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On the Limits of Lossy Compression for Human Activity Recognition in Sensor networks 48

On the Limits of Lossy Compression for Human Activity Recogn...

引用

48th conference on local computer networks

作者： Schulz, Jonas Radak, Hristina Nguyen, Phuong T. Nguyen, Giang T. Fitzek, Frank H. P. Tech Univ Dresden Deutsch Telekom Chair Commun Networks Dresden Germany Univ LAquila Dept Informat Engn Comp Sci & Math Laquila Italy Tech Univ Dresden Hapt Commun Syst Dresden Germany Ctr Tactile Internet Human Loop CeTI Dresden Germany Joint Project 6G Life Dresden Germany

ISBN: (纸本)9798350300741;9798350300734

Human activity recognition is crucial for tactile internet, virtual reality, and digital-twin applications. Previous works have analyzed machine learning for this purpose but often need to pay more attention to typical challenges when deploying these machine learning models in production. First, data must be transmitted to the network node on which the machine-learning model is running. However, scaling human activity recognition by the number of devices and users puts additional constraints on the available transmission channel. While transmitting less data saves bandwidth, removing redundant information from the data can also benefit machine learning. This paper addresses the problem of transmitting wearable sensor data for human activity recognition. To this end, we analyze the extent to which wearable sensor data can be compressed via sparse coding without sacrificing loss in recognition performance. We empirically illustrate, on various datasets, that only a fraction of sensor information is relevant for human activity recognition.

关键词： Wearable sensors

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Unifying Global and local Scene Entities Modelling for Precise Action Spotting

Unifying Global and Local Scene Entities Modelling for Preci...

引用

International Joint conference on Neural networks (IJCNN)

作者： Tran, Kim Hoang Do, Phuc Vuong Ly, Ngoc Quoc Le, Ngan FPT Software Ctr Can Tho City Vietnam Naver Vietnam Dev Ctr Ho Chi Minh City Vietnam VNUHCM Univ Sci Ho Chi Minh City Vietnam Univ Arkansas Dept Comp Sci Fayetteville AR 72701 USA

ISBN: (纸本)9798350359329;9798350359312

Sports videos pose complex challenges, including cluttered backgrounds, camera angle changes, small action-representing objects, and imbalanced action class distribution. Existing methods for detecting actions in sports videos heavily rely on global features, utilizing a backbone network as a black box that encompasses the entire spatial frame. However, these approaches tend to overlook the nuances of the scene and struggle with detecting actions that occupy a small portion of the frame. In particular, they face difficulties when dealing with action classes involving small objects, such as balls or yellow/red cards in soccer, which only occupy a fraction of the screen space. To address these challenges, we introduce a novel approach that analyzes and models scene entities using an adaptive attention mechanism. Particularly, our model disentangles the scene content into the global environment feature and local relevant scene entities feature. To efficiently extract environmental features while considering temporal information with less computational cost, we propose the use of a 2D backbone network with a time-shift mechanism. To accurately capture relevant scene entities, we employ a Vision-Language model in conjunction with the adaptive attention mechanism. Our model has demonstrated outstanding performance, securing the 1st place in the SoccerNet-v2 Action Spotting, FineDiving, and FineGym challenge with a substantial performance improvement of 1.6, 2.0, and 1.3 points in avg-mAP compared to the runner-up methods. Furthermore, our approach offers interpretability capabilities in contrast to other deep learning models, which are often designed as black boxes. Our code and models are released at:https://***/Fsoft-AIC/unifying-global-local-feature.

关键词： computer Vision Action Spotting Deep Learning Sports Videos Global Environment local Scene Entities

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Heroes: Lightweight Federated Learning with Neural Composition and Adaptive local Update in Heterogeneous Edge networks

Heroes: Lightweight Federated Learning with Neural Compositi...

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ieee conference on computer Communications (ieee INFOCOM)

作者： Yan, Jiaming Liu, Jianchun Wang, Shilong Xu, Hongli Liu, Haifeng Zhou, Jianhua Univ Sci & Technol China Sch Comp Sci & Technol Langfang Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Suzhou Peoples R China Guangdong OPPO Mobile Telecommun Corp Ltd Dongguan Guangdong Peoples R China

ISBN: (纸本)9798350383515;9798350383508

Federated Learning (FL) enables distributed clients to collaboratively train models without exposing their private data. However, it is difficult to implement efficient FL due to limited resources. Most existing works compress the transmitted gradients or prune the global model to reduce the resource cost, but leave the compressed or pruned parameters under-optimized, which degrades the training performance. To address this issue, the neural composition technique constructs size-adjustable models by composing low-rank tensors, allowing every parameter in the global model to learn the knowledge from all clients. Nevertheless, some tensors can only be optimized by a small fraction of clients, thus the global model may get insufficient training, leading to a long completion time, especially in heterogeneous edge scenarios. To this end, we enhance the neural composition technique, enabling all parameters to be fully trained. Further, we propose a lightweight FL framework, called Heroes, with enhanced neural composition and adaptive local update. A greedy-based algorithm is designed to adaptively assign the proper tensors and local update frequencies for participating clients according to their heterogeneous capabilities and resource budgets. Extensive experiments demonstrate that Heroes can reduce traffic consumption by about 72.05% and provide up to 2.97x speedup compared to the baselines.

关键词： Federated Learning Heterogeneity Neural Composition local Update Frequency

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Raising the AWAREness of BFT Protocols for Soaring Network Delays 47

Raising the AWAREness of BFT Protocols for Soaring Network D...

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47th ieee conference on local computer networks (LCN)

作者： Nischwitz, Martin Esche, Marko Tschorsch, Florian Phys Tech Bundesanstalt Berlin Germany Tech Univ Berlin Dept Distributed Secur Infrastruct Berlin Germany

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

Classic BFT protocols are often deployed in the LAN setting, with low delays and reliable links. Recent publications of BFT protocols have decreased drastically in their communication complexity and the application to previously unsuited areas such as mobile or sensor networks is getting more traction. To facilitate that development and showcase suitability under harsher network conditions, we take one of the most recent BFT protocols, HotStuff, and analyze the impact of increased and varying network delays on its performance. We apply the delay prediction scheme AWARE and make some simple modifications to the prediction algorithm in order to increase its performance even further.

关键词： Delay-tolerant networks Vehicular networks Wireless ad hoc Byzantine Fault Tolerance

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An agent-based approach to disintegrate and modularise Software Defined networks controller 47

An agent-based approach to disintegrate and modularise Softw...

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47th ieee conference on local computer networks (LCN)

作者： Chemalamarri, Vijaya Durga Abolhasan, Mehran Braun, Robin Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

The Software Defined Network paradigm deviates from traditional networks by logically centralising and physically separating the control plane from the data plane. In this work, we present the idea of a modular, agent-based SDN controller. We first highlight issues with current SDN controller designs, followed by a description of the proposed framework. We present a prototype for our design to demonstrate the controller in action using a few common use-cases. We continue the discussion by highlighting areas that require further research.

关键词： Multi-agent systems modular SDN controller SDN OpenFlow

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Blockchain-Based Decentralized Authentication for Information-Centric 5G networks 47

Blockchain-Based Decentralized Authentication for Informatio...

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47th ieee conference on local computer networks (LCN)

作者： Hassan, Muhammad Pesavento, Davide Benmohamed, Lotfi Natl Inst Stand & Technol NIST Gaithersburg MD 20899 USA

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

The 5G research community is increasingly lever-aging the innovative features offered by Information Centric Networking (ICN). However, ICN's fundamental features, such as in-network caching, make access control enforcement more challenging in an ICN-based 5G deployment. To address this shortcoming, we propose a Blockchain-based Decentralized Authentication Protocol (BDAP) which enables efficient and secure mobile user authentication in an ICN-based 5G network. We show that BDAP is robust against a variety of attacks to which mobile networks and blockchains are particularly vulnerable. Moreover, a preliminary performance analysis suggests that BDAP can reduce the authentication delay compared to the standard 5G authentication protocols.

关键词： Access control Protocols 5G mobile communication Information-centric networking Authentication computer architecture computer networks

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Convolution-Enhanced Transformer with Frequency Domain Contrastive Learning for Image Deraining

Convolution-Enhanced Transformer with Frequency Domain Contr...

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International Joint conference on Neural networks (IJCNN)

作者： Wang, Cheng Li, Wei Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China Jiangnan Univ Jiangsu Key Lab Media Design & Software Technol Wuxi Jiangsu Peoples R China Jiangnan Univ Engn Res Ctr Itelligent Technol Healthcare Minist Educ Wuxi Jiangsu Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Rainfall, as a ubiquitous atmospheric phenomenon, causes visual image degradation through rain streaks, which in turn hinders human vision and degrades the performance of subsequent computer vision algorithms. In order to further improve the performance of visual algorithms in harsh rain environments, image rain removal technology has become a hot topic in this field. In computer vision, the integration of the Vision Transformer (ViT) marks an important progress, and its transfer application to the image rain removal task has achieved remarkable success. However, the application of transformers in this task still faces challenges. While they excel at modelling long-range dependencies, transformers are less adept at modelling local contexts for image restoration tasks. Moreover, the internal transmission of features within dense transformer layers often needs more finesse. In this paper, we tackle these shortcomings by incorporating convolutional mechanisms within both the self-attention components of the transformer blocks and the subsequent feed-forward networks. We refine the feature flows amalgamated between recursive transformer blocks by utilizing feature fusion strategies. To further refine the high-frequency textural details of images, we employ frequency domain contrastive learning techniques to augment the delineation of contrastive sample information, ensuring that the restored image closely resembles the clear image in terms of texture structure while maintaining a distinction from the rain image. Extensive quantitative and qualitative experimental results show that the proposed deraining network surpasses mainstream methods on public datasets.

关键词： Image deraining Neural networks Transformer Feature fusion Frequency domain contrastive learning

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XGRADATE: Graph Anomaly Detection via Multi-Scale Cross-Layer Contrastive Learning networks 7

XGRADATE: Graph Anomaly Detection via Multi-Scale Cross-Laye...

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7th ieee International conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Zhu, Kaixu College of Computer and Information Science Southwest University Chongqing China

ISBN: (纸本)9798350350760

Graph anomaly detection is a crucial area in data mining and machine learning, with applications in network security, fraud detection, and health monitoring. Current methods, however, face limitations such as over-reliance on local node information or inefficiency with large-scale graphs. To address these issues, we propose XGRADATE, a novel graph anomaly detection network based on cross-layer contrastive multi-scale contrastive learning. XGRADATE comprises two components: the cross-layer contrastive and the graph contrastive network. The cross-layer contrastive module captures local features and structural information more effectively by considering embedding features from different layers of the graph convolutional network. The graph contrastive network module integrates various types of anomaly information to thoroughly consider global and local features. Our method is evaluated on multiple datasets, demonstrating its effectiveness and practicality in improving the accuracy of graph anomaly detection tasks. XGRADATE provides a new solution for anomaly detection in graph data by integrating anomaly information at different levels and capturing local features and structural information more effectively. ©2024 ieee.

关键词： Contrastive Learning

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th ieee International conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 ieee.

关键词： Convolutional neural networks

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Eliminating Routing Loops and Oscillations in BGP Using Total Ordering 47

Eliminating Routing Loops and Oscillations in BGP Using Tota...

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47th ieee conference on local computer networks (LCN)

作者： Garcia-Luna-Aceves, J. J. Univ Calif Santa Cruz Comp Sci & Engn Dept Santa Cruz CA 95064 USA

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

OPERA is a framework recently introduced that formalizes routing etiquettes based on path information. New rules derived from OPERA to provide total ordering among paths are added to the policy mechanisms used in IBGP and EBGP, which results in OPERA-based BGP (OBGP). OBGP is a complete loop-free inter-domain multi-path routing solution based on IBGP and EBGP. OBGP is proven to be stable and loop-free at every instant. Well-known examples of systems in which IBGP and EBGP do not converge are used to illustrate the benefits of OBGP.

关键词： Routing computer networks Proposals Oscillators Convergence

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