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Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images

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arXiv 2021年

作者： Li, Jiaxuan Jin, Peiyao Zhu, Jianfeng Zou, Haidong Xu, Xun Tang, Min Zhou, Minwen Gan, Yu He, Jiangnan Ling, Yuye Su, Yikai John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai200240 China Department of Preventative Ophthalmology Shanghai Eye Disease Prevention and Treatment Center Shanghai Eye Hospital Shanghai200040 China Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University Shanghai200080 China Department of Electrical and Computer Engineering The University of Alabama AL35487 United States State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. To address this issue, we developed a novel graph convolutional network (GCN)-assisted two-stage framework to simultaneously label the nine retinal layers and the optic disc. Specifically, a multi-scale global reasoning module is inserted between the encoder and decoder of a U-shape neural network to exploit anatomical prior knowledge and perform spatial reasoning. We conducted experiments on human peripapillary retinal OCT images. The Dice score of the proposed segmentation network is 0.820 ± 0.001 and the pixel accuracy is 0.830 ± 0.002, both of which outperform those from other state-of-the-art techniques. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical tomography

Low-Latency Layer-Aware Proactive and Passive Container Migration in Meta Computing

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arXiv 2024年

作者： Liu, Mengjie Li, Yihua Mou, Fangyi Tang, Zhiqing Lou, Jiong Guo, Jianxiong Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University China Faculty of Arts and Sciences Beijing Normal University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Informatics University of Zurich Zurich Switzerland

关键词： Containers

Collaborative Edge Service Placement for Maximizing QoS with Distributed Data Cleaning

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Collaborative Edge Service Placement for Maximizing QoS with...

International Workshop on Quality of Service

作者： Yuzhu Liang Wenhua Wang Xi Zheng Qin Liu Liang Wang Tian Wang School of Artificial Intelligence Beijing Normal University Beijing China School of Computing Macquarie University NSW Australia College of Computer Science and Electronic Engineering Hunan University Changsha China School of Computer Science Northwestern Polytechnical University Xi'an China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai China

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines.

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A New Hybrid Cipher based on Prime Numbers Generation Complexity: Application in Securing 5G networks

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A New Hybrid Cipher based on Prime Numbers Generation Comple...

ACS/IEEE International Conference on computer Systems and Applications

作者： Adda Boualem Abdelkader Berrouachedi Marwane Ayaida Hisham Kholidy Elhadj Benkhelifa Department of Computer Science Ibn Khaldoun University Tiaret Algeria Paragraphe Lab University of Paris 8 Paris France Polytechnique Hauts-de-France CNRS Univ. Lille UMR 8520 - IEMN France CReSTIC EA 3804 Univ. Reims Champagne Ardenne France Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica New Yourk USA Smart Systems AI and Cybersecurity Research Centre Staffordshire Univ UK

Today’s cellular networks (known as 2G, 3G, and 4G) provide a solid foundation for connecting things. The Internet of Things (IoT) is helping people live and work smarter and take full control of their lives. In addition to providing smart devices for home automation, IoT is also critical for businesses. The security of classical cryptosystems is characterized by their simple arithmetic complexity. This type uses; shifts, arrangements, permutations, and substitutions of letters, words, or phrases to encrypt a given message. Moreover, this kind of cryptosystem is easy to break, because the complexity of their scheme is convergent. On the other hand, the security of modern cryptosystems is characterized by complex arithmetic, using the concept of keys; private keys to encrypt and public keys to decrypt. This type is hard to break because of the complexity of their scheme being divergent. This paper aims to secure the 5G networks with a new variant of the classical Polybius Checkerboard Cipher (HPCC). The system of letter substitution is based on the complexity and the divergence of private key generation. Filling a magic square with primes is an NP-hard task, which shows the complexity and divergence of the strategy adopted in this variant.

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A Mixed-Signal Framework for Modelling Fourier-Domain Optical Coherence Tomography

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A Mixed-Signal Framework for Modelling Fourier-Domain Optica...

Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Yuye Ling Mengyuan Wang Yu Gan Xinwen Yao Leopold Schmetterer Chuanqing Zhou Yikai Su John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China University of Alabama AL USA Singapore Eye Research Institute Singapore National Eye Centre Singapore School of Chemical and Biomedical Engineering Nanyang Technological University Singapore School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China State Key Lab of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China

We investigate the signal digitization procedure in FD-OCT and propose a novel mixed-signal framework to model its image formation. It turns out FD-OCT is a shift-variant system, if conventional IDFT is used to recons... 详细信息

ISBN: (数字)9781943580828

ISBN: (纸本)9781728176567

关键词： Optical coherence tomography Two dimensional displays Asia Optical fiber communication Image reconstruction Photonics

Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

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arXiv 2023年

作者： Cui, Hanshuai Tang, Zhiqing Lou, Jiong Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519087 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27% compared to baseline algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework

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arXiv 2024年

作者： Yao, Zhi Tang, Zhiqing Lou, Jiong Shen, Ping Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai519087 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China

The Large Language Model (LLM) has gained significant popularity and is extensively utilized across various domains. Most LLM deployments occur within cloud data centers, where they encounter substantial response delays and incur high costs, thereby impacting the Quality of Services (QoS) at the network edge. Leveraging vector database caching to store LLM request results at the edge can substantially mitigate response delays and cost associated with similar requests, which has been overlooked by previous research. Addressing these gaps, this paper introduces a novel Vector database-assisted cloud-Edge collaborative LLM QoS Optimization (VELO) framework. Firstly, we propose the VELO framework, which ingeniously employs vector database to cache the results of some LLM requests at the edge to reduce the response time of subsequent similar requests. Diverging from direct optimization of the LLM, our VELO framework does not necessitate altering the internal structure of LLM and is broadly applicable to diverse LLMs. Subsequently, building upon the VELO framework, we formulate the QoS optimization problem as a Markov Decision Process (MDP) and devise an algorithm grounded in Multi-Agent Reinforcement Learning (MARL) to decide whether to request the LLM in the cloud or directly return the results from the vector database at the edge. Moreover, to enhance request feature extraction and expedite training, we refine the policy network of MARL and integrate expert demonstrations. Finally, we implement the proposed algorithm within a real edge system. Experimental findings confirm that our VELO framework substantially enhances user satisfaction by concurrently diminishing delay and resource consumption for edge users utilizing LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Vectors

Study on user's identification system in insider threats

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Tongxin Xuebao/Journal on Communications 2009年第11 A期30卷 121-126页

作者： Pei, Qing-Qi Zhao, Peng Zhang, Hong-Bin Wang, Chao Yin, Hao Institute of China Electronic System Engineering Corporation Beijing 100141 China Key Lab. of Computer Networks and Information Security of Ministry of Education Xidian University Xi'an 710071 China Institute of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang 050018 China

Monitoring user's abnormal behaviors, which is an effective method to detect impersonation, is used for impersonation detection in insider threats. A model is built by using TAN-based Bayesian network to reflect the characteristics of user's behavior. When the deviation from the model is found, the system can determine the identity of the user. As a result, experiments show that the monitoring numbers of processes called by users can be very effective on detecting impersonation and can identify the identity of the attacker.

关键词： Bayesian networks

The TREND experimental activities on “green” communication networks

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The TREND experimental activities on “green” communication...

Tyrrhenian International Workshop on Digital Communications - Enhanced Surveillance of Aircraft and Vehicles, TIWDC/ESAV

作者： Michela Meo Yi Zhang Yige Hu Filip Idzikowski Łukasz Budzisz Fatemeh Ganji Ivaylo Haratcherev Alberto Conte Antonio Cianfrani Luca Chiaraviglio Angelo Coiro Raffaele Bolla Olga Jaramillo Ortiz Roberto Bruschi Roberto Gonzalez Carmen Guerrero Edion Tego Francesco Matera Stratos Keranidis Giannis Kazdaridis Thanasis Korakis Dipartimento di Elettronica e Telecomunicazioni Politecnico di Torino Italy Networking Technology Domain Alcatel-Lucent Bell Labs Nozay France DIET Department University of Rome “Sapienza” Italy Fondazione Ugo Bordoni Rome Italy Telecommunication Networks Group Technische Universität Berlin Germany Telecommunication Networks and Telematics Lab RU of CNIT c/o University of Genoa Italy Departamento de Ingeniería Telemática Universidad Carlos III de Madrid Spain Department of Computer and Communication Engineering University of Thessaly Greece

Aimed at answering important questions about the energy demand of current telecom infrastructure and the design of sustainable and energy-efficient future networks, the research of a number of European partners is brought together in the TREND project. In this paper we present the achievements of the Work Package coordinating the experimental activities of the project - WP4. Although not presenting a completely finished portrait yet, the results shown help building a better global view on the “big picture” in the field of energy-efficient networking.

关键词： Power demand Monitoring Switches Energy consumption Power measurement Voltage measurement DVD