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46th International Conference on software engineering: Companion, ICSE-Companion 2024

作者： Niu, Feifei Shao, Junqian Xu, Chaofan Mayr-Dorn, Christoph Assunção, Wesley K. G. Huang, Liguo Li, Chuanyi Ge, Jidong Luo, Bin Egyed, Alexander State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science North Carolina State University North Carolina United States Department of Computer Science and Engineering Southern Methodist University DallasTX United States

ISBN: (纸本)9798400705021

History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source code elements break such history. To this end, this paper presents RAT, a refactoring-aware tool for keeping track of code elements evolution across time, not just in terms of revisions but also in terms of refactoring. This is the first tool that enables fine-grained code element traceability of the whole repository. Empirical evaluation of leveraging our tool in three bug localization techniques relying on code history shows significant improvement in localization accuracy. Based on our findings, we believe that many of the state-of-the-art approaches using past source code data would benefit from our tool. Demo Tool: https://***/feifeiniu-se/RAT-Demo. Demo Video: https://***/VI-xwUaIPp4. © 2024 IEEE Computer Society. All rights reserved.

关键词： Rats

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A Peridynamic Model for the Dynamics of Defects with Asymmetric Potential Wells

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Acta Mechanica Solida Sinica 2021年第6期34卷 844-861页

作者： Jiacheng Xing Jianxiang Wang Linjuan Wang State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of EngineeringPeking UniversityBeijing100871China School of Astronautics Beihang UniversityBeijing100191China

The peridynamic model of a solid is suitable for studying the dynamics of defects in *** use the bond-based peridynamic theory to propose a one-dimensional nonlocal continuum model to study a defect in equilibrium and in steady *** the defect propagates,the material particles undergo a transition between two *** using the Wiener–Hopf method,an explicit analytical solution of the problem is *** relation between the applied force and the propagation speed of the defect is determined;our results show that the defect does not propagate if the applied force is less than a critical value,whereas propagation occurs when the force exceeds that *** energy properties of the system are investigated.

关键词： Peridynamic theory Defect propagation Phase transformation The Wiener-Hopf method

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Smooth and Time-optimal Improved-Bezier Trajectory Planning Based on Dynamics for Industrial Robots

Smooth and Time-optimal Improved-Bezier Trajectory Planning ...

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第43届中国控制会议

作者： Wei Cai Yahui Gan Fang Fang Bo Zhou Xianzhong Dai School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper presents a smooth and time-optimal improved-Bezier trajectory planning algorithm that addresses the avoidance of solving switching points and solves the problem of discontinuous torque on the basis of the time-optimal path parameterization(TOPP) algorithm. The proposed approach employs the TOPP-RA algorithm with a friction model, to construct the optimal velocity trajectory under the constraints of the geometric paths and joint torques. Moreover, the proposed algorithm utilizes an enhanced Bezier curve instead of the jerk constraint in the trajectory optimization model to achieve a smooth trajectory. The modified Bezier curve optimizes the peaks of the pseudo-velocity curve in the phase plane to change the discontinuity of joint torque. The proposed algorithm ensures absolute time optimality and achieve a s mooth tr ajectory. The results of the MATLAB simulation experiment on a two-link motion system indicate the validity of the proposed algorithm.

关键词： time-optimal smooth trajectory planning improved-Bezier trajectory

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Neighborhood Subgraph-based Illicit Transaction Detection in Cryptocurrency Networks

Neighborhood Subgraph-based Illicit Transaction Detection in...

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International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Shenghao Jin Qiwen Yang Shengyu Chen Hui Zhang State Key Laboratory of Complex & Critical Software Environment School of Computer Science & Engineering Beihang University Beijing China

ISBN: (数字)9798331510633

ISBN: (纸本)9798331510640

With the widespread adoption of cryptocurrencies, associated illicit activities such as money laundering, fraud, extortion, and Ponzi schemes have garnered significant attention. Traditional studies have extensively employed graph neural network technologies to identify illicit transactions, typically treating the entire transaction network as input to classify its nodes. Although these methods perform well on small-scale networks, they face performance limitations when processing large-scale blockchain transaction data. To address this issue, we propose a neighborhood subgraph-based method for detecting illicit transactions, integrating GCN and LSTM. In this approach, the GCN captures and integrates information from adjacent nodes for each transaction, facilitating a deep understanding of network structures. The LSTM is tasked with capturing the sequence and variations in fund flows, effectively tracking the direction and temporal characteristics of the money flow. Experimental results indicate that when using 3-hop neighborhood subgraphs, our model’s performance surpasses other GCN-based methods. Compared to previous models that require considering the entire transaction network, our approach only needs to utilize information from an average of 80 nodes, significantly improving efficiency.

关键词： Knowledge engineering Graph neural networks Cryptocurrency Fraud Blockchains Internet of Things Long short term memory Faces

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In-situ growth of heterophase Ni nanocrystals on graphene for enhanced catalytic reduction of 4-nitrophenol

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Nano Research 2022年第2期15卷 1230-1237页

作者： Jiahao Zhuang Feng He Xianglin Liu Pengchao Si Fangna Gu Jing Xu Yu Wang Guangwen Xu Ziyi Zhong Fabing Su School of Chemical Engineering University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Key Laboratory on Resources Chemicals and Materials of Ministry of Education Shenyang University of Chemical TechnologyShenyang 110142China State Key Laboratory of Chemical Engineering School of Chemical EngineeringEast China University of Science and TechnologyShanghai 200237China Department of Chemical Engineering Guangdong Technion-Israel Institute of Technology(GTIIT)Shantou 515063China Technion-Israel Institute of Technology(IIT) Haifa 32000Israel

Generating heterophase structures in nanomaterials,e.g.,heterophase metal nanocrystals,is an effective way to tune their physicochemical properties because of their high-energy nature and unique electronic environment of the generated ***,the direct synthesis of heterophase metal nanocrystals remains a great challenge due to their unstable ***,we report the in situar direct synthesis of heterophase Ni nanocrystals on *** heterostructure of face-centered cubic(fee)and hexagonal close-packed(hep)phase was generated via the epitaxial growth of hep Ni and the partial transformation of fee Ni and stabilized by the anchoring effect of graphene toward fee Ni nanocrystal and the preferential adsorption of surfactant polyethylenimine(PEI)toward epitaxial hep *** with the fee Ni nanocrystals grown on graphene,the heterophase(fcc/hcp)Ni nanocrystals in situ grown on graphene showed a greatly improved catalytic activity and reusability in 4-nitrophenol(4-NP)reduction to 4-aminophenol(4-AP).The measured apparent rate constant and the activity parameter were 2.958 min^(-1) and 102 min^(-1)·mg^(-1),respectively,higher than that of the best reported non-noble metal catalysts and most noble metal *** control experiments and density functional theory calculations reveal that the interface of the fee and hep phases enhances the adsorption of substrate 4-NP and thus facilitates the reaction *** work proves the novel idea for the rational design of heterophase metal nanocrystals by employing the synergistic effect of surfactant and support,and also the potential of creating the heterostructure for enhancing their catalytic reactivity.

关键词： heterophase structure Ni nanocrystals graphene 4-nitrophenol(4-NP)reduction catalysts

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Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing 42

Enabling Communication-Efficient Federated Learning via Dist...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Guan, Yixuan Liu, Xuefeng Ren, Tao Niu, Jianwei Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Sciences Laboratory for Internet Software Technologies Institute of Software Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach. © 2023 IEEE.

关键词： Compressed sensing

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FEAT: Towards Fast Environment-Adaptive Task Offloading and Power Allocation in MEC 42

FEAT: Towards Fast Environment-Adaptive Task Offloading and ...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Ren, Tao Hu, Zheyuan He, Hang Niu, Jianwei Liu, Xuefeng Institute of Software Chinese Academy of Sciences Laboratory for Internet Software Technologies Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Mobile edge computing (MEC) has been proposed to provide mobile devices with both satisfactory computing resources and latency. key issues in MEC include task offloading and power allocation (TOPA), for which deep reinforcement learning (DRL) is becoming a popular methodology. However, most DRL-based TOPA approaches are typically developed in a certain environment, lacking the adaptability to unseen environments. Motivated by this, this paper proposes a Fast Environment-Adaptive TOPA (FEAT) approach that could adapt to unseen environments with little fine-tuning. Specifically, we first split MEC states into the internal state and environmental state. Then, based on these two types of states, we develop two main components of FEAT - a group of internal state-dependent TOPA meta-policies and an environmental state-embedded steerer. Meta-policies learn TOPA skills within the internal state space (allowing reusing meta-policies in different environments), while the steerer learns to choose appropriate meta-policies according to embedded environmental states. When encountering an unseen environment with the same internal state space, FEAT only needs to fine-tune the steerer using the newly embedded environmental state with few internal state explorations. Extensive experimental results on simulation and testbed demonstrate that FEAT outperforms the state-of-the-art by more than 16.4% in terms of fine-tuning speeds. © 2023 IEEE.

关键词： Mobile edge computing

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Shock-induced instability of dual-layer dilute gas-particle mixture

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Physical Review Fluids 2024年第12期9卷 124301-124301页

作者： Yifeng He Baoqing Meng Baolin Tian Yue Yang State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing 100871 China and HEDPS-CAPT Peking University Beijing 100871 China

We investigate the two-dimensional shock-induced instability of a dual-layer gas-particle (DLGP) mixture, with particle volume fractions on the order of 10−3 and moderate mass loading at the initial time. We elucidate the mechanism underlying the instability in the DLGP mixture using vorticity budgets, wave system analysis, and particle dynamics. The mechanism is distinct from that in the dual-layer gas (DLG) mixture. In the DLG mixture, the shock-induced instability is driven by the baroclinic vorticity. By contrast, in the DLGP mixture, the vorticity budgets suggest that the instability is not triggered by the baroclinic vorticity, because its contribution is negligible. Instead, the shock-induced instability in the DLGP mixture is induced by the pressure perturbation near the perturbed interface. The particle dynamics show that the velocity difference in particles near the perturbed interface is driven by the velocity difference in gas through the drag force. Thus, the velocity difference in- gas induced by the pressure perturbation drives the particle interface to grow via dragcoupling effects. Inspired by the interfacial instability mechanism, we estimate the growth of the particle interface in the linear stage. The model incorporates the multiphase Atwood number and the Stokes number, and well captures the linear growth rate of the particle-phase interface amplitude at moderate Atwood and Stokes numbers.

关键词： Particle-laden flows Richtmyer-Meshkov instability

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Improving prediction of preferential concentration in particle-laden turbulence using the neural-network interpolation

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Physical Review Fluids 2024年第3期9卷 034606-034606页

作者： Jiajun Hu Zhen Lu Yue Yang State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing 100871 China HEDPS-CAPT Peking University Beijing 100871 China

We develop a neural-network interpolation (NNI) to improve the prediction of preferential concentration in simulations of particle-laden turbulence. The NNI uses the particle position and velocity on neighboring grid points to estimate the fluid velocity at the particle position via fully connected neural networks. By avoiding the requirement for superresolution of the entire field and additional interpolations, the NNI offers computational efficiency and simplifies implementation. To evaluate the effectiveness of NNI and compare it with other interpolation methods, we conduct simulations on two-dimensional homogeneous isotropic turbulence subjected to high-wave-number forcing. This specific turbulent flow has a long inertial range and rich small-scale structures, posing a challenge for velocity interpolations and subsequently accurate prediction of preferential concentration. We compare the results on flow fields, energy spectra, and preferential concentration against the reference data obtained from direct numerical simulations at a range of the Stokes number from 0.1 to 5.0. The comparison demonstrates that the NNI can recover the effect of small-scale motion on particle distribution, so it improves the prediction accuracy of the preferential concentration from a priori test results of the large-eddy simulation on coarse grids.

关键词： Particle-laden flows Turbulence simulations Two-dimensional turbulence Fluid-particle interactions Direct numerical simulations Large eddy simulations Machine learning

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End-to-end multi-task reinforcement learning-based UAV swarm communication attack detection and area coverage

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Knowledge-Based systems 2025年 316卷

作者： Yu, Jin Zhang, Ya Sun, Changyin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

This paper proposes an innovative end-to-end multi-task reinforcement learning framework designed to enhance the security and operational stability of UAVs in complex communication attacks environments. In scenarios involving multiple UAV coverage, the framework classifies attack types and predicts UAV behaviors, which enables rapid response and compromised data recovery. First, a spatiotemporal attention mechanism is employed to strengthen the network by capturing temporal dependencies in the communication data and improving the recognition of hybird attack mode, thereby facilitating the integration of detection and repair. Second, the causal inference mechanism introduced in this study extracts true causal relationships from interference, which ensures the accuracy and stability of action predictions for area coverage under attack conditions. Finally, the entire process is modeled as a multi-task Markov decision process, optimizing overall decision-making efficiency. Additionally, the dynamic pheromone induction mechanism is proposed to realize the adaptive regional cooperative coverage of multiple UAVs. The experimental results demonstrate that the proposed algorithm can effectively detect hybrid attacks and predict UAV actions, protecting the multi-UAV cooperative area coverage. © 2025

关键词： Reinforcement learning

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