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检索条件"任意字段=8th International Conference on Distributed Computing and Networking"

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DSANE:A Dual Structure-Aware Network Embedding Approach for User Identity Linkage

DSANE:A Dual Structure-Aware Network Embedding Approach for ...

引用

IEEE international conference on Big Data Analysis (ICBDA)

作者： Zhihao Xiong Xiaqing Xie Xu Wu Yuan Peng Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education BUPT China school of cyberspace security BUPT China Libarary of BUPT China

Increasing popularity and diversity of social media networks has encouraged people to participate on multiple online social networks. thus, linking account across different networks belonging to the same person becomes an emerging task and is important to many downstream tasks. the mainstream approach is to regard users as nodes in networks and embed them in a low-dimensional latent space, making it convenient to input into subsequent predictive models. Existing methods are still faced up with challenges of incompleteness of networks and insufficiency performance. We propose a novel method called DSANE-Dual Structure-Aware Network Embedding for user identity linkage based on deep neural network, which firstly optimizes networks by a complete-filter method, and then embeds both local and global information into nodes’ vectors, thus greatly enriches nodes’ representation. Experiments on real-world datasets show that our method greatly outperforms baselines.

关键词： Couplings Deep learning Social networking (online) Neural networks Diversity reception Predictive models Feature extraction

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602933

the proceedings contain 158 papers. the special focus in this conference is on Life System Modeling and Simulation and Intelligent computing for Sustainable Energy and Environment. the topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater Image Recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General Pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602315

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A distributed Dependency-Aware Offloading Scheme for Vehicular Edge computing Based on Policy Gradient 8

A Distributed Dependency-Aware Offloading Scheme for Vehicul...

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8th IEEE international conference on Cyber Security and Cloud computing and 2021 7th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2021

作者： Liu, Haoqiang Zhao, Hongbo Geng, Liwei Wang, Yujie Feng, Wenquan Beihang University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9781665443777

the proliferation of smart transportation has significantly promoted explosive growth of the Internet of Vehicles (IoV). Especially, with the rapid development of the 5-th generation mobile networks (5G), amounts of computation-intensive and latency-sensitive vehicle applications impose heavy pressures to such limited vehicles. To address this issue, Multi-access Edge computing (MEC) has served as a promising paradigm to facilitate those devices at the edge of wireless networks. Computation offloading is a critical technology that decides which tasks should be offloaded for minimizing the energy and time cost. However, conventional methods are inefficient to deal with dependency-aware subtask topologies from vehicles, which leads to low efficiency and waste of edge resources, especially for multi-vehicles scenarios. In this paper, we investigate the subtask topologies of IoV applications. Directed acyclic graphs (DAGs) are utilized to obtain the priority of task scheduling. Further, take privacy protection and optimal resource allocation into consideration, we put forward a distributed deep reinforcement learning (DRL) strategy based on policy gradient without information sharing in edge computing. Especially, both actor and critic network employ convolutional layer and transformer to learn the optimal mapping from the input states to the offloading decision for each subtasks. Numerical results show that, for various experimental settings, compared with the existing offloading methods, the proposed scheme achieves the superior performance. © 2021 IEEE.

关键词： Vehicles

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Collaborative Mobile Edge computing through UPF Selection 18th

Collaborative Mobile Edge Computing Through UPF Selection

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18th European-Alliance-for-Innovation (EAI) international conference on Collaborative computing - networking, Applications and Worksharing (CollaborateCom)

作者： Li, Yuanzhe Zhou, Ao Ma, Xiao Wang, Shangguang Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9783031243851;9783031243868

the distributed deployment and the relatively limited resource of one edge node make it quite challenging to effectively manage resources at the edge. Inappropriate scheduling may result in a quality of service deterioration and brings significant cost. In this paper, we propose a per-user level management mechanism for joint scheduling of user requests and container resources at the edge and study how to minimize average cost as well as satisfy delay constraints. the cost model of the system consists of operating cost, switching cost and delay violation cost. the key idea is to deploy a deep reinforcement learning-based scheduler in the core network to conduct joint network and computation management. To evaluate the performance, we build a test bed namely MiniEdgeCore that contains a full user plane protocol stack and deploy a real-time video inference application on it. A real-world dataset is used as the workload sequence to conduct experiments. the results show that the proposed method can reduce average costs effectively.

关键词： Mobile edge computing 5G Request dispatching Container management

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602247

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Panoramic Video Quality Assessment Based on Spatial-Temporal Convolutional Neural Networks 8th

Panoramic Video Quality Assessment Based on Spatial-Temporal...

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8th international conference on Signal and Information Processing, Network and Computers, ICSINC 2021

作者： An, Tingting Sun, Songlin Liu, Rui School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Trusted Distributed Computing and Services Ministry of Education Key Laboratory Beijing China San Jose State University San JoseCA95192 United States

ISBN: (纸本)9789811933868

the development of 5G technology and Ultra HD video provide the basis for panoramic video, namely virtual reality (VR). At present, the traditional VQA method is not effective on panoramic video. therefore, it is crucial to design objective VQA models for the standardization of panoramic video industry. With the development of deep learning, excellent algorithms of VQA methods based on convolutional neural network have emerged. In this paper, we propose a full reference VQA model based on spatial-temporal 3D convolutional neural network, the feature extraction combined the time and spatial information. we verify and optimize the proposed VQA model based on VQA-ODV panoramic video database, its objective score has a higher correlation with subjective scores than that of traditional VQA methods. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Dual Image Reversible Data Hiding Based on Coordinate Vector Directions via Communication networking 23

Dual Image Reversible Data Hiding Based on Coordinate Vector...

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8th international conference on Cloud computing and Internet of things, CCIOT 2023

作者： Lee, Chin-Feng Chan, Kuo-Chung Dept. of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9798400708046

In this paper, an innovative reversible dual image information hiding technique is proposed, which utilizes the coordinate vectors generated by the original pixel pairs to hide secret information at two combined locations in four directions. In the process of image transmission, it is not easy to be detected, and the receiver of the image data can completely restore the stego image to the original image, which is very suitable for the transmission of confidential information such as medicine or intelligent transportation. In recent years, many researchers have used this technique to improve the quality of embedded steganographic images and the ability to hide secret information. In the field of digital image information hiding, embedding capacity and Peak Signal-to-Noise Ratio (PSNR) are two important quantitative evaluation indicators. the higher the PSNR value, the closer the distorted digital image quality is to the original digital image. If the PSNR is higher than 30dB, it is generally considered to be ideal image quality. We extract two pixels from the original image and embed the secret information according to different hidden parameters and . the minimum hiding ability is 1 bit per pixel (bpp) or above. According to our experimental results, when the hidden capacity of the steganographic image is 1 bpp, the PSNR value of the image is 50.65 dB, and when the hidden capacity is 2 bpp, the PSNR value of the image is 37.88 dB. this means that our proposed method has good image quality and embedding capacity. © 2023 ACM.

关键词： Pixels

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An Autonomic Chess Bot Detection & Defeat System

An Autonomic Chess Bot Detection & Defeat System

引用

Cyber Security in networking conference (CSNet)

作者： Andrew Bengtson Christopher Rouff Ali Tekeoglu Johns Hopkins University Applied Physics Laboratory & JHU Whiting School of Engineering Computer Science Department Johns Hopkins University Whiting School of Engineering Leidos Innovations Center & Johns Hopkins University Whiting School of Engineering

ISBN: (数字)9798331534103

ISBN: (纸本)9798331534110

Due to the rise of online chess, the increasing dominance of automated chess bots, and high-profile cheating allegations in chess recently, there has been a growing interest in applying bot detection for chess. the existing methods in this field primarily rely on player history, anomaly detection, and engine analysis to attempt to identify engine play after it has occurred. these post-facto analysis methods generalize poorly to real-time autonomy detection domains such as real-time cybersecurity. this paper suggests a new challenge in which bot detection must be done on an opposing player mid-game, allowing for a dynamic strategy based on the identification made of the opponent. this paper then suggest an autonomic system which could use the self-chop properties in order to meet this challenge.

关键词： Deepfakes Runtime Social networking (online) Games Real-time systems Complexity theory History distributed computing Engines Testing

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Improved Federated Average Algorithm for Non-independent and Identically distributed Data 8th

Improved Federated Average Algorithm for Non-independent and...

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8th international conference on Signal and Information Processing, Network and Computers, ICSINC 2021

作者： Zhou, Zhongchang Sun, Fenggang Lan, Peng Wang, Zhijun College of Information Science and Engineering Shandong Agricultural University Tai’an271000 China

ISBN: (纸本)9789811933868

Federated learning is a distributed machine learning model that can protect data privacy, where a large number of clients and servers participate in the joint train procedure. However, the problem of heterogeneous data has been a potential obstacle for federated learning. Traditional joint averaging algorithms suffer from low accuracy and slow convergence on non-IID (identical and independently distributed) data. To this end, this paper proposes an improved FedAvg based algorithm called FedAvg-Z. We introduce penalty coefficients and adaptive parameter averaging methods in our algorithm, which adjusts the influence of clients on the global model as a way to increase the convergence and stability of the model. We first demonstrate the reliability of our method on the dataset and then compare it experimentally with the traditional algorithm. the experiments show that the algorithm has higher accuracy and convergence speed in the non-IID case. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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