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Straightforward Guess and Determine Analysis Based on Genetic Algorithm

Journal of systems Science & Complexity 2022年第5期35卷 1988-2003页

作者： CAO Chunping CEN Zhe FENG Xiutao WANG Zhangyi ZHU Yamin Department of Computer Science and Technology University of Shanghai for Science and TechnologyShang-hai 200093China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China School of Science Xi'an Technological UniversityXi'an 710021China

With the development of artificial intelligence,the genetic algorithm has been widely used in many *** cryptography,the authors find it is natural to code an individual and design its fitness in a genetic algorithm for a straightforward guess and determine analysis(SGDA,in short).Based on this observation,the authors propose an SGDA based on genetic *** it with the other three SGDAs based on exhaustive search,MILP method and CPP method respectively,the authors illustrate its effectiveness by three stream ciphers:Small scale SNOW 2.0,medium scale Enocoro-128v2 and large scale *** results show our method is significantly superior to them,especially for Trivium,the method can find a solution of 165 variables in less than one hour,while the other three methods are not applicable due to its enormous search space of size 2^(619.37).As far as we know,it is a best solution in an SGDA for Trivium so far.

关键词： Enocoro-128v2 genetic algorithm guess and determine analysis SNOW 2.0 trivium

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Research on Vegetable Commodity Pricing and Replenishment Strategy Based on Simulated Annealing Algorithm 3

Research on Vegetable Commodity Pricing and Replenishment St...

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3rd International Signal Processing, Communications and Engineering Management Conference, ISPCEM 2023

作者： Shi, Jinfeng Zhang, Rui Feng, Siqi Zhang, Qiang School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China College of Science Wuhan University of Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Systems Science in Metallurgical Process Wuhan China

ISBN: (纸本)9798350307023

In fresh food superstores, the freshness period of most vegetable products is relatively short, and the quality of vegetables will deteriorate due to the increase in storage time and cannot be sold on the next day, so it is very important to determine the total daily replenishment for superstores. This paper discusses the optimization problem of daily replenishment total and pricing decisions. In this paper, firstly, the bivariate correlation coefficient analysis is carried out using SPSS to calculate the correlation coefficients between the total sales volume of each category and the unit price of sales;secondly, the regression model is established to derive the fitting equations and the R2 the test is carried out. For the superstore revenue optimization problem, firstly, the total daily replenishment strategy model of each vegetable category was established, and secondly, the pricing strategy model of each vegetable category was established through the wholesale price and sales unit price. Considering the transportation loss and deterioration of vegetables, the goods will be sold at a discount, this paper establishes the pricing strategy model of undiscounted and discounted sales. Finally, the optimal model is obtained based on the simulated annealing algorithm to ensure revenue maximization. The final maximum returns (RMB) from July 1-7, 2023 are 8091.56, 8102.48, 8097.47, 8114.66, 8166.51, 8115.96, and 8126.11, respectively. © 2023 IEEE.

关键词： Vegetables

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Simultaneous Estimation of Human Motion Intention and Time-Varying Arm Stiffness for Enhanced Human-Robot Interaction

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IEEE Transactions on Cognitive and Developmental systems 2024年第3期17卷 510-524页

作者： Wu, Huayang Zhu, Chengzhi Cheng, Long Yang, Chenguang Li, Yanan University of Sussex Department of Engineering and Design BrightonBN1 9RH United Kingdom South China University of Technology Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering Guangzhou510640 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China University of Liverpool Department of Computer Science LiverpoolL69 3BX United Kingdom

Recent advances in physiological human motor control research indicate that human endpoint stiffness magnitude increases linearly with grasp force. Based on these findings, a scheme was proposed in this paper to integrate the linear quadratic estimation (LQE) filter with the stiffness model inferred from grasp force, which can simultaneously estimate the human arm's stiffness and motion intention. Then, an online variable impedance controller (VIC) was designed based on previous estimations for physical human-robot interaction (pHRI). The proposed stiffness model and estimation method were validated through experiments using a planar robotic interface. In order to assess its performance in practical pHRI tasks, the implementation of human arm stiffness and intention estimation combining with VIC was extended to a teleoperation peg-in-hole and robot-assisted rehabilitation tasks. The experimental results demonstrate that the proposed method can effectively estimate human motion intention and arm stiffness simultaneously. Compared to existing methods, the proposed VIC enhances pHRI in terms of increased flexibility, effective guidance, and reduced human effort. © 2016 IEEE.

关键词： Human robot interaction

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A Reputation-Aware Randomization Consensus Algorithm for Performance Optimization in Blockchain systems

A Reputation-Aware Randomization Consensus Algorithm for Per...

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International Conference on computer Supported Cooperative Work in Design

作者： Jingyu Zhang Yongtao Sun Zhennan Zhang Weifeng Ren Lailong Luo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Blockchain technology, due to its decentralized, traceable, and tamper-resistant characteristics, has been applied in a wide range of collaborative computing scenarios, including smart grid, industrial production, and smart cities. Consensus algorithm, as the key blockchain technology, plays a decisive role in terms of performance, security, and scalability, ensuring that system nodes reach consensus on transaction data. However, with the increase of user nodes and the diversity of application scenarios, existing consensus algorithms face some performance and security challenges. To overcome these challenges, this article proposes a novel blockchain consensus mechanism, namely the Reputation-Aware Randomization Consensus Algorithm (RRCA). The proposed mechanism first constructs a dynamic reputation evaluation model to divide nodes into ordinary ones and candidate ones. Secondly, a selection model is constructed for candidate nodes to select consensus nodes, which run the consensus algorithm. Finally, a random selection mechanism for the leader node of the proposed consensus algorithm is constructed to ensure the unpredictability and reduce the probability of the leader node being subjected to malicious attacks. Theoretical analysis and experimental results indicate that the RRCA can significantly reduce consensus latency and increase the unpredictability of the leader node, which improves the performance and security of blockchain systems.

关键词： Smart cities Federated learning Scalability Evaluation models Consensus algorithm Production Blockchains

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3D semantic segmentation under adverse weather conditions 12

3D semantic segmentation under adverse weather conditions

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SPIE 12th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2023

作者： Liu, Tong Lin, Yunhan Wuhan University of Science and Technology College of Computer Science and Technology No. 2 Huangjiahu West Road Hongshan District Wuhan City Hubei Province Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Systems No. 947 Heping Avenue Qingshan District Wuhan City Hubei Province Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

The accurate 3D comprehension of point cloud scenes in diverse weather conditions holds paramount significance in various applications such as autonomous driving in contemporary automobiles, outdoor operations of robots, and autonomous drones. Presently, the majority of studies on semantic segmentation algorithms for 3D point clouds primarily focus on clear weather conditions. However, adverse weather conditions introduce specific types of noise that significantly deteriorate the quality of point clouds. Consequently, this poses a challenge in achieving high accuracy and efficiency in point cloud semantic segmentation for outdoor large-scale scenarios. To tackle this issue, this paper presents a novel semantic segmentation method designed for large scenes encompassing point cloud and foggy weather conditions. We further validate our approach using the Foggy Semantickitti dataset, thereby effectively enhancing the average cross-parallel ratio while maintaining computational efficiency. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Semantic Segmentation

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InfinityRand: Blockchain Non-Interactive Randomness Beacon Protocol Based on Trapdoor Verifiable Delay Function

InfinityRand: Blockchain Non-Interactive Randomness Beacon P...

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International Conference on computer Supported Cooperative Work in Design

作者： Jingyu Zhang Jiejun Ou Di Lan Bojian Ma Lailong Luo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Due to the prosperity of Decentralized Finance (DeFi) ecosystems and the rise of Decentralized Autonomous Organization (DAO) groups, blockchain as the underlying revolutionary theory has been attracted a lot of attention. How to achieve cryptographically unpredictable randomness in the publicly verifiable blockchain network, one of the typical collaborative systems, is a critical issue. Since Ethereum finished merging its mainnet with beacon chain, the research on randomness beacon in the blockchain field has become a hotspot. Most of the current distributed randomness beacon schemes are interactive protocols. They are constructed with Public Verifiable Secret Sharing (PVSS), leading high communication complexity O(n 2 ). In contrast, randomness beacons constructed based on Verifiable Delay Functions (VDFs) rely on the sequentiality and uniqueness of VDFs could solve this problem. This paper proposes a blockchain non-interactive randomness beacon protocol: InfinityRand (IR), which decoupled from the underlying message distribution mechanism. It could generate publicly verifiable, strongly bias-resistant, and fair random numbers. In designing InfinityRand, we also design a new trapdoor VDF scheme, which is constructed using negative wrapped convolution (NWC) based number theoretic transform (NTT) on polynomial ring. We conduct security analysis and evaluation experiments. Experiments show that InfinityRand could provide well unpredictability, leader election fairness and scalability guarantees.

关键词： Protocols Scalability Decentralized autonomous organization Voting Finance Collaboration Polynomials

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An Overview of Artificial Intelligence Ethics

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 799-819页

作者： Huang, Changwu Zhang, Zeqi Mao, Bifei Yao, Xin Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Southern University of Science and Technology Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Shenzhen518055 China Huawei Technologies Company Ltd. Trustworthiness Theory Research Center Shenzhen518055 China University of Birmingham School of Computer Science BirminghamB15 2TT United Kingdom

Artificial intelligence (AI) has profoundly changed and will continue to change our lives. AI is being applied in more and more fields and scenarios such as autonomous driving, medical care, media, finance, industrial robots, and internet services. The widespread application of AI and its deep integration with the economy and society have improved efficiency and produced benefits. At the same time, it will inevitably impact the existing social order and raise ethical concerns. Ethical issues, such as privacy leakage, discrimination, unemployment, and security risks, brought about by AI systems have caused great trouble to people. Therefore, AI ethics, which is a field related to the study of ethical issues in AI, has become not only an important research topic in academia, but also an important topic of common concern for individuals, organizations, countries, and society. This article will give a comprehensive overview of this field by summarizing and analyzing the ethical risks and issues raised by AI, ethical guidelines and principles issued by different organizations, approaches for addressing ethical issues in AI, and methods for evaluating the ethics of AI. Additionally, challenges in implementing ethics in AI and some future perspectives are pointed out. We hope our work will provide a systematic and comprehensive overview of AI ethics for researchers and practitioners in this field, especially the beginners of this research discipline. © 2020 IEEE.

关键词： Industrial robots

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GraphTensor: Comprehensive GNN-Acceleration Framework for Efficient Parallel Processing of Massive Datasets

GraphTensor: Comprehensive GNN-Acceleration Framework for Ef...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Junhyeok Jang Miryeong Kwon Donghyun Gouk Hanyeoreum Bae Myoungsoo Jung Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology (KAIST)

We present GraphTensor, a comprehensive open-source framework that supports efficient parallel neural network processing on large graphs. GraphTensor offers a set of easy-to-use programming primitives that appreciate both graph and neural network execution behaviors from the beginning (graph sampling) to the end (dense data processing). Our framework runs diverse graph neural network (GNN) models in a destination-centric, feature-wise manner, which can significantly shorten training execution times in a GPU. In addition, GraphTensor rearranges multiple GNN kernels based on their system hyperparameters in a self-governing manner, thereby reducing the processing dimensionality and the latencies further. From the end-to-end execution viewpoint, GraphTensor significantly shortens the service-level GNN latency by applying pipeline parallelism for efficient graph dataset preprocessing. Our evaluation shows that GraphTensor exhibits 1.4× better training performance than emerging GNN frameworks under the execution of large-scale, real-world graph workloads. For the end-to-end services, GraphTensor reduces training latencies of an advanced version of the GNN frameworks (optimized for multi-threaded graph sampling) by 2.4×, on average.

关键词：

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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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Efficient and high precision target-driven fluid simulation based on spatial geometry features

Efficient and high precision target-driven fluid simulation ...

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作者： Zhou, Xiangyang Liu, Sinuo Zeng, Haokai Wang, Xiaokun Ban, Xiaojuan Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems University of Science and Technology Beijing Beijing China School of Computer Science Peking University Beijing China National Centrefor Computer Animation Bournemouth University Poole United Kingdom Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang China

We proposed a novel target-driven fluid simulation method based on the weighted control model derived from the spatial geometric features of the target shape. First, the spatial geometric characteristics of the target model are taken into account to set the color field weights of control particles. This enabled the full expression of geometric characteristics of the target model, and improve the shape accuracy of controlled fluid. Then, the fluid is controlled to form the target shape under driving constraints, wherein we proposed a new adaptive constraint mechanism that enables efficient target shape generation. Finally, a new density constraint between the control particles and the controlled fluid particles is proposed to ensure the incompressibility of fluid during control. Compared to the state-of-the-art target-driven fluid control methods, our method achieves higher precision fluid control with higher efficiency. © 2023 John Wiley & Sons Ltd.

关键词： Hydrodynamics

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