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检索条件"任意字段=2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020"

共 5726 条记录，以下是441-450 订阅

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BK-MSSNN: BI-Kernel Based Multi-Scale Subgraph Neural Networks 22

BK-MSSNN: BI-Kernel Based Multi-Scale Subgraph Neural Networ...

引用

22nd international conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2024

作者： Jiang, Kang Peng, Jia-Wang Yuan, Xin Xu, Wei Tong, He Chen, Sheng-Bing Xu, Li-Xiang College of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia College of Aeronautical Engineering Beijing Polytechnic Beijing100176 China College of Artificial Intelligence and Big Data Hefei University Hefei230601 China School of Artificial Intelligence Hefei Institute of Technology Hefei238076 China

ISBN: (纸本)9798331528072

Graph Neural Networks (GNNs) have achieved remarkable success in handling graph-related tasks. Most GNN frameworks are based on Message Passing Neural Networks (MPNNs). However, subgraph structures are often complex and multi-scale, and the performance of MPNN in graph isomorphism discrimination tasks may have certain limitations. To address this issue, this paper investigates the semantic associations between subgraphs of different scales and proposes a novel Bi-Kernel Multi-Scale Subgraph Neural Network (BK-MSSNN) to represent more finegrained subgraph semantic information. Specifically, we sample global subgraphs and local subgraphs of different scales, and integrate two graph kernel methods, namely the RW kernel and the Weisfeiler-Lehman kernel, to construct multiple associative relationships between subgraphs for updating node embeddings. Experimental results on five public datasets demonstrate that our proposed model outperforms other benchmark models in graph classification tasks. © 2024 IEEE.

关键词： Graph neural networks

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Halving the Number of Qubits of Quantum Comparators 15th

Halving the Number of Qubits of Quantum Comparators

引用

15th international conference on Parallel processing and Applied Mathematics, PPAM 2024

作者： Donaire, Laura María Ortega, Gloria Garzón, Ester M. Orts, Francisco Paulavičius, Remigijus Filatovas, Ernestas University of Almería Almería Spain Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania

ISBN: (纸本)9783031856990

Quantum comparators are of significant importance within the realm of various quantum algorithms. In this work we improve the number of qubits needed to perform a comparison of two N-bit strings from 2N+1 qubits to N+2. To achieve this, we resort to an encoding of the bits in which one qubit is not wasted for each bit entered. This encoding is based on the one proposed in the work of Pérez-Salinas et al. (2020), but in our case, we adapt it for use in arithmetic operations. We also use the implementation of the AND operation proposed by Gidney (2018) to reduce the number of T gates (significantly more expensive than the rest of the gates) necessary for comparison. The result is a circuit that equals the T-count of the best comparator for quantum computing currently available while halving the number of qubits required. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Qubits

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AGMV Approach for Reduce Complexity of Classification Tasks 6

AGMV Approach for Reduce Complexity of Classification Tasks

引用

6th international Scientific conference on Information, Control, and Communication Technologies, ICCT 2022

作者： Ivchenko, Alexander Izyumov, Pavel Keldysh Institute of Applied Mathematics Kiam Big Data and Intelligent Systems Laboratory Moscow Russia Moscow Institute of Physics and Technology Mipt Department of Multimedia Technologies and Telecommunications Dolgoprudny Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

We present a research of applying a statistical method of data processing and generation of a new features for Accumulated Generalized Mean Values (AGMV) for internet traffic analysis. The approach is based on the accumulation of averaged values and application of fractional powers at various scales. We tested 'classical' Machine Learning (ML) algorithms for solving classification tasks and show quality improvement. The method was applied on several datasets for investigation the possibilities and to prove the effectiveness of the AGMV: encrypted and unencrypted traffic ISCXVPN (2016) dataset and Android Malware dataset CCCS-CICAndMal (2020). We demonstrate that AGMV preprocessing method significantly improves the performance of ML algorithms. Source code is stored at github. com/AleksandrIvchenko/agmv. © 2022 IEEE.

关键词： Machine learning

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Carbon Emission Prediction in Thermoelectric Coupled Systems Based on Improved Ensemble Empirical Mode Decomposition and Reinforcement Learning

Carbon Emission Prediction in Thermoelectric Coupled Systems...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Yunfei Tian Huihui Xu Jing Peng Yi Chai Yong Li State Grid Gansu Electric Power Company Lanzhou China School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic University Shanghai China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

Aiming at the difficult problem of carbon emission prediction under the thermoelectric coupling system, the IEEMD-GRU model was innovatively proposed in this study, which combined with improved ensemble empirical mode decomposition and gated cycle unit technology, significantly improved the prediction accuracy. Compared with the three types of models of traditional methods, IEEMD-GRU performs well in trend prediction and data fitting, especially in robustness and accuracy when dealing with non-abnormal data points. In view of its excellent performance, IEEMD-GRU models are recommended as a priority in carbon intensity prediction to obtain more reliable and refined results to assist environmental policy making and energy strategic planning.

关键词： Accuracy Empirical mode decomposition Computational modeling Fitting Carbon dioxide Predictive models Market research data models Robustness Trajectory

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A Robust Approach to Adversarial Attack on Tabular data for Classification Algorithm Testing 8

A Robust Approach to Adversarial Attack on Tabular Data for ...

引用

8th international conference on Big data and Information Analytics, BigDIA 2022

作者： Ju, Lunhao Cui, Ruijing Sun, Jianbin Li, Zituo College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

Adversarial machine learning (AML) is a significant measure to detect the performance and robustness of the machine learning models, and it has obtained intense interest. While research on the topic has mainly been focused on a specific model or classifier, the generality of adversarial perturbations has been constrained a lot. To tackle this issue, a robust attack approach based on data distribution features has been proposed is this paper. Firstly, the objective function is selected with the distribution features of the data accordingly. Then, the perturbation vector is so obtained from the objective function. Finally, the rationality and feasibility of the robust attack approach are verified by testing on specific cases. Experiments on comparing with other commonly used attack algorithms show that the new method outperforms the state-of-the-art. © 2022 IEEE.

关键词： Classification (of information)

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MC-GAN: An Adversarial Sample Defense Algorithm 20

MC-GAN: An Adversarial Sample Defense Algorithm

引用

20th international Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Chenli, Wang Juyang, W.U. Xing, Yang Junfei, Wang Jian, S.H.U. Jiazhong, L.U. Yuanyuan, Huang Shuo, H.A.N. School of Cybersecurity Chengdu University of Information Technology Sichuan Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Sichuan Chengdu610225 China Northwestern University Department of Statistics and Data Sciences EvanstonIL60202 United States

ISBN: (纸本)9798350318982

The current adversarial sample defense algorithms are plagued by issues such as poor defense effectiveness and high training costs. In order to enhance sample classification accuracy while reducing training costs, we have introduced a novel defense algorithm based on model compression within the framework of Generative Adversarial Networks (GANs). This algorithm integrates GANs with model compression techniques, and the results of testing on public datasets have demonstrated an improved accuracy in defending against adversarial samples along with reduced training costs. © 2023 IEEE.

关键词： Generative adversarial networks

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Deep Variational Bayesian Modeling of Haze Degradation Process 23

Deep Variational Bayesian Modeling of Haze Degradation Proce...

引用

32nd ACM international conference on Information and Knowledge Management (CIKM)

作者： Im, Eun Woo Shin, Junsung Baik, Sungyong Kim, Tae Hyun Hanyang Univ Dept Artificial Intelligence Seoul South Korea Hanyang Univ Dept Data Sci Seoul South Korea Hanyang Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9798400701245

Relying on the representation power of neural networks, most recent works have often neglected several factors involved in haze degradation, such as transmission (the amount of light reaching an observer from a scene over distance) and atmospheric light. These factors are generally unknown, making dehazing problems ill-posed and creating inherent uncertainties. To account for such uncertainties and factors involved in haze degradation, we introduce a variational Bayesian framework for single image dehazing. We propose to take not only a clean image and but also transmission map as latent variables, the posterior distributions of which are parameterized by corresponding neural networks: dehazing and transmission networks, respectively. Based on a physical model for haze degradation, our variational Bayesian framework leads to a new objective function that encourages the cooperation between them, facilitating the joint training of and thereby boosting the performance of each other. In our framework, a dehazing network can estimate a clean image independently of a transmission map estimation during inference, introducing no overhead. Furthermore, our model-agnostic framework can be seamlessly incorporated with other existing dehazing networks, greatly enhancing the performance consistently across datasets and models.

关键词： Image dehazing Variational Bayesian method Computer vision Machine learning Image processing

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Research on Submarine Localization Based on K-Means Clustering Algorithm

Research on Submarine Localization Based on K-Means Clusteri...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Haoling Xie Yuyang Hua Weixiang Qiu Fuzhou University Fuzhou China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

With the development of submarines and the increasing concern about the safety of this program, this study constructs a localization model and a search model for search and rescue after an accident. In addition, we provide a series of equipment recommendations and fundamentals to minimize the risk of accidents. We deeply analyze the influencing factors and obtain the key factors (ocean currents, seawater density, and seafloor topography), and then we process the obtained data related to the key factors, such as clustering, rasterization, and visualization. Using the processed data, we propose a three-dimensional physical model to analyze the impact on the submersible. Based on this physical model, we constructed a multilayer cross-location model (MLCL) to predict the positional trajectory of the submersible over time. To verify the accuracy of the model predictions, we propose a probability distribution model to comparatively validate the MLCL model. The final results show that the accuracy of the MLCL model is above 91%.

关键词： Location awareness Analytical models Accuracy Computational modeling data visualization Predictive models data models Trajectory Underwater vehicles Accidents

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APCCPA '22: 1st international Workshop on Advances in Point Cloud Compression, processing and Analysis 22

APCCPA '22: 1st International Workshop on Advances in Point ...

引用

30th ACM international conference on Multimedia (MM)

作者： Gao, Wei Li, Ge Yuan, Hui Hamzaoui, Raouf Li, Zhu Liu, Shan Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Shandong Univ Jinan Peoples R China De Montfort Univ Leicester Leics England Univ Missouri Kansas City Kansas City MO USA Tencent Shenzhen Peoples R China

ISBN: (纸本)9781450392037

Point clouds are attracting much attention from academia, industry and standardization organizations such as MPEG, JPEG, and AVS. 3D Point clouds consisting of thousands or even millions of points with attributes can represent real-world objects and scenes in a way that enables an improved immersive visual experience and facilitates complex 3D vision tasks. In addition to various point cloud analysis and processing tasks (e.g., segmentation, classification, 3D object detection, registration), efficient compression for these large-scale 3D visual data is essential to make point cloud applications more effective. This workshop focuses on point cloud processing, analysis, and compression in challenging situations to further improve visual experience and machine vision performance. Both learning-based and non-learning-based perception-oriented optimization algorithms for compression and processing are solicited. Contributions that advance the state-of-the-art in analysis tasks, are also welcomed.

关键词： Point cloud compression Point cloud processing Point cloud analysis Deep learning Quality assessment

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A Verifiable Decentralized data Modification Mechanism Supporting Accountability for Securing Industrial IoT 24th

A Verifiable Decentralized Data Modification Mechanism Suppo...

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24th international conference on algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Yang, Changsong Wu, Junfu Liang, Hai Ding, Yong Wang, Yujue Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Nanning China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong Hangzhou Innovation Institute of Beihang University Hangzhou China

ISBN: (纸本)9789819615247

The widespread application of Industrial Internet of Things (IIoT) has profoundly transformed modern industrial production methods. However, the large scale of industrial data, the diversity of data sources, and the heterogeneity of data structures make data maintenance exceptionally challenging. The editability techniques have been introduced into blockchain to leverage its distributed and editable features for data maintenance and management. However, existing solutions mainly focus on achieving editability while overlooking data management personnel’s privacy protection and lacking effective modification verification mechanisms to ensure data reliability. To address these issues, this paper proposes a verifiable decentralized data modification mechanism supporting accountability (DDMA) for securing IIoT. Our DDMA mechanism not only achieves the redactability of blockchain but also protects the privacy of data management personnel and provides reliable clues for subsequent accountability. Additionally, the DDMA mechanism offers an efficient method for modification verification, enabling rapid validation of whether on-chain data has been modified. Theoretical analysis demonstrates that DDMA meets the requirements of modification authorization, behavior traceability, and efficient modification verification. Experimental results indicate that DDMA can reduce modification and verification overheads. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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