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IEEE International Conference on Systems, Man and Cybernetics

作者： Feiyu Wang Jian-tao Zhou College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.

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Combining Kernelized Autoencoding and Centroid Prediction for Dynamic Multi-objective Optimization

arXiv

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arXiv 2023年

作者： Hou, Zhanglu Zou, Juan Ruan, Gan Liu, Yuan Xia, Yizhang Hunan Engineering Research Center of Intelligent System Optimization and Security Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China CERCIA School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

Evolutionary algorithms face significant challenges when dealing with dynamic multi-objective optimization because Pareto optimal solutions and/or Pareto optimal fronts change. This paper proposes a unified paradigm, which combines the kernelized autoncoding evolutionary search and the centriod-based prediction (denoted by KAEP), for solving dynamic multi-objective optimization problems (DMOPs). Specifically, whenever a change is detected, KAEP reacts effectively to it by generating two subpopulations. The first subpoulation is generated by a simple centriod-based prediction strategy. For the second initial subpopulation, the kernel autoencoder is derived to predict the moving of the Pareto-optimal solutions based on the historical elite solutions. In this way, an initial population is predicted by the proposed combination strategies with good convergence and diversity, which can be effective for solving DMOPs. The performance of our proposed method is compared with five state-of-the-art algorithms on a number of complex benchmark problems. Empirical results fully demonstrate the superiority of our proposed method on most test instances. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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International Conference on Computer Supported Cooperative Work in Design

作者： Feiyu Wang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

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Interactive Prototype Learning and Self-Learning for Few-Shot Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Song, Yuhui Xu, Chenchu Wang, Boyan Du, Xiuquan Chen, Jie Zhang, Yanping Li, Shuo School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230051 China Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Engineering Case Western Reserve University Cleveland44106 United States

Few-shot learning alleviates the heavy dependence of medical image segmentation tasks on large-scale labeled data, but it shows a strong performance gap when dealing with new targets and new tasks compared with traditional deep learning. Existing few-shot medical image segmentation models mainly realize segmentation by learning the semantic class knowledge of a few known (support) samples and extending it to unknown (query) samples. However, the neglect of the differences between known and unknown samples leads to a certain deviation in the transfer of class knowledge, and the trained model tends to overfit the class knowledge of training classes, which greatly reduces the segmentation and generalization performance of few-shot model. In this work, we propose an interactive prototype learning and self-learning network to alleviate the above problems. Specifically, we propose an interactive prototype learning network to promote feature consistency between support images and query images to improve the accuracy of prototype metric learning. In addition, to avoid overfitting the training class knowledge and reduce the over-dependence of query segmentation on the class knowledge, we introduce a query features-guided self-learning network to achieve the finer segmentation of query images through its rich feature information. Our model obtained the competitive segmentation results on three multi-organ and multi-modal public datasets that show a substantial improvement in generalization ability. © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Joint relay selection and power allocation protocol for two-way relaying system

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International Journal of Wireless and Mobile computing 2019年第4期17卷 349-360页

作者： Zhi, Hui Zhu, Jun Hu, Yanjun Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui230601 China

The Joint Relay Selection and Power Allocation (JRSPA) for Amplify-and-Forward (AF) relaying (JRSPA-AF) combines Power Allocation (PA) and Relay Selection (RS) very well in Two- Way Relaying (TWR) system. In order to explore the system performance of JRSPA-AF protocol, the outage probability and Diversity-Multiplexing Trade-off (DMT) of JRSPA-AF protocol are analysed. One upper bound, two lower bounds and approximate expressions of outage probability along with the DMT are derived. Based on this, the idea of JRSPA is introduced into Decode-and- Forward (DF) relaying TWR system, and the JRSPA-DF protocol is proposed. Moreover, the exact expressions of outage probability and DMT for JRSPA-DF are derived. Simulations are developed to validate the analytical results, the results show that the approximation outage probability of JRSPA-AF can be used well to evaluate system outage probability over the entire transmission SNR region, and the analysis results of outage probability of JRSPA-DF are consistent with its simulation results. In addition, the simulation results indicate that JRSPA-DF can achieve lower outage probability than JRSPA-AF and other protocols. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Probability

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PD-SDF: Dynamic Surface Reconstruction Based on Plane Decomposition for Single View RGB-D Videos

PD-SDF: Dynamic Surface Reconstruction Based on Plane Decomp...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jinwen Li Weixing Xie Junfeng Yao Shaoqi Wu Youhong Peng Mengyuan Ge Xiao Dong Center for Digital Media Computing School of Film School of Informatics Xiamen University National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Jiujiang Research Institute of Xiamen University Jiujiang China Guangdong Provincial/Zhuhai Key Laboratory of IRADS BNU-HKBU United International College Zhuhai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Surface reconstruction of dynamic scenes from single view videos is a challenging task due to the highly ill-posed and under-constrained nature. Existing single view reconstruction methods suffer from severe quality issues, such as surface distortion and mesh adherison. In this paper, we propose an efficient dynamic representation network, PD-SDF, which consists of a 4D motion field and a 3D geometry field. The explicit disentanglement of motion and geometry based on planar factorization guarantees the mesh consistency across frames. Specifically, to address the mesh adhesion problem, we design a depth-guided sampling strategy to focus on optimizing the SDF field near the object surface. Due to insufficient geometric cues, we design various regularization strategies to constrain smoothness and topological correcness of the scene geometry. Extensive experiments show that our method outperforms existing methods in both appearance and geometry reconstruction. The project page: https://***/

关键词： Geometry Training Surface reconstruction Three-dimensional displays Noise Reconstruction algorithms Stability analysis Speech processing Surface treatment Videos

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On the coset graph construction of distance-regular graphs

arXiv

引用

arXiv 2022年

作者： Shi, Minjia Krotov, Denis S. Solé, Patrick Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China Sobolev Institute of Mathematics Novosibirsk630090 Russia I2M CNRS Centrale Marseille University of Aix-Marseille Marseille France

We show that no more new distance-regular graphs in the tables of the book of (Brouwer, Cohen, Neumaier, 1989) can be produced by using the coset graph of additive completely regular codes over finite *** Codes 05E30,... 详细信息

关键词： Graph theory

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Constructing MRD codes by switching

arXiv

引用

arXiv 2022年

作者： Shi, Minjia Krotov, Denis S. Özbudak, Ferruh Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei230601 China Sobolev Institute of Mathematics Novosibirsk630090 Russia Department of Mathematics Institute of Applied Mathematics Middle East Technical University Ankara Turkey

MRD codes are maximum codes in the rank-distance metric space on m-by-n matrices over the finite field of order q. They are diameter perfect and have the cardinality qm(n-d+1) if m ≥ n. We define switching in MRD codes as replacing special MRD subcodes by other subcodes with the same parameters. We consider constructions of MRD codes admitting such switching, including punctured twisted Gabidulin codes and direct-product codes. Using switching, we construct a huge class of MRD codes whose cardinality grows doubly exponentially in m if the other parameters (n, q, the code distance) are fixed. Moreover, we construct MRD codes with different affine ranks and aperiodic MRD *** Codes 94B25 Copyright © 2022, The Authors. All rights reserved.

关键词： Switching

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Latent multi-view subspace clustering based on Laplacian regularized representation

Latent multi-view subspace clustering based on Laplacian reg...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Wei Guo Hangjun Che Man-Fai Leung Nankun Mu Xiangguang Dai Yuming Feng School of Electronic and Information Engineering Southwest University Chongqing China School of Electronic Information Engineering Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing China School of Computing and Information Science Faculty of Science and Engineering Anglia Ruskin University Cambridge UK Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China

We propose a latent multi-view subspace clustering model based on Laplacian regularized representation. To emphasize the information of the representation matrix at the local level and reflect the grouping effect of clustering, a Laplacian regularization is imposed on the representation matrix. Additionally, To boost the efficiency o f c lustering, we apply nonnegative constraints on the representation matrix. A unified framework is designed to solve the proposed model by using ALM and LADMAP methods. The proposed model has excellent performance, as demonstrated by a number of experimental findings.

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CSCL-DTI: predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: predicting drug-target interaction through cross-v...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Xi Zhang Zu-Guo Yu Yahui Long Xiangxiang Zeng Philip S. Yu College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research (A*STAR) Singapore College of Information Science and Engineering Hunan University China Department of Computer Science University of Illinois Chicago USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI 1 outperforms six state-of-the-art methods.

关键词： Drugs Proteins Representation learning Protein engineering Adaptation models Contrastive learning Predictive models Transformers Amino acids Diffusion tensor imaging

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