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Analyses of geographical observations in the Heihe River Basin: Perspectives from complexity theory

Journal of Geographical sciences 2019年第9期29卷 1441-1461页

作者： Gao Jianbo FANG Peng YUAN Lihua State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Institute of Complexity Science and Big Data Technology Guangxi UniversityNanning 530004China Wuhan National Laboratory for Optoelectronics Key Laboratory of In formation Storage SystemEngineering Research Center of Data Storage Systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Center for GeoData and Analysis Faculty of Geographical ScieneeBeijing Normal UniversityBeijing 100875China

Since 2005,dozens of geographical observational stations have been established in the Heihe River Basin(HRB),and by now a large amount of meteorological,hydrological,and ecological observations as well as data pertaining to water resources,soil and vegetation have been *** adequately analyze these available data and data to be further collected in future,we present a perspective from complexity *** concrete materials covered include a presentation of adaptive multiscale filter,which can readily determine arbitrary trends,maximally reduce noise,and reliably perform fractal and multifractal analysis,and a presentation of scale-dependent Lyapunov exponent(SDLE),which can reliably distinguish deterministic chaos from random processes,determine the error doubling time for prediction,and obtain the defining parameters of the process *** adaptive filter is illustrated by applying it to obtain the global warming trend and the Atlantic multidecadal oscillation from sea surface temperature data,and by applying it to some variables collected at the HRB to determine diurnal cycle and fractal *** SDLE is illustrated to determine intermittent chaos from river flow data.

关键词： Heihe River basin geographical observation complexity theory adaptive multiscale filter fractal analysis scale-dependent Lyapunov exponent

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zkrpChain: Privacy-Preserving data Auditing for Consortium Blockchains Based on Zero-Knowledge Range Proofs

zkrpChain: Privacy-Preserving Data Auditing for Consortium B...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Shiwei Xu Xiaowen Cai Yizhi Zhao Zhengwei Ren Lingjuan Wu Huanguo Zhang Le Du Yan Tong College of Informatics Huazhong Agricultural University Wuhan P. R. China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan P. R. China College of Cyber Science and Engineering Wuhan University Wuhan P. R. China Wuhan Donghu Big Data Trading Center Co. Ltd. Wuhan P. R. China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Consortium blockchain has been widely used in different scenarios, where blockchain members demand that their uploaded data could be audited under their identities without exposing the data themselves. However, so far, no solution of privacy-preserving data auditing has been proposed. To address the problem, we propose zkrpChain, which focuses on protection of the integrity and privacy of the data uploaded by blockchain members while leaving their identities public. In zkrpChain, which is based on Hyperledger Fabric and Bulletproofs, both standard-range and arbitrary-range zero-knowledge range proofs generation and verification are supported. To improve the efficiency, the aggregation of multiple proofs and batch verification are also developed. For further development, we provide the client codes, chaincodes and related APIs. Finally, we conduct experiments to evaluate the performance of zkrpChaln, and the results show that the consumed time (in an 8-thread scheme) of chaincodes and needed on-chain space of zkrpChain are very close to Bulletproofs, which is evaluated in a single-machine environment.

关键词： data privacy Privacy Distributed ledger Focusing Blockchain Fabrics security

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The phosphorylation of CD147 by Fyn plays a critical role for melanoma cells growth and metastasis (vol 39, pg 4183, 2020)

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ONCOGENE 2021年第10期40卷 1925-1925页

作者： Zhang, Xu Huang, Zunnan Guo, Yeye Xiao, Ta Tang, Ling Zhao, Shuang Wu, Lisha Su, Juan Zeng, Weiqi Huang, Hongbin Li, Zheng Tao, Juan Zhou, Jianda Chen, Xiang Peng, Cong The Department of Dermatology Xiangya Hospital Central South University Changsha Hunan China Hunan Key Laboratory of Skin Cancer and Psoriasis Xiangya Hospital Central South University Changsha Hunan China Hunan Engineering Research Center of Skin Health and Disease Xiangya Hospital Central South University Changsha Hunan China Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University Research Platform Service Management Center Guangdong Medical University Dongguan 523808 Guangdong China The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health Xiangya Hospital Central South University Changsha Hunan China Department of Dermatology Affiliated Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Plastic Surgery of Third Xiangya Hospital Central South University Changsha China

A Correction to this paper has been published: https://***/10.1038/s41388-021-01641-8

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A Domain-Oriented Double Dictionary Forward Traversal Method for Fine Corpus Extraction

A Domain-Oriented Double Dictionary Forward Traversal Method...

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International Conference on Measurement, Information and Control (MIC)

作者： Gang LIU Sen-nan ZHANG Wang-yang LIU Yang CAO Xue-feng LI College of Computer Science and Technology Harbin Engineering University Harbin China General Technology Research Center CETC Big Data Research Institute Company Limited Guiyang China Generic Technology Research Center CETC Big Data Research Institute Company Limited Guiyang China

ISBN: (数字)9781728130354

ISBN: (纸本)9781728130361

Improving audit intelligence requires computers to understand the semantics of audit information. At present, the researches on the related field of the intelligent information processing show that, the basis of intelligent information processing is the natural language understanding. This paper which combines the construction technology of corpus researches the basic methods and techniques on the construction of audit corpus. According to the text features in social security audit field, the paper proposed dual dictionary secondary forward traversal keyword extraction method which combines the specialized dictionaries obtained and general dictionaries, which is applied to text processing in social security audit, acquiring corpus of the field. The experimental results show that the proposed method can well divide, extract and discover the conceptual knowledge of field.

关键词： Dictionaries security Vocabulary Natural languages Feature extraction Business Classification algorithms

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A Novel research in Internet Finance Combining With Blockchain and Network Coding

A Novel Research in Internet Finance Combining With Blockcha...

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Smart BlockChain (SmartBlock), International Conference on

作者： Lijun xiao Zuoting Ning Wei Liang Jin Guo Lu Li Big data development and Research Center Guangzhou College of Technology and Business Guanqzhou China Hunan Police Academy Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China School of Economic Peking University Beijing China Information Centre Chinese GreatWall Technology group Co. LTD Changsha China

Internet finance has developed rapidly in the Internet Plus wave, however, some urgent issues, such as security, trustworthiness, and network performance, are emerging. This paper explores these problems for Internet finance. Based on the combination of blockchain and network coding, we propose innovative solutions to overcome these problems for internet finance to promote the development of internet finance. Experimental results and analysis demonstrate that the proposed scheme harvests good performance in terms of security and delay.

关键词： Internet security Finance Blockchain Network coding Delays security Communication networks

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RETRACTED ARTICLE: Smart IoT information transmission and security optimization model based on chaotic neural computing

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Neural Computing and Applications 2019年第21期32卷 16491-16504页

作者： Deng, Lianbing Li, Daming Cai, Zhiming Hong, Lin The Post-Doctoral Research Center Zhuhai Da Hengqin Science and Technology Development Co.Ltd Hengqin China Huazhong University of Science and Technology Wuhan China Macau Big Data Research Centre for Urban Governance City University of Macao Macau China School of Management Beijing Normal University Zhuhai China

The improvement of human quality of life is inseparable from the support of information technology, and the development of information technology has made human life more convenient. The current era is the information age, and the level of informatization has gradually become one of the important indicators to measure the comprehensive level of a country. The emergence of the Internet of Things has led to rapid development of technologies such as data perception, wireless data transmission, and intelligent information processing. With the increasing use of information transmission, people gradually realize the impact of security issues on themselves and society. In this paper, a smart IoT information transmission and security optimization model based on chaotic neural computing model is proposed. Simulation and analysis show that the proposed algorithm can ensure the availability and confidentiality of data at the same time.

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Front Cover: International Journal of Intelligent Systems, Volume 37 Issue 11 November 2022

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International Journal of Intelligent Systems 2022年第11期37卷

作者： Xianjia Meng Yong Yang Ximeng Liu Nan Jiang School of Information Science and Technology Northwest University Xi'an People's Republic of China Fujian Province Key Laboratory of Information Security of Network System and College of Computer and Data Science Fuzhou University Fuzhou People's Republic of China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen People's Republic of China Department of Internet of Things School of Information Engineering East China Jiao Tong University Nanchang People's Republic of China

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Onionchain: Towards balancing privacy and traceability of blockchain-based applications

arXiv

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arXiv 2019年

作者： Zhang, Yue Weng, Jian Weng, Jiasi Li, Ming Luo, Weiqi College of Informatin Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China

With the popularity of Blockchain comes grave security related concerns. Achieving privacy and traceability simultaneously remains an open question. Efforts have been made to address the issues, while they may subject to specific scenarios. This paper studies how to provide a more general solution for this open question. Concretely, we propose Onionchain, featuring a suite of protocols, offering both traceability and privacy. As the term implies, our Onionchain is inspired by Onion routing. We investigate the principles of Onion routing carefully and integrate its mechanism together with Blockchain technology. We advocate the Blockchain community to adopt Onionchain with the regards of privacy and traceability. To this end, a case-study of Onionchain, which runs in the context of Vehicular Ad Hoc Networks (VANETs), is proposed, providing the community a guideline to follow. Systematic security analysis and extensive experiments are also conducted to validate our secure and cost-effective Onionchain. Copyright © 2019, The Authors. All rights reserved.

关键词： Blockchain

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Multi-Keyword Search Guaranteeing Forward and Backward Privacy over Large-Scale Cloud data

Multi-Keyword Search Guaranteeing Forward and Backward Priva...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Li Gong Hongwei Li Guowen Xu Xizhao Luo Mi Wen School of Computer Science and Engineering University of Electronic Science and Technology of China China CETC Big Data Research Institute Co. Ltd. Guiyang China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen China Science and Technology on Communication Security Laboratory Chengdu China School of Computer Science and Technology Soochow University Suzhou China School of Computer Science and Technology Shanghai University of Electric Power Shanghai China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Using searchable encryption (SE), users' data can be outsourced to an untrusted server while ensuring privacy of both the queries and the data. Meanwhile, to efficiently support data updating, dynamic SE (DSE) has also been proposed and applied to a variety of scenarios. However, recent work shows that even with little information leakage on updated keywords, most of existing DSE schemes are also vulnerable to adaptative attacks breaking the privacy of the queries. To address this problem, several privacy-preserving DSE have been exploited to mitigate the two major privacy issues in the data update process: i.e., Forward privacy and Backward privacy. Nevertheless, it is still an open problem to support clients multi-keyword-based searching over dynamic cloud data. In reality, as a promising query requirement, it is assurance that the cost of all participants can be fundamentally reduced by implementing multi-keyword-based querying. To combat that, in this paper, we design the first multi-keyword based search proposals ensuring forward and backward privacy over dynamic cloud data. Specifically, we utilize Symmetric Hidden Vector Encryption (SHVE) as the underlying structure to build multi-keyword search protocol. Then, Bloom filter integrating with pseudo-random function will be further adopted to enhance query efficiency. The security analysis proves the high security of our model, and extensive experiments conducted on real-world data also demonstrate the practical performance of our proposed scheme.

关键词： data privacy Servers Encryption Indexes Privacy

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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