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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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Structure Aware Loss for Deep Unsupervised Industrial Image Registration

Structure Aware Loss for Deep Unsupervised Industrial Image ...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Yong Shi Mengjin Lyu Zhiquan Qi Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China College of Information Science and Technology University of Nebraska at Omaha Omaha USA School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Image registration is extensively utilized in industrial applications. However, specific challenges inherent to these environments—such as low texture, significant displacement, and poor lighting conditions—complicate the registration process. This paper introduces a structure-aware loss module tailored to the unique characteristics of industrial images. This module promotes the network’s focus on salient regions while emphasizing structural features. Additionally, it is designed as a plug-in component, facilitating integration with any loss function and homography estimation method. We develop an unsupervised registration network and create an industrial dataset to evaluate the module’s effectiveness. A series of experiments demonstrate that our structure-aware module significantly enhances registration performance.

关键词： Deep learning Image registration Conferences Lighting Estimation data mining

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Understanding and Detecting Privacy Leakage Vulnerabilities in Hyperledger Fabric Chaincodes

Understanding and Detecting Privacy Leakage Vulnerabilities ...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Ziming Chen Yue Li Jianbo Gao Jiashuo Zhang Ke Wang Jianbin Hu Zhi Guan Zhong Chen School of Computer Science Peking University Beijing China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

The application on a blockchain cannot maintain secrecy because its data is replicated across all peers in the network. To remedy this problem, Hyperledger Fabric introduces private data collection (PDC) into its smart contract (i.e. chaincode) to facilitate applications that require privacy. However, recent studies have revealed that PDC is too complex for chaincode developers to fully understand and use correctly, leading to privacy leaks vulnerabilities. In this paper, we present an empirical study on the prevalence of PDC misuse in chaincodes by extracting privacy leakage cases from StackOverflow posts and Hyperledger Fabric repositories on GitHub. Subsequently, we systematically categorize the misuse of PDC into three categories of vulnerabilities resulting in the leakage of private data and provide formal definitions for them. Furthermore, we develop PDChecker, an automated security analysis framework for identifying the privacy and security vulnerabilities in Fabric chaincodes. We evaluated PDChecker on 956 real-world chaincodes applying PDC and found that 67.78% of them contain at least one privacy leakage vulnerability. In addition, PDChecker uncovered 10 zero-day vulnerabilities documented by the China National Vulnerability database.

关键词： Privacy data privacy Distributed ledger databases Smart contracts Fabrics Blockchains Software reliability Security Software development management

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Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation

arXiv

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arXiv 2023年

作者： Wang, Yu Zheng, Tongya Liu, Shunyu Feng, Zunlei Chen, Kaixuan Hao, Yunzhi Song, Mingli The College of Computer Science Zhejiang university Hangzhou China Big Graph Center School of Computer and Computing Science Hangzhou City University Hangzhou China The College of Software Technology Zhejiang University Hangzhou China The State Key Laboratory of Blockchain and Security Zhejiang University Hangzhou China

Human mobility patterns have shown significant applications in policy-decision scenarios and economic behavior researches. The human mobility simulation task aims to generate human mobility trajectories given a small set of trajectory data, which have aroused much concern due to the scarcity and sparsity of human mobility data. Existing methods mostly rely on the static relationships of locations, while largely neglect the dynamic spatiotemporal effects of locations. On the one hand, spatiotemporal correspondences of visit distributions reveal the spatial proximity and the functionality similarity of locations. On the other hand, the varying durations in different locations hinder the iterative generation process of the mobility trajectory. Therefore, we propose a novel framework to model the dynamic spatiotemporal effects of locations, namely SpatioTemporal-Augmented gRaph neural networks (STAR). The STAR framework designs various spatiotemporal graphs to capture the spatiotemporal correspondences and builds a novel dwell branch to simulate the varying durations in locations, which is finally optimized in an adversarial manner. The comprehensive experiments over four real datasets for the human mobility simulation have verified the superiority of STAR to state-of-the-art methods. Our code is available at https://***/Star607/STAR-TKDE. © 2023, CC BY.

关键词： Location

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Selection for Cheaper Amino Acids Drives Nucleotide Usage at the Start of Translation in Eukaryotic Genes

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 949-957页

作者： Na L.Gao Zilong He Qianhui Zhu Puzi Jiang Songnian Hu Wei-Hua Chen Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imagingDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Institute for Computer Science and Cluster of Excellence on Plant Sciences Heinrich Heine UniversityD-40225 DuesseldorfGermany CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China State Key Laboratory of Microbial Resources Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Interdisciplinary Innovation Institute of Medicine and EngineeringBeihang UniversityBeijing 100191China University of Chinese Academy of Sciences Beijing 100049China

Coding regions have complex interactions among multiple selective forces,which are manifested as biases in nucleotide *** studies have revealed a decreasing GC gradient from the 5′-end to 3′-end of coding regions in various *** confirmed that this gradient is universal in eukaryotic genes,but the decrease only starts from the~25th *** trend is mostly found in nonsynonymous(ns)sites at which the GC gradient is universal across the eukaryotic *** GC contents at ns sites result in cheaper amino acids,indicating a universal selection for energy efficiency toward the N-termini of encoded *** a genome,the decreasing GC gradient is intensified from lowly to highly expressed genes(more and more protein products),further supporting this *** reveals a conserved selective constraint for cheaper amino acids at the translation start that drives the increased GC contents at ns *** GC contents can facilitate transcription but result in a more stable local secondary structure around the start codon and subsequently impede translation ***,the GC gradients at four-fold and two-fold synonymous sites vary across *** could decrease or increase,suggesting different constraints acting at the GC contents of different codon sites in different *** study reveals that the overall GC contents at the translation start are consequences of complex interactions among several major biological processes that shape the nucleotide sequences,especially efficient energy usage.

关键词： Macroevolution Prioritization of selective forces Energy efficiency Transcription Translation initiation

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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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Ciphertext-policy attribute-based proxy re-encryption via constrained PRFs

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science China(Information sciences) 2021年第6期64卷 242-243页

作者： Zengpeng LI Vishal SHARMA Chunguang MA Chunpeng GE Willy SUSILO College of Computer Science and Technology Qingdao University Guizhou Provincial Key Laboratory of Public Big Data Guizhou University ISTD Pillar Singapore University of Technology and Design (SUTD) College of Computer Science and Engineering Shandong University of Science and Technology College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Computing and Information Technology University of Wollongong

Dear editor,To maintain the confidentiality of the sensitive data, users tend to encrypt their data under an associated access policy(or attributes) before outsourcing them to the *** the traditional access control model via cryptographic approaches, e.g., ciphertext-policy attribute-based encryption(or CP-ABE), users are allowed to formulate access policies and let the encrypted data associated with access policies. Only authenticated users, whose own attributes matching their formulated access policies, have the privilege to recover the ciphertext. However, once the access policies are formulated, the authorized user cannot update it unless the user downloads and re-encrypts the file under the new access policy. Hence, to achieve dynamic access control, the model of ciphertext-policy attribute-based proxy re-encryption(or CP-AB-PRE) is adopted by integrating the proxy re-encryption(or PRE).

关键词： Ciphertext-policy attribute-based proxy re-encryption via constrained PRFs

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High-Frequency Stereo Matching Network

High-Frequency Stereo Matching Network

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Haoliang Zhao Huizhou Zhou Yongjun Zhang Jie Chen Yitong Yang Yong Zhao Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Artificial Intelligence Guizhou University Guiyang Guizhou China Ghost-Valley AI Technology Shenzhen Guangdong China School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangzhou China The Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University China

In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency information. We propose the Decouple module to alleviate the problem of data coupling and allow features containing subtle details to transfer across the iterations which proves to alleviate the problem significantly in the ablations. To further capture high-frequency details, we propose a Normalization Refinement module that unifies the disparities as a proportion of the disparities over the width of the image, which address the problem of module failure in cross-domain scenarios. Further, with the above improvements, the ResNet-like feature extractor that has not been changed for years becomes a bottleneck. Towards this end, we proposed a multi-scale and multi-stage feature extractor that introduces the channel-wise self-attention mechanism which greatly addresses this bottleneck. Our method (DLNR) ranks 1st on the Middlebury leaderboard, significantly outperforming the next best method by 13.04%. Our method also achieves SOTA performance on the KITTI-2015 benchmark for D1-fg. Code and demos are available at: https://***/David-Zhao-1997/High-frequency-Stereo-Matching-Network.

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OFFLINE MODEL-BASED OPTIMIZATION BY LEARNING TO RANK

arXiv

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arXiv 2024年

作者： Tan, Rong-Xi Xue, Ke Lyu, Shen-Huan Shang, Haopu Wang, Yao Wang, Yaoyuan Fu, Sheng Qian, Chao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China Advanced Computing and Storage Lab Huawei Technologies Co. Ltd. China

Offline model-based optimization (MBO) aims to identify a design that maximizes a black-box function using only a fixed, pre-collected dataset of designs and their corresponding scores. This problem has garnered significant attention from both scientific and industrial domains. A common approach in offline MBO is to train a regression-based surrogate model by minimizing mean squared error (MSE) and then find the best design within this surrogate model by different optimizers (e.g., gradient ascent). However, a critical challenge is the risk of out-of-distribution errors, i.e., the surrogate model may typically overestimate the scores and mislead the optimizers into suboptimal regions. Prior works have attempted to address this issue in various ways, such as using regularization techniques and ensemble learning to enhance the robustness of the model, but it still remains. In this paper, we argue that regression models trained with MSE are not well-aligned with the primary goal of offline MBO, which is to select promising designs rather than to predict their scores precisely. Notably, if a surrogate model can maintain the order of candidate designs based on their relative score relationships, it can produce the best designs even without precise predictions. To validate it, we conduct experiments to compare the relationship between the quality of the final designs and MSE, finding that the correlation is really very weak. In contrast, a metric that measures order-maintaining quality shows a significantly stronger correlation. Based on this observation, we propose learning a ranking-based model that leverages learning to rank techniques to prioritize promising designs based on their relative scores. We show that the generalization error on ranking loss can be well bounded. Empirical results across diverse tasks demonstrate the superior performance of our proposed ranking-based method than twenty existing methods. Our implementation is available at https://***/lam

关键词： Linear regression

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Homogeneous and Heterogeneous Graph Learning for Hybrid Beamforming in mmWave Systems

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IEEE Transactions on Wireless Communications 2025年

作者： Li, Yuhang Lu, Yang Zhang, Guangyang Ai, Bo Niyato, Dusit Cui, Shuguang State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Shenzhen518066 China

Hybrid analog and digital beamforming (HBF) is a cost-efficient technique to achieve high data rates in millimeterwave (mmWave) communication systems. This paper applies the emerging graph neural networks (GNNs) to HBF by leveraging the topological information in wireless networks for better adaptation to dynamic environments. To address the issue of limited feature extraction capability of the existing single-type GNNs, such as node-GNN or edge-GNN, we model the mmWave communication systems as both homogeneous and heterogeneous graphs, and separate the HBF design into node- and edge-level subtasks. Then, the two graphs are presented by two novel models based on homogeneous graph attention network (GAT) and heterogeneous GAT (HGAT), respectively, and mapped to the desired power allocation, radio frequency precoder and baseband precoder. Both the proposed GAT and HGAT are generalizable in the user variation scenarios, while the HGAT is also generalizable in antenna variation scenarios through the incorporation of a complex embedding layer. Furthermore, we introduce a constraint adaptive layer in the GAT and HGAT to ensure feasible outputs. Extensive numerical results based on the public dataset DeepMIMO are provided to evaluate the GAT and HGAT. The proposed approaches generally outperform existing baselines in terms of adaptability to system settings and generalizability to (unseen) problem parameters/sizes, while the HGAT can even achieve faster and better inference than traditional optimization approaches. © 2002-2012 IEEE.

关键词： Network theory (graphs)

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