作者机构：
[Dai, Zhenyang; Yang, Zhimin; Ge, Fei; Qiu, Han; Tan, Liansheng; Li, Jiayuan; Hu, Jianyuan] Cent China Normal Univ, Sch Comp Sci, Wuhan 430070, Peoples R China.;[Tan, Liansheng] Univ Tasmania, Sch Technol Environm & Design, Hobart, Tas 7001, Australia.
通讯机构：
[Yang, ZM ; Ge, F] C;Cent China Normal Univ, Sch Comp Sci, Wuhan 430070, Peoples R China.
关键词：
Attention;channel state information (CSI);convolutional neural networks;human activity recognition
摘要：
In recent years, the use of WiFi Channel State Information (CSI) for Human Activity Recognition (HAR) has attracted widespread attention, thanks to its low cost and non-intrusive advantages. Previous research mostly used models based on Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN) for activity recognition. However, these methods fail to achieve good parallelism while learning global features and fine-grained features, so they often cannot achieve the ideal recognition effect or training speed. In light of this, we propose an ensemble deep learning model based on CNN and Transformer, ConTransEn. Specifically, we first use CNN to extract spatial features of the sequence, and then use Vision Transformer (ViT) to further extract temporal features. The Transformer introduces self-attention mechanism, enabling the model to fully consider information from other positions in the sequence, rather than being limited to the current input. Furthermore, due to the increased parallelism, Transformer has an advantage in training speed over RNN. In order to further improve the accuracy and robustness of the model, we adopt a bagging ensemble learning strategy, integrating the prediction results of multiple homogeneous base models using a soft voting method to obtain the final classification result. This ensemble learning method reduces the risk of model overfitting, and improves the overall accuracy and reliability of the model. We extensively evaluated the model on two publicly available datasets, and achieved excellent recognition results, indicating its good performance and robustness. The average recognition accuracy on the UT-HAR dataset reached 99.41%, surpassing existing solutions.
摘要：
Distributed optimization is a powerful paradigm to solve various problems in machine learning over networked systems. Existing first-order optimization methods perform cheap gradient descent by exchanging information per iteration only with single-hop neighbours in a network. However, in many agent networks such as sensor and robotic networks, it is prevalent that each agent can interact with other agents over multi-hop communication. Therefore, distributed optimization method over multi-hop networks is an important but overlooked topic that clearly needs to be developed. Motivated by this observation, in this paper, we apply multi-hop transmission to the outstanding distributed gradient descent (DGD) method and propose two typical versions (i.e., consensus and diffusion) of multi-hop DGD method, which we call CM-DGD and DM-DGD, respectively. Theoretically, we present the convergence guarantee of the proposed methods under mild assumptions. Moreover, we confirm that multi-hop strategy results in higher probability to improve the spectral gap of the underlying network, which has been shown to be a critical factor improving performance of distributed optimizations, thus achieves better convergence metrics. Experimentally, two distributed machine learning problems are picked to verify the theoretical analysis and show the effectiveness of CM-DGD and DM-DGD by using synthesized and real data sets.
期刊：
IEEE Open Journal of the Computer Society,2021年2:393-406 ISSN：2644-1268
作者机构：
[Fei Ge; Wei Zhang; Ming Liu] Computer Science Department, Central China Normal University, Wuhan, Hubei, P. R. China;Discipline of ICT, School of Technology, Environments and Design, University of Tasmania, Hobart, TAS, Australia;Computer Science Department, Huazhong Normal University, Wuhan, Hubei, P. R. China;[Xun Gao] Electronic Engineering Department, Wuhan University, Wuhan, Hubei, P. R. China;[Juan Luo] College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, P. R. China
期刊：
Security and Communication Networks,2021年2021:1-17 ISSN：1939-0114
通讯作者：
Tan, Liansheng
作者机构：
[Huang, Kaijiao; Tan, Liansheng] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.;[Tan, Liansheng] Univ Tasmania, Coll Sci & Engn, Hobart, Tas 7001, Australia.;[Peng, Gang] Shenzhen Inst Informat Technol, Sch Comp Sci, Shenzhen 518172, Peoples R China.
通讯机构：
[Tan, Liansheng] C;[Tan, Liansheng] U;Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.;Univ Tasmania, Coll Sci & Engn, Hobart, Tas 7001, Australia.
摘要：
<jats:p>The Internet is nowadays suffering dramatically serious attacks, with the distributed denial of service (DDoS) attacks being the representative and dominant ones. It is seen that, to stabilize the buffer queue length around a given target under DDoS attacks in the relevant routes is vitally important and helpful to mitigate the attacks and to improve the quality of service (QoS) for normal users. In the current paper, a stochastic queue dynamic model with <jats:inline-formula>
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</jats:inline-formula> jump noise, the mechanism that characterizes the behavior of the queue at routers is presented for stabilizing the queue length under constant-rate DDoS attacks. By applying the stochastic control theory into analyzing the performance of queue dynamic under constant-rate DDoS attacks, some explicit conditions are established under which the instantaneous queue length converges to any given target in a route. Simulation results demonstrate the satisfaction of the proposed defense mechanism with sharp contrast to the state of the art active queue management (AQM) schemes.</jats:p>
作者机构：
[Liansheng Tan; Yan Yang; Wei Zhang] Computer Science Department, Central China Normal University, Wuhan, China;[M. Zukerman] Australian Research Council Special Research Centre for Ultra-Broadband Information Networks (CUBIN), an affiliated program of National ICT Australia, EEE Department, University of Melbourne, VIC, Australia
摘要：
Based on control theory, this letter provides guidelines for the selection of the control gain for dynamic-RED to stabilize a congested queue at a target and hence to improve network performance. Simulations demonstrate that indeed satisfactory performance can be achieved if the control gain is selected based on the guidelines.
期刊：
2003 European Control Conference (ECC),2003年:3603-3608
作者机构：
[Qin Liu] Department of Computer Science, Central China Normal University, Wuhan 430079, PR China;[S. H. Yang] Department of Computer Science, Loughborough University, Loughborough, Leicestershire, UK;[Liansheng Tan] Department of Computer Science, Central China Normal University, Wuhan, PR China
会议名称：
2003 European Control Conference (ECC)
关键词：
ABR service;ATM networks;congestion control;PID controller;performance evaluation
摘要：
In this paper, we present a novel method to the design of closed-loop rate-based flow controller for high-speed networks. In this method, a proportional-integral-plus-derivative (PID) controller is adopted, where the control parameters are designed to ensure the stability of the control loop in a control theoretic sense. Based on a general traffic model of computer network and on system stability criterion, it is shown that under PID controller the source rates are regulated, the congestion-controlled network is asymptotically stable in terms of both the buffer occupancy of the destination node and the user transmission rates, and the bandwidth fairness is achieved. The basic control theory approach for the algorithm is firstly presented, and steady state analysis is subsequently given to show how the max/min fairness is achieved in a natural way without additional computation. We then use simulations to show the good dynamic performance of the PID congestion control scheme under a variety of networking configurations and traffics.
