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2nd World Symposium on Software Engineering, WSSE 2020

作者： Hu, Zheyuan Cai, Jun Wang, Huiwei Zhang, Dehao Liu, Yang Li, Xin Li, Yanlong Zhao, Fengyan Zhang, Hongjun Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Shanxi zhen'An Pumped Storage Co. Ltd Shanxi711500 China Beijing Nelda Technology Co. Ltd. Beijing100194 China

ISBN: (纸本)9781450387873

Faced with the complex environment and difficult construction of infrastructure projects, designing an intelligent safety monitoring and early warning system for construction sites can effectively detect existing violations and reduce the probability of accidents. Existing violation detection methods for construction sites mainly include hand-crafted feature extraction and deep neural network. However, the method of extracting features is usually difficult to design and the architecture of the deep learning-based method is simple, which might lead to poor detection performance in extreme cases (Insufficient light, small detection object, occlusion, etc.) and cannot be used in actual detection environment. Therefore, we improve the existing target detection algorithm by adding image preprocessing module, multi-scale feature fusion module, and repulsion loss term. We also use the KCF algorithm to continuously track targets to identify specific violations. On this basis, we develop an intelligent safety monitoring and early warning system to classify the detected violations and send the information to the responsible person in time, which significantly improves the management capacity at the construction site. Through a series of experiments, we compared the impact of different modules on detection accuracy. The results show that our model has a significant improvement compared to existing methods on our dataset, especially in harsh environments. © 2020 ACM.

关键词： Object detection

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Recent advances in distributed adaptive consensus control of uncertain nonlinear multi-agent systems

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Journal of Control and Decision 2020年第1期7卷 44-63页

作者： Wei Wang Jiang Long Changyun Wen Jiangshuai Huang School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191People’s Republic of China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191People’s Republic of China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Automation Chongqing UniversityChongqingPeople’s Republic of China

So far,distributed adaptive consensus problems for uncertain nonlinear multi-agent systems have not yet been extensively *** with centralised adaptive control,some new challenges need to be well addressed,for examples,(i)how to reach asymptotically consensus tracking with directed topology condition,by using totally distributed adaptive control strategies;(ii)how to ensure globally uniform boundedness of closed-loop systems while achieving leaderless consensus with semi-positive definite Laplacian matrix;(iii)how to maintain system performance while effectively reducing the communication burden among connected *** paper is mainly devoted to report some recent advances in distributed adaptive consensus ***,some interesting research topics which are worthy of further investigation will also be discussed.

关键词： Distributed adaptive control consensus nonlinear multi-agent systems uncertainties

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Structured probabilistic end-to-end learning from crowds 29

Structured probabilistic end-to-end learning from crowds

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Chen, Zhijun Wang, Huimin Sun, Hailong Chen, Pengpeng Han, Tao Liu, Xudong Yang, Jie SKLSDE Lab School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China Web Information Systems Delft University of Technology Netherlands

ISBN: (纸本)9780999241165

End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy crowdsourced annotations. It models the relationship between true labels and annotations with a specific type of neural layer, termed as the crowd layer, which can be trained using pure backpropagation. Parameters of the crowd layer, however, can hardly be interpreted as annotator reliability, as compared with the more principled probabilistic approach. The lack of probabilistic interpretation further prevents extensions of the approach to account for important factors of annotation processes, e.g., instance difficulty. This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which allows to explicitly model annotator reliability while benefiting from the end-to-end training of neural networks. Moreover, we propose SpeeLFC-D, which further takes into account instance difficulty. Extensive validation on real-world datasets shows that our methods improve the state-of-the-art. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Deep neural networks

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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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Comprehensive Characteristic Decomposition of Parametric Polynomial Systems 21

Comprehensive Characteristic Decomposition of Parametric Pol...

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46th International Symposium on Symbolic and Algebraic Computation, ISSAC 2021

作者： Dong, Rina Lu, Dong Mou, Chenqi Wang, Dongming Chongqing Key Laboratory of Automated Reasoning and Cognition Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Mathematical Sciences Beihang University Beijing100191 China LMIB-School of Mathematical Sciences Beihang University Beijing100191 China Centre National de la Recherche Scientifique Paris cedex 1675794 France

ISBN: (纸本)9781450383820

This paper presents an algorithm that decomposes an arbitrary set F of multivariate polynomials involving parameters into finitely many sets Gi of (lexicographical) Gröbner bases G ij such that associated with each Gi there is a system Ai of parametric constraints, all the Ai's partition the parameter space, all the Gij's in each Gi remain Gröbner bases under specialization of the parameters satisfying the constraints in Ai, and for each i the Gröbner bases Gij together with their corresponding W-characteristic sets form a normal characteristic decomposition of F. The sets of Gröbner bases computed by the algorithm provide a comprehensive characteristic decomposition of F that is structure-invariant under the associated constraints and possesses many algebraic and geometric properties, including most of the notable properties on comprehensive Gröbner systems and comprehensive triangular decomposition. Some of these properties are highlighted in the paper and advantages of the proposed algorithm are discussed briefly from the aspects of methodological simplicity and computational performance using illustrative examples and preliminary experiments. © 2021 ACM.

关键词： Polynomials

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Multi-scale Hierarchy Feature Fusion Generative Adversarial Network for Low-Dose CT Denoising 20

Multi-scale Hierarchy Feature Fusion Generative Adversarial ...

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Proceedings of the 2020 9th International Conference on Bioinformatics and Biomedical Science

作者： Ying Bai Haifeng Zhao Shaojie Zhang Dong Nie Zhenyu Tang School of Computer Science and Technology Anhui University Department of Computer Science University of North Carolina at Chapel Hill Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

ISBN: (纸本)9781450388658

Image noise is an inherent issue in low-dose CT (LDCT). Increasing radiation dose can alleviate this problem to some extent, but it also brings potential risks to the patients. Thus, LDCT denoising has raised increasing attention from researchers. Currently, many deep learning based LDCT denoising methods have been proposed with success, such as encoder-decoder. In this paper, we propose a novel multi-scale hierarchy feature fusion based encoder-decoder network within the GAN framework for LDCT denoising. Specifically, a four-stage multi-scale dilated blocks is introduced to integrate low-level features with high-level features. Comparing with the conventional skip connection, which ignores the semantic gap between low-level features and high-level features, the advantage of our method is the effective use of low-level information. In addition, residual learning is also adopted to boost the training of the network. Experimental results on public dataset have demonstrated the superiority of our method over the state-of-the-art methods under comparison in both visual quality and quantitative evaluation.

关键词： Image denoising Low-dose CT Encoder-decoder Deep learning Wasserstein GAN

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Secure data Transmission of Smart Home Networks Based on Information Hiding 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Deng, Haiyu Yang, Lina Dang, Xiaocui Tang, Yuan Yan Wang, Patrick School of Computer Electronics and Information Guangxi University Nanning530004 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Computer and Information Science Northeastern University Boston02115 United States

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Smart home is an emerging form of the Internet of things (IoT), which can enable people to master the conditions of their smart homes remotely. However, privacy leaking in smart home network is neglected. Therefore, it is strongly necessary to design a secure and effective scheme to ensure the privacy protection of the smart home network. Considering the privacy leaking in the smart home network system, this paper proposes a scheme combining encryption and information hiding to ensure the security of data transmission. In the scheme, the sensitive data are encrypted to ciphertext before it is transmitted, and then the ciphertext is embedded into the Least Significant Bit (LSB) of the cover image. On the receiving terminal, the ciphertext can be extracted from the stego image, then the ciphertext is decrypted into the sensitive data. Comparing to the method of using encryption algorithm merely, the scheme greatly improved the security of data transmission, making it more suitable for the real environment. © 2020, Springer Nature Switzerland AG.

关键词： Home networks

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Stabilization of Continuous-Time Markov/Semi-Markov Jump Linear Systems Via Finite data-Rate Feedback

SSRN

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SSRN 2022年

作者： Wang, Jingyi Feng, Jianwen Xu, Chen Wu, Xiaoqun Lü, Jinhu College of Mathematics and Statistics Shenzhen University Shenzhen518060 China School of Mathematics and Statistics Hubei Key Laboratory of Computational Science Wuhan University Wuhan China State Key Laboratory of Software Development Environment School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

This letter investigates almost sure exponential stabilization of continuous-time Markov jump linear systems (MJLSs) under communication data-rate constraints by introducing sampling and quantization into the feedback control. Different from previous works, the sampling times and the jump times are independent of each other in this letter. The quantization is recursively adjusted on the sampling time, and its updating strategy does not depend on the switching in a sampling interval. In other words, the explicit value of the switching signal in a sampling interval is not necessary. The numerically testable condition is developed to ensure almost sure exponential stabilization of MJLSs under the proposed communication and control protocols. We also drop the assumption of stabilizability of all individual modes required in previous works about the switched systems. Moreover, we extend the result to the case of continuous-time semi-Markov jump linear systems (semi-MJLSs) via the semi-Markov kernel approach. Finally, some numerical examples are presented to illustrate the effectiveness on stabilization of the proposed communication and control protocols. © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Collective firing patterns of neuronal networks with short-term synaptic plasticity

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Physical Review E 2021年第2期103卷 022312-022312页

作者： Chong-Yang Wang Ji-Qiang Zhang Zhi-Xi Wu Jian-Yue Guan Lanzhou Center for Theoretical Physics and Key Laboratory of Theoretical Physics of Gansu Province Lanzhou University Lanzhou Gansu 730000 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China School of Physics and Electronic-Electrical Engineering Ningxia University Yinchuan 750021 China

We investigate the occurrence of synchronous population activities in a neuronal network composed of both excitatory and inhibitory neurons and equipped with short-term synaptic plasticity. The collective firing patterns with different macroscopic properties emerge visually with the change of system parameters, and most long-time collective evolution also shows periodic-like characteristics. We systematically discuss the pattern-formation dynamics on a microscopic level and find a lot of hidden features of the population activities. The bursty phase with power-law distributed avalanches is observed in which the population activity can be either entire or local periodic-like. In the purely spike-to-spike synchronous regime, the periodic-like phase emerges from the synchronous chaos after the backward period-doubling transition. The local periodic-like population activity and the synchronous chaotic activity show substantial trial-to-trial variability, which is unfavorable for neural code, while they are contrary to the stable periodic-like phases. We also show that the inhibitory neurons can promote the generation of cluster firing behavior and strong bursty collective firing activity by depressing the activities of postsynaptic neurons partially or wholly.

关键词： Neuronal dynamics Population dynamics Synapses Synchronization Collective dynamics Neuronal avalanches Neuronal networks Integrate-and-fire model

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A novel in-memory computing scheme based on toggle spin torque MRAM 20

A novel in-memory computing scheme based on toggle spin torq...

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30th Great Lakes Symposium on VLSI, GLSVLSI 2020

作者： Bai, Yining Zhang, Yue Wang, Jinkai Wang, Guanda Zhang, Zhizhong Zheng, Zhenyi Zhang, Kun Zhao, Weisheng Fert Beijing Institute School of Microelectronics Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China Nanoelectronics Science and Technology Center Hefei Innovation Research Institute Beihang University Hefei China School of Electronics Engineering Beihang University Beijing China

ISBN: (纸本)9781450379441

This paper proposes a novel in-memory computing (IMC) scheme based on toggle spin torque magnetic random access memory (TST-MRAM), called TST-IMC, which makes full use of the unique TST writing mechanism. In this scheme, all of the computing results are directly written in bit-cells without transferring data out of the memory array. Varied Boolean logic operations, such as, NAND, NOR and XOR, can be achieved by specially configuring decision cells. We can also implement three-input majority logic through replacing a decision cell with a datum cell, which can further be used to realize the carry of full-adder. By using 28 nm CMOS technology node and 50 nmdiameter TST-MRAM, we perform mixed simulations to validate the functionality of the proposed TST-IMC scheme. Simulation results show that XOR logic operation can be carried out within 4 ns at 1.8 V supply voltage while the other basic logic operations can be faster, i.e. within 2 ns. In addition, TST-IMC 33% less time and 44% energy saved comparing with existing IMC schemes. © 2020 Association for computing Machinery.

关键词： Cells

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