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IEEE Conference on Global Communications (GLOBECOM)

作者： Menghan Wu Dawei Wang Lingtong Min Yixin He Chen Gao Li Zhen Keping Yu School of Electronics and Information Northwestern Polytechnical University Xi'an China College of Information Science and Engineering Jiaxing University Jiaxing China Jiaxing Key Laboratory of Smart Transportations Jiaxing China Department of Electrical and Computer Engineering National University of Singapore Singapore Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China Graduate School of Science and Engineering Hosei University Tokyo Japan

The proposed study introduces a reconfigurable intelligent surface (RIS)-aided hybrid radio frequency (RF)/free space optical (FSO) system with an unmanned aerial vehicle (UAV) relay to enable an ultra-dense sixth-generation (6G) network. The channels for RF and FSO are represented by Rayleigh and Gamma-Gamma probability distributions, correspondingly. Additionally, the network includes a terrestrial eavesdropper that follows the Nakagami-m distribution, attempting to breach confidential information. To counter this threat, RIS technology is used to enhance the hybrid system's secrecy. The study conducts a closed-form analysis of the secrecy outage probability (SOP) and obtains its asymptotic expression for determining the diversity order and coding gain. Theoretical findings have been confirmed through thorough numerical simulations implemented with the Monte-Carlo approach. The findings demonstrate the RIS technology's effectiveness in enhancing the network's secrecy performance.

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SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation

arXiv

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arXiv 2022年

作者： Zhu, Wanqing Yin, Jia-Li Chen, Bo-Hao Liu, Ximeng Fujian Province Key Laboratory of Information Security of Network System Fuzhou350108 China College of Computer Science and Big Data Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering Yuan Ze University Taiwan

As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target dataset, is gaining increasing popularity. While extensive studies have been devoted to improving the model accuracy on target domain, an important issue of model robustness is neglected. To make things worse, conventional adversarial training (AT) methods for improving model robustness are inapplicable under UDA scenario since they train models on adversarial examples that are generated by supervised loss function. In this paper, we present a new meta self-training pipeline, named SRoUDA, for improving adversarial robustness of UDA models. Based on self-training paradigm, SRoUDA starts with pre-training a source model by applying UDA baseline on source labeled data and taraget unlabeled data with a developed random masked augmentation (RMA), and then alternates between adversarial target model training on pseudo-labeled target data and fine-tuning source model by a meta step. While self-training allows the direct incorporation of AT in UDA, the meta step in SRoUDA further helps in mitigating error propagation from noisy pseudo labels. Extensive experiments on various benchmark datasets demonstrate the state-of-the-art performance of SRoUDA where it achieves significant model robustness improvement without harming clean accuracy. Code is available at https://***/VisionFlow/SRoUDA main. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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MOAFL: Potential Seed Selection with Multi-Objective Particle Swarm Optimization 21

MOAFL: Potential Seed Selection with Multi-Objective Particl...

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Proceedings of the 7th International Conference on Communication and Information Processing

作者： Jinman Jiang Rui Ma Xiajing Wang Jinyuan He Donghai Tian Jiating Li Beijing Key Laboratory of Software Security Engineering Technology School of Computer Science&Technology Beijing Institute of Technology China CASIC General Department of Network&Information China and CASIC Network&Information Development Corporation Limited China

ISBN: (纸本)9781450385190

Fuzzing has become one of the most widely used technology for discovering software vulnerabilities thanks to its effectiveness. However, even the state-of-the-art fuzzers are not very efficient at identifying promising seeds. Coverage-guided fuzzers like American Fuzzy Lop (AFL) usually employ single criterion to evaluate the quality of seeds that may pass up potential seeds. To overcome this problem, we design a potential seed selection scheme, called MOAFL. The key idea is to measure seed potential utilizing multiple objectives and prioritize promising seeds that are more likely to generate interesting seeds via mutation. More specifically, MOAFL leverages lightweight swarm intelligence techniques like Multi-Objective Particle Swarm Optimization (MOPSO) to handle multi-criteria seed selection, which allows MOAFL to choose promising seeds effectively. We implement this scheme based on AFL and our evaluations on LAVA-M dataset and 7 popular real-world programs demonstrate that MOAFL significantly increases the code coverage over AFL.

关键词： Multi-Objective Particle Swarm Optimization (MOPSO) AFL Multiple Criteria Seed Selection

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Compromising Rechargeable Sensor networks in Marine Environment

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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Low-modal-crosstalk doped-fiber amplifiers in few-mode-fiber-based systems

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Photonics Research 2024年第8期12卷 1768-1775页

作者： SHUAILUO HUANG LEI SHEN GANG QIAO YUANPENG DING YUYANG GAO JIAN CUI BAOLONG ZHU SIYUAN LIU MINGQING ZUO JINGLONG ZHU LEI ZHANG JIE LUO YONGQI HE ZHANGYUAN CHEN JUHAO LI State Key Laboratory of Advanced Optical Communication Systems and Networks Peking UniversityBeijing 100871China State Key Laboratory of Optical Fiber and Cable Manufacture Technology Yangtze Optical Fibre and Cable Joint Stock Limited CompanyWuhan 430073China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Networks China Mobile Communications Group Co.Ltd.Beijing 100033China Department of Fundamental Network Technology China Mobile Research InstituteBeijing 100053China Pengcheng Laboratory Shenzhen 518055China

Independent light propagation through one or multiple modes is commonly considered as a basic demand for mode manipulation in few-mode fiber(FMF)-or multimode fiber(MMF)-based optical systems such as transmission links,optical fiber lasers,or distributed optical fiber ***,the insertion of doped-fiber amplifiers always kills the entire effort by inducing significant modal *** this paper,we propose the design of doped-fiber amplifiers in FMF-based systems adopting identical multiple-ring-core(MRC)index profiles for both passive and doped fibers to achieve low modal *** develop the direct-glass-transition(DGT)modified chemical vapor deposition(MCVD)processing for precise fabrication of few-mode erbium-doped fibers(FM-EDFs)with MRC profiles of both refractive index and erbium-ion doping ***,a few-mode erbium-doped-fiber amplifier(FM-EDFA)with a maximum gain of 26.08 dB and differential modal gain(DMG)of 2.3 dB is realized based on fabricated FM-EDF matched with a transmission FMF supporting four linearly polarized(LP)*** the insertion of the FM-EDFA,60+60 km simultaneous LP_(01)∕LP_(11)∕LP_(21)∕LP_(02)transmission without inter-modal multiple-input multiple-output digital signal processing(MIMO-DSP)is successfully *** proposed design of low-modal-crosstalk doped-fiber amplifiers provides,to our knowledge,new insights into mode manipulation methods in various applications.

关键词： fiber crosstalk amplifiers

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COVID TCL:A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

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computer Systems Science & engineering 2023年第10期47卷 187-204页

作者： Rui Wen Jie Zhou Zhongliang Shen Xiaorui Zhang Sunil Kumar Jha Department of Electromechanical Engineering Zhejiang Business Technology InstituteNingbo315012China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Department of Physics Faculty of ScienceAdani UniversityAhmedabadIndia

Convolution Neural networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of ***,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 *** reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 *** solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class ***,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation ***,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation *** experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference *** COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.

关键词： Covid-19 diagnose convolutional neural networks XGBoost COVID triplet-center loss early and incubation COVID-19 patients

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State Estimation of UAV under network Attack

State Estimation of UAV under Network Attack

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Xiaoqi Liu Jiangning Zhu Qingquan Liu College of Equipment Engineering Shenyang Ligong University Shenyang China Department of mathematics and computer Chaoyang Teachers College Chaoyang China Fujian Zhongrui Network Co. LTD Fuzhou China

When the sensors of UAVs send back their state information, they may be attacked by the network, resulting in a large gap between the state information sent back by the sensors and the actual state information. This paper studies the state estimation of UAVs under network attack. First of all, the state equation of the sensor of the UAV is established when it is attacked by the network and interfered with by the input after sending the state information. Secondly, the judgment conditions of UAVs being attacked by networks are analyzed. Then, the model of UAV state estimation is established under the condition that network attack and interference exist simultaneously. Finally, the algorithm of UAV state estimation is designed. The rationality of the algorithm is verified by comparing the state estimation value with the state data of the UAV with and without a network attack.

关键词： High performance computing Interference Mathematical models Data models Sensors Linear matrix inequalities State estimation

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Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval

arXiv

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arXiv 2022年

作者： Wang, Zhaowei Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Special Administrative Region China Beijing Laiye Network Technology Co. Ltd Beijing China

Legal case retrieval, which aims to retrieve relevant cases given a query case, plays an essential role in the legal system. While recent research efforts improve the performance of traditional ad-hoc retrieval models, legal case retrieval is still challenging since queries are legal cases, which contain hundreds of tokens. Legal cases are much longer and more complicated than keywords queries. Apart from that, the definition of legal relevance is beyond the general definition. In addition to general topical relevance, the relevant cases also involve similar situations and legal elements, which can support the judgment of the current case. In this paper, we propose an interaction-focused network for legal case retrieval with a multi-view contrastive learning objective. The contrastive learning views, including case-view and element-view, aim to overcome the above challenges. The case-view contrastive learning minimizes the hidden space distance between relevant legal case representations produced by a pre-trained language model (PLM) encoder. The element-view builds positive and negative instances by changing legal elements of cases to help the network better compute legal relevance. To achieve this, we employ a legal element knowledge-aware indicator to detect legal elements of cases. We conduct extensive experiments on the benchmark of relevant case retrieval. Evaluation results indicate our proposed method obtains significant improvement over the existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning systems

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Reinforcement learning beyond expectation

arXiv

引用

arXiv 2021年

作者： Ramasubramanian, Bhaskar Niu, Luyao Clark, Andrew Poovendran, Radha Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

The inputs and preferences of human users are important considerations in situations where these users interact with autonomous cyber or cyber-physical systems. In these scenarios, one is often interested in aligning behaviors of the system with the preferences of one or more human users. Cumulative prospect theory (CPT) is a paradigm that has been empirically shown to model a tendency of humans to view gains and losses differently. In this paper, we consider a setting where an autonomous agent has to learn behaviors in an unknown environment. In traditional reinforcement learning, these behaviors are learned through repeated interactions with the environment by optimizing an expected utility. In order to endow the agent with the ability to closely mimic the behavior of human users, we optimize a CPT-based cost. We introduce the notion of the CPT-value of an action taken in a state, and establish the convergence of an iterative dynamic programming-based approach to estimate this quantity. We develop two algorithms to enable agents to learn policies to optimize the CPT-value, and evaluate these algorithms in environments where a target state has to be reached while avoiding obstacles. We demonstrate that behaviors of the agent learned using these algorithms are better aligned with that of a human user who might be placed in the same environment, and is significantly improved over a baseline that optimizes an expected utility. © 2021, CC BY.

关键词： Autonomous agents

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Automatic Diagnosis of Early Pregnancy Fetal Nasal Bone Development Based on Complex Mid-Sagittal Section Ultrasound Imaging

SSRN

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SSRN 2024年

作者： Chen, Xi Xu, Xiaoyu Tong, Lyuyang Zhao, Huangxuan Du, Bo School of Computer Science Wuhan University Wuhan430072 China Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China National Engineering Research Center for Multimedia Software Wuhan University Wuhan430072 China The Obstetrical Department Wuhan University People’s Hospital East Branch Wuhan430072 China Department of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430072 China

Early prenatal screening of fetal nasal bone (FNB) development is crucial for detecting chromosomal abnormalities. Existing deep learning approaches primarily focus on detection rather than diagnosis of FNB. This paper introduces an early prenatal FNB development automated diagnostic system (FNB-ADS), which employs a cascaded hierarchical filtering method to reduce noise interference in mid-sagittal plane ultrasound images. Specifically, the system employs YOLOv8 for precise FNB localization, segments the nasal bone, tip, and prenasal skin using a specially designed lightweight segmentation network, and diagnoses developmental abnormalities using Resnet34 classification methods. Furthermore, this paper has collected and publicly released the FNB-UDV dataset, which includes a detection subset and a video subset. The detection subset contains 1,007 two-dimensional ultrasound images, while the video subset comprises 12 ultrasound videos. Upon a comprehensive evaluation, the diagnostic accuracy of FNB-ADS reaches 92.37% with a processing time of 0.14 seconds per image, and the video diagnostic accuracy is 98.69% with a per-frame inference speed of 0.37 seconds in the FNB-UDV dataset. Representing the first deep-learning approach tailored specifically for early pregnancy FNB ultrasound video diagnosis, FNB-ADS significantly enhances the standardization of diagnostic procedures and reduces the dependence on subjective clinical assessments. © 2024, The Authors. All rights reserved.

关键词： Ultrasonic imaging

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