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Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, and Challenges

IEEE Communications Surveys and Tutorials 2025年

作者： Satpathy, Anurag Sahoo, Manmath Narayan Swain, Chittaranjan Bellavista, Paolo Guizani, Mohsen Muhammad, Khan Bakshi, Sambit Missouri University of Science and Technology Department of Computer Science RollaMO United States National Institute of Technology Visual Surveillance Laboratory Department of Computer Science and Engineering Odisha Rourkela769008 India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India Department of Computer Science and Engineering Italy Machine Learning Department Abu Dhabi United Arab Emirates Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In addition, this survey originally contributes to the literature by proposing a novel taxonomy for existing VNE solutions and providing a thorough comparative study of their strategies. © 1998-2012 IEEE.

关键词： Virtual machine

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Expert with Outlier Exposure for Continual Learning of New Diseases

Expert with Outlier Exposure for Continual Learning of New D...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xu, Zhengjing Chen, Kanghao Zheng, Wei-Shi Tan, Zhijun Yang, Xiaobo Wang, Ruixuan Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE China State Key Laboratory of Dampness Syndrome of Chinese Medicine Guangzhou China Peng Cheng Laboratory Department of Network Intelligence Shenzhen China

ISBN: (纸本)9781665468190

Current intelligent diagnosis systems struggle to continually learn to diagnose more and more diseases due to catastrophic forgetting of old knowledge when learning new knowledge. Although storing small old data for subsequent continual learning can effectively help alleviate the forgetting issue, the heavy data imbalance between old classes and to-be-learned new classes in classifier training often causes biased prediction towards the new classes just learned by the updated classifier. In this study, an outlier detection technique is novelly applied to train an additional expert classifier for new classes to help alleviate the class imbalance issue and discriminate the learned new classes from old classes during inference (instead of the training phase). Specially, the stored small data of old classes are considered as outliers during training the expert classifier, such that the output probability distributions from the expert classifier are expected to be obviously different between test data of the old classes and those of the new classes. Such difference between old classes and new classes can be used to fine-tune the original output from the updated classifier which is responsible for prediction of all learned (old and new) classes. During inference, a novel ensemble strategy is proposed to combine the predictions from the updated classifier, the expert classifier, and the previously learned old classifier. The proposed learning and inference framework can be easily combined with existing continual learning strategies. Empirical evaluations on three medical image datasets and one natural image dataset show that the proposed framework can effectively improve continual learning performance. © 2022 IEEE.

关键词： Forecasting

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Skeleton-Embedded Network for action recognition

Skeleton-Embedded Network for action recognition

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Luo, Li Ren, Ziliang Qin, Yong Zhang, Qieshi Gao, Xiangyang Dongguan University of Technology Department of Computer Science Dongguan52300 China Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China The Chinese University of HongKong Hong Kong Hong Kong

ISBN: (纸本)9798350327182

Multimodality-based human action recognition is becoming an increasingly attractive topic as different modalities can provide complementary information. RGB and skeleton data have their pros and limitations for action recognition because they describe the action from distinctive views. Therefore, the fusion of these two modalities using their complementarity is meaningful to action recognition. However, it is difficult to completely utilize the supplementarity of RGB and skeleton data. In this paper, to take advantage of their complementary at the semantic level, A skeleton-embedded network(SE-Net) is proposed by us. The architecture is based on integrating their feature in the network not only a post-hoc fusion. With a sparse sampling strategy, which is efficient for learning using the whole action video. To examine the performance of our method, we conducted experiments on the wildly used datasets, obtaining an accuracy of 86.4% in C-sub and 88.2% in C-view on NTU-RGB+D 60. For NTU-RGB+D 120, the accuracy of 81.3% in C-sub and 83.4% in C-set is obtained. © 2023 IEEE.

关键词： Semantics

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Faster approximate subgraph counts with privacy 23

Faster approximate subgraph counts with privacy

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Dung Nguyen Mahantesh Halappanavar Venkatesh Srinivasan Anil Vullikanti Department of Computer Science and UVA Biocomplexity Institute and Initiative University of Virginia Data Sciences and Machine Intelligence Group Pacific Northwest National Laboratory Department of Mathematics and Computer Science Santa Clara University

One of the most common problems studied in the context of differential privacy for graph data is counting the number of non-induced embeddings of a subgraph in a given graph. These counts have very high global sensitivity. Therefore, adding noise based on powerful alternative techniques, such as smooth sensitivity and higher-order local sensitivity have been shown to give significantly better accuracy. However, all these alternatives to global sensitivity become computationally very expensive, and to date efficient polynomial time algorithms are known only for few selected subgraphs, such as triangles, k-triangles, and k-stars. In this paper, we show that good approximations to these sensitivity metrics can be still used to get private algorithms. Using this approach, we much faster algorithms for privately counting the number of triangles in real-world social networks, which can be easily parallelized. We also give a private polynomial time algorithm for counting any constant size subgraph using less noise than the global sensitivity; we show this can be improved significantly for counting paths in special classes of graphs.

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TAMBRIDGE: Bridging Frame-Centered Tracking and 3D Gaussian Splatting for Enhanced SLAM

arXiv

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arXiv 2024年

作者： Jiang, Peifeng Liu, Hong Li, Xia Wang, Ti Zhang, Fabian Buhmann, Joachim National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School China Institute for Machine Learning Department of Computer Science ETH Zurich Switzerland

The limited robustness of 3D Gaussian Splatting (3DGS) to motion blur and camera noise, along with its poor real-time performance, restricts its application in robotic SLAM tasks. Upon analysis, the primary causes of these issues are the density of views with motion blur and the cumulative errors in dense pose estimation from calculating losses based on noisy original images and rendering results, which increase the difficulty of 3DGS rendering convergence. Thus, a cutting-edge 3DGS-based SLAM system is introduced, leveraging the efficiency and flexibility of 3DGS to achieve real-time performance while remaining robust against sensor noise, motion blur, and the challenges posed by long-session SLAM. Central to this approach is the Fusion Bridge module, which seamlessly integrates tracking-centered ORB Visual Odometry with mapping-centered online 3DGS. Precise pose initialization is enabled by this module through joint optimization of re-projection and rendering loss, as well as strategic view selection, enhancing rendering convergence in large-scale scenes. Extensive experiments demonstrate state-of-the-art rendering quality and localization accuracy, positioning this system as a promising solution for real-world robotics applications that require stable, nearreal-time performance. Our project is available at https://***/TAMBRIDGE/ Copyright © 2024, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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Matching individual Ladoga ringed seals across short-term image sequences (vol 102, pg 957, 2022)

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MAMMALIAN BIOLOGY 2022年第3期102卷 1045-1045页

作者： Nepovinnykh, E. Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering School of Engineering Science Lappeenranta-Lahti University of Technology LUT P.O.Box 20 53851 Lappeenranta Finland Department of Artificial Intelligence Institute of Computer Science and Technology Peter the Great St. Petersburg Polytechnic University Polytechnicheskaya 29 Saint Petersburg Russian Federation 195251 Department of Computer Science and Computational Experiment Southern Federal University Rostov-on-Don Russian Federation 344006 Interregional Charitable Public Organization “Biologists for Nature Conservation” (BFNC) 24 line 3-7 Saint Petersburg Russian Federation 199106

Automated wildlife reidentification has attracted increasing attention in recent years as it provides a non-invasive tool to identify and to track individual wild animals over time. In this paper, the first steps are taken towards the automatic photo-identification of the Ladoga ringed seals (Pusa hispida ladogensis). A method is proposed that takes a sequence of images, each containing multiple individuals as the input, and produces cropped images of seals grouped based on one certain individual per group. The method starts by detecting each seal from the images and proceeds to matching the individual seals between the images. It is shown that high grouping accuracy can be obtained with a general-purpose image retrieval method on an image sequence taken from the same location within a relatively short period of time. Each resulting group contains multiple images of one individual with slightly different variations, for example, in pose and illumination. Utilizing these images simultaneously provides more information for the individual re-identification compared to the traditional approach, i.e., which utilizes just one image at a time. It is further demonstrated that a convolutional neural network based method can be used to extract the unique pelage patterns of the seals despite the low contrast. Finally, a method is proposed and experiments with the novel Ladoga ringed seals data are carried out to provide a proof-of-concept for the individual re-identification.

关键词： Animal re-identification Convolutional neural networks Instance segmentation Ladoga ringed seal Photo-identification

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SEFA: A Shared Encoder and Feature Adaptation Framework for Breast MRI Segmentation

SEFA: A Shared Encoder and Feature Adaptation Framework for ...

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2023 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Xue, Hongwei Zhong, Ke Wu, Xiaofen Gao, Yan Wu, Yujiao Zhang, Lei Qian, Guangwu Wang, Peijun Tongji Hospital School of Medicine Tongji University Department of Radiology Shanghai China College of Computer Science Sichuan University Machine Intelligence Laboratory Chengdu China The Commonwealth Scientific and Industrial Research Organisation Hobart Australia Sichuan University-Pittsburgh Institute Sichuan University Chengdu China

ISBN: (纸本)9798350309003

Significant progress has been made in developing accurate automatic segmentation systems for various biomedical applications using convolutional networks (CNNs). However, these systems often lose effectiveness when they encounter a domain shift caused by variations in imaging protocols. Traditional supervised transfer learning approaches are not ideal because manually annotating new data for every testing domain is impractical, while unsupervised domain adaptation is a challenging subject in the field of biomedical image analysis. In this paper, we introduce a new framework for unsupervised domain adaptation called Shared Encoder and Feature Adaptation (SEFA). This framework focuses on cross-sequences adaptation and is more resilient to disparities in input data and does not require any annotations on the test domain. Specifically, our segmenter model is based on a compact fully convolutional network designed for breast mask prediction. First, the shared encoder is designed to minimize the distribution disparity between the source and target domains. Then, a domain discriminator is set up to discriminate the feature space of both domains. Our model is optimized by using unpaired TSE/DCE sequences in an unsupervised manner. This eliminates the need for labeling additional medical datasets. Using our unsupervised approach, we achieve segmentation accuracies that are comparable to those obtained through supervised training. These accuracies are measured by metrics such as Case/Global Dice Score and average symmetric surface distance (ASD), which are close to the best possible performance. © 2023 IEEE.

关键词： Medical applications

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CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

arXiv

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arXiv 2023年

作者： Sun, Shoukun Xian, Min Xu, Fei Capriotti, Luca Yao, Tiankai Machine Intelligence and Data Analytics Lab Department of Computer Science University of Idaho United States Idaho National Laboratory United States

The click-based interactive segmentation aims to extract the object of interest from an image with the guidance of user clicks. Recent work has achieved great overall performance by employing feedback from the output. However, in most state-of-theart approaches, 1) the inference stage involves inflexible heuristic rules and requires a separate refinement model, and 2) the number of user clicks and model performance cannot be balanced. To address the challenges, we propose a click-based and mask-guided interactive image segmentation framework containing three novel components: Cascade-Forward Refinement (CFR), Iterative Click Loss (ICL), and SUEM image augmentation. The CFR offers a unified inference framework to generate segmentation results in a coarse-to-fine manner. The proposed ICL allows model training to improve segmentation and reduce user interactions simultaneously. The proposed SUEM augmentation is a comprehensive way to create large and diverse training sets for interactive image segmentation. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach on five public datasets. Remarkably, our model reduces by 33.2%, and 15.5% the number of clicks required to surpass an IoU of 0.95 in the previous state-of-the-art approach on the Berkeley and DAVIS sets, respectively. © 2023, CC BY.

关键词： Image segmentation

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Network Attack Detection in the PfSense Firewall via Deep Learning

Network Attack Detection in the PfSense Firewall via Deep Le...

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Mechatronic Systems Supervision (IW_MSS), IEEE International Workshop on

作者： Merzoug Soltane Aimen Gasmi Laboratory of vision and intelligence (LAVIA) Echahid Cheikh Larbi Tebessi University Tebessa Algeria Department of Mathematics and Computer Science Echahid Cheikh Larbi Tebessi University Tebessa Algeria

ISBN: (数字)9798350378382

ISBN: (纸本)9798350378399

The detection of network attacks is a crucial aspect in ensuring the sustainability and proper functioning of information systems. Complex threat patterns and malicious actors possess the ability to inflict significant damage to cyber systems. In this study, we propose novel approaches utilizing deep learning techniques to identify and respond to threats and alerts found within network logs acquired through pfSense, an open-source firewall software. pfSense incorporates various robust security services including firewall capabilities, URL filtering, and virtual private networking (VPN), among others. The primary objective of this research is to analyze the acquired logs from a local installation of pfSense software, in order to develop a potent and efficient solution that can effectively manage traffic flow by automatically recognizing patterns through the proposed deep learning architectures, which present a significant challenge. To accomplish this, we have devised an attack detection system based on the CICDDOS2019 dataset, leveraging a deep neural network (DNN) model that has been seamlessly integrated into pfSense, enabling automatic identification and prevention of attacks.

关键词： Deep learning Uniform resource locators Firewalls (computing) Prevention and mitigation Artificial neural networks Network security Software Virtual private networks Pattern recognition Sustainable development

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Control of defensive behavior by the nucleus of Darkschewitsch GABAergic neurons

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National science Review 2024年第4期11卷 70-83页

作者： Huiying Zhao Jinrong Liu Yujin Shao Xiang Feng Binhan Zhao Li Sun Yijun Liu Linghui Zeng Xiao-ming Li Hongbin Yang Shumin Duan Yan-qin Yu Department of Neurology of Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Nanhu Brain-Computer Interface Institute Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province School of Medicine Hangzhou City University

The nucleus of Darkschewitsch(ND), mainly composed of GABAergic neurons, is widely recognized as a component of the eye-movement controlling system. However, the functional contribution of ND GABAergic neurons(NDGABA) in animal behavior is largely unknown. Here, we show that NDGABA neurons were selectively activated by different types of fear stimuli, such as predator odor and foot *** and chemogenetic manipulations revealed that NDGABA neurons mediate freezing ***, using circuit-based optogenetic and neuroanatomical tracing methods, we identified an excitatory pathway from the lateral periaqueductal gray(l PAG) to the ND that induces freezing by exciting ND inhibitory outputs to the motor-related gigantocellular reticular nucleus, ventral part(Gi V). Together,these findings indicate the NDGABA population as a novel hub for controlling defensive response by relaying fearful information from the l PAG to Gi V, a mechanism critical for understanding how the freezing behavior is encoded in the mammalian brain.

关键词： nucleus of Darkschewitsch GABAergic population defensive behavior freezing neural circuitry

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