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22nd IEEE International Conference on High Performance computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Zhu, Jianyong Wang, Xu Wo, Tianyu Hu, Chunming Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9781728176499

Recently Cyber-Physical-Human Systems (CPHS) have been attracted much attention. Unlike existing computing paradigms such as Cloud computing and Edge computing, CPHS usually comprise many types of hybrid resources such as software and hardware in the cloud layer, network devices and edge servers in the network layer, as well as the physical devices and human participation. Such hybrid resource makes it challenging to efficient resource scheduling for CPHS. Previous studies on CPHS often focus on improving scheduling efficiency for specific application scenarios and thus are limited for other CPHS. In this work, we present a unified hybrid resource scheduling framework for CPHS, including a hybrid resource model for multiple types of resource providers and participants in CPHS, a hybrid resource orchestration tool that can describe our hybrid resource model as the resource requirements, and a scheduling mechanism to satisfy the resource requirements especially by considering the resource constraints and crosslayer scheduling in CPHS. We perform the case study of typical CPHS scenarios and show that our hybrid resource model and scheduling framework is effective, and provide a new view for improving the resource provision efficiency in CPHS. © 2020 IEEE.

关键词： Network layers

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Identifying spatiotemporal traffic patterns in large-scale urban road networks using a modified nonnegative matrix factorization algorithm

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Journal of Traffic and Transportation Engineering(English Edition) 2020年第4期7卷 529-539页

作者： Xiaolei Ma Yi Li Peng Chen School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Beiing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeiing 100191China

The identification and analysis of spatiotemporal traffic patterns in road networks constitute a crucial process for sophisticated traffic management and *** methods based on mathematical equations and statistical models can hardly be applicable to large-scale urban road networks,where traffic states exhibit high degrees of dynamics and ***,advances in data collection and processing have provided new opportunities to effectively understand spatiotemporal traffic patterns in large-scale road networks using data-driven ***,limited efforts have been exerted to explore the essential structure of the networks when conducting a spatiotemporal analysis of traffic *** this end,this study proposes a modified nonnegative matrix factorization algorithm that processes high-dimensional traffic data and provides an improved representation of the global traffic *** matrix factorization,cluster analysis is conducted based on the obtained low-dimensional representative matrices,which contain different traffic patterns and serve as the basis for exploring the temporal dynamics and spatial structure of network *** applicability and effectiveness of the proposed approach are examined in a road network of Beijing,*** show that the methods exhibit considerable potential for identifying and interpreting the spatiotemporal traffic patterns over the entire network and provide a systematic and efficient approach for analyzing the network-level traffic state.

关键词： Large-scale network Traffic state Spatiotemporal patterns Nonnegative matrix factorization

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Collusion-Proof Result Inference in Crowdsourcing

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Journal of Computer Science & Technology 2018年第2期33卷 351-365页

作者： Peng-Peng Chen Hai-Long Sun Yi-Li Fang Jin-Peng Huai State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing 100191 China

In traditional crowdsourcing, workers are expected to provide independent answers to tasks so as to ensure the diversity of answers. However, recent studies show that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. To pursue more rewards with little effort, some workers may collude to provide repeated answers, which will damage the quality of the aggregated results. Nonetheless, there are few efforts considering the negative impact of collusion on result inference in crowdsourcing. In this paper, we are specially concerned with the Collusion-Proof result inference problem for general crowdsourcing tasks in public platforms. To that end, we design a metric, the worker performance change rate, to identify the colluded answers by computing the difference of the mean worker performance before and after removing the repeated answers. Then we incorporate the collusion detection result into existing result inference methods to guarantee the quality of the aggregated results even with the occurrence of collusion behaviors. With real-world and synthetic datasets, we conducted an extensive set of evaluations of our approach. The experimental results demonstrate the superiority of our approach in comparison with the state-of-the-art methods.

关键词： crowdsourcing quality control collusion collaborative crowdsourcing result inference

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Graph algorithms: parallelization and scalability

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Science China(Information Sciences) 2020年第10期63卷 234-254页

作者： Wenfei FAN Kun HE Qian LI Yue WANG School of Informatics University of Edinburgh Shenzhen Institute of Computing Sciences Shenzhen University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen University

For computations on large-scale graphs, one often resorts to parallel algorithms. However, parallel algorithms are difficult to write, debug and analyze. Worse still, it is difficult to make algorithms parallelly scalable, such that the more machines are used, the faster the algorithms run. Indeed, it is not yet known whether any PTIME computational problems admit parallelly scalable algorithms on shared-nothing *** it possible to parallelize sequential graph algorithms and guarantee convergence at the correct results as long as the sequential algorithms are correct? Moreover, does a PTIME parallelly scalable problem exist on shared-nothing systems? This position paper answers both questions in the affirmative.

关键词： parallelization parallel scalability PTIME problems graph algorithms shared-nothing systems

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Automated Classification of Attacker Privileges Based on Deep Neural Network 1

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4th International Conference on Smart computing and Communications, SmartCom 2019

作者： Liu, Hailong Li, Bo Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (数字)9783030341398

ISBN: (纸本)9783030341381

Attack graphs generated from the detected vulnerabilities in a network depict all possible attack paths that an intruder can take. Conventional approaches to generating attack graphs require well-categorized data of prerequisites and postconditions for the identified vulnerabilities. However, generating them in an automated way is an open issue. Hence automatic classification methods are desirable to effectively generate attacker privilege fields as prerequisites and postconditions, improve the generation of the attack graph, and reduce the security risks of the system. In this paper, we propose a new automatic attacker privilege model (IG-DNN). The information gain (IG) is used for obtaining an optimal set of feature words from vulnerability description, and the deep neural network model is served as an automatic attacker privilege classifier. We use the National Vulnerability database (NVD) to validate the effectiveness of the IG-DNN model. We observe that prerequisite and postcondition privileges can be generated with overall average F-measure of 99.53% and 98.90% with the IG-DNN models, respectively. Moreover, compared with Naïve Bayes, KNN, and SVM, the IG-DNN model has achieved the best performance in precision, recall, and F-measure. © 2019, Springer Nature Switzerland AG.

关键词： Deep neural networks

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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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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Intelligent identification of two-dimensional nanostructures by machine-learning optical microscopy

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Nano Research 2018年第12期11卷 6316-6324页

作者： Xiaoyang Lin Zhizhong Si Wenzhi Fu Jianlei Yang Side Guo Yuan Cao Jin Zhang Xinhe Wang Peng Liu Kaili Jiang Weisheng Zhao Fert Beijing Research Institute School ofMicroelectronics & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China Beihang-GoertekJoint Microelectronics Institute Qingdao Research InstituteBeihang UniversityQingdao 266000China Fert Beijing Research Institute School of Computer Science and Engineering & Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics & Tsinghua-Foxconn Nanotechnology Research CenterCollaborative Innovation Center of Quantum MatterTsinghua UniversityBeijing 100084China

Two-dimensional (2D) materials and their heterostructures, with wafer-scale synthesis methods and fascinating properties, have attracted significant interest and triggered revolutions in corresponding device applications. However, facile methods to realize accurate, intelligent, and large-area characterizations of these 2D nanostructures are still highly desired. Herein, we report the successful application of machine-learning strategy in the optical identification of 2D nanostructures. The machine-learning optical identification (MOI) method endows optical microscopy with intelligent insight into the characteristic color information of 2D nanostructures in the optical photograph. The experimental results indicate that the MOI method enables accurate, intelligent, and large-area characterizations of graphene, molybdenum disulfide, and their heterostructures, including identifications of the thickness, existence of impurities, and even stacking order. With the convergence of artificial intelligence and nanoscience, this intelligent identification method can certainly promote fundamental research and wafer-scale device applications of 2D nanostructures.

关键词： machine-learning optical identification two-dimensional (2D) material heterostructure artificial intelligence

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Stability of connected and automated vehicles platoon considering communications failures

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Chinese Physics B 2023年第7期32卷 598-609页

作者：刘润坤于海洋任毅龙崔志勇 School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety ControlBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China

As a form of a future traffic system,a connected and automated vehicle(CAV)platoon is a typical nonlinear physical *** can communicate with each other and exchange ***,communication failures can change the platoon system *** characterize this change,a dynamic topology-based car-following model and its generalized form are proposed in this ***,a stability analysis method is ***,taking the dynamic cooperative intelligent driver model(DC-IDM)for example,a series of numerical simulations is conducted to analyze the platoon stability in different communication topology *** results show that the communication failures reduce the stability,but information from vehicles that are farther ahead and the use of a larger desired time headway can improve ***,the critical ratio of communication failures required to ensure stability for different driving parameters is studied in this work.

关键词： connected and automated vehicle car-following model communication failures string stability

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An Agent-Based Approach for Time-Series Momentum and Reversal

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Journal of Systems Science & Complexity 2020年第2期33卷 461-474页

作者： WANG Zhaoyuan LIU Shancun YANG Haijun WU Harris School of Economics and Management Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China College of Business and Public Administration Old Dominion UniversityNorfolkVirginiaUSA

This paper proposes a novel agent-based model combining private information diffusion to explain time-series momentum and *** information transmission allows heterogeneous trading strategies coexist in the artificial *** experiments reproduce momentum in short horizon and reversal in long horizon in the artificial financial ***,the authors also analyze how the private information contagion affects the ***,the authors find the significant price trend and excess volatility of volume when private information diffuses gradually.

关键词： Agent-based model excess volatility of volume price trend private information contagion time-series momentum

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