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Game Control of Attack and Defense in Cyber Physical System

Procedia Computer Science 2021年 187卷 488-494页

作者： Xiaobo Cai Baijuan Wang Zhiyong Cao Haitao Zhang Ke Han Min Chen Jiajun Xu Caifei ZI Wendou Wu College of big data Yunnan Agricultural University Kunming 650201 China Yunnan organic tea industry Intelligent Engineering Research Center Kunming 650201 China School of electrical and mechanical engineering Kunming Metallurgy College Kunming 650033 China

Cyber physical system (CPS) mainly relies on wireless network to realize information transmission from sensors to controllers and from the controllers to actuators. This paper studies the cyber-physical fusion system that is attacked by data packet timing. The attack and defense process is abstracted as a game process. Based on the two-person zero-sum game model of non-cooperative game, a variable delay situation is designed. In addition, this paper also compares the designed maximum-minimum controller with the linear quadratic Gaussian (LQG) control and carried out the simulation verification. It can be found that the controller designed in this paper can finally achieve stable control under attack.

关键词： Zero-sum game maximum-minimum control LQG control packet timing attack

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LI-FPN: Depression and Anxiety Detection from Learning and Imitation

LI-FPN: Depression and Anxiety Detection from Learning and I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid Network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

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Motif-preserving temporal network embedding 29

Motif-preserving temporal network embedding

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Huang, Hong Fang, Zixuan Wang, Xiao Miao, Youshan Jin, Hai National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China Beijing University of Posts and Telecommunications China Microsoft Research Asia

ISBN: (纸本)9780999241165

Network embedding, mapping nodes in a network to a low-dimensional space, achieves powerful performance. An increasing number of works focus on static network embedding, however, seldom attention has been paid to temporal network embedding, especially without considering the effect of mesoscopic dynamics when the network evolves. In light of this, we concentrate on a particular motif - triad - and its temporal dynamics, to study the temporal network embedding. Specifically, we propose MTNE, a novel embedding model for temporal networks. MTNE not only integrates the Hawkes process to stimulate the triad evolution process that preserves motif-aware high-order proximities, but also combines attention mechanism to distinguish the importance of different types of triads better. Experiments on various real-world temporal networks demonstrate that, compared with several state-of-the-art methods, our model achieves the best performance in both static and dynamic tasks, including node classification, link prediction, and link recommendation. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Network embeddings

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data-Driven Control Based on Function Approximation with Binomial Coefficients

Data-Driven Control Based on Function Approximation with Bin...

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第33届中国控制与决策会议

作者： Guoliang Zhu Lei Chen Kexin Liu Jinhu Lü School of Automation Science and Electrical Engineering Beihang Uniersity Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates data-driven control methods based on function approximation with binomial coefficients and sampling *** propose two approximate models and corresponding controllers formed from sampled *** shows that the information truncated in the first approximate model is saved in the latter with parameter estimation *** results validate the effectiveness of proposed approaches.

关键词： Sampled-data driven systems Function approximation Approximate theory Adaptive control Binomial coefficients

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Matter power spectrum induced by primordial magnetic fields: from the linear to the non-linear regime

arXiv

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arXiv 2024年

作者： Ralegankar, Pranjal Garaldi, Enrico Viel, Matteo SISSA - International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy INFN National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INAF Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 TriesteI-34131 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing via Magnanelli 2 Casalecchio di Reno40033 Italy

Linear theory predicts that primordial magnetic fields (PMFs) enhance the matter power spectrum on small scales. However, the linear approximation breaks down on sufficiently small scales where PMF-induced baryon perturbations back-react onto the magnetic fields. Previous studies assumed that the baryon power spectrum would be sharply suppressed in this non-linear regime, based on arguments related to the magnetic Jeans scale. For the first time, we perform dedicated magnetohydrodynamic (MHD) simulations to investigate the transition from the linear to the non-linear regime. Our simulations confirm the expected linear behavior on large scales. In the non-linear regime, however, we find that the dimensionless baryon power spectrum saturates to an O(1) value, which contrasts with previous analytical expectations. Additionally, our results show that several past studies overestimated the total matter power spectrum by orders of magnitude near the transition to non-linearity. Thus, the results presented in this work are useful to obtain more accurate constraints on PMFs from structure formation processes and/or different tracers of cosmic structures. © 2024, CC BY.

关键词： Magnetic fields

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Deep learning-based hyperspectral and multispectral fusion techniques: Review, optimization, and perspectives

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Information Fusion 2025年 124卷

作者： Sen Luo Yurong Qian Lu Bai Yingying Fan Yuanxu Wang WeiQuan Kong Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi 830091 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi 830046 China School of Computer Science and Technology Xinjiang University Urumqi 830046 China

Hyperspectral and Multispectral Image Fusion (HMIF) aim to acquire high spatial and spectral resolution images, which overcomes the inherent limitations of a single imaging device and effectively extends the applicability of spectral images on various downstream tasks. In recent years, Deep Learning (DL)-based image fusion technology breaks through the many limitations of traditional methods, rapidly develops a variety of neural network architectures and improvement strategies, and gradually overcomes a series of practical difficulties. However, a comprehensive overview of these deep learning architectures and various improvement techniques is still lacking. In this paper, we present a comprehensive review of deep learning techniques on HMIF, organize the fusion paradigms according to different researchers’ understanding and hypotheses of the problem, and sort out the common optimization strategies of neural network models, such as attention mechanism, multi-feature learning, combined with the variational and unmixing models of deep learning. In addition, we also provide a detailed compendium of the practical challenges and corresponding solution strategies for the first time, which involves the spatial-temporal misregistration, generalization enhancement and high-magnification fusion problems. Finally, we sort out and categorize the commonly used evaluation metrics for fused datasets, and discuss the potential research directions in HMIF. Through these works, we hope to provide a quick and clear reference for aspiring researchers in this area.

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research on DNS Resolution Technology based on Blockchain

Research on DNS Resolution Technology based on Blockchain

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Chinese Automation Congress (CAC)

作者： Wang Hu-qing Zhang Ying-jiao Zhang Xu Sun Zhi-xin Post Industry Technology Research and Development Center of the State Posts Bureau(Intemet of Things Technology) Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Aahui Yougu Express Intelligent Technology Co. LTD Nanjing China

DNS completes the resolution of domain names to IP addresses, which is an effective means to realize various In-ternet applications, and is also an indispensable part of Internet infrastructure, playing an important role. However, the resolution efficiency and security of DNS have always been a hot spot in the industry. This study deeply discusses the principle of Blockchain-based DNS resolution and conducts extensive re-search on Blockchain-based DNS resolution schemes. Based on compatibility with traditional DNS, these schemes are divided into two categories: compatible and incompatible. For different scenarios, we conduct a detailed analysis and comparison, focusing on the technical characteristics and performance of each so-lution. Finally, the future research direction is looked forward to, and the full text is summarized.

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Two-Stream Regression Network for Dental Implant Position Prediction

arXiv

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arXiv 2023年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Chen, Wenting Shen, Linlin Li, Xin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China Department of Electrical Engineering City University of Hong Kong Hong Kong

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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research on SDN Multi Controller Deployment based on K-means++

Research on SDN Multi Controller Deployment based on K-means...

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2020 International Conference on 5G Mobile Communication and Information Science, MCIS-5G 2020

作者： Yue, Li Junyan, Chen Chuxin, Liang Xiaochun, Lei School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Cloud Computing and Big Data Collaborative Innovation Center Guilin541001 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

In large-scale SDN network, a single centralized controller can not meet the demand, and multiple controllers are needed to deal with the problem, which leads to the problem of multi control balanced deployment. In this paper, the topology of SDN switches and links is known. The main research contents are as follows: The mathematical model of SDN multi controller deployment is established, and the appropriate network topology is selected and the multi controller deployment problem is solved. In the research, the number of controllers needed in the network and the switches managed by each controller is determined by the algorithm results, and the mapping relationship between controllers and switches is established. By analyzing the deployment results of the same network topology under different algorithms, the influence of different clustering algorithms on the experimental results is obtained. At the same time, the better deployment experiment results are simulated. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Controllers

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Passenger Waiting Time Prediction Based on Density Spatiotemporal Clustering and Time Series Feature Processing

Passenger Waiting Time Prediction Based on Density Spatiotem...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Ruixi Huang Dawen Xia Jian Geng Wenyong Zhang Yang Hu Yantao Li Huaqing Li College of Data Science and Information Engineering Guizhou Minzu University Guiyang China Engineering Research Center of Micro-nano and Intelligent Manufacturing Ministry of Education Kaili University Kaili China College of Big Data Engineering & College of Microelectronics and Artificial Intelligence Kaili University Kaili China Party School of the Anshun Municipal Committee of C.P.C Anshun China Department of Automotive Engineering Guizhou Traffic Technician and Transportation College Guiyang China College of Computer Science Chongqing University Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Accurate passenger waiting time prediction addresses the taxi supply-demand imbalance in intelligent Transport Systems (ITS). To capture the dependencies of passenger waiting time on urban taxi distribution, we propose a Density Spatiotemporal Clustering and Time Series Feature Processing (DSC-TSFP) method that discovers potential feature relationships of taxi GPS trajectory data. Specifically, a Multilevel Spatiotemporal Density-Based Spatial Clustering of Applications with Noise (MST-DBSCAN) is proposed to cluster taxi GPS trajectory data to obtain waiting time series with spatiotemporal correlations. Furthermore, a time series processing module based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Normalization (CEEMDANN) is designed to enhance prediction accuracy by handling non-stationarity and numerical discrepancy, which solves the residual white noise problem in Intrinsic Modal Functions (IMFs) of EEMD and CEEMD. Next, a waiting time prediction module oriented to a Convolutional Long Short-Term Memory Network (ConvLSTM) is incorporated to compensate for the defects of spatially localized dependencies that LSTM cannot capture. Finally, a CEEMDANN-ConvLSTM model is constructed to predict the passenger waiting time by combining the time series processing and waiting time prediction module. Based on real-world taxi GPS trajectory data, experimental results demonstrate that DSC-TSFP outperforms comparable methods regarding prediction accuracy.

关键词： Accuracy Time series analysis White noise Predictive models Trajectory Spatiotemporal phenomena Informatics Public transportation Long short term memory Global Positioning System

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