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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RETRACTED ARTICLE: Smart IoT information transmission and security optimization model based on chaotic neural computing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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The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future (vol 398, pg 1619, 2021)

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LANCET 2021年第10317期398卷 2148-2148页

作者： Romanello, M. McGushin, A. Di Napoli, C. [a]Institute for Global Health University College London London UK [b]Institute for Sustainable Resources University College London London UK [c]UCL Energy Institute University College London London UK [d]Institute for Environmental Design and Engineering University College London London UK [e]Department of Geography University College London London UK [f]The Bartlett School of Sustainable Construction University College London London UK [g]Centre for Human Health and Performance University College London London UK [h]School of Agriculture Policy and Development University of Reading Reading UK [i]Department of Meteorology University of Reading Reading UK [j]The Walker Institute University of Reading Reading UK [k]Department of Health Sciences University of York York UK [l]Institute for Environment and Human Security United Nations University Bonn Germany [m]Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK [n]Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK [o]Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK [p]Department of Earth System Science Tsinghua University Beijing China [q]Department of Environment Climate Change and Health World Health Organization Geneva Switzerland [r]Institute for Environmental Sciences World Health Organization Geneva Switzerland [s]Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff UK [t]Yale Center on Climate Change and Health Yale University New Haven CT USA [u]School of Government University of Birmingham Birmingham UK [v]Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy [w]Natural Resources Institute University of Greenwich London UK [x]Department of Global Health University of Washington Seattle WA USA [y]Centre for He

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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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ReRAM Crossbar-Based Analog Computing Architecture for Naive Bayesian Engine

ReRAM Crossbar-Based Analog Computing Architecture for Naive...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Bing Wu Dan Feng Wei Tong Jingning Liu Chengning Wang Wei Zhao Mengye Peng Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology (School of Computer Science and Technology Huazhong University of Science and Technology) Ministry of Education of China

Recent advances in Resistive RAM (ReRAM) have explored the in-situ Matrix-Vector Multiplication (MVM) ability of crossbar arrays to achieve high energy-efficiency Process-In-Memory (PIM) architectures for Convolutional Neural Network (CNN), image processing, and so on. However, the existing ReRAM-based PIM architectures suffer from considerable additional auxiliary logic and device variations. In this work, we propose a novel analog computing architecture NB Engine for classification by implementing Naive Bayesian (NB) algorithm on ReRAM crossbar arrays. The two key steps of the NB algorithm, that is, probability calculation and electing the class that has the highest probability, are elaborately accomplished in our architecture. The ReRAM arrays are both used as storage and computation components. We store the pre-calculated prior probabilities and conditional probabilities of every class in crossbar arrays. Then the probability calculation step is completed in parallel through the MVM operation of the array. In general, the election step is a multiple-comparison procedure and is normally implemented by a comparison tree. Here, we reuse the max pooling module in a conventional CNN PIM architecture to realize a compatible comparison logic. However, neither of the two designs can avoid the overhead of costly high bit-precision Analog-to-Digital Converters (ADCs). So we introduce a novel analog parallel comparison design which does not need any ADCs or other computing logic with better energy-saving and area-efficiency. Our proposed NB Engine is tested by 11 various datasets. The influence of several non-ideal device properties is discussed and the NB Engine exhibits great tolerance to these variations. The experiment results show that our design offers a runtime speedup up to 2289.6x compared with the software-implemented NB classifier with negligible accuracy loss. In addition, the NB Engine saves 96.2% energy consumption and 45.2% array area compared with the CNN P

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SRSC: Improving Restore Performance for Deduplication-Based Storage Systems

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ZTE Communications 2019年第2期17卷 59-66页

作者： ZUO Chunxue WANG Fang TANG Xiaolan ZHANG Yucheng FENG Dan Key Laboratory of Information Storage Systems Engineering Research Center of Data Storage Systems and TechnologyHuazhong University of Science and TechnologyWuhanHubei 430074China 5G Application Product Line ZTE CorporationShenzhenGuangdong 518057China

Modern backup systems exploit data deduplication technology to save stor-age space whereas suffering from the fragmentation problem caused by *** degrades the restore performance because of restoring the chunks thatare scattered all over different containers. To improve the restore performance, thestate-of-the-art History Aware Rewriting Algorithm(HAR) is proposed to collect frag-mented chunks in the last backup and rewrite them in the next backup. However, dueto rewriting fragmented chunks in the next backup, HAR fails to eliminate internalfragmentation caused by self-referenced chunks(that exist more than two times in abackup) in the current backup, thus degrading the restore performance. In this paper,we propose Selectively Rewriting Self-Referenced Chunks(SRSC), a scheme that de-signs a buffer to simulate a restore cache, identify internal fragmentation in the cacheand selectively rewrite them. Our experimental results based on two real-world datas-ets show that SRSC improves the restore performance by 45% with an acceptable sac-rifice of the deduplication ratio.

关键词： data deduplication fragmentation restore performance

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A survey on graph processing accelerators: Challenges and opportunities

arXiv

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arXiv 2019年

作者： Gui, Chuang-Yi Zheng, Long He, Bing-Sheng Liu, Cheng Chen, Xin-Yu Liao, Xiao-Fei Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computing National University of Singapore 117418 Singapore

Graph is a well known data structure to represent the associated relationships in a variety of applications, e.g., data science and machine learning. Despite a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures, dedicated hardware solutions, also referred to as graph processing accelerators, are essential and emerging to provide the benefits significantly beyond those pure software solutions can offer. In this paper, we conduct a systematical survey regarding the design and implementation of graph processing accelerator. Specifically, we review the relevant techniques in three core components toward a graph processing accelerator: preprocessing, parallel graph computation and runtime scheduling. We also examine the benchmarks and results in existing studies for evaluating a graph processing accelerator. Interestingly, we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and complexity of hardware configurations. We finially present to discuss several challenges in details, and to further explore the opportunities for the future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy efficiency

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