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CoRE:Constrained Robustness Evaluation of Machine Learning-Based Stability Assessment for Power systems

IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 557-559页

作者： Zhenyong Zhang David K.Y.Yau the State Key Laboratory of Public Big Data and the College of Computer Science and Technology Guizhou UniversityGuiyang 550025China IEEE the Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingapore 487372Singapore

Dear Editor,Machine learning(ML) approaches have been widely employed to enable real-time ML-based stability assessment(MLSA) of largescale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems.

关键词： enable constraints Power

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Distinguishing Sensitive and Insensitive Options for the Winograd Schema Challenge 28th

Distinguishing Sensitive and Insensitive Options for the ...

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28th International Conference on database systems for Advanced Applications, DASFAA 2023

作者： Li, Dong Wang, Pancheng He, Liangliang Pang, Kunyuan Li, Shasha Tang, Jintao Wang, Ting College of Computer Science and Technology National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China

ISBN: (纸本)9783031306747

The Winograd Schema Challenge (WSC) is a popular benchmark for commonsense reasoning. Each WSC instance has a component that corresponds to the mention of the correct answer option of the two options in the context. We observe that the answers of many instances are insensitive to the options. In this paper, based on this observation, we propose an approach based on fine-tuning the pre-trained language model for WSC by distinguishing sensitive and insensitive options. First, we split WSC instances into option-sensitive and insensitive categories, and use option expanding and option masking strategies to weaken the options so that the model does not pay attention to options when they are insensitive during fine-tuning. Second, we treat the two categories as intermediate-task of each other, and use transfer learning to improve the performance. We fine-tune BERT-Large and T5-XXL with our approach on WINOGRANDE, a new dataset of WSC, and the experiment shows our method outperforms baselines by a large margin, achieving state-of-the-art, which indicates the effectiveness of our instance-distinguishing strategy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

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Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

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Computer systems science & Engineering 2023年第6期45卷 2505-2519页

作者： Yizhi Liu Rutian Qing Yijiang Zhao Xuesong Wang Zhuhua Liao Qinghua Li Buqing Cao School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Northwestern Institute on Complex Systems Northwestern UniversityEvanstonIL60208USA Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Xiangtan411201China

Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the *** how to alleviate sparsity and further enhance accuracy is still *** at these issues,we propose to fuse spatio-temporal contexts into deep factorization machine(STC_DeepFM)offline for pick-up area recommendation,and within the area to recommend pick-up points online using factorization machine(FM).Firstly,we divide the urban area into several grids with equal ***-temporal contexts are destilled from pick-up points or points-of-interest(POIs)belonged to the preceding ***,the contexts are integrated into deep factorization machine(DeepFM)to mine high-order interaction relationships from *** a novel algorithm named STC_DeepFM is presented for offline pick-up area ***,we devise the architecture of offline-to-online(O2O)recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and *** experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts,different recommendation models,and the O2O *** results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods,and the O2O architecture achieves excellent real-time performance.

关键词： Location-based service(LBS) trajectory data mining offline-toonline(O2O)recommendation deep factorization machine(DeepFM) spatiotemporal context

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Mask-guided Multi-scale Spatial-Spectral Transformer for Snapshot Compressive Imaging

Mask-guided Multi-scale Spatial-Spectral Transformer for Sna...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yin, Heyuan Yang, Jingxiang Liu, Jia Xiao, Liang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Analysis of Mathematical Theory and Modeling of Complex Systems Ministry of Industry and Information Technology China

ISBN: (纸本)9798350368741

Effectively reconstructing 3D hyperspectral images (HSIs) from 2D measurements presents a significant challenge in Coded Aperture Snapshot Spectral Imaging (CASSI) systems. While recent transformers exhibit potential in HSI reconstruction, they often suffer from inadequate exploration of multi-scale spatial-spectral self-similarity, leading to mean effects and information loss. Additionally, these methods struggle with insufficient modeling of the degradation inherent in the compressive imaging process. To address these issues, we propose a novel Mask-guided Multi-scale Spatial-Spectral Transformer (MMSST). Specifically, we introduce a Degradation Aware Mask Attention (DAMA) module to incorporate degradation information of the compressive imaging process. Furthermore, MMSST leverages Local-Regional SpAtial attention (LRSA) and Global-Regional SpEctral attention (GRSE) to effectively exploit multi-scale self-similarity across spatial and spectral dimensions. Extensive experimental results demonstrate the effectiveness of our MMSST. © 2025 IEEE.

关键词： hyperspectral reconstruction Snapshot Compressive Imaging spatial-spectral transformer

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AMCheX: Accurate Analysis of Missing-Check Bugs for Linux Kernel

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Journal of Computer science & Technology 2021年第6期36卷 1325-1341页

作者： Ying-Jie Wang Liang-Ze Yin Wei Dong Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense TechnologyChangsha 410073China

The Linux kernel adopts a large number of security checks to prevent security-sensitive operations from being executed under unsafe *** a security-sensitive operation is unchecked,a missing-check issue *** check is a class of severe bugs in software programs especially in operating system kernels,which may cause a variety of security issues,such as out-of-bound accesses,permission bypasses,and privilege *** to the lack of security specifications,how to automatically identify security-sensitive operations and their required security checks in the Linux kernel becomes a challenge for missing-check *** this paper,we present an accurate missing-check analysis method for Linux kernel,which can automatically infer possible security-sensitive ***,we first automatically identify all possible security check functions of *** according to their callsites,a two-direction analysis method is leveraged to identify possible security-sensitive operations.A missing-check bug is reported when the security-sensitive operation is not protected by its corresponding security *** have implemented our method as a tool,named AMCheX,on top of the LLVM(Low Level Virtual Machine)framework and evaluated it on the Linux *** reported 12 new missing-check bugs which can cause security *** of them have been confirmed by Linux maintainers.

关键词： security check function security-sensitive operation program analysis missing-check

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SSpose: Self-Supervised Spatial-Aware Model for Human Pose Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5403-5417页

作者： Yu, Linfang Qin, Zhen Xu, Liqun Qin, Zhiguang Choo, Kim-Kwang Raymond University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Industrial Research Institute Chengdu610213 China University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering Chengdu610054 China Industrial Research Institute Chengdu610213 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human pose estimation (HPE) relies on the anatomical relationships among different body parts to locate keypoints. Despite the significant progress achieved by convolutional neural networks (CNN)-based models in HPE, they typically fail to explicitly learn the global dependencies among various body parts. To overcome this limitation, we propose a spatial-aware HPE model called SSpose that explicitly captures the spatial dependencies between specific key points and different locations in an image. The proposed SSpose model adopts a hybrid CNN-Transformer encoder to simultaneously capture local features and global dependencies. To better preserve image details, a multiscale fusion module is introduced to integrate coarse- and fine-grained image information. By establishing a connection with the activation maximization (AM) principle, the final attention layer of the Transformer aggregates contributions (i.e., attention scores) from all image positions and forms the maximum position in the heatmap, thereby achieving keypoint localization in the head structure. Additionally, to address the issue of visible information leakage in convolutional reconstruction, we have devised a self-supervised training framework for the SSpose model. This framework incorporates mask autoencoder (MAE) technology into SSpose models by utilizing masked convolution and hierarchical masking strategy, thereby facilitating efficient self-supervised learning. Extensive experiments demonstrate that SSpose performs exceptionally well in the pose estimation task. On the COCO val set, it achieves an AP and AR of 77.3% and 82.1%, respectively, while on the COCO test-dev set, the AP and AR are 76.4% and 81.5%. Moreover, the model exhibits strong generalization capabilities on MPII. © 2024 IEEE.

关键词： Supervised learning

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Bi-objective approach for lot-sizing problem in cold rolling production planning

Bi-objective approach for lot-sizing problem in cold rolling...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Jia, Weiyan Tang, Lixin Yang, Yang Dong, Yun National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China

ISBN: (纸本)9798400704956

Planning in the cold rolling is an increasingly important vital aspect in steel industry. In this study, we investigate a cold rolling planning problem derived from a steel company, which is characterized by multi-item considerations with sequence-dependent setups in a single period. To tackle this problem, we introduce a bi-objective modified lot-sizing model, aiming at minimizing both the capacity waste and setup cost. A dedicated multi-objective evolutionary algorithm is proposed to solve this problem. The experiment results demonstrate the effectiveness of the proposed algorithm in achieving near-optimal solutions within a reasonable time, which can facilitate reductions in both production costs and time. © 2024 Copyright held by the owner/author(s).

关键词： evolutionary algorithm

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Multi-agent Reinforcement Learning for a Special Formation Problem 5th

Multi-agent Reinforcement Learning for a Special Formation P...

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Qu, Changsheng Ke, Liangjun Xuan, Shuzhe Wang, Zhigang State Key Laboratory for Manufacturing Systems Engineering School of Automation Science and Engineering Xi’an Jiaotong University 710049 Xi’an China CETC Key Laboratory of Data Link Technology Xi’an China

ISBN: (纸本)9789811939976

For a long time, the formation control problem has been one of the core problems in the field of multi-agent collaboration. It’s goal is to make multiple agents form a formation in the tasks and move to a designated target point. In this paper, the reinforcement learning method is used to deal with a special formation problem, and the mean field theory is applied to Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to make it effective in large-scale multi-agent formation problems. The algorithm is compared with MADDPG and Deep Deterministic Policy Gradient (DDPG) algorithm. In the simulation experiment, a team of UAVs are initialized at random positions in the two-dimensional space. The goal is to make the UAVs form an equilateral triangle with the shortest total displacement. We model the formation problem. In our algorithm, a reward function is designed based on the goal of making equilateral triangle formation and requirement of the shortest total displacement;Moreover, we define the equilateral triangle criterion and use it to evaluate the formation effect of multi-agent reinforcement learning. The results show that, compared with DDPG and MADDPG algorithms, MADDPG algorithm using the mean field method has obvious advantages over MADDPG and DDPG in terms of convergence speed and success rate in the formation problem. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mean field theory

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Testing for Error Correlation in Semi-Functional Linear Models

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Journal of systems science & complexity 2023年第4期36卷 1697-1716页

作者： YANG Bin CHEN Min ZHOU Jianjun Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan UniversityKunming 650091China City College Kunming University of Science and TechnologyKunming 650051China School of Mathematical Sciences Shanxi UniversityTaiyuan 030006China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China

Existing methods for analyzing semi-functional linear models usually assumed that random errors are not serially correlated or serially correlated with the known ***,in some applications,these assumptions on random errors may be unreasonable or *** this end,this paper aims at testing error correlation in a semi-functional linear model(SFLM).Based on the empirical likelihood approach,the authors construct an empirical likelihood ratio statistic to test the serial correlation of random errors and identify the order of autocorrelation if the serial correlation *** proposed test statistic does not need to estimate the variance as it is data adaptive and possesses the nonparametric version of Wilks'*** studies are conducted to investigate the performance of the proposed test *** real examples are illustrated by the proposed test method.

关键词： Empirical likelihood error correlation functional principal component analysis semifunctional linear model spline estimation Wilks'theorem

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