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IEEE International Conference on Joint Cloud computing (JCC)

作者： Ying Zhang Tianyu Wo Kun Fan Tianyu Ye Jiwei Zhang Junhua Zhang Xiao Feng Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University China Electronics Technology Taiji Group Corporation Limited Beijing China

Forming a collaborative computing network among the deployed satellites in space can process data rapidly and reduce data transmission delay by leveraging the communication, storage, and computing capacities of the satellites. Due to the dynamics of satellite orbits, the distance between satellites varies over time, bringing new challenges to the designing of dedicated distributed file systems for collaborative satellites. Traditional distributed file systems did not consider the network topology and dynamics of satellite clouds and the limited computing resources of satellite node cores. In view of the torus network topology of satellite clouds, we design a highly available distributed metadata management mechanism for satellite clouds to ensure efficient metadata reading and reliability. On this basis, a topology-aware replica placement strategy is proposed to minimize communication costs and energy consumption for replica placement. Based on the metadata strategy and replica placement strategy mentioned above, we design and implement a distributed file system for satellite clouds. Experimental results demonstrate that our proposed placement strategy can improve the data transmission performance of the replica placement by more than double compared to the random placement strategy.

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Cooperative differential games guidance laws for multiple attackers against an active defense target

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Chinese Journal of Aeronautics 2022年第5期35卷 374-389页

作者： Fei LIU Xiwang DONG Qingdong LI Zhang REN School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100083China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of ChinaChongqing UniversityChongqing 400044China

This paper is concerned with a scenario of multiple attackers trying to intercept a target with active *** types of agents are considered in the guidance:The multiple attackers,the target and the defender,where the attackers aim to pursuit the target from different directions and evade from the defender *** guidance engagement is formulated in the framework of a zero-sum two-person differential game between the two opposing teams,such that the measurements on the maneuver of the target or estimations on the defending strategy of the defender can be *** of the attackers resides in two aspects:redundant interception under the threat of the defender and the relative intercept geometry with the *** miss distances,the relative intercept angle errors and the costs of the agents are combined into a single performance index of the *** formulation enables a unitary approach to the design of guidance laws for the *** minimize the control efforts and miss distances for the attackers,an optimization method is proposed to find the best anticipated miss distances to the defender under the constraint that the defender is endowed with a capture *** simulations with two cases are conducted to illustrate the effectiveness of the proposed cooperative guidance law.

关键词： Active defense Cooperative guidance Differential games Minimum effort optimization Relative intercept angle

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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Learning from Noisy Crowd Labels with Logics 39

Learning from Noisy Crowd Labels with Logics

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Chen, Zhijun Sun, Hailong He, Haoqian Chen, Pengpeng Beihang University Sklsde Lab Beijing China Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China China's Aviation System Engineering Research Institute Beijing China

ISBN: (纸本)9798350322279

This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels. We introduce Logic-guided Learning from Noisy Crowd Labels (Logic-LNCL), an EM-alike iterative logic knowledge distillation framework that learns from both noisy labeled data and logic rules of interest. Unlike traditional EM methods, our framework contains a "pseudo-E-step"that distills from the logic rules a new type of learning target, which is then used in the "pseudo-M-step"for training the classifier. Extensive evaluations on two real-world datasets for text sentiment classification and named entity recognition demonstrate that the proposed framework improves the state-of-the-art and provides a new solution to learning from noisy crowd labels. © 2023 IEEE.

关键词： Crowdsourcing

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Detecting Informationally-Dense Subsets

Detecting Informationally-Dense Subsets

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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Chao Zhao Ali Al-Bashabsheh Chung Chan Department of Computer Science City University of Hong Kong. Big Data and Brain Computing (BDBC) center at Beihang University Beijing China

ISBN: (数字)9798350348934

ISBN: (纸本)9798350348941

Identifying locally dense subgraphs aims to pinpoint subgraphs characterized by tight internal connectivity. However, existing methods for identifying dense subgraphs based on density can lead to loose internal connections. This paper addresses this issue by introducing a concept of strength to detect strong subsets. Our approach encompasses the existing work that finds a nested chain of densest k-subgraphs as a special case and reveals subgraphs that have tight internal connections overlooked by existing methods. The strong subsets exhibit a laminar structure and can be computed in polynomial time. In contrast to previous works defining locally densest subgraphs without a natural extension to weighted graphs, our method accommodates both weighted and unweighted, directed and undirected graphs, as well as hypergraphs. Furthermore, it extends to a broader notion of information density, surpassing the scope of weighted graphs.

关键词： Conferences Minimization Polynomials Information theory

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Asymmetric scattering behaviors of spin wave dependent on magnetic vortex chirality

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Chinese Physics B 2023年第10期32卷 635-640页

作者：张雪枫沈帝虎马晓萍宋成于海明朴红光 Hubei Engineering Research Center of Weak Magnetic-Field Detection China Three Gorges UniversityYichang 443002China Department of Physics College of ScienceYanbian UniversityYanji 133002China Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Fert Beijing Institute School of Integrated Circuit Science and EngineeringBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China

We investigate asymmetric spin wave scattering behaviors caused by vortex chirality in a cross-shaped ferromagnetic system by using the micromagnetic *** the system,four scattering behaviors are found:(i)asymmetric skew scattering,depending on the polarity of vortex core,(ii)back scattering(reflection),depending on the vortex core stiffness,(iii)side deflection scattering,depending on structural symmetry of the vortex circulation,and(iv)geometrical scattering,depending on waveguide *** first and second scattering behaviors are attributed to nonlinear topological magnon spin Hall effect related to magnon spin-transfer torque effect,which has value for magnonic exploration and application.

关键词： magnonics magnetic vortex spin wave magnetic chirality

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Three-dimensional cooperative guidance strategy and guidance law for intercepting highly maneuvering target

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Chinese Journal of Aeronautics 2021年第5期34卷 485-495页

作者： Ziyan CHEN Jianglong YU Xiwang DONG Zhang REN School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data cmd Brain Computing Beihang UniversityBeijing 100191China

Cooperative interception of the target with strong maneuverability by multiple missiles with weak maneuverability in the three-dimensional nonlinear model is ***,the three-dimensional nonlinear model of cooperative guidance is *** three-dimensional reachable region is represented composed of lateral acceleration and longitudinal acceleration in the two-dimensional coordinate ***,the problem of the multiple missile’s reachable coverage area is transformed into the problem of cooperative coverage.A cooperative coverage strategy is proposed and an algorithm for quickly calculating the number of required missiles is ***,the guidance law based on the cooperative coverage strategy is proposed,and it is proved that cooperative interception of the target can be achieved under the acceleration ***,the relations among the number of missiles,the initial array position of terminal guidance and the coverage area of the target’s large maneuver are *** dynamic adjustment strategy of guidance parameters is proposed to reduce the guidance ***,simulation results show that multiple missiles with low maneuverability can achieve effective interception of target with strong maneuverability through the proposed cooperative strategy and cooperative guidance method.

关键词： Acceleration constraint Cooperative interception Coverage strategy Guidance law Highly maneuvering target

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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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Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction 23

Self-training through Classifier Disagreement for Cross-Doma...

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2023 World Wide Web Conference, WWW 2023

作者： Sun, Kai Zhang, Richong Samuel, Mensah Nikolaos, Aletras Mao, Yongyi Liu, Xudong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Sheffield Sheffield United Kingdom School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9781450394161

Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts. © 2023 ACM.

关键词： Sentiment analysis

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Privacy-preserving graph publishing with disentangled variational information bottleneck

Privacy-preserving graph publishing with disentangled variat...

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作者： Ma, Liya Li, Chen Sun, Siqi Guo, Shu Wang, Lihong Li, Jianxin School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China ARC LAB Tencent Shenzhen China China Academy of Industrial Internet Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

Social networks collect enormous amounts of user personal and behavioral data, which could threaten users' privacy if published or shared directly. Privacy-preserving graph publishing (PPGP) can make user data available while protecting private information. For this purpose, in PPGP, anonymization methods like perturbation and generalization are commonly used. However, traditional anonymization methods are challenging in balancing high-level privacy and utility, ineffective at defending against both various link and hybrid inference attacks, as well as vulnerable to graph neural network (GNN)-based attacks. To solve those problems, we present a novel privacy-disentangled approach that disentangles private and non-private information for a better privacy-utility trade-off. Moreover, we propose a unified graph deep learning framework for PPGP, denoted privacy-disentangled variational information bottleneck (PDVIB). Using low-dimensional perturbations, the model generates an anonymized graph to defend against various inference attacks, including GNN-based attacks. Particularly, the model fits various privacy settings by employing adjustable perturbations at the node level. With three real-world datasets, PDVIB is demonstrated to generate robust anonymous graphs that defend against various privacy inference attacks while maintaining the utility of non-private information. © 2023 John Wiley & Sons Ltd.

关键词： Graph neural networks

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