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检索条件"任意字段=IEEE Conference on Local Computer Networks"

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GLAD: A Global-to-local Anomaly Detector 23

GLAD: A Global-to-Local Anomaly Detector

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

作者： Artola, Aitor Kolodziej, Yannis Morel, Jean-Michel Ehret, Thibaud Univ Paris Saclay CNRS ENS Paris Saclay Ctr Borelli Paris France Visionairy Orsay France

ISBN: (纸本)9781665493468

Learning to detect automatic anomalies in production plants remains a machine learning challenge. Since anomalies by definition cannot be learned, their detection must rely on a very accurate "normality model". To this aim, we introduce here a global-to-local Gaussian model for neural network features, learned from a set of normal images. This probabilistic model enables unsupervised anomaly detection. A global Gaussian mixture model of the features is first learned using all available features from normal data. This global Gaussian mixture model is then localized by an adaptation of the K-MLE algorithm, which learns a spatial weight map for each Gaussian. These weights are then used instead of the mixture weights to detect anomalies. This method enables precise modeling of complex data, even with limited data. Applied on WideResnet50-2 features, our approach outperforms the previous state of the art on the MVTec dataset, particularly on the object category. It is robust to perturbations that are frequent in production lines, such as imperfect alignment, and is on par in terms of memory and computation time with the previous state of the art.

关键词： Adaptation models computer vision Perturbation methods Neural networks Production Machine learning Detectors

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Global-to-local Modeling for Video-based 3D Human Pose and Shape Estimation

Global-to-Local Modeling for Video-based 3D Human Pose and S...

引用

ieee/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Shen, Xiaolong Yang, Zongxin Wang, Xiaohan Ma, Jianxin Zhou, Chang Yang, Yi Zhejiang Univ CCAI ReLER Hangzhou Zhejiang Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Video-based 3D human pose and shape estimations are evaluated by intra-frame accuracy and inter-frame smoothness. Although these two metrics are responsible for different ranges of temporal consistency, existing state-of-the-art methods treat them as a unified problem and use monotonous modeling structures (e.g., RNN or attention-based block) to design their networks. However, using a single kind of modeling structure is difficult to balance the learning of short-term and long-term temporal correlations, and may bias the network to one of them, leading to undesirable predictions like global location shift, temporal inconsistency, and insufficient local details. To solve these problems, we propose to structurally decouple the modeling of long-term and short-term correlations in an end-to-end framework, Global-to-local Transformer (GLoT). First, a global transformer is introduced with a Masked Pose and Shape Estimation strategy for long-term modeling. The strategy stimulates the global transformer to learn more inter-frame correlations by randomly masking the features of several frames. Second, a local transformer is responsible for exploiting local details on the human mesh and interacting with the global transformer by leveraging cross-attention. Moreover, a Hierarchical Spatial Correlation Regressor is further introduced to refine intra-frame estimations by decoupled global-local representation and implicit kinematic constraints. Our GLoT surpasses previous state-of-the-art methods with the lowest model parameters on popular benchmarks, i.e., 3DPW, MPI-INF-3DHP, and Human3.6M. Codes are available at https://***/sxl142/GLoT.

关键词： body gesture Humans: Face movement pose

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Sampling-Based Density Peaks Clustering for Energy-Aware Industrial IoT networks

Sampling-Based Density Peaks Clustering for Energy-Aware Ind...

引用

2025 ieee International Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Rajkumar, S. Gopalakrishnan, R. Shreeraksha, T.S. Dhanaraj, S. Kabiyashwanth, V. K. S. Rangasamy College of Technology Computer Science and Engineering 637 215 India K. S. Rangasamy College of Technology Electrical and Electronics Engineering 637 215 India

ISBN: (纸本)9798331529833

In Industrial Internet of Things (IIoT) settings, effective data processing and energy management are essential due to the resource-limited characteristics of the linked equipment. This research introduces a unique Sampling-Based Density Peaks Clustering (SDPC) algorithm designed for energy-efficient clustering in IIoT networks. The suggested method improves classic Density Peak Clustering by incorporating a sampling mechanism that decreases computational burden while preserving excellent clustering precision. SDPC efficiently clusters devices by determining cluster centers through local density and distance parameters, hence reducing communication costs and prolonging the network's operational lifespan. The energy consumption model is integrated into the clustering process, prioritizing devices with greater energy reserves as cluster heads, hence enhancing energy load distribution throughout the network. Simulation results indicate that SDPC realizes substantial enhancements, featuring a 30% decrease in energy usage and a 25% augmentation in network durability relative to leading clustering methods. This method has significant potential for enhancing energy management in IIoT networks, facilitating more sustainable and scalable industrial processes. © 2025 ieee.

关键词： Energy utilization

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AmGNN: A Framework for Adaptive Processing of Inter-layer Information in Multi-layer Graph 16th

AmGNN: A Framework for Adaptive Processing of Inter-layer In...

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16th International conference on Social networks Analysis and Mining

作者： Zhu, Huaisheng Wu, Zongyu Zhao, Tianxiang Wang, Suhang Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9783031785405;9783031785412

Graphs play a vital role in various applications. Graph Neural networks (GNNs) excel at capturing topology information by using a message-passing mechanism to enrich node representations with local neighborhood information. Despite their success in modeling single-layer graphs, real-world scenarios often involve multi-layer graphs where nodes can have multiple edges or relationships represented as different layers. Existing methods of multi-layer graph learning struggle to efficiently process inter-layer information, as they mainly focus on preserving similar layers or shared invariant information, which may not be suitable for all situations. We propose a novel framework called Adaptive Multi-layer Graph Neural networks (AmGNN) to address this challenge. AmGNN learns shared invariant information for nodes that need it and selectively preserves relevant layers' information for nodes not requiring shared invariance. We introduce multi-layer graph contrastive learning to efficiently capture invariant information and learn weights for adaptive processing. Our experiments on real-world multi-layer graphs validate the effectiveness of AmGNN in node classification tasks.

关键词： Multi-layer Graph Node Classification Graph Neural networks

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Mobility-Focused Joining in TSCH networks 47

Mobility-Focused Joining in TSCH Networks

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47th ieee conference on local computer networks (LCN)

作者： Nielsen, Oliver Schnugger, Lucas Koefoed Orfanidis, Charalampos Fafoutis, Xenofon Tech Univ Denmark DTU DTU Compute Lyngby Denmark

ISBN: (纸本)9781665480017

TSCH is a MAC protocol able to provide reliable wireless communication to IoT networks. For applications where mobility is necessary research has shown that TSCH presents a suboptimal performance. One of the main issues is that the time required for a device to join a TSCH network is unpredictable and theoretically has no upper limit. The joining process to a TSCH network can take several minutes in some cases which can ensue significant delay and power overheads. In this paper we examine how TSCH can be adapted to support mobile applications, by improving the joining and re-joining procedure. Relevant information about the network topology is aggregated throughout the network to be utilized during the joining process. The results show that the joining time can be reduced by 50% and the re-joining time by 90%.

关键词： Internet of Things TSCH Reliability Dependable IoT

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Deep Transfer Model Based local Climate Zone Classification Using SAR/Multispectral Images 4

Deep Transfer Model Based Local Climate Zone Classification ...

引用

4th International conference on computer Communication and Artificial Intelligence (CCAI)

作者： Nawaz, Amjad Chen, Jingyi Yang, Wei Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Beijing 101 Middle Sch Beijing Peoples R China

ISBN: (纸本)9798350362770;9798350362763

The deep transfer model described in this research is made up of several sub-networks that are individually optimized by an unsupervised consistency loss and a supervised task-oriented loss. Annotations are used by the supervised loss function to achieve the intended outcome. The consistency loss promotes various sub-networks to communicate gained knowledge and motivates the network to learn the target domain data distribution. We employ the suggested model to deal with a classification of local climate zones worldwide. The dataset comprises 48,307 samples from 10 additional cities in the target domain and 352,366 training samples from 42 locations in the source domain. We provide a unique method for local climate zone (LCZ) categorization utilizing deep learning and big data analytics to merge publically available global radar and multi-spectral satellite data, gathered by the Sentinel-1 and Sentinel-2 satellites. A consistent classification scheme for characterizing the local physical structure worldwide is provided by LCZ. Due to the rapid advancement of satellite imaging techniques, multispectral (MS) and synthetic aperture radar (SAR) data have become increasingly common in LCZ classification jobs. As demonstrated by our experiments;the suggested model improves the performance as compared to baseline model.

关键词： deep learning local climate zone classification Sentinel-1 Sentinel-2

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Reputation-Based Data Carrying for Web3 networks 47

Reputation-Based Data Carrying for Web3 Networks

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47th ieee conference on local computer networks (LCN)

作者： Stokkink, Quinten Ileri, Can Umut Pouwelse, Johan Delft Univ Technol Delft Netherlands

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

Web3 networks are emerging to replace centrally-governed networking infrastructure. The integrity of the shared public infrastructure of Web3 networks is guaranteed through data sharing between nodes. However, due to the unstructured and highly partitioned nature of Web3 networks, data sharing between nodes in different partitions is a challenging task. In this paper we present the TSRP mechanism, which approaches the data sharing problem through nodes auditing each other to enforce carrying of data between partitions. Reputation is used as an analogue for the likelihood of nodes interacting with nodes from other partitions in the future. The number of copies of data shared with other nodes is inversely related to the nodes' reputation. We use a real-world trace of Twitter to show how our implementation can converge to an equal number of copies as structured approaches.

关键词： infrastructure integrity reputation,Web3 peer-to-peer partitioning split

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STAR-RIS based Resource Scheduling and Mode Selection for Drone assisted 5G communications

STAR-RIS based Resource Scheduling and Mode Selection for Dr...

引用

ieee conference on computer Communications (ieee INFOCOM)

作者： Chaudhary, Shivam Nehra, Anushka Budhiraja, Ishan Chaudhary, Rajat Bansal, Abhay Bennett Univ Sch Comp Sci Engn & Technol Greater Noida India Thapar Inst Engn & Technol Comp Sci & Engn Patiala Punjab India

ISBN: (纸本)9798350384482;9798350384475

For a range of future vehicle usage in 5G, communication between vehicle road cooperative systems (VRCS) is crucial. With the advent of 5G deployments, vehicle-to-everything (V2X) communications is enabled to boost network density, optimize transmission mode selection, expand resource capacity, and provide near-ubiquitous connectivity between vehicles with extremely dependable and low latency. On the other hand, mode selection and resource scheduling in V2X communications come with intrinsic difficulties. In this paper, we present a joint optimization approach to address the problems associated with scheduling resources and choosing a transmission mode for the drone-mounted Simultaneous Transmitting and Reflecting-Intelligent Reflecting Surface (STAR-RIS) infrastructure for virtual reality surveillance. The Markov decision process is used to formulate the problem, and the deep reinforcement learning (DRL)-based STAR-RIS drone mounted approach maximizes vehicle-to-infrastructure user capacity while meeting the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs. Moreover, a DRL approach called PDQN (Parametrised Deep Q Network) is employed to generate resilient models while considering the training constraints of local DRL models. The results of the simulation validate the PDQN DRL algorithm's superiority for V2V pairs and show that the proposed DRL-based algorithm outperforms current baseline techniques.

关键词： Drone communications 5G networks STAR-RIS resource scheduling mode selection

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Multi-Scale Non-local Sparse Attention for Single Image Super-Resolution

Multi-Scale Non-Local Sparse Attention for Single Image Supe...

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International Joint conference on Neural networks (IJCNN)

作者： Xiao, Xianwei Zhong, Baojiang Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9781665488679

The non-local attention (NLA) has demonstrated its success in deep learning to solve various image processing and computer vision tasks. However, the NLA over the entire input image requires extremely high computational complexity and inevitably introduces irrelevant information since all the feature points are involved in calculating attention map. To address these problems, a novel attention module, called the multi-scale non-local sparse attention (MNSA), is proposed in this paper. In our MNSA, attention calculation is constrained within nonoverlapping windows, and then only the most relevant feature points are selected to compute an attention map. The resulting sparse attention prevents the model from attending to irrelevant information and noise while reducing the computational complexity from quadratic to linear with respect to the input image size. To obtain receptive fields at different scales, our MNSA is further performed by exploiting different sizes of windows. Moreover, a novel local feature extraction (LFE) is proposed to extract the local structural information of natural images. To verify the effectiveness of our proposed attention module, a MNSA network is finally developed for conducting single image super-resolution (SISR). Extensive experimental results have clearly shown that our MNSA network can deliver superior performance over a number of state-of-the-art SISR methods.

关键词： Non-local attention single image superresolution multi-scale deep learning

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PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch 23

PatchZero: Defending against Adversarial Patch Attacks by De...

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

作者： Xu, Ke Xiao, Yao Zheng, Zhaoheng Cai, Kaijie Nevatia, Ram

ISBN: (纸本)9781665493468

Adversarial patch attacks mislead neural networks by injecting adversarial pixels within a local region. Patch attacks can be highly effective in a variety of tasks and physically realizable via attachment (e.g. a sticker) to the real-world objects. Despite the diversity in attack patterns, adversarial patches tend to be highly textured and different in appearance from natural images. We exploit this property and present PatchZero, a general defense pipeline against white-box adversarial patches without retraining the downstream classifier or detector. Specifically, our defense detects adversaries at the pixel-level and "zeros out" the patch region by repainting with mean pixel values. We further design a two-stage adversarial training scheme to defend against the stronger adaptive attacks. PatchZero achieves SOTA defense performance on the image classification (ImageNet, RESISC45), object detection (PASCAL VOC), and video classification (UCF101) tasks with little degradation in benign performance. In addition, PatchZero transfers to different patch shapes and attack types.

关键词： Training Degradation Shape Pipelines Neural networks Object detection Robustness

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