the attention-based Resnet and the attention-based BiLSTM. At first, ECG signals are divided into several signal segments with the same length. Then multi-scale features are extracted by our attention-based Resnet through signal segments. Next, these multi-scale features from a same ECG signal are integrated in chronological order. In the end, our attention-based BiLSTM classifies cardiac arrhythmias according to combined features. Our method achieved a good result with an average F1score of 0.8757 on a multi-label arrhythmias classification problem in the First China ECG Intelligent Competition. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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Generative Adversarial Networks for Image Super-Resolution: A Survey

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zhang, Xuanyu Zhu, Qi Zhang, Bob Lin, Jerry Chun-Wei School of Software Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-space-Ground-Ocean Big Data Application Technology 1 Dongxiang Rd Shaanxi Xi’an710129 China School of Software Northwestern Polytechnical University 1 Dongxiang Rd Shaanxi Xi’an710129 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics 101 Section 2 Kuang-Fu Road Jiangsu Nanjing China PAMI Research Group University of Macau 101 Section 2 Kuang-Fu Road Macao999078 China Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

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Be2C5 Monolayer with Quasiplanar Pentacoordinate Carbon Atoms and Ultrahigh Energy Density as a Dirac Anode for Potassium-Ion Batteries

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PRX Energy 2023年第3期2卷 033012-033012页

作者： Feilong Wang Meiling Xu Shuyi Lin Jian Hao Yanchao Wang Hong Jian Zhao Yinwei Li Laboratory of Quantum Functional Materials Design and Application School of Physics and Electronic Engineering Jiangsu Normal University Xuzhou 221116 China International Center for Computational Method and Software State Key Laboratory of Superhard Materials Key Laboratory of Physics and Technology for Advanced Batteries Ministry of Education College of Physics Jilin University Changchun 130012 China

K-ion batteries (KIBs) are of potential importance in the future energy industries. Recent efforts were committed to increasing the energy storage density in KIBs. Here, we search for anode materials with high energy density that are compatible with KIBs, by a combination of the swarm-intelligence structure search method and first-principles calculations. We predict the Be2C5 monolayer to be a kind of material that showcases ultrahigh energy density (∼5455 mWh/g, referenced to the standard hydrogen electrode potential), thanks to an exceptional specific capacity of 2060 mAh/g and low average open-circuit voltage of 0.28 V. This energy density value is much larger than that of most currently known 2D KIB-based anode materials. Furthermore, the Be2C5 anode presents other intriguing behavior, such as (i) the fast diffusion of K ions, as suggested by an ultralow barrier energy of about 0.074 eV; (ii) excellent mechanical strength, with a Young’s modulus of about 243 N/m; (iii) robust compatibility with electrolytes; (iv) small-scale volume expansion of 0.68% during the process of potassiation; and (v) a unique “self-doping” effect, which is beneficial for high electronic conductivity.

关键词： Lithium-alternative batteries 2-dimensional systems First-principles calculations

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Automated test generation for IEC 61131-3 ST programs via dynamic symbolic execution 13

Automated test generation for IEC 61131-3 ST programs via dy...

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13th International Symposium on Theoretical Aspects of software engineering, TASE 2019

作者： Hao, Li Shi, Jianqi Su, Ting Huang, Yanhong National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai China Hardware/software Co-Design Technology and Application Engineering Research Center Shanghai China Nanyang Technological University Singapore Singapore Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)9781728133423

A programmable logic controller (PLC) is essentially a computer dedicated to industrial control which is widely used in the field of global automation control. However, PLC software bugs can result in economic losses and even personal safety issues. PLC software must be thoroughly tested regarding function, structure, safety, and other aspects to avoid accidents. Existing PLC tools are mainly based on the manual setting of input data, which is not only unable to be well automated but also cannot provide information about code coverage. This paper presents an automated test case generation approach for a Structured Text (ST) language to reduce the cost of testing, using dynamic symbolic execution. We apply this method to implement the coverage-based automated test case generation tool STAutoTester. We have evaluated STAutoTester on 21 programs. The experimental results show that STAutoTester can effectively handle these programs. For 11 ST programs, STAutoTester reduces, on average, 87.5% of generated test cases compared to SYMPLC. © 2019 IEEE.

关键词： software testing

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A novel measure to quantify the robustness of social network under the virus attacks 6th

A novel measure to quantify the robustness of social network...

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6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020

作者： Song, Bo Jing, Zhengjun Jay Guo, Y. Liu, Ren Ping Zhou, Qian School of Modern Posts and Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing210003 China University of Technology Sydney SydneyNSW2007 Australia Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210003 China Nanjing University of Posts and Telecommunications Nanjing210003 China Jiangsu University of Technology Changzhou213001 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China

ISBN: (纸本)9789811590306

Combining the spread velocity, the epidemic threshold and the infection scale at steady state, a new network robust measure with respect to the virus attacks is proposed in this paper. Through examples, we show that spread velocity plays an important role on the network robustness. By using the SI and SIS epidemic model, we analyze the robustness of homogeneous networks. The results show that the irregularity in node degrees decreases the robustness of the networks. Moreover, the simulation results show that the network becomes more fragile as the average degree grows in both homogeneous and heterogeneous networks. © Springer Nature Singapore Pte Ltd 2020.

关键词： Heterogeneous networks

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Web Service Discovery Based on Knowledge Graph and Similarity Network

Web Service Discovery Based on Knowledge Graph and Similarit...

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IEEE World Congress on Services (SERVICES)

作者： Yang Yu Jun Zeng Juan Yao Junhao Wen Bin Xing School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Innovation Center of Industrial Big-Data Co. Ltd National Engineering Laboratory for Industrial Big-data Application Technology Chongqing China

The following topics are dealt with: cloud computing; mobile computing; Internet; learning (artificial intelligence); Internet of Things; Web services; software engineering; optimisation; workflow management software;... 详细信息

ISBN: (数字)9781728182032

ISBN: (纸本)9781728182049

关键词： Knowledge engineering Web services Semantics Neural networks Feature extraction Data mining Context modeling

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NDRIDC: NMF-based deep representation algorithm for incomplete data clustering

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Knowledge-Based Systems 2025年 324卷

作者： Dexian Wang Zonglin Li Sha Yang Tianrui Ren Pengfei Zhang Ping Deng Tianrui Li Chengdu University of Traditional Chinese Medicine Chengdu 611137 China Chinese Academy of Medical Sciences & Peking Union Medical College Beijing 100730 China School of Computer and Software Engineering Xihua University Chengdu 610039 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu 611756 China

Incomplete data clustering is a hot research topic in the field of data mining. Many methods based on incomplete clustering have two issues: (1) their models do not have non-negative constraints; (2) their model building strategies do not consider combining traditional machine learning and deep learning. To address these issues, we proposed the NMF-based deep representation algorithm for incomplete data clustering (NDRIDC). First, the element-based objective function is constructed based on the known elements in the incomplete data matrix with matrix factorization principle. Then the NDRIDC deep network is constructed based on the element update gradient and nonlinear function with non-negative value domain. The learning of the NDRIDC network enables the optimization of the objective function and the filling of missing values. Further, based on the same learning framework, the NDRIDC network is employed to perform low-dimensional representation of the filled matrix. Finally, extensive experiments on regular and large-scale datasets show that our proposed algorithm has good performance.

关键词：

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Synchronous Motion Generation of Multiple Continuum Robots Based on a Jacobian-Estimation Strategy

Synchronous Motion Generation of Multiple Continuum Robots B...

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IEEE International Conference on Robotics and Biomimetics

作者： Ning Tan Ruikun Hu Yuyang Wu Xinyu Zhang Fenglei Ni Zhenglong Sun School of Computer Science and Engineering Sun Yat-sen University Guangzhou PR China State Key Laboratory of Robotics and Systems (HIT) Harbin PR China Engineering Research Center of Software/Hardware Co-Design Technology and Application Ministry of Education (East China Normal University) Shanghai PR China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9781665405362

In this paper, a novel model-free control scheme is proposed for synchronous motion control of multiple continuum robots. The control problem is formulated as two convex optimization routines in an adaptive and model-free closed-loop framework. The proposed approach is heading to address cooperative task execution for multiple continuum robots. The model-free feedback control guarantees the high portability for a wide range of continuum robots. Simulation studies are conducted on two kinds of tasks, which are cooperative task execution with two robots and simultaneously tracking control with three robots. Besides, the superiority of proposed approach is validated through comparisons with other methods. Simulation results prove the high accuracy and efficiency of this model-free feedback control scheme.

关键词： Jacobian matrices Adaptation models Tracking Simulation Conferences Convex functions Feedback control

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