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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

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

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

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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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Graph Contrastive Learning with Cohesive Subgraph Awareness 24

Graph Contrastive Learning with Cohesive Subgraph Awareness

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33rd ACM Web Conference, WWW 2024

作者： Wu, Yucheng Wang, Leye Han, Xiao Ye, Han-Jia Key Lab of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China National Key Laboratory for Novel Software Technology Nanjing University School of Artificial Intelligence Nanjing University Nanjing China

ISBN: (纸本)9798400701719

Graph contrastive learning (GCL) has emerged as a state-of-the-art strategy for learning representations of diverse graphs including social and biomedical networks. GCL widely uses stochastic graph topology augmentation, such as uniform node dropping, to generate augmented graphs. However, such stochastic augmentations may severely damage the intrinsic properties of a graph and deteriorate the following representation learning process. We argue that incorporating an awareness of cohesive subgraphs during the graph augmentation and learning processes has the potential to enhance GCL performance. To this end, we propose a novel unified framework called CTAug, to seamlessly integrate cohesion awareness into various existing GCL mechanisms. In particular, CTAug comprises two specialized modules: topology augmentation enhancement and graph learning enhancement. The former module generates augmented graphs that carefully preserve cohesion properties, while the latter module bolsters the graph encoder's ability to discern subgraph patterns. Theoretical analysis shows that CTAug can strictly improve existing GCL mechanisms. Empirical experiments verify that CTAug can achieve state-of-the-art performance for graph representation learning, especially for graphs with high degrees. The code is available at https://***/10.5281/zenodo.10594093, or https://***/wuyucheng2002/CTAug. © 2024 ACM.

关键词： Stochastic systems

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Idea Mining from Online Reviews Using Transformation-Based Natural Language Processing Tasks 6

Idea Mining from Online Reviews Using Transformation-Based N...

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6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

作者： Mahdi, Hussain Falih Gupta, Lav Kumar Choudhury, Tanupriya Bansal, Nikunj University of Diyala Department of Computer and Software Engineering Baquba Iraq School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India

ISBN: (数字)9781665470131

ISBN: (纸本)9781665470131

Product reviews from online communities are good sources to innovative ideas. Making use of this unstructured data from these reviews is a complicated process. The major task of identifying good ideas is ensuring the correct reviews are picked up for consideration. We propose a method of mining ideas by exploring online reviews data with a particular interest in discovering helpful suggestions in innovation. We use a classifier model to categorize whether the reviews from an online community provide a suggestion. The non-suggestive texts were discarded from the further analysis. The texts are then processed further by a sentence similarity comparison module that uses the Bidirectional Encoder Representations from Transformers (BERT) model. We use the sentence similarity techniques to filter common suggestions that are attractive to many reviewers. We describe how to use BERT based model for Natural Language Tasks. We conclude that mining reviews can help extract ideas from online communities. This is a generic implementation and can be adopted in any domain. We also suggest a human intervention to assess the context of the generated ideas. This method can help researchers and professionals discover ideas buried in unstructured online reviews. © 2022 IEEE.

关键词： Online systems

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Difficulty-Aware Data Augmentor for Scene Text Recognition 48

Difficulty-Aware Data Augmentor for Scene Text Recognition

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Meng, Guanghao Dai, Tao Chen, Bin Li, Naiqi Jiang, Yong Xia, Shu-Tao Tsinghua University Tsinghua Shenzhen International Graduate School China Peng Cheng Laboratory Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Harbin Institute of Technology Department of Computer Science and Technology Shenzhen China

ISBN: (纸本)9781728163277

Deep neural network (DNN) based scene text recognition (STR) methods usually require a large amount of annotated data for training, which is time-consuming and cost-expensive in practice. To address this issue, many data augmentation methods have been developed to train recognizers by improving the diversity of training samples. However, most existing methods neglect the difficulty inherent in samples, and easily suffer from the problem of over-diversity, i.e., the distribution of the augmented data significantly deviates from that of clean data. In this paper, we propose a novel difficulty-aware data augmentation framework for scene text recognition, which jointly considers the difficulty of samples and the strength of augmentations. Specifically, our framework first predicts the sample difficulty, followed by an adaptive data augmentation strategy. Furthermore, we build a more diverse set of augmentation methods for STR and integrate it into our augmentation framework. Extensive experiments on scene text recognition benchmarks show that our augmentation framework significantly improves the performance of recognizers. © 2023 IEEE.

关键词： data augmentation deep neural network difficulty-aware Scene text recognition

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Federated Flowchart: Overview of State-of-the-Arts based on Federated Learning Process 13

Federated Flowchart: Overview of State-of-the-Arts based on ...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Oh, Junsuk Lee, Donghyun Ha, Taeyun Jeon, Yongin Noh, Woniong Cho, Sungrae School of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of School of Software Hallym University Chuncheon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Federated learning centrally trains global statistical models by aggregating the local models trained on each device with localized data. Accordingly, federated learning protects the privacy of each device by eliminating the need to transmit distributed data generated in real-time on each device to a central data center. However, unlike other machine learning paradigms, this new paradigm raises new problems in large-scale and high-density networks composed of various devices, such as privacy protection due to the numerous devices, and distributed optimization due to the different resources of each device. In this paper, we describe the characteristics, challenges, and process of federated learning, and provide an overview of state-of-the-arts according to each phase of the process. © 2022 IEEE.

关键词： Learning systems

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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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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors 8

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Qian, Hong Wu, Wei Zhu, Qi Wang, Fei Zhao, Jinwei Zheng, Yan School of Computer Science and Technology University of Science and Technology of China Hefei China Jiangnan Institute of Computing Technology System Software Research Department Wuxi China Tsinghua University Department of Computer Science and Technology Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665456128

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs. © 2023 IEEE.

关键词： Supercomputers

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Container-Aware Service Function Chains Placement and Optimization in Vehicular Edge Computing

Container-Aware Service Function Chains Placement and Optimi...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Kaixin Zhang Shihong Hu Zhihao Qu Baoliu Ye School of Computer Science and Software Engineering Hohai University National Key Laboratory for Novel Software Technology Nanjing University

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In the domain of Vehicular Edge Computing (VEC), this paper addresses the complex problem of Service Function Chain (SFC) placement, which is crucial for the efficient deployment of cloud applications in vehicular environments. Our approach leverages Virtual Network Function (VNF) technology, transitioning network services from traditional hardware to more flexible, container-based edge computing frameworks. The primary objective is to organize these VNFs into functional SFCs, thereby minimizing service delays in VEC systems. SFC placement faces two significant challenges. The first is the sequential dependency among VNFs within an SFC, which adds substantial complexity to container deployment. The second challenge is the cold start delay of VNF containers, a critical issue in scenarios requiring swift response times, which negatively impacts service quality in VEC applications. To address these challenges, we propose a comprehensive model for SFC placement that considers the deployment status of VNF containers, acknowledging the complexity of this NP-hard problem. The core of our contribution is the development of the Single SFC Placement Algorithm (SSPA), a sophisticated, greedy-based approach designed for the effective placement of individual SFCs. We enhance this algorithm by incorporating a Particle Swarm Optimization (PSO) technique, making it capable of efficiently handling the placement of multiple SFCs. Our solution aims to improve edge resource utilization and mitigate startup delays associated with the initial activation of containers, thereby reducing service delays. Extensive experimental evaluations demonstrate that our algorithm achieves a 34.3% average reduction in service delay compared to four baselines and a 5.8% average reduction compared to other container-aware algorithms.

关键词： Service function chaining NP-hard problem Containers Hardware Delays Time factors Resource management Particle swarm optimization Optimization Edge computing

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Adversarial Guided Gradient Estimation Hashing for Cross-modal Retrieval 8

Adversarial Guided Gradient Estimation Hashing for Cross-mod...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Lu, Kangkang Liang, Meiyu Xue, Zhe Cao, Xiaowen Yin, Mengran Zhao, Zehua Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science National Pilot Software Engineering School Beijing100876 China

ISBN: (纸本)9781665477352

Due to low storage cost and fast query speed, deep hashing methods are widely used in cross-modal retrieval. However, the 'heterogeneous gap' between multi-modal data is still a challenge. Moreover, a major difficulty in deep hashing lies in the discrete constraints imposed on the network output. Existing solutions usually use relaxation techniques, but this inevitably produces quantization error, leading to sub-optimal hash code. In this paper, we propose Adversarial Guided Gradient Estimation Hashing (AGEH). Firstly, in order to bridge the heterogeneous gap between different modal data, a cross-modal adversarial feature learning network is constructed to learn cross-modal semantic associations. Secondly, to solve the discrete optimization problem of hash code, we propose a hashing optimization strategy based on gradient estimation for sign function, which strictly uses sign function to maintain discrete constraints in forward propagation, while in back propagation, the gradient is directly transmitted to the previous layer and thus avoid quantization error. Extensive experiments conducted on two cross-modal benchmark datasets show that our proposed AGEH outperforms several state-of-the-art methods. © 2022 IEEE.

关键词： Backpropagation

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