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Reliable Routing and Scheduling in Time Sensitive Networks based on Reinforcement Learning

IEEE Transactions on Network science and engineering 2025年第4期12卷 2415-2427页

作者： Cheng, Hao Yang, Lei Zhang, Qingfeng Zhu, Weiping South China University of Technology School of Software Engineering Guangzhou510006 China Wuhan University School of Computer Science Wuhan430072 China

Time Sensitive Network (TSN) provides strict low latency and bounded jitter requirements for applications such as industrial systems, autonomous driving, etc. One of the important problems in TSN is to achieve high reliability and low latency by effectively routing and scheduling time-sensitive data flows. Existing work applies heuristic or integer programming to address flow routing and scheduling, yet often fail to achieve optimal solutions quickly. In this paper, we propose a new Reinforcement Learning (RL) based approach for routing and scheduling of redundant data flows, aiming to achieve load balancing on the network links as well as meeting the reliability and delay constraints. Our approach first leverages a simple heuristic algorithm to decide the redundant path candidate set, and then incorporates Proximal Policy Optimization (PPO) method to choose the most suitable multi-routing flows from the candidates, which can be aware of the network status dynamically to reduce the load on the bottleneck link of the network. On this basis, we further retrain the RL model by fine-tuning to adapt to the online environment. The simulation results show that our proposed solution outperforms the benchmark algorithms in terms of the degree of network balance by 38.7% in offline network environments and in terms of average delay by 14.0% in online network environments. © 2025 IEEE. All rights reserved.

关键词： Delay tolerant networks

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Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks

arXiv

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arXiv 2024年

作者： Xu, Zhengjia Lyu, Dingyang Zhang, Jinghui College of Software Engineering Southeast University China School of Computer Science and Engineering Southeast University China

As graphs grow larger, full-batch GNN training becomes hard for single GPU memory. Therefore, to enhance the scalability of GNN training, some studies have proposed sampling-based mini-batch training and distributed graph learning. However, these methods still have drawbacks, such as performance degradation and heavy communication. This paper introduces SliceGCN, a feature-sliced distributed large-scale graph learning method. SliceGCN slices the node features, with each computing device, i.e., GPU, handling partial features. After each GPU processes its share, partial representations are obtained and concatenated to form complete representations, enabling a single GPU's memory to handle the entire graph structure. This aims to avoid the accuracy loss typically associated with mini-batch training (due to incomplete graph structures) and to reduce inter-GPU communication during message passing (the forward propagation process of GNNs). To study and mitigate potential accuracy reductions due to slicing features, this paper proposes feature fusion and slice encoding. Experiments were conducted on six node classification datasets, yielding some interesting analytical results. These results indicate that while SliceGCN does not enhance efficiency on smaller datasets, it does improve efficiency on larger datasets. Additionally, we found that SliceGCN and its variants have better convergence, feature fusion and slice encoding can make training more stable, reduce accuracy fluctuations, and this study also discovered that the design of SliceGCN has a potentially parameter-efficient nature. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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A GNN-Based Learning Approach for Energy Optimization in Relay-Assisted IoT Networks

A GNN-Based Learning Approach for Energy Optimization in Rel...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Yang, Huapeng Ji, Han Huang, Zhangqin Wu, Xiping College of Computer Science Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing100124 China School of Electrical and Electronic Engineering University College Dublin DublinD04 V1W8 Ireland School of Information Science and Engineering Southeast University The National Mobile Communications Research Laboratory Nanjing210096 China

ISBN: (纸本)9798350368369

Minimizing energy consumption is critical for the long-range (LoRa) Internet of Things (IoT) networks, to extend the battery lifetime of end devices (EDs) while reducing the maintenance cost. To overcome the excessive computational complexity required by the traditional optimization methods, learning approaches such as deep neural network (DNN) and reinforcement learning (RL) have been researched in wireless networks, where star topologies are widely employed. In contrast, LoRa usually deploys relays to assist the connection between the EDs and the gateway (GW), leading to a much more complex network topology. Consequently, DNN and RL would become less effective in LoRa, since those methods are difficult to learn the complex network topology constructed by LoRa. In this paper, we propose a learning method based on graph neural network (GNN), which is known for its prominent ability to capture and represent intricate graph-structural dependencies, to tackle the energy optimization problem for relay-assisted LoRa. Specifically, the multi-hop LoRa network is modeled as a directed graph, with channel state information (CSI) defined as node features, while the spreading factor and transmission power are deemed labels. A hierarchical message aggregation mechanism is proposed to effectively capture the multi-hop structural dependencies, followed by the process of inductive learning. Results show that against conventional optimization algorithms, the proposed method can achieve near-optimal energy consumption with a gap of 10% to 16%, while reducing the runtime by about six orders of magnitude. Compared to DNN, the GNN-based model can provide an energy saving of up to 32%, at a similar level of inference time. © 2025 IEEE.

关键词： Reinforcement learning

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Image Reconstruction Improvement of Variable Coded Aperture using Deep Learning Method for Gamma and Lensless Imaging Applications

Image Reconstruction Improvement of Variable Coded Aperture ...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Schwarz, Ariel Shemer, Amir Danan, Eliezer Cohen, Noa E. Danan, Yossef Department of Electrical and Electronics Engineering Azrieli College of Engineering Jerusalem9103501 Israel Faculty of Engineering Bar-Ilan University Ramat-Gan5290002 Israel School of Software Engineering and Computer Science Azrieli College of Engineering Jerusalem9103501 Israel

ISBN: (纸本)9798350345995

In gamma ray imaging for nuclear medicine, coded aperture is used to improve sensitivity. one of the main reconstructing methods is inverse filtering (deconvolution), where the recorded image is cross-correlated with periodic inverse filter of coded array. The reconstruction is free of coding noise for arbitrary array. However, amplification of quantum noise affect the reconstructed image. Although it is improved by Wiener filtering, the major problem is small terms in the spectral distribution of coded masks. Statistically and experimentally pinhole arrays have at least one term which is zero, resulting unacceptably noisy reconstruction. In our previous research we presented new approach of variable coded aperture (VCA) design for far and near field imaging applications [1-4]. The imaging system is based on time multiplexing method using variable multi pinhole array. The unique variable design enables to overcome the spatial frequencies cutoff and small terms in the Fourier transform exists in static multi pinhole array. The overall pinholes positions are designed to avoid spatial frequencies loss. However, traces of duplications can still be detected in reconstruction using Wiener filtering. Furthermore, since coded aperture blocks most of the photons that enter the detector, the dynamic range of the image is limited, thus leading to low contrast image and inadequate colors gamut. © 2023 IEEE.

关键词： Quantum noise

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DCQNet: Collaborative Camouflaged Object Detection Using Cross-Sample and Cross-Scale Network

DCQNet: Collaborative Camouflaged Object Detection Using Cro...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Panrui Tang Zuping Zhang Yubin Sheng Bo Huang Yao Xiao Lin Shen School of Computer Science and Engineering Central South University School of Software Xinjiang University Changsha China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Camouflaged object detection (COD) aims to identify objects that blend into the surrounding backgrounds, which has been a hot topic in recent years, with many different optimization strategies being explored. Among these, collaborative detection, a recently proposed solution for COD, has shown outstanding performance. However, current collaborative detection methods adopt a "one-to-many" pattern, failing to fully leverage the advantages of collaborative detection. Moreover, existing approaches to disguised target detection overlook the importance of high-level features. To address these issues, we propose a novel dualcross query network (DCQNet). It effectively capitalizes on the commonalities among objects and the directive role of high-level features in enhancing low-level features. Specifically, we designed a cross-sample query module and a cross-scale query module to collaboratively locate the object and guide low-level features, respectively. Extensive experimental results demonstrate that DCQNet outperforms state-of-the-art (SOTA) methods on the CoCOD8K dataset.

关键词： Representation learning Accuracy Semantics Neural networks Collaboration Object detection Feature extraction

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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Multi Task-Based Facial Expression Synthesis with Supervision Learning and Feature Disentanglement of Image Style

Multi Task-Based Facial Expression Synthesis with Supervisio...

引用

IEEE International Conference on Image Processing

作者： Wenya Lu Zhibin Peng Cheng Luo Weicheng Xie Jiajun Wen Zhihui Lai Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University

Image-to-Image synthesis paradigms have been widely used for facial expression synthesis. However, current generators are apt to either produce artifacts for largely posed and non-aligned faces or unduly change the identity information like AdaIN-based generator. In this work, we suggest to use image style feature to surrogate the expression cues in the generator, and propose a multi-task learning paradigm to explore this style information via the supervision learning and feature disentanglement. While the supervision learning can make the encoded style specifically represent the expression cues and enable the generator to produce correct expression, the feature disentanglement of content and style cues enables the generator to better preserve the identity information in expression synthesis. Experimental results show that the proposed algorithm can well reduce the artifacts for the synthesis of posed and non-aligned expressions, and achieves competitive performances in terms of FID, PNSR and classification accuracy, compared with four publicly available GANs. The code and pre-trained models are available at https://***/lumanxi236/MTSS.

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Usability Evaluation of Kids' Learning Apps 2

Usability Evaluation of Kids' Learning Apps

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ibrahim, Amer Al-Rajab, Murad Hamid, Khalid Aqeel, Muhamamd Muneer, Salman Parveen, Mehvish Saleem, Maryam American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates Abu Dhabi University Computer Scienc and IT College of Engineering United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Superior University Department of Software Engineering Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NCBAE School of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The main objective of learning applications is to use modern electronic technologies to impart knowledge, communicate knowledge, and Direct learning models with their learning activities in a timely and effective manner. The preponderance of the apps were designed to up-skill the kids in the fundamentals of letters and numbers. Generally, they were grounded and practice skills in nature, based on a lack of abilities in critical thinking, favoring parrot-fashion skills, and contributing to a deep conceptual comprehension of more specific concepts are unable. By using appropriate evaluation methodologies, the purposed study observes essential characteristics and aspects that affect the usability of interfaces. An evaluation of the app's features will figure out to ensure user contentment. When a kid interacts with a learning interface, the ease of use provided by the interface is validated. Because a large number of the apps were designed to train kids' fundamentals of alphabetic and numeric. The focus of this research is to see whether self-proclaimed learning apps for kids are built according to developmentally appropriate standards to help kids in formal and informal learning settings develop socially, emotionally, and academically. © 2023 IEEE.

关键词： Learning systems

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A Survey on Anti-Money Laundering Techniques in Blockchain Systems

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Strategic Study of CAE 2025年第2期27卷 287-303页

作者： Leyuan, Liu Xiangye, Li Tian, Lan Yakun, Cheng Wei, Chen Zhixin, Li Sheng, Cao Weili, Han Xiaosong, Zhang Hongfeng, Chai School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science Fudan University Shanghai200433 China Institute of Financial Technology Fudan University Shanghai200433 China

As the global financial landscape undergoes profound transformation, the blockchain technology has emerged as a cornerstone of Web 3.0 finance and a pivotal frontier in financial technology innovation. However, its decentralized and pseudonymous nature has also been exploited by malicious actors to circumvent regulatory oversight, facilitate money laundering, and conduct other illicit financial activities, posing substantial risks to both national and global financial security. Consequently, there is an urgent need to systematically assess the current progress in anti-money laundering (AML) research, anticipate future directions in blockchain-based AML technologies, and develop effective countermeasures to mitigate the evolving financial security challenges associated with blockchain applications. This study provides a comprehensive review of AML research in blockchain systems, examining the foundational AML frameworks, including traditional AML models and blockchain-based money laundering methodologies. It categorizes existing AML techniques into three primary approaches: rule-based methods, such as transaction parameter threshold setting, address-entity association analysis, and cross-chain association analysis;machine learning-based approaches, including support vector machines, logistic regression, decision trees, random forests, k-means clustering, and combining off-chain information;and deep learning-based methodologies, encompassing convolutional neural networks, recurrent neural networks, graph neural networks, and transformer-based models. Furthermore, this study discusses the practical applications of these techniques and reviews commonly used datasets that support AML research. Looking ahead, the advancement of AML technologies in blockchain systems necessitates progress in several critical areas: the construction of AML datasets capable of addressing data imbalance and annotation uncertainty, development of trusted AML algorithms, design of detection mec

关键词： Blockchain

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E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning

arXiv

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arXiv 2024年

作者： Qu, Qiang Shen, Yiran Chen, Xiaoming Chung, Yuk Ying Liu, Tongliang School of Computer Science The University of Sydney Australia School of Software Shandong University China School of Computer Science and Engineering Beijing Technology and Business University China

The bio-inspired event cameras or dynamic vision sensors are capable of asynchronously capturing per-pixel brightness changes (called event-streams) in high temporal resolution and high dynamic range. However, the non-structural spatial-temporal event-streams make it challenging for providing intuitive visualization with rich semantic information for human vision. It calls for events-to-video (E2V) solutions which take event-streams as input and generate high quality video frames for intuitive visualization. However, current solutions are predominantly data-driven without considering the prior knowledge of the underlying statistics relating event-streams and video frames. It highly relies on the non-linearity and generalization capability of the deep neural networks, thus, is struggling on reconstructing detailed textures when the scenes are complex. In this work, we propose E2HQV, a novel E2V paradigm designed to produce high-quality video frames from events. This approach leverages a model-aided deep learning framework, underpinned by a theory-inspired E2V model, which is meticulously derived from the fundamental imaging principles of event cameras. To deal with the issue of state-reset in the recurrent components of E2HQV, we also design a temporal shift embedding module to further improve the quality of the video frames. Comprehensive evaluations on the real world event camera datasets validate our approach, with E2HQV, notably outperforming state-of-the-art approaches, e.g., surpassing the second best by over 40% for some evaluation metrics. © 2024, CC BY.

关键词： Cameras

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