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International Conference on Computing, Networking and Communications (ICNC)

作者： Liang Zhang Bijan Jabbari Department of Electrical and Computer Engineering George Mason University Fairfax VA USA Department of Electrical and Computing Engineering Communications and Networks Laboratory George Mason University Fairfax VA USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Internet of Things devices (IoTDs) that deployed for various applications have limited size, power, storage, and computing capabilities, and many of them are used for low-latency applications. Mobile edge computing (MEC) can be leveraged to reduce the latency of IoTDs, and judicious caching placement can help further to improve the quality of service. In this paper, we study multi-content placement (MCP) in the MEC networks for IoTDs by considering the background data caching in the edge nodes and the data collection of IoTDs. The MCP problem is formulated by considering the caching, the IoTD assignment, and the communication and computing resources allocation to minimize the average latency of all IoTDs. As the MCP problem is NP-hard, we propose a deep reinforcement machine learning algorithm to obtain the caching placement and IoTD assignment jointly to solve the MCP problem. We use an optimal joint resource-scheduling algorithm to assign the resources. Our results demonstrate that considerable latency improvement can be achieved through the proposed deep reinforcement machine learning algorithm as compared to baseline algorithms.

关键词： Machine learning algorithms Multi-access edge computing Scheduling algorithms Simulation Quality of service Data collection Deep reinforcement learning

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Coherent stacking of 128 pulses amplified in the large nonlinear regime with pre-amplitude modulation

Coherent stacking of 128 pulses amplified in the large nonli...

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CLEO: Science and Innovations, S and I 2022

作者： Zhou, Kangzhu Yang, Bowei Zhang, Zhigang Li, Qian School of Electronic and Computer Engineering Peking University Shenzhen518055 China State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing100871 China

ISBN: (纸本)9781557528209

关键词： Amplitude modulation

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Dynamic computation offloading in time-varying environment for ultra-dense networks:a stochastic game approach

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The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 24-37页

作者： Xie Renchao Liu Xu Duan Xuefei Tang Qinqin Yu Fei Richard Huang Tao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Guangdong Communications and Networks Institute Guangzhou 510700China Department of Future Networks Purple Mountain LaboratoryNanjing 211111China Department of Systems and Computer Engineering Carleton UniversityOttawaCanada

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks(UDNs)and mobile edge computing(MEC)are considered as important *** the MEC enabled UDNs,one of the most important issues is computation *** a number of work have been done toward this issue,the problem of dynamic computation offloading in time-varying environment,especially the dynamic computation offloading problem for multi-user,has not been fully ***,in order to fill this gap,the dynamic computation offloading problem in time-varying environment for multi-user is considered in this *** considering the dynamic changes of channel state and users’queue state,the dynamic computation offloading problem for multi-user is formulated as a stochastic game,which aims to optimize the delay and packet loss rate of *** find the optimal solution of the formulated optimization problem,Nash Q-learning(NQLN)algorithm is proposed which can be quickly converged to a Nash equilibrium ***,extensive simulation results are presented to demonstrate the superiority of NQLN *** is shown that NQLN algorithm has better optimization performance than the benchmark schemes.

关键词： dynamic computation offloading time-varying environment stochastic game ultra-dense networks(UDNs) mobile edge computing(MEC)

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Encrypted Malicious Traffic Detection Based on Albert and Deep Learning

SSRN

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SSRN 2024年

作者： Zang, Xiaodong Wang, Tongliang Zhang, Xinchang Gong, Jian Gao, Peng Zhang, Guowei School of Cyber Science and Engineering Qufu Normal University Qufu China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks Jinan China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

The focus on privacy protection has brought much-encrypted network traffic. However, attackers always abuse traffic encryption to conceal malicious behaviors. Although researchers have proposed several enlightening detection methods, they must enhance the generalization ability or improve detection performance. Our inspiration is that the packet header fields, as do the underlying grammatical rules for constructing sentences, have a strict order. We consider the original packet as text and devise a robust approach with natural language processing and a deep learning model to improve the generalization ability and detection performance. We capture the critical keywords as characteristic representations of the traffic and design an adaptive domain generalization algorithm with a new loss function. It is robust against various datasets and can augment the minority malicious samples to balance the data. Simultaneously, we design an efficient feature selection algorithm, which obtains an optimal feature subset and reduces feature dimensions by 75.3%. We conducted extensive experiments with two open-source datasets and the actual Internet backbone traffic(CERNET). Experimental results demonstrate our proposal's efficiency and robustness, outperforming state-of-the-art detection schemes. Specifically, the accuracy rate of our work can improve by at most 22.8% and achieve the average detection latency of 0.73s in the backbone. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Two-Stage Tripletnet: Light Weight Remote Sensing Scene Classification

Two-Stage Tripletnet: Light Weight Remote Sensing Scene Clas...

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IEEE International Conference on Image Processing

作者： Xianbin Hu Wei Wu Zhu Li Xueliang Luo Zhengfeng Chen State Key Laboratory of Integrated Services Networks Xidian University China Department of Computer Science& Electrical Engineering University of Missouri-Kansas City USA

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Remote sensing scene classification (RSSC) seeks to allocate correct semantic labels to remote sensing images. Recently, numerous algorithms have made significant contributions to enhancing the accuracy of RSSC. However, models with high parameters and computational complexity still dominate. To address this issue, we propose a lightweight network architecture, namely Two-Stage TripletNet. In this proposed algorithm, we employ a two-stage optimizing strategy involving label optimization and loss function optimization. First, a KD-tree generated by remote sensing image features is utilized to produce visual labels. Secondly, we establish the triplet sampling method based on the visual and semantic labels of the images. Finally, the triplet loss and cross-entropy loss are jointly applied to train our model. Experimental results on mainstream datasets demonstrate the effectiveness of our proposed framework. Meanwhile, the two-stage optimizing strategy renders our model more competitive compared to other state-of-the-art algorithms.

关键词： Visualization Scene classification Adaptation models Accuracy Semantics Network architecture Sampling methods

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Fine-grained Semantic Alignment with Transferred Person-SAM for Text-based Person Retrieval 24

Fine-grained Semantic Alignment with Transferred Person-SAM ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yihao Yang, Meng Cao, Rui School of Computer Science and Engineering Sun Yat-Sen University Guangdong Guangzhou China State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi'an China Ministry of Education Guangdong Guangzhou China School of Information Science and Technology State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest University Shaanxi Xi'an China

ISBN: (纸本)9798400706868

Addressing the disparity in description granularity and information gap between images and text has long been a formidable challenge in text-based person retrieval (TBPR) tasks. Recent researchers tried to solve this problem by random local alignment. However, they failed to capture the fine-grained relationships between images and text, so the information and modality gaps remain on the table. We align image regions and text phrases at the same semantic granularity to address the semantic atomicity gap. Our idea is first to extract and then exploit the relationships between fine-grained locals. We introduce a novel Fine-grained Semantic Alignment with Transferred Person-SAM (SAP-SAM) approach. By distilling and transferring knowledge, we propose a Person-SAM model to extract fine-grained semantic concepts at the same granularity from images and texts of TBPR and its relationships. With the extracted knowledge, we optimize the fine-grained matching via Explicit Local Concept Alignment and Attentive Cross-modal Decoding to discriminate fine-grained image and text features at the same granularity level and represent the important semantic concepts from both modalities, effectively alleviating the granularity and information gaps. We evaluate our proposed approach on three popular TBPR datasets, demonstrating that SAP-SAM achieves state-of-the-art results and underscores the effectiveness of end-to-end fine-grained local alignment in TBPR tasks. © 2024 ACM.

关键词： Visual languages

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An Adaptive UAV Scheduling Process to Address Dynamic Mobile Network Demand Efficiently

An Adaptive UAV Scheduling Process to Address Dynamic Mobile...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Ruide Cao Jiao Ye Jin Zhang Qian You Chao Tang Yan Liu Yi Wang Institute of Future Networks DET of Heyuan School of Architecture and Urban Planning SZU Department of Computer Science and Engineering Peng Cheng Laboratory School of Design SUSTech

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Benefiting from high flexibility and probability of line-of-sight, deploying unmanned aerial vehicles (UAV s) as aerial access points has emerged as a promising solution for ensuring reliable wireless connectivity in crowded events. This paper introduces a UAV scheduling process adaptive to dynamic mobile network demand, including three phases. In the sensing phase, the user distribution is sensed, and user number thresholds are set to determine whether UAV assistance is needed. The planning phase presents an enhanced mean shift algorithm to find suitable locations to deploy UAVs with a dynamic bandwidth derived from the user distribution, the UAV's maximum capacity, and the UAV's maximum throughput. The deploying phase dispatches and recalls UAV s based on planning results. Comprehensive simulation experiments are conducted on OMNeT ++ using real-world data. Results show that the proposed process shows great adaptivity, with an efficiency increase of 18.7% and a fairness increase of 28.9 % compared to the existing related works on average.

关键词： Adaptive scheduling Adaptive systems Heuristic algorithms Bandwidth Autonomous aerial vehicles Dynamic scheduling Throughput

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Cluster Representation of the Structural Description of Images for Effective Classification

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computers, Materials & Continua 2022年第12期73卷 6069-6084页

作者： Yousef Ibrahim Daradkeh Volodymyr Gorokhovatskyi Iryna Tvoroshenko Medien Zeghid Department of Computer Engineering and Networks College of Engineering at Wadi AddawasirPrince Sattam Bin Abdulaziz UniversityWadi Addawasir11991Saudi Arabia Department of Informatics Kharkiv National University of Radio ElectronicsKharkiv61166Ukraine Electrical Engineering Department College of Engineering at Wadi AddawasirPrince Sattam Bin Abdulaziz UniversityWadi Addawasir11991Saudi Arabia Electronics and Micro-Electronics Laboratory Faculty of SciencesUniversity of MonastirMonastir5000Tunisia

The problem of image recognition in the computer vision systems is being *** results of the development of efficient classification methods,given the figure of processing speed,based on the analysis of the segment representation of the structural description in the form of a set of descriptors are *** propose three versions of the classifier according to the following principles:“object-etalon”,“object descriptor-etalon”and“vector description of the object-etalon”,which are not similar in level of integration of researched data *** options for constructing clusters over the whole set of descriptions of the etalon database,separately for each of the etalons,as well as the optimal method to compare sets of segment centers for the etalons and object,are *** experimental rating of the efficiency of the created classifiers in terms of productivity,processing time,and classification quality has been realized of the *** proposed methods classify the set of etalons without *** have formed the inference about the efficiency of classification approaches based on segment *** time of image processing according to the developedmethods is hundreds of times less than according to the traditional one,without reducing the accuracy.

关键词： Cluster representation computer vision description relevance descriptor image classification keypoint processing speed vector space

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Up to 320-GBaud Coherent Optical System Based on Broadband Segmented TFLN I/Q Modulator

Up to 320-GBaud Coherent Optical System Based on Broadband S...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Lingjun Zhou Xiansong Fang Yixiao Zhu Junbo Zhu Yunchen Li Xiaomin Nie Xian Zhou Chuanchuan Yang Lei Wang Weisheng Hu Ke Li Zhixue He Fan Zhang School of Electronic and Computer Engineering Peking University Shenzhen China Pengcheng Laboratory Shenzhen China State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing China State Key Laboratory of Advanced Optical Communication System and Networks Shanghai Jiao Tong University Shanghai China School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

We demonstrate the record 320-GBd single-polarization QPSK and 300-GBd dual-polarization QPSK signal generation and intradyne coherent detection. This result is achieved with a novel broadband segmented TFLN I/Q modul... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Partial response signaling Phase shift keying Optical recording Optical device fabrication Lithium Optical fiber communication Encoding Broadband communication Optical modulation Photonics

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Parameter Estimation of Synchronous Generator Using Neural Controlled Differential Equations

Parameter Estimation of Synchronous Generator Using Neural C...

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International Conference on Control and Automation (ICCA)

作者： Zhun Yin Hong Wang Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper introduces a synchronous generator modeling method based on neural controlled differential equations (neural CDEs) using online sampled data. This method begins with a fifth-order generator model, where every trainable parameter is regarded as one parameter of the fifth-order generator model. The objective is to use the real-time data to learn these parameters. A training algorithm has been formulated, and it has been shown that the combination of the proposed neural CDEs and the fifth-order model can produce desired online parameter estimations for the synchronous generator. The simulation results show that the proposed method can generate a very accurate estimation and model predictions with the mean absolute percentage error of 0.04638%.

关键词： Training Parameter estimation Accuracy Voltage Differential equations Predictive models Mathematical models

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