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A TCP Throughput Model for ieee 802.11 Linear Multi-hop networks 3

A TCP Throughput Model for IEEE 802.11 Linear Multi-hop Netw...

引用

3rd International conference on Electronic Information Engineering and computer Communication, EIECC 2023

作者： Liu, Jianzhao Zhang, Junxiong Ge, Xiaohu School of Electronic Information and Communication Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350359961

Nowadays, ieee 802.11 has become the most widely used standard for the wireless local area network (WLAN), and transport control protocol (TCP) is a dominant communication protocol in networks. Besides, the linear multi-hop networks are applied in various scenarios to overcome the limitations of communication range. This paper proposed a novel TCP throughput model for ieee 802.11 linear multi-hop networks by analyzing distributed coordination function (DCF) and frame transmit queues. It is evaluated the effects of minimum contention window sizes on the throughput by mathematical analysis, and the optimal parameters for the best throughput performance are obtained. The validity of the model is verified through the simulation results. © 2023 ieee.

关键词： ieee 802.11 multi-hop throughput transport control protocol

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Point Cloud Classification Based on local Fusion and Dense Connection Structure 4

Point Cloud Classification Based on Local Fusion and Dense C...

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4th International conference on Neural networks, Information and Communication Engineering, NNICE 2024

作者： Huang, Yansen Zhang, Lujian Bi, Yuanwei Liu, Yaowen Yantai University School of Computer and Control Engineering Shandong Yantai China

ISBN: (纸本)9798350394375

Due to the influence of objective factors such as scene noise and hardware equipment, compared to CAD modeling point clouds, the real point clouds collected by LiDAR often have strong non-uniformity and irregularity, which leads to the low ability of traditional point cloud classification models to extract point cloud features in real scenes and poor classification robustness. To address this issue, we have added a local feature fusion module and a dense connected feature fusion method to PointMLP, thereby encoding broader point cloud information into local features. The simple stacking of multiple MLP and Mixer blocks constitutes our network model-PointMM. The shallow features of the point cloud are used as input and enter the local feature fusion module to perform feature fusion within and between point sets. Finally, by maximizing the pooling layer and selecting points with stronger semantic features as inputs to the second stage feature extraction module. This method has been tested on the ScanObjectNN dataset and shows that it can approach SOTA with a classification accuracy of 0.8%, surpassing other mainstream models. © 2024 ieee.

关键词： Classification (of information)

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Ranking-based Diversified Influence Maximization for Dynamic Social networks 17

Ranking-based Diversified Influence Maximization for Dynamic...

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17th International conference on COMmunication Systems and networks, COMSNETS 2025

作者： Meena, Sunil Kumar Singh, Shashank Sheshar Singh, Kuldeep University of Delhi New Delhi Department of Computer Science New Delhi India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (纸本)9798331531195

Influence maximization (IM) is the task of selecting the most influential nodes in the network. IM achieves the goal of spreading information, influencing behaviour, or promoting sales of products. Existing studies in IM focus on maximizing the activated nodes in the static network and neglecting the communities. In this paper, we give a novel ranking-based algorithm that ranks the nodes based on their reachability and diversity. This paper proposes an objective function that considers both diversity and activated nodes to determine the diversity capability of the nodes. The proposed algorithm utilizes previous computation and integrates reachability centrality with the objective function to rank the nodes at a timestamp in a dynamic network. We execute the proposed and benchmark algorithms on five datasets. The experiments show that the suggested algorithm produces superior and competitive outcomes in comparison to the benchmark algorithms. © 2025 ieee.

关键词： Diversified Influence Maximization Dynamic networks Influence Maximization local Reaching Centrality Social Network Analysis

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Accurate Real-Time Labeling of Application Traffic 47

Accurate Real-Time Labeling of Application Traffic

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47th ieee conference on local computer networks (LCN)

作者： Schaefer, Sebastian Loebel, Alexander Meyer, Ulrike Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9781665480017

In this paper, we present the design and implementation of ATLAS, a novel tool for automatically labeling network packets with the process responsible for them. Our tool is able to label all kinds of outbound packets based on Windows events and TCP stream information with ground-truth accuracy. Additionally, it is able to label DNS packets with the correct process name instead of just the DNS resolver. Using ATLAS, it is possible to create large datasets, e.g., to create software fingerprints or train machine learning classifiers. Another use-case is to inspect the network traffic of a machine to determine which application is communicating with whom. We evaluate the performance considering different load scenarios to demonstrate the real-time capacity of ATLAS. Additionally, we analyze the communication endpoints of a Windows 10 host and compare the results before and after disabling all privacy related settings.

关键词： Privacy Telecommunication traffic Machine learning Fingerprint recognition Real-time systems Software computer networks

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PICNIC: Refinement of AlphaFold2 tertiary structure predictions using deep 3D residual neural networks

PICNIC: Refinement of AlphaFold2 tertiary structure predicti...

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2024 ieee International conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Stewart, Ross Campbell Zhao, Chenguang Liu, Tong Wang, Zheng University of Miami Department of Computer Science Coral GablesFL United States St. Ambrose University Department of Computer Science DavenportIA United States

ISBN: (纸本)9798350386226

In this paper, we present two novel approaches, PICNIC2-local and PICNIC2-GLOBAL, for refining AlphaFold protein tertiary structure (TS) predictions using deep 3D residual neural networks. These approaches aim to improve the accuracy and consistency of TS predictions, addressing a key challenge in protein structure prediction. PICNIC2-local and PICNIC2-GLOBAL effectively learn by putting each atom of the AlphaFold model in a voxel grid, with feature extractions of each atom, to learn the structural conformations and deviations to the native structure. PICNIC2-local learns the features locally, in which we used a smaller voxel grid with the target atom put in the center. Whereas for PICNIC2-GLOBAL, the voxel grid covers the whole protein. We trained on 30,077 AlphaFold TS predictions from the UniProt database and performed a blind-test evaluation on AlphaFold TS predictions from CASP15. The results indicate consistent improvement in atom-specific (reduction of per-atom deviation to native) and whole-protein (measured by TM-score) structural accuracy. Both models excel at refining the FM/TBM and FM target proteins in CASP15. PICNIC2-local and PICNIC2-GLOBAL can be freely accessed at https://***/zwang-bioinformatics/PICNIC/ and http://***/PICNIC/. © 2024 ieee.

关键词： Digital elevation model

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Adaptive Edge Content Delivery networks for Web-Scale File Systems 47

Adaptive Edge Content Delivery Networks for Web-Scale File S...

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47th ieee conference on local computer networks (LCN)

作者： Tiago, Joao Dias, David Veiga, Luis ULisboa INESC ID Tecn Lisboa Lisbon Portugal Protocol Labs Lisbon Portugal

ISBN: (纸本)9781665480017

The InterPlanetary File System (IPFS) is an hypermedia distribution protocol, addressed by content and identities. It aims to make the web faster, safer, and more open. The JavaScript implementation of IPFS runs on the browser, benefiting from the mass adoption potential that it yields. Startrail takes advantage of the IPFS ecosystem and strives to further evolve it, making it more scalable and performant through the implementation of an adaptive network caching mechanism. Our solution aims to add resilience to IPFS and improve its overall scalability, by avoiding overloading the nodes providing highly popular content, particularly during flash-crowd-like conditions where popularity and demand grow suddenly. We add a novel crucial key component to enable an IPFS-based decentralized Content Distribution Network (CDN). Following a peer-to-peer architecture, it runs on a scalable, highly available network of untrusted nodes that distribute immutable authenticated objects which are cached progressively towards the sources of requests.

关键词： Adaptive systems Protocols File systems Content distribution networks Scalability Memory management Ecosystems

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DUAL RANK-1 TENSOR ATTENTION MODULE FOR CONVOLUTIONAL NEURAL networks 49

DUAL RANK-1 TENSOR ATTENTION MODULE FOR CONVOLUTIONAL NEURAL...

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49th ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lin, Baihong Chi, Hanxing Lin, Zengrong Hu, Jun Wang, Liang Zou, Jianxiao Fan, Shicai UESTC Shenzhen Inst Adv Study Shenzhen Peoples R China Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Channel-spatial attention mechanisms have been extensively investigated in computer vision. However, it is still a difficult problem that how to efficiently utilize global and local contextual information laid in a feature tensor to generate an accurate 3D attention map. This paper proposes a novel attention module for convolutional neural networks named Dual Rank1 Tensor Attention Module, which can reach a good balance between global and local contextual information utilization for attention map generation. In our module, given a feature tensor, we sequentially generate two rank-1 3D tensor attention maps, i.e., the initial rank-1 tensor attention map containing global contextual information, and the complement rank-1 tensor attention map containing partial local contextual information. Then, we obtain a 3D tensor attention map based on the combination of these two rank-1 tensor attention maps for feature recalibration. Experimental results on ImageNet-1K and PASCAL VOC datasets demonstrate that the proposed module can achieve competitive performance compared with other state-of-the-art attention modules. The source code will be available at https://***/KevinBHLin/.

关键词： Global and local contextual information rank-1 tensor residual attention learning CP decomposition

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Plant Disease Prediction with Fertilizer Recommendation Engine

Plant Disease Prediction with Fertilizer Recommendation Engi...

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2024 ieee International conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Kumar, Vipul Raghav, Sonia Rajpal, Manmohan Vaidya, Himadri Rawat, Ruchira Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382693

Agricultural productivity has a critical role in maintaining economies, especially in nations where a significant proportion of the population is engaged in farming. Plant diseases are a serious risk to agricultural productivity because they can result in losses that are both quantitative and qualitative. To identify plant illnesses, this study suggests a complete system that makes use of deep learning and image processing methods, particularly Convolutional Neural networks (CNNs).mAn artifi-cial neural network with specific capabilities for processing grid-structured data-especially images-is called a convolutional neural network (CNN). CNNs are particularly good at extracting hierarchical features from visual input. They are made up of pooling layers for spatial dimension reduction, activation functions for adding nonlinearity, and convolutional layers that apply filters to extract local patterns. © 2024 ieee.

关键词： Convolutional neural networks

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Learning Non-local Spatial-Angular Correlation for Light Field Image Super-Resolution

Learning Non-Local Spatial-Angular Correlation for Light Fie...

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ieee/CVF International conference on computer Vision (ICCV)

作者： Liang, Zhengyu Wang, Yingqian Wang, Longguang Yang, Jungang Zhou, Shilin Guo, Yulan Natl Univ Def Technol Changsha Peoples R China Aviat Univ Air Force Changchun Jilin Peoples R China

ISBN: (纸本)9798350307184

Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities among LF images. Although many deep neural networks (DNNs) have been developed for LF image SR and achieved continuously improved performance, existing methods cannot well leverage the long-range spatial-angular correlation and thus suffer a significant performance drop when handling scenes with large disparity variations. In this paper, we propose a simple yet effective method to learn the non-local spatial-angular correlation for LF image SR. In our method, we adopt the epipolar plane image (EPI) representation to project the 4D spatial-angular correlation onto multiple 2D EPI planes, and then develop a Transformer network with repetitive self-attention operations to learn the spatial-angular correlation by modeling the dependencies between each pair of EPI pixels. Our method can fully incorporate the information from all angular views while achieving a global receptive field along the epipolar line. We conduct extensive experiments with insightful visualizations to validate the effectiveness of our method. Comparative results on five public datasets show that our method not only achieves state-of-the-art SR performance but also performs robust to disparity variations. Code is publicly available at https://***/ZhengyuLiang24/EPIT.

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Enriched CNN-Transformer Feature Aggregation networks for Super-Resolution 23

Enriched CNN-Transformer Feature Aggregation Networks for Su...

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23rd ieee/CVF Winter conference on Applications of computer Vision (WACV)

作者： Yoo, Jinsu Kim, Taehoon Lee, Sihaeng Kim, Seung Hwan Lee, Honglak Kim, Tae Hyun Hanyang Univ Seoul South Korea LG AI Res Seoul South Korea

ISBN: (纸本)9781665493468

Recent transformer-based super-resolution (SR) methods have achieved promising results against conventional CNN-based methods. However, these approaches suffer from essential shortsightedness created by only utilizing the standard self-attention-based reasoning. In this paper, we introduce an effective hybrid SR network to aggregate enriched features, including local features from CNNs and long-range multi-scale dependencies captured by transformers. Specifically, our network comprises transformer and convolutional branches, which synergetically complement each representation during the restoration procedure. Furthermore, we propose a cross-scale token attention module, allowing the transformer branch to exploit the informative relationships among tokens across different scales efficiently. Our proposed method achieves state-of-the-art SR results on numerous benchmark datasets.

关键词： computer vision Aggregates Superresolution computer architecture Benchmark testing Transformers Cognition

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