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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Li, Lianghao He, Cheng Cheng, Ran Pan, Linqiang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783030720612

Mating restriction strategies are capable of restricting the distribution of parent solutions for effective offspring generation in evolutionary algorithms (EAs). Studies have shown the importance of these strategies in improving the performance of EAs for multiobjective optimization. Our previous study proposed a specific manifold learning inspired mating restriction (MLMR) strategy. It has shown promising capability of solving multiobjective optimization problems (MOPs) with complicated Pareto set shapes. However, the effect of mating restriction strategies in solving constrained MOPs is yet to be well studied. Here, we investigate the effectiveness of MLMR for solving constrained MOPs. The MLMR strategy is embedded into some representative multiobjective EAs and tested on various benchmark constrained MOPs. Experimental results indicate the encouraging performance of MLMR in constrained multiobjective optimization. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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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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Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation 11th

Operator-Adapted Evolutionary Large-Scale Multiobjective Opt...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Huang, Changwu Li, Lianghao He, Cheng Cheng, Ran Yao, Xin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030720612

Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications, and they are challenging for existing evolutionary algorithms due to their massive volume of search space. Despite that a number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been proposed in recent years, their effectiveness in solving LSMOPs remains unsatisfactory. One main reason is that most existing LSMOEAs may fail to balance convergence enhancement and diversity maintenance, especially for solving real-world problems. To address this issue, we propose to use a hybridized LSMOEA with adaptive operator selection (AOS) to handle real-world LSMOPs. Specifically, the proposed hybridized LSMOEA with AOS (AOS-LSMOEA) includes multiple different offspring generation and environmental selection strategies extracted from some existing LSMOEAs. Then it uses the AOS to adaptively determine the application rates of different offspring generation and environmental selection operators in an online manner. The proposed approach is capable of taking advantage of existing LSMOEAs, and the AOS enables the algorithm to choose suitable operators for solving different LSMOPs. In this study, the proposed algorithm is expected to solve the voltage transformer ratio error estimation (TREE) problems effectively. Experimental results show that AOS-LSMOEA achieves significant performance improvement due to the hybridization of different operators and the adoption of AOS method. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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CoChain: High Concurrency Blockchain Sharding via Consensus on Consensus 42

CoChain: High Concurrency Blockchain Sharding via Consensus ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Li, Mingzhe Lin, You Zhang, Jin Wang, Wei Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Computer Science and Engineering Department China Hong Kong University of Science and Technology Computer Science and Engineering Department Hong Kong Institute of High Performance Computing and Agency for Science Technology and Research Singapore

ISBN: (纸本)9798350334142

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes ( © 2023 IEEE.

关键词： Blockchain

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Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

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Frontiers of Information technology & Electronic Engineering 2023年第2期24卷 187-202页

作者： Jiaqi GAO Jingqi LI Hongming SHAN Yanyun QU James ZWANG Fei-Yue WANG Junping ZHANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201210China School of Information Science and Technology Xiamen UniversityXiamen 361005China College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPA 16802USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain ***-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are *** overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd *** goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental ***,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning *** experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

关键词： Crowd counting Knowledge distillation Lifelong learning

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Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary Algorithms 13

Last-X-Generation Archiving Strategy for Multi-Objective Evo...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Shu, Tianye Nan, Yang Shang, Ke Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time. © 2024 IEEE.

关键词： Optimization algorithms

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Generating Redstone Style Cities in Minecraft 5

Generating Redstone Style Cities in Minecraft

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5th Annual IEEE Conference on Games, CoG 2023

作者： Huang, Shuo Hu, Chengpeng Togelius, Julian Liu, Jialin Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China New York University Tandon School of Engineering Department of Computer Science and Engineering New York10012 United States

ISBN: (纸本)9798350322774

Procedurally generating cities in Minecraft provides players more diverse scenarios and could help understand and improve the design of cities in other digital worlds and the real world. This paper presents a city generator that was submitted as an entry to the 2023 Edition of Minecraft Settlement Generation Competition for Minecraft. The generation procedure is composed of six main steps, namely vegetation clearing, terrain reshaping, building layout generation, route planning, streetlight placement, and wall construction. Three algorithms, including a heuristic-based algorithm, an evolving layout algorithm, and a random one are applied to generate the building layout, thus determining where to place different redstone style buildings, and tested by generating cities on random maps in limited time. Experimental results show that the heuristic-based algorithm is capable of finding an acceptable building layout faster for flat maps, while the evolving layout algorithm performs better in evolving layout for rugged maps. A user study is conducted to compare our generator with outstanding entries of the competition's 2022 edition using the competition's evaluation criteria and shows that our generator performs well in the adaptation and functionality criteria. © 2023 IEEE.

关键词： Heuristic algorithms

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Overexpression of Sirt6 ameliorates sleep deprivation induced-cognitive impairment by modulating glutamatergic neuron function

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Neural Regeneration Research 2023年第11期18卷 2449-2458页

作者： Jinpiao Zhu Chang Chen Zhen Li Xiaodong Liu Jingang He Ziyue Zhao Mengying He Binbin Nie Zili Liu Yingying Chen Kuanpin Su Xiang Li Juxiang Chen Hongbing Xiang Fuqiang Xu Kangguang Lin Zongze Zhang Jie Wang Department of Anesthesiology Brain Research CenterDepartment of NeurosurgeryZhongnan HospitalWuhan UniversityWuhanHubei ProvinceChina Department of Anesthesiology and Pain Medicine Tongji Hospital of Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanHubei ProvinceChina Department of Anesthesia and Intensive Care Peter Hung Pain Research InstituteThe Chinese University of Hong KongHong Kong Special Administration RegionChina Key Laboratory of Magnetic Resonance in Biological Systems State Key Laboratory of Magnetic Resonance and Atomic and Molecular PhysicsNational Center for Magnetic Resonance in WuhanWuhan Institute of Physics and MathematicsInnovation Academy for Precision Measurement Science and TechnologyChinese Academy of Sciences-Wuhan National Laboratory for OptoelectronicsWuhanHubei ProvinceChina Key Laboratory of Nuclear Radiation and Nuclear Energy Technology Institute of High Energy PhysicsChinese Academy of SciencesBeijingChina The Brain Cognition and Brain Disease Institute(BCBDI) NMPA Key Laboratory for Research and Evaluation of Viral Vector Technology in Cell and Gene Therapy Medicinal ProductsShenzhen Key Laboratory of Viral Vectors for BiomedicineShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research InstitutionsShenzhenGuangdong ProvinceChina Mind-Body Interface Research Center(MBI-Lab) China Medical University HospitalTaichungTaiwanChina An-Nan Hospital China Medical UniversityTainanTaiwanChina Department of Neurosurgery Changhai HospitalNaval Medical UniversityShanghaiChina Department of Affective Disorders The Affiliated Brain Hospital of Guangzhou Medical UniversityGuangzhouGuangdong ProvinceChina Institute of Neuroscience and Brain Diseases Xiangyang Central HospitalAffiliated Hospital of Hubei University of Arts and ScienceXiangyangHubei ProvinceChina University of Chinese Academy of Sciences BeijingChina

Sleep benefits the restoration of energy metabolism and thereby suppo rts neuronal plasticity and cognitive ***6 is a NAD+-dependent protein deacetylase that has been recognized as an essential regulator of energy metabolism because it modulates various transcriptional regulators and metabolic *** aim of this study was to investigate the influence of Sirt6 on cerebral function after chronic sleep deprivation(CSD).We assigned C57BL/6J mice to control or two CSD groups and subjected them to AAV2/9-CMV-EGFP or AAV2/9-CMV-Sirt6-EGFP infection in the prelimbic cortex(PrL).We then assessed cerebral functional connectivity(FC) using resting-state functional MRI,neuron/astrocyte metabolism using a metabolic kinetics analysis;dendritic spine densities using sparse-labeling;and miniature excitato ry postsynaptic currents(mEPSCs) and action potential(AP) firing rates using whole-cell patchclamp *** addition,we evaluated cognition via a comprehensive set of behavioral *** with controls,Sirt6 was significantly decreased(P<0.05) in the PrL after CSD,accompanied by cognitive deficits and decreased FC between the PrL and accumbens nucleus,piriform cortex,motor co rtex,somatosensory co rtex,olfactory tubercle,insular cortex,and ***6 ove rexpression reve rsed CSD-induced cognitive impairment and reduced *** analysis of metabolic kinetics using [1-13C] glucose and [2-13C] acetate showed that CSD reduced neuronal Glu4and GABA2synthesis,which could be fully restored via forced Sirt6 ***,Sirt6 ove rexpression reversed CSD-induced decreases in AP firing rates as well as the frequency and amplitude of mEPSCs in PrL pyramidal *** data indicate that Sirt6 can improve cognitive impairment after CSD by regulating the PrL-associated FC network,neuronal glucose metabolism,and glutamatergic ***,Sirt6 activation may have potential as a novel strategy for treating sleep disorder-related diseases.

关键词： chronic sleep deprivation cognitive impairment functional connectivity glutamatergic neurons metabolic kinetics neuronal-astrocytic glucose metabolism prelimbic cortex REM sleep Sirt6 synaptic function

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Negatively Correlated Search for Constrained Optimization

Negatively Correlated Search for Constrained Optimization

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Li, Yuan Lu, Xiaofen Yao, Xin Southern University of Science and Technology The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

ISBN: (纸本)9798350314588

Evolutionary algorithms (EAs) combined with constraint handling techniques (CHTs) are very effective in solving constrained optimization problems (COPs). Recently, negatively correlated search (NCS) has been shown to be powerful for real-world multimodal optimization problems (MMOP). However, there has been few works on utilizing NCS to solve COPs. In this paper, we present a novel constrained optimization evolutionary algorithm (COEA) named NCS-E which combined NCS with a new CHT ensemble method to deal with COPs. It integrates three complementary CHTs as voters, and individuals are first voted by each voter, then the individual with more weighted votes is considered better. In addition, the negative correlation information of the voted individuals is utilized to adaptively adjust the voting weights. To demonstrate the performance of NCS-E, NCS-E is compared with four NCS-based COEAs and two state-of-the-art methods, MAgES and MAgES-VMCH, on 57 real-world COPs. The statistical results show that NCS-E exhibits the best performance among the compared methods. Moreover, NCS-E was applied to solve the Deep Neural Network pruning problem and compared with the state-of-the-art pruning method OLMP on three reference models. Empirical results show that the proposed method improves the performance of OLMP on all three DNN pruning tasks. © 2023 IEEE.

关键词： Constrained optimization

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Aerial-ground collaborative routing with time constraints

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Chinese Journal of Aeronautics 2023年第2期36卷 270-283页

作者： Jiahao XING Lichen SU Wenjing HONG Lu TONG Renli LYU Wenbo DU School of Electronics and Information Engineering Beihang UniversityBeijing 100091China School of Future Aerospace Technology/Shen Yuan Honors College Beihang UniversityBejing 100091China School of Automation Science and Electrical Engineering Beihang UniversityBejing 100091China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Shenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Research Institute of Frontier Science Beihang UniversityBeijing 100083China CAAC Key Laboratory of General Aviation Operation Civil Aviation Management Institute of ChinaBeijing 100102China

The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery *** reach a balance between green transportation and competitive edge,the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery,where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily *** this paper,we propose the Vehicle Routing Problem with Drones under Time constraints(VRPD-T)and focus on the time constraints involved in realistic scenarios during the delivery.A mixed-integer linear programming model has been developed to minimize the total delivery completion ***,to overcome the limitations of standard solvers in handling large-scale complex issues,a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality *** numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.

关键词： Aerial-ground collaborative delivery Mixed-integer programming Space-time hybrid heuristic algorithm Vehicle routing problem with drones Time-dependent travel times

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