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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental Science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities Artificial intelligence Artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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piRT-IFC:Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties

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Microsystems & Nanoengineering 2023年第3期9卷 347-356页

作者： Xiaofeng Luan Pengbin Liu Di Huang Haiping Zhao Yuang Li Sheng Sun Wenchang Zhang Lingqian Zhang Mingxiao Li Tian Zhi Yang Zhao Chengjun Huang Institute of Microelectronics of the Chinese Academy of Sciences BeijingChina University of Chinese Academy of Sciences BeijingChina State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijingChina Cerebrovascular Diseases Research Institute Xuanwu Hospital of Capital Medical UniversityBeijingChina

Real-time transformation was important for the practical implementation of impedance flow *** major obstacle was the time-consuming step of translating raw data to cellular intrinsic electrical properties(e.g.,specific membrane capacitance C_(sm) and cytoplasm conductivityσ_(cyto)).Although optimization strategies such as neural network-aided strategies were recently reported to provide an impressive boost to the translation process,simultaneously achieving high speed,accuracy,and generalization capability is still *** this end,we proposed a fast parallel physical fitting solver that could characterize single cells’C_(sm)andσ_(cyto)within 0.62 ms/cell without any data preacquisition or pretraining *** achieved the 27000-fold acceleration without loss of accuracy compared with the traditional *** on the solver,we implemented physics-informed real-time impedance flow cytometry(piRT-IFC),which was able to characterize up to 100,902 cells’C_(sm) andσ_(cyto)within 50 min in a real-time *** to the fully connected neural network(FCNN)predictor,the proposed real-time solver showed comparable processing speed but higher ***,we used a neutrophil degranulation cell model to represent tasks to test unfamiliar samples without data for *** being treated with cytochalasin B and N-Formyl-Met-Leu-Phe,HL-60 cells underwent dynamic degranulation processes,and we characterized cell’s C_(sm)andσ_(cyto)using *** to the results from our solver,accuracy loss was observed in the results predicted by the FCNN,revealing the advantages of high speed,accuracy,and generalizability of the proposed piRT-IFC.

关键词： impedance intrinsic flow

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An Eye-Gaze Tracking Method Based on a 3D Ocular Surface Fitting Model 4th

An Eye-Gaze Tracking Method Based on a 3D Ocular Surface Fit...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Ling, Zhang Zongxin, Ma MingYu, Yan Wenchao, Jiang Muhammad Computer Faculty Guangdong University of Technology Guangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Management and Technology Lahore54770 Pakistan

ISBN: (纸本)9783031200984

Nowadays gaze tracking systems have several common drawbacks: low suitability, high complexity and low accuracy. To solve these problems, this paper presents a novel gaze tracking method, which improves the performance of gaze tracking system in two aspects. A 3D ocular quadratic surface model is constructed with eyeball’s point cloud data, which are collected by a pair of binocular cameras in a free space, allowing head movement and without any wearable devices. Geometrical features of ocular surface, including principal direction, Gaussian curvature and radius are exploited to calculate ocular optical axis direction and person-specific parameters, and then to estimate ocular visual axis direction, i.e. gaze direction without any personal calibration procedures. For this purpose, two person-specific parameters, the radius of cornea curvature and the distance between the center of cornea curvature and the pupil on the ocular model, are inputted into a GRNN network to estimate the value of kappa angle, which is an angle between ocular optical axis and visual axis and has important effect on estimation accuracy of the ocular visual axis direction by the ocular optical axis direction. Compared with the pupil center cornea reflection method, the following experimental results show that the method presented in this paper is effective and accurate in kappa angle estimating and gaze tracking. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Eye tracking

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Early Execution for Soft Error Detection

Early Execution for Soft Error Detection

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International Conference on VLSI Design

作者： Raj Kumar Choudhary Janeel Patel Virendra Singh Computer Architecture and Dependable Systems Laboratory Indian Institute of Technology Bombay India

Single event upsets have made modern integrated circuits more susceptible to soft errors, making their operation less reliable. In order to detect induced computational faults, we propose a fault-tolerant, low-overhead microarchitecture that detects soft errors using temporal redundancy. The idea of introducing an Early Execution Unit (EXU) at the processor frontend has been explored by previous proposals in order to execute dynamic instructions with short dependency chains expeditiously for performance improvement. However, we observe that the functional units in the EXU are idle for a significant portion of the program execution duration. Our proposed microarchitecture utilises these inactive frontend functional units in order to re-execute dynamic instructions to ascertain computational correctness. A low-overhead backend verifier leverages idle resources for redundant execution using the proposed priority-based scheduling algorithm that interleaves program execution with re-execution for error detection. Our proposal provides exhaustive transient fault coverage while improving performance by 7.5% over an existing restricted OoO microarchitecture, Freeflow Core, delivering IPC close to that of an out-of-order baseline, while being $1.78 \times $ more energy-efficient than the involuted, power-hungry out-of-order design.

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Path Optimization Method Under UAV Charging Scheduling Network 24th

Path Optimization Method Under UAV Charging Scheduling Netwo...

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24th International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Yang, Tingting Wang, Yiqian Zhu, Jie Chang, Shuyu Huang, Haiping Nanjing University of Posts and Telecommunications Nanjing210003 China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing100864 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (纸本)9789819615278

Unmanned Aerial Vehicles (UAVs) have emerged as integral components in logistics systems, where their potential for efficient delivery services is being explored. However, the limited battery capacity of UAVs poses a significant challenge for long-distance delivery applications. In this paper, we investigate a hybrid problem involving UAV trajectory planning and charging scheduling for long-distance logistics systems with a charging network. In the problem under study, multiple UAVs are assigned long-distance delivery tasks. A UAV can stop at any charging station in the charging network to charge if it is in a low-energy state. An Ant Colony Optimization-based UAV Trajectory Planning and Charging Scheduling (ACO-TPCS) algorithm is proposed to minimize task completion time by optimizing UAV trajectory and charging plans. The main framework of the ACO-TPCS algorithm consists of several key components, including reachable graph construction, candidate flight path generation, pheromone matrix construction, ant generation, feasible solution generation, and pheromone update method. All these components are delicately designed. Through extensive experimentation and comparison with baseline algorithms, we demonstrate the effectiveness of the ACO-TPCS algorithm in addressing the problem under study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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DRS: A deep reinforcement learning enhanced Kubernetes scheduler for microservice-based system

DRS: A deep reinforcement learning enhanced Kubernetes sched...

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作者： Jian, Zhaolong Xie, Xueshuo Fang, Yaozheng Jiang, Yibing Lu, Ye Dash, Ankan Li, Tao Wang, Guiling College of Computer Science Nankai University Tianjin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Cyber Science Nankai University Tianjin China Department of Computer Science New Jersey Institute of Technology NewarkNJ United States

Recently, Kubernetes is widely used to manage and schedule the resources of microservices in cloud-native distributed applications, as the most famous container orchestration framework. However, Kubernetes preferentially schedules microservices to nodes with rich and balanced CPU and memory resources on a single node. The native scheduler of Kubernetes, called Kube-scheduler, may cause resource fragmentation and decrease resource utilization. In this paper, we propose a deep reinforcement learning enhanced Kubernetes scheduler named DRS. We initially frame the Kubernetes scheduling problem as a Markov decision process with intricately designed state, action, and reward structures in an effort to increase resource usage and decrease load imbalance. Then, we design and implement DRS mointor to perceive six parameters concerning resource utilization and create a thorough picture of all available resources globally. Finally, DRS can automatically learn the scheduling policy through interaction with the Kubernetes cluster, without relying on expert knowledge about workload and cluster status. We implement a prototype of DRS in a Kubernetes cluster with five nodes and evaluate its performance. Experimental results highlight that DRS overcomes the shortcomings of Kube-scheduler and achieves the expected scheduling target with three workloads. With only 3.27% CPU overhead and 0.648% communication delay, DRS outperforms Kube-scheduler by 27.29% in terms of resource utilization and reduces load imbalance by 2.90 times on average. © 2023 John Wiley & Sons, Ltd.

关键词： Reinforcement learning

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Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection 7th

Block-gram: Mining Knowledgeable Features for Smart Contrac...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Li, Tao Wang, Haolong Fang, Yaozheng Jian, Zhaolong Wang, Zichun Xie, Xueshuo Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031281235

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Sampling Methods for Efficient Training of Graph Convolutional Networks:A Survey

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 205-234页

作者： Xin Liu Mingyu Yan Lei Deng Guoqi Li Xiaochun Ye Dongrui Fan State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100086 School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049 IEEE Department of Precision Instrument Center for Brain Inspired Computing ResearchTsinghua UniversityBeijing 100084China

Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces *** a GCN model for large-scale graphs in a conventional way requires high computation and storage ***,motivated by an urgent need in terms of efficiency and scalability in training GCN,sampling methods have been proposed and achieved a significant *** this paper,we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of *** highlight the characteristics and differences of sampling methods,we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all ***,we discuss some challenges and future research directions of the sampling methods.

关键词： Efficient training graph convolutional networks(GCNs) graph neural networks(GNNs) sampling method

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Stochastic Task Offloading Problems for Edge Computing 17th

Stochastic Task Offloading Problems for Edge Computing

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Ding, Kexin Zhong, Zhi Zhu, Jie Nanjing University of Posts and Telecommunications Nanjing210003 China Institute of Computing Technology Academy of Sciences State Key Laboratory Chinese of Computer Architecture Beijing100864 China

ISBN: (纸本)9789819923557

The edge-cloud computing is extensively deployed to provide convenient computing. A stochastic task offloading problem in edge-cloud environment is considered. For the problem under study, a greedy simulated annealing heuristic algorithm based online offloading framework (SAOF) is proposed. In consideration of the stochastic arrival of independent tasks and the heterogeneity of resources, SAOF offloads tasks to cloud servers or edge servers dynamically. During the offloading process, each server preemptively schedules the uncompleted tasks according to the latency-sensitivity of the task. The optimization objective is to minimize the total weighted response time. Experiments are delicately designed on the testing instances with various settings. SAOF is compared with the baseline algorithm. Experimental results indicate that SAOF can more effectively reduce the total weighted response time of all tasks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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Accelerate Bit Manipulation in XiangShan Processer Using RISC-V B Extension and Instruction Fusion

Accelerate Bit Manipulation in XiangShan Processer Using RIS...

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2022 International Conference on Cloud Computing, Internet of Things, and computer Applications, CICA 2022

作者： Zhang, Fawang Tang, Dan Cai, Ye College of Computer Science and Software Engineering Shenzhen University Shenzhen China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China

ISBN: (纸本)9781510656673

In the field of high-performance computing, some application scenarios make extensive use of bit manipulation. RISC-V foundation issues B extension to reduce the number of instructions during the static compilation. But this extension does not cover the more frequent operations fully. Instruction fusion technology can fuse multiple instructions into one instruction at the decode stage to dynamically reduce the number of instructions. Therefore, this technology can supplement RISC-V B extension uncovered instruction, enhancing the processor’s capabilities for bit manipulation. This paper implements the RISC-V B extension 1.0 version on the Xiangshan processor. For accelerating more bit manipulation, we profiled general benchmarks by the simulator, proposed ten new instructions fusion targets, and implemented a fusion decode unit. Finally, we use Coremark and SPEC CPU 2006 integer to evaluate the performance. The Coremark results show that dynamic instructions reduce by 18.14%, and the performance improves by 12.09%. © 2022 SPIE.

关键词： Decoding

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