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Mitigation of Optical Multipath Interference Algorithms in IM-DD Transmission System

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China Communications 2024年第4期21卷 1-9页

作者： Huo Jiahao Zhu Jin Zhu Zuqing Zhang Xiaoying Liu Shaonan Tao Jianlong Wei Huangfu School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of information science and technology University of Science and Technology of ChinaHefei 230026China

This paper aimed to propose two algorithms,DA-M and RF-M,of reducing the impact of multipath interference(MPI)on intensity modulation direct detection(IM-DD)systems,particularly for four-level pulse amplitude modulation(PAM4)***-M reduced the fluctuation by averaging the signal in blocks,RF-M estimated MPI by subtracting the decision value of the corresponding block from the mean value of a signal block,and then generated interference-reduced samples by subtracting the interference signal from the product of the corresponding MPI estimate and then weighting *** paper firstly proposed to separate the signal before decision-making into multiple blocks,which significantly reduced the complexity of DA-M and *** results showed that the MPI noise of 28 GBaud IMDD system under the linewidths of 1e5 Hz,1e6 Hz and 10e6 Hz can be effectively alleviated.

关键词： IM-DD multipath interference PAM4

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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Computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng department of Computer science&Engineering Ramaiah Institute of TechnologyBangalore560054India department of Artificial Intelligence&Data science Ramaiah Institute of TechnologyBangalore560054India School of information Engineering Changchun Sci-Tech UniversityChangchun130600China department of Computer science and information Engineering Chaoyang University of TechnologyTaichung41349Taiwan department of information science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

Cybernet Model:A New Deep Learning Model for Cyber DDoS Attacks Detection and Recognition

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Computers, Materials & Continua 2024年第1期78卷 1275-1295页

作者： Azar Abid Salih Maiwan Bahjat Abdulrazaq department of information technology Technical College of DuhokDuhok Polytechnic UniversityDuhokIraq department of Computer science Faculty of ScienceUniversity of ZakhoDuhokIraq

Cyberspace is extremely dynamic,with new attacks arising *** cybersecurity controls is vital for network *** Learning(DL)models find widespread use across various fields,with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and *** capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic *** study presents novel lightweight DL models,known as Cybernet models,for the detection and recognition of various cyber Distributed Denial of Service(DDoS)*** models were constructed to have a reasonable number of learnable parameters,i.e.,less than 225,000,hence the name“lightweight.”This not only helps reduce the number of computations required but also results in faster training and inference ***,these models were designed to extract features in parallel from 1D Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM),which makes them unique compared to earlier existing architectures and results in better performance *** validate their robustness and effectiveness,they were tested on the CIC-DDoS2019 dataset,which is an imbalanced and large dataset that contains different types of DDoS *** results revealed that bothmodels yielded promising results,with 99.99% for the detectionmodel and 99.76% for the recognition model in terms of accuracy,precision,recall,and F1 ***,they outperformed the existing state-of-the-art models proposed for the same ***,the proposed models can be used in cyber security research domains to successfully identify different types of attacks with a high detection and recognition rate.

关键词： Deep learning CNN LSTM Cybernet model DDoS recognition

Realization of Quantum Secure Direct Communication with Continuous Variable

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Research 2024年第2期6卷 159-170页

作者： Zhengwen Cao Yuan Lu Geng Chai Hao Yu Kexin Liang Lei Wang School of information science and technology Northwest UniversityXi'an710127China

With the progress of theoretical and applied technologies,the communication system based on the classical encryption is seriously threatened by quantum computing and distributed computing.A communication method that directly loads confidential information on the quantum state,quantum secure direct communication(QSDC),came into being for resisting security ***,we report the first continuous-variable QSDC(CV-QSDC)experimental demonstration for verifying the feasibility and effectiveness of the CV-QSDC protocol based on Gaussian mapping and propose a parameter estimation for signal classification under the actual *** our experiment,we provided 4×10^(2) blocks,where each block contains 105 data for direct information *** the transmission distance of 5 km in our experiment,the excess noise is 0.0035 SNU,where SNU represents the unit of shot-noise *** 4.08×10^(5) bit per second experimental results firmly demonstrated the feasibility of CV-QSDC under the fiber *** proposed grading judgment method based on parameter estimation provides a practical and available message processing scheme for CV-QSDC in a practical fiber channel and lays the groundwork for the grading reconciliation.

关键词： quantum estimation verify

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-Sheng Jia Ying-Xiang Hu Hong-Mei Sun the College of Computer science and Engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of information science and Engineering Ocean University of ChinaQingdao 266000China the College of Computer science and Engineering Shandong University of Science and TechnologyQingdao 266590China the College of Computer science and Engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning

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A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

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Tsinghua science and technology 2024年第2期29卷 617-633页

作者： Weiqi Zhang Zhenzhen Xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin College of Computer science and technology Harbin Engineering UniversityHarbin 150001China School of Computer science and technology Shandong UniversityQingdao 266237China department of Computer science Georgia State UniversityAtlantaGA 30303USA College of information science and technology Jinan UniversityGuangzhou 510632China

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

关键词： local-differential privacy stay points detection trajectory data areas of interest temporal-spatial clustering

THE LONG TIME BEHAVIOR OF THE FRACTIONAL ORNSTEIN-UHLENBECK PROCESS WITH LINEAR SELF-REPELLING DRIFT

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Acta Mathematica Scientia 2024年第2期44卷 671-685页

作者：夏晓宇闫理坦杨晴 College of information science and technology Donghua UniversityShanghai201620China department of Statistics College of ScienceDonghua UniversityShanghai201620China

Let B^(H) be a fractional Brownian motion with Hurst index 1/2≤H<*** this paper,we consider the equation(called the Ornstein-Uhlenbeck process with a linear self-repelling drift)dX_(t)^(H)=dB_(t)^(H)+σ X_(t)^(H)dt+vdt-θ(∫_(0)^(t)(X_(t)^(H)-X_(s)^(H))ds)dt,whereθ<0,σ,v∈ℝ.The process is an analogue of self-attracting diffusion(Cranston,Le *** Ann,1995,303:87–93).Our main aim is to study the large time behaviors of the *** show that the solution X^(H)diverges to infinity as t tends to infinity,and obtain the speed at which the process X^(H)diverges to infinity.

关键词： fractional Brownian motion stochastic difference equations rate of convergence asymptotic

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Rethinking attribute localization for zero-shot learning

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science China(information sciences) 2024年第7期67卷 184-196页

作者： Shuhuang CHEN Shiming CHEN Guo-Sen XIE Xiangbo SHU Xinge YOU Xuelong LI School of Electronic information and Communication Huazhong University of Science and Technology School of Computer science and Engineering Nanjing University of Science and Technology School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University

Recent advancements in attribute localization have showcased its potential in discovering the intrinsic semantic knowledge for visual feature representations, thereby facilitating significant visual-semantic interactions essential for zero-shot learning(ZSL). However, the majority of existing attribute localization methods heavily rely on classification constraints, resulting in accurate localization of only a few attributes while neglecting the rest important attributes associated with other classes. This limitation hinders the discovery of the intrinsic semantic relationships between attributes and visual features across all *** address this problem, we propose a novel attribute localization refinement(ALR) module designed to enhance the model's ability to accurately localize all attributes. Essentially, we enhance weak discriminant attributes by grouping them and introduce weighted attribute regression to standardize the mapping values of semantic attributes. This module can be flexibly combined with existing attribute localization *** experiments show that when combined with the ALR module, the localization errors in existing methods are corrected, and state-of-the-art classification performance is achieved.

关键词： zero-shot learning attention mechanism attribute localization image classification

Understanding adversarial attacks on observations in deep reinforcement learning

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science China(information sciences) 2024年第5期67卷 69-83页

作者： You QIAOBEN Chengyang YING Xinning ZHOU Hang SU Jun ZHU Bo ZHANG department of Computer science and technology Beijing National Research Center for Information Science and Technology Tsinghua-Bosch Joint Center for Machine Learning Institute for Artificial Intelligence Tsinghua University Peng Cheng Laboratory

Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease the cumulative expected reward of a victim by manipulating its observations. Despite the efficiency of previous optimization-based methods for generating adversarial noise in supervised learning, such methods might not achieve the lowest cumulative reward since they do not generally explore the environmental ***, a framework is provided to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space. The reformulation approach adopted herein generates an optimal adversary in the function space of targeted attacks, repelling them via a generic two-stage framework. In the first stage, a deceptive policy is trained by hacking the environment and discovering a set of trajectories routing to the lowest reward or the worst-case performance. Next, the adversary misleads the victim to imitate the deceptive policy by perturbing the observations. Compared to existing approaches, it is theoretically shown that our adversary is strong under an appropriate noise level. Extensive experiments demonstrate the superiority of the proposed method in terms of efficiency and effectiveness, achieving state-of-the-art performance in both Atari and MuJoCo environments.

关键词： deep learning reinforcement learning adversarial robustness adversarial attack