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第43届中国控制会议

作者： Mei Zhang Ye Jin Jinhui Zhu School of Automation Science and Engineering South China University of Technology School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Multi-exposure image fusion is a cost-effective method to improve the dynamic range of images. For the problems of inadequate detail extraction in bright and dark areas and the over simplicity of existing fusion rules by current deep learning multi-exposure image fusion algorithms, we propose a DenseNet-based framework using adaptive Gabor convolution. Building on the Dense network architecture, we design an adaptive Gabor convolution module, which the parameters of the Gabor filter can be updated along with the network, enhancing the ability of Gabor to extract image features. Additionally, a cross-domain aggregation attention module is proposed, which effectively aggregates and enhances information. Comparing our method with seven advanced methods on the multi-exposure image fusion benchmark dataset, the subjective and objective evaluations outperform the other methods, proving the effectiveness and superiority of our method. The experiments of different exposure images fusion demonstrate the good generalization performance of our method.

关键词： Multi-exposure Image Fusion Adaptive Gabor Convolution Cross-domain Aggregation Attention

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Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods

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Computer Modeling in engineering & Sciences 2021年第10期129卷 31-45页

作者： Lejun Gong Xingxing Zhang Li Zhang Zhihong Gao Jiangsu Key Laboratory of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China

Novel coronavirus disease 2019(COVID-19)is an ongoing health *** studies are related to ***,its molecular mechanism remains *** rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational *** paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine *** method obtains seed genes based on prior *** genes are mined from biomedical *** candidate genes are scored by machine learning based on the similarities measured between the seed and candidate ***,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of *** results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.

关键词： COVID-19 SARS-CoV-2 computational method bioinformatics genotype machine learning

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Large Language Model Supply Chain: A Research Agenda

arXiv

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arXiv 2024年

作者： Wang, Shenao Zhao, Yanjie Hou, Xinyi Wang, Haoyu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

The rapid advancement of large language models (LLMs) has revolutionized artificial intelligence, introducing unprecedented capabilities in natural language processing and multimodal content generation. However, the increasing complexity and scale of these models have given rise to a multifaceted supply chain that presents unique challenges across infrastructure, foundation models, and downstream applications. This paper provides the first comprehensive research agenda of the LLM supply chain, offering a structured approach to identify critical challenges and opportunities through the dual lenses of software engineering (SE) and security & privacy (S&P). We begin by establishing a clear definition of the LLM supply chain, encompassing its components and dependencies. We then analyze each layer of the supply chain, presenting a vision for robust and secure LLM development, reviewing the current state of practices and technologies, and identifying key challenges and research opportunities. This work aims to bridge the existing research gap in systematically understanding the multifaceted issues within the LLM supply chain, offering valuable insights to guide future efforts in this rapidly evolving domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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An Image Super Resolution Algorithm Using Reparameterization 18th

An Image Super Resolution Algorithm Using Reparameterization

引用

18th Chinese Intelligent Systems Conference, CISC 2022

作者： Cai, Qiang Chang, Haodong Li, Haisheng Wan, Ruyi Chen, Xiaoming School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China National Engineering Laboratory for Agri-Product Quality Traceability Beijing100048 China

ISBN: (纸本)9789811962257

At present, the residual-based deep network model mainly adopts a multi-branch structure, but this structure increases the memory access cost, and the reasoning time is not as good as that of a single-branch network. In this work, we propose a re-parameterized construction structure based on EDSR, namely Re-parameterized Deep Super-Resolution (RepEDSR). The training and inference processes are decoupled to speed up inference without sacrificing performance. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm outperforms existing methods in terms of speed and accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical resolving power

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation

arXiv

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arXiv 2024年

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm’s generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024, CC BY.

关键词： Semantic Segmentation

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Energy-Efficient Synthesis Method and Mechanism Investigation of Sr2mgsi2o7: Eu2+, Dy3+ Long Afterglow Phosphor

SSRN

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SSRN 2024年

作者： Zhang, Chi Han, Xu Zhang, Min Cui, Ruirui Deng, Chaoyong Yan, Jingjing Hang, Yuan Ji, Miaoxin Dong, Zhe You, Xianglong College of Electrical Engineering Henan University of Technology Zhengzhou450001 China Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China Guiyang University China

Solid state reaction method is most commonly used to synthesize Sr2MgSi2O7: Eu2+, Dy3+ phosphors. In order to avoid the energy waste caused by high sintering temperature, we systematically studied and optimized the preparation process of Sr2MgSi2O7: Eu2+, Dy3+ long afterglow phosphors by increasing the Si/Sr ratio in the raw materials. The appropriate addition of silica not only facilitates the preparation of single phase Sr2MgSi2O7: Eu2+, Dy3+ phosphor but also enhances the rate of solid state reaction, so as to achieve the purpose of reducing the sintering temperature. The photoluminescence property of Si/Sr=1.3 sample sintered at 1300℃ exhibits a similarity to that of Si/Sr=1 sample sintered at 1400℃, while in terms of the afterglow property, the former demonstrates an enhancement by a factor of 3.5 compared to the latter. Thus, the sintering temperature can be reduced by at least 100℃. Additionally, the electronic structures of the Sr2MgSi2O7 host, the doped luminescence center Eu, the oxygen vacancies and the co-doped activator Dy have been methodically investigated through first-principles calculations. By integrating the findings on the distribution of the dopants and defects levels, we predict and propose the photoluminescence and afterglow mechanisms of Sr2MgSi2O7: Eu2+, Dy3+. The afterglow performance primarily depends on the concentration of electron traps. The calculation results demonstrate that the trap levels that can effectively capture electrons include both oxygen vacancies (VO and VO2+ shallow traps) and Dy 4f levels (deep traps). The increase of Si/Sr ratio can enhance the number of oxygen vacancies and the effective density of Dy3+ ions in the host, thus increasing the number of captured electrons and improving the afterglow performance. These studies suggest an efficient, cheap and energy-saving strategy for the preparation of Sr2MgSi2O7: Eu2+, Dy3+ long afterglow phosphor, and reveal the roles of oxygen vacancies and co-doped Dy in the aftergl

关键词： Magnesium compounds

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Overview of Chinese electronic medical records Information Extraction 7

Overview of Chinese electronic medical records Information E...

引用

7th International Conference on big data and Information Analytics, bigDIA 2021

作者： Zhao, Dongchen Chang, Xia Xue, Zhenxia School of Computer Science and Engineering Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan China School of Math and Information Science North Minzu University Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan China

ISBN: (纸本)9781665424660

Wise medical can't be separated from the analysis and processing of medical big data, electronic medical records as the main component of medical data have important research value. Chinese electronic medical records (CEMR) as the core records patient's disease, diagnosis and treatment information, the study of CEMR information extraction can better help doctors diagnose the disease and predict the development of the disease, in order to make more effective treatment plans. This paper divides information extraction technology into two subtasks of named entity recognition and relationship extraction, analyzes and compares the mechanism, algorithm and models of its existing methods, and finally points out the development direction of information extraction in the future. © 2021 IEEE.

关键词： Diagnosis

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On Convergence in Distribution of Stein's Unbiased Risk Hyper-parameter Estimator for Regularized System Identification 41

On Convergence in Distribution of Stein's Unbiased Risk Hype...

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第41届中国控制会议

作者： Yue Ju Tianshi Chen Biqiang Mu Lennart Ljung School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of Sciences Division of Automatic Control Department of Electrical EngineeringLink?ping University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Asymptotic theory for the regularized system identification has received increasing interests in recent *** this paper,for the finite impulse response(FIR) model and filtered white noise inputs,we show the convergence in distribution of the Stein's unbiased risk estimator(SURE) based hyper-parameter estimator and find factors that influence its convergence *** particular,we consider the ridge regression case to obtain closed-form expressions of the limit of the regression matrix and the variance of the limiting distribution of the SURE based hyper-parameter estimator,and then demonstrate their relation numerically.

关键词： Asymptotic theory Stein's unbiased risk estimator Hyper-parameter estimator Regularized system identification

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Analysis of Bamboo Resource Survey Application Based on Remote Sensing Images 6

Analysis of Bamboo Resource Survey Application Based on Remo...

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6th International Conference on Computer Information Science and Application Technology, CISAT 2023

作者： Liu, Xiaohong Yan, Kai Sun, Maosheng College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650000 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Forestry Southwest Forestry University Kunming650224 China

ISBN: (纸本)9781510668546

Understanding the distribution range of bamboo resources in Yiliang county is important for the ecological services and economic benefits of the county. In this paper, we investigated the distribution of bamboo resources by using 3S (GPS, RS, GIS) technology with remote sensing images and field survey. The survey results show that: the total area of bamboo forest in the county is 65863.72×10-2 km2;the origin of bamboo resources is mainly plantation and natural forest;the types of bamboo forest are pure bamboo forest, tree forest and mixed bamboo forest;the applications are mainly nature reserve bamboo forest, protective bamboo forest, timber bamboo forest and bamboo forest for bamboo shoots;There are 9 kinds of bamboo species, including Jinfo Mountain Fang, Xiaochaoba Fang, Yushan Bamboo, Cixi Bamboo, Mao Bamboo, Bambusa Bamboo, Shui Bamboo, Yongsan Fang and Siban Bamboo;the protection level is Ⅰ, Ⅱ, Ⅲ and Ⅳ respectively. © 2023 SPIE.

关键词： Bamboo

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