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共 64336 条记录，以下是321-330 订阅

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Understanding Different Transport Coexistence in Datacenter networks

Understanding Different Transport Coexistence in Datacenter ...

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1st international Conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Hu, Dinghuang Dong, Dezun Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

ISBN: (纸本)9798350364613;9798350364606

Nowadays, numerous transport protocols utilizing different congestion signals are being developed to address network congestion in datacenter networks (DCNs) and meet the requirements of low latency and high throughput. As new transport protocols are gradually introduced, DCNs become heterogeneous and enter a different transport coexistence state where they often encounter unfair bandwidth allocation. Nevertheless, existing research has not provided a comprehensive and in-depth understanding of the underlying causes of this fairness issue. In this paper, we scrutinize the performance of numerous datacenter transport protocols in different transport coexistence scenarios and observe that this fairness issue is closely linked to the steady-state buffer length of a transport protocol. Through our in-depth analysis, we ascertain that the earlier a protocol reacts to network congestion, the more likely it is to experience bandwidth depletion.

关键词： Datacenter networks different transport coexistence Fairness

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A Detailed Simulation Model of Residential LV networks for Harmonic Resonance Studies 13

A Detailed Simulation Model of Residential LV Networks for H...

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13th international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Kannan, Shrinath Meyer, Jan Schegner, Peter Tech Univ Dresden IEEH Dresden Germany

ISBN: (数字)9781665466189

ISBN: (纸本)9781665466189

Harmonic resonance is increasingly observed in many European residential low-voltage networks, warranting its characterization and detection. Various simulation models of such networks were developed to perform harmonic studies, but they are not optimized to represent the varying harmonic resonance characteristics and the parameters it affects. To bridge this gap, a detailed simulation model in the frequency domain is proposed in this paper. The model represents the low-voltage network including its residential customers. One of the novel contributions of this paper is the realistic modeling of customer-side harmonic impedance. Equivalent circuits of various time-dependent combinations of household devices are derived, which were identified based on customer behavior. The simulation model is validated against measurements obtained from German low-voltage networks for their representativeness. An application example is provided to highlight the usefulness of the proposed model.

关键词： Simulation model Customer-side frequency-dependent impedance time-dependent behavior Harmonic resonance

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Convolutional Neural Network with Improved Optimizer for Plant Disease Detection and Classification

Convolutional Neural Network with Improved Optimizer for Pla...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Prakash Kumar, H.R. Halaswamy, B.M. Shivakumar, B.K. Government Polytechnic Department of Electronics and Communication Hosadurga India Government Polytechnic Department of Electronics and Communication Chitradurga India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375442

Recently, plant disease detection has become a main concern in the field of agriculture. The early detection and identification of plant disease supports the farmers for considering precaution metrics by protecting the diseases spread to various phases of plants. The existing techniques to detect plant diseases majorly concentrated on manual feature extraction, making made it expensive. Here, the Convolutional Neural Network (CNN) with improved optimizer is developed to detect and classify the plant leaf disease with high classification accuracy. The deep meaningful features are automatically extracted by using the convolutional layer of CNN. Then, the improved Stochastic Gradient Descent (SGD) optimizer is used in the CNN model which enhances the classification performance. The performance measures considered to evaluate the performance of the developed method are, accuracy, f1-score, recall and precision. The CNN with improved optimizer attains 99.5% accuracy, 98.7% precision, 98.2% recall and 98.5% f1-score, proving superior to the existing techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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GYMetricPose: A light-weight angle-based graph adaptation for action quality assessment 37

GYMetricPose: A light-weight angle-based graph adaptation fo...

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37th international symposium on computer-Based Medical systems (CBMS)

作者： Gallardo, Ulises Caro, Fernando Hernandez, Eluney Espinosa, Ricardo Ochoa-Ruiz, Gilberto Univ Panamer Fac Ingn Aguascalientes 20290 Aguascalientes Mexico Tecnol Monterrey Escuela Ingn & Ciencias Monterrey 64849 NL Mexico Univ Lorraine CRAN UMR 7039 F-54518 Vandoeuvre Les Nancy France CNRS F-54518 Vandoeuvre Les Nancy France

ISBN: (纸本)9798350384734;9798350384727

Improving the overall quality of exercise is crucial for achieving effective and safe techniques during gym workouts. Moreover, identifying errors during workouts can optimize training benefits and minimize the risk of injury. In this paper, we propose a cross-domain method to exploit angle information in human pose skeletons, aiming to detect fine-grained posture problems in complex real-world environments. Specifically, we integrate the Geometric Representation Extraction (GRE) module along with transformer-based pose estimation. Our approach demonstrates efficacy on the Fitness-AQA dataset, which comprises authentic exercise samples captured in real-world gym settings. This performance is achieved after pose estimation with approximately 164k parameters in its base configuration. The experimental results highlight that our method is a competitive approach compared to self-supervised video/image approaches in complex environments. In the Back Squat exercise, our method outperforms Motion Disentangling (MD) in detecting Knee Inward Error (KIE) with an F1-score of 0.4398. For Static Shallow Squat Error, it achieved the second-best F1-score of 0.8677, just 0.0017 below Cross-View Cross-Subject Pose Contrastive Learning (CVCSPC). In the Overhead Press exercise, the method significantly improved the detection of Knee error, achieving an Fl-score of 0.8160, surpassing CVCSPC and other methods. Overall, these results demonstrate that the proposed method provides competitive performance compared to the state-of-the-art models while using 187x fewer parameters than the model with the highest performance in the AQA dataset, the Motion Disentangling (MD) approach. Code will be available at: https://***/CaroFernando/G3inPose

关键词： Action Quality Assessment graph convolutional neural networks human action understanding

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Beyond the Horizon: Drone-Assisted HAR Through Cutting-Edge Caps Net and Optimization Techniques

Beyond the Horizon: Drone-Assisted HAR Through Cutting-Edge ...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Meesala, Mohan Kumar Vallabhaneni, Rohith Mathapati, Mahantesh Pareek, Piyush Kumar Metan, Jyoti University of the Cumberlands Department of Information Technology Williamsburg United States University of the Cumberlands Williamsburg United States Amruta Institute of Engineering & Management Sciences Department of Cse Bengaluru India And Ipr Cell Nitte Meenakshi Institute of Technology Department of Aiml Bengaluru India Atria Institute of Technology Department of Ise Bengaluru India

ISBN: (纸本)9798350375442

The intricate stances, the need to comprehend multiple points of view, and the varied settings in which the action unfolds are the main obstacles. In this paper, a new Capsule Network (CapsNet) is proposed as a solution to these problems. By removing the pooling layers and replacing them with capsule layers, CapsNet overcomes the major limitations of Convolutional Neural networks (CNNs). In order to enhance the classification accuracy, the parameters of the suggested model are fine-tuned using the rider optimisation algorithm (ROA). To optimise current deep CNN designs for learning temporal information, the suggested network can be employed as a plug-in module, doing away with the necessity for a distinct temporal stream. Two freely available benchmark datasets, MOD20 and Okutama, have been used to test it. Associated to the prior state-of-the-art performances, the suggested perfect is light years ahead with an accuracy of 97.49% and 98.12% on the relevant datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving EFL Students' Academic Success with Positive Psychology and Graph Attention networks

Improving EFL Students' Academic Success with Positive Psych...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Nayak, Ipsita Shukla, Tushar Dhar Shiny, K.P. Raghavendrareddy, C. Pulugu, Dileep Raihana, A. Kiit D University Department of English Bhubaneswar India University Institute of Physical Education & Sports Chandigarh University Mohali India Department of English Tenali India Mohan Babu University Department of English Tirupati India Malla Reddy College of Engineering and Technology Department of Cse Hyderabad India Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350375442

Students in content-based courses in Asia sometimes become passive in class as a result of the prevalence of traditional teacher-fronted instruction and lecture-based learning. As a result, there has been a current push to promote reforms in EFL teaching approaches. In this situation, new approaches to teaching English as a foreign language (EFL), such as flipped classrooms, are required. The popularity of flipped classes has increased alongside the proliferation of useful *** three steps that comprise this approach are preprocessing, feature selection, and model training. At its core, data preparation is about getting the raw data ready to use data mining algorithms. A feature evaluator and a CFS make up the feature selection. The model was trained using a GAT. With an average accuracy of 91.34%, the suggested method surpasses ANN and SVM. © 2024 IEEE.

关键词： Students

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Adaptive Load Balancing for distributed Multi-Controller SDNs through Switch Migration Strategy 17

Adaptive Load Balancing for Distributed Multi-Controller SDN...

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17th international Conference on COMmunication systems and networks, COMSNETS 2025

作者： Rao, Allu Sudharshan Khanal, Bipal Alam Ansari, Md. Sarfaraj National Institute of Technology Sikkim Department of Computer Science and Engineering Sikkim Ravangla India

ISBN: (纸本)9798331531195

Software-defined networking (SDN) is a transformative technology that systematically centralises and manages network resources. This paradigm shift allows for greater flexibility, agility, and efficiency in network management. In the ever-evolving landscape of distributed software-defined networks, the escalating demand for cloud services and data traffic underscores the critical need for a sophisticated load-balancing scheme. This paper addresses the pivotal challenge of optimising resource utilisation in SDN controllers to mitigate bottlenecks, enhance responsiveness, and ensure efficient network operations. This paper proposes a switch migration algorithm for efficient load balancing in distributed multi-controller topology. The proposed framework uses resource utilisation, load balancing rate, and re-association time for performance measurement. The proposed algorithm considers the switch to controller distance and remaining processing capacity to select an immigration controller. It also minimizes re-association time in the process of switch migration. The proposed algorithms are compared with benchmark algorithms like 'Closest Switch Migration' and 'Maximum Utilization Switch Migration' using various performance metrics. It has shown promising results and outperforms the benchmark algorithms. © 2025 IEEE.

关键词： Benchmarking

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Deadline-aware and energy-efficient distributed scheduling algorithm in ground-air cooperative networks 9

Deadline-aware and energy-efficient distributed scheduling a...

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9th international Conference on computer and Communication systems (ICCCS)

作者： Xu, Hairui Shao, Yanling Liu, Liming Shan, Pingping Guo, Junying Xu, Wenxuan Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Nanyang Inst Technol Sch Comp & Software Nanyang 473000 Peoples R China

ISBN: (纸本)9798350350227;9798350350210

The scarcity of energy resources and computational capabilities poses significant challenges for Unmanned Aerial Vehicles (UAVs) when tasked with executing time-sensitive and complex demands, such as those necessary for Artificial Intelligence (AI)-enabled applications. Integrating Unmanned Ground Vehicles (UGVs) in Ground-Air Cooperative systems offers a promising solution by providing additional computational and storage capacities near the point of need. However, these systems' distributed, dynamic, and resource-constrained nature presents a critical challenge for efficient task scheduling. This study introduces a novel two-tiered distributed online task scheduling approach aimed at optimizing the resource utilization of UGVs in edge computing environments. Our methodology encompasses two algorithms: a task dispatching algorithm designed to identify a UGV server that meets the deadline requirements while minimizing energy consumption and a computing scheduling algorithm that employs the Earliest Deadline First (EDF) strategy to refine the sequence in which tasks are processed, thereby reducing the average task completion time. We thoroughly assess the performance of the suggested algorithm with state-of-the-art methods using the OMNeT++ network simulator. The outcomes indicate that the proposed method significantly reduces the average task latency and energy consumption while meeting task deadline requirements.

关键词： ground-air cooperative distributed scheduling delay-aware energy-efficient

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distributed Pooled Data Intrusion Detection: Lessons Learned from Quantitative Group Testing 44

Distributed Pooled Data Intrusion Detection: Lessons Learned...

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44th IEEE international Conference on distributed Computing systems (ICDCS)

作者： Hahn-Klimroth, Max Kaaser, Dominik Rau, Malin Goethe Univ Frankfurt Frankfurt Germany TU Hamburg Christian Doppler Lab Blockchain Technol Internet Hamburg Germany Univ Hamburg Hamburg Germany

ISBN: (纸本)9798350386066;9798350386059

The goal of (network) intrusion detection systems is to identify unauthorized or malicious activities within a computer network. In this work we consider the following theoretical model for intrusion detection systems in large data center networks. We assume that the network is modeled as a leaf-spine-architecture with m spine nodes and n leaves. In a sequence of observation periods, each spine node stores a snapshot of the communication graph and accumulates (an approximation of) the number of alerts caused by suspicious behavior. To identify the responsible malicious nodes, we apply a distributed reconstruction algorithm based on quantitative group testing: In quantitative group testing we are given a binary signal sigma of Hamming weight k along with a querying method. Each query pools multiple entries of s together and returns the sum of the entries in the pool. The goal is to reconstruct s using as few queries as possible. Our contributions in this paper are three-fold. First we mathematically analyze a distributed reconstruction algorithm for the quantitative group testing instance induced by our intrusion detection model. In particular, we analyze the performance assuming a communication graph where each leaf sends Geom(p) many packets to the spine nodes in each time interval, where p is a parameter of the model. Second, we prove that our algorithm achieves a performance that is optimal up to logarithmic factors. Finally, we simulate our approach and provide empirical data that show that our approach works well in practice. The main novelty of our analysis is that the test-design is given by the communication graphs that are accumulated in multiple observation periods. This is in contrast to classical group testing where the algorithm is allowed to decide on the test design, and we believe that our analysis of non-standard test designs is of independent interest to the distributed group testing community.

关键词： Reconstruction Algorithm Group Testing Pooled Data Intrusion Detection Leaf-Spine Architecture

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Accountable Bulletin Boards: Definition and Provably Secure Implementation 37

Accountable Bulletin Boards: Definition and Provably Secure ...

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37th IEEE computer Security Foundations symposium (CSF)

作者： Graf, Mike Kuesters, Ralf Rausch, Daniel Egger, Simon Bechtold, Marvin Flinspach, Marcel Univ Stuttgart Stuttgart Germany

ISBN: (纸本)9798350362046;9798350362039

Bulletin boards (BB) are important cryptographic building blocks that, at their core, provide a broadcast channel with memory. BBs are widely used within many security protocols, including secure multi-party computation protocols, e-voting systems, and electronic auctions. Even though the security of protocols crucially depends on the underlying BB, as also highlighted by recent works, the literature on constructing secure BBs is sparse. The so-far only provably secure BBs requiretrusted components and sometimes also networks without message loss, which makes them unsuitable for applications with particularly high security needs where these assumptions might not always be met. In this work, we fill this gap by leveraging the concepts of accountability and universal composability (UC). More specifically, we propose the first ideal functionality for accountable BBs that formalizes the security requirements of such BBs in UC. We then propose Fabric*(BB) as a slight extension designed on top of Fabric*, which is a variant of the prominent Hyperledger Fabric distributed ledger protocol, and show that Fabric*(BB) UC-realizes our ideal BB functionality. This result makes Fabric*(BB) the first provably accountable BB, an often desired, but so far not formally proven property for BBs, and also the first BB that has been proven to be secure based only on standard cryptographic assumptions and without requiring trusted BB components or network assumptions. Through an implementation and performance evaluation we show that Fabric*(BB) is practical for many applications of BBs.

关键词： Accountability Blockchain Bulletin Board distributed Ledger E-voting Foundations Hyperledger Fabric Ideal Bulletin Board Functionality Universal Composability

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