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Joint Scheduling, Computing, and Load Balancing for Time Sensitive Traffic in SDN-Enabled Space-Air-Ground Integrated 6 G Networks: A Federated Reinforcement Learning Approach

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IEEE Transactions on Mobile Computing 2025年

作者： Sun, Haitong Zhang, Haijun Ma, Hui Leung, Victor C.M Hebei Key Laboratory of Space-Air-Ground Intelligent Communication Beijing100083 China University of Science and Technology Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China Shenzhen University Department of Computer Science and Software Engineering Shenzhen518061 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Low Earth Orbit (LEO) constellations and Unmanned Aerial Vehicle (UAV) networks enable wide coverage for the sixth generation (6G) mobile communication. However, it is a challenge to achieve high scheduling success rate, ultra-low latency, and efficient load balance in the Space-Air-Ground Integrated 6G Network (SAGGIN). This paper addresses the following issue: How to effectively and orderly transmit time-sensitive traffic in SAGGIN under strict deadlines, limited computational ability, and restrained link capacity? Specifically, this paper uses software-Defined Networking (SDN) and designs a joint optimization method to enhance the traffic transmission ability of SAGGIN. Considering response time, computing cost, and link capacity in SAGGIN, the scheduling, computing, and load balance issues are modeled as a multi-objective optimization problem that minimizes the worst-case response time and computing cost of data frames while maximizing the network flow. Then, this paper leverages a Federated Reinforcement Learning (FRL) scheme to solve the problem. Results show that the FRL could achieve great scheduling, computing, and load balance performance. Specifically, our method can successfully schedule 80% of the traffic at most when the current network load is around 90%. Furthermore, the computational delay could reduce around 50%. © 2002-2012 IEEE.

关键词： Reinforcement learning

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TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation

arXiv

引用

arXiv 2023年

作者： Xian, Zixiang Huang, Rubing Towey, Dave Fang, Chunrong Chen, Zhenyu School of Computer Science and Engineering Macau University of Science and Technology Taipa999078 China School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo315100 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China

Artificial intelligence (AI) has revolutionized software engineering (SE) by enhancing software development efficiency. The advent of pre-trained models (PTMs) leveraging transfer learning has significantly advanced AI for SE. However, existing PTMs that operate on individual code tokens suffer from several limitations: They are costly to train and fine-tune;and they rely heavily on labeled data for fine-tuning on task-specific datasets In this paper, we present TransformCode, a novel framework that learns code embeddings in a contrastive learning manner. Our framework is encoder-agnostic and language-agnostic, which means that it can leverage any encoder model and handle any programming language. We also propose a novel data-augmentation technique called abstract syntax tree (AST) transformation, which applies syntactic and semantic transformations to the original code snippets, to generate more diverse and robust samples for contrastive learning. Our framework has several advantages over existing methods: (1) It is flexible and adaptable, because it can easily be extended to other downstream tasks that require code representation (such as code-clone detection and classification);(2) it is efficient and scalable, because it does not require a large model or a large amount of training data, and it can support any programming language;(3) it is not limited to unsupervised learning, but can also be applied to some supervised learning tasks by incorporating task-specific labels or objectives;and (4) it can also adjust the number of encoder parameters based on computing resources. We evaluate our framework on several code-related tasks, and demonstrate its effectiveness and superiority over the state-of-the-art methods such as SourcererCC, Code2vec, and InferCode. © 2023, CC BY.

关键词： Semantics

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Evaluation and Analysis of Hallucination in Large Vision-Language Models

arXiv

引用

arXiv 2023年

作者： Wang, Junyang Zhou, Yiyang Xu, Guohai Shi, Pengcheng Zhao, Chenlin Xu, Haiyang Ye, Qinghao Yan, Ming Zhang, Ji Zhu, Jihua Sang, Jitao Tang, Haoyu School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Software Engineering Xi’an Jiaotong University Xi’an China School of Software Shandong University Jinan China Beijing China DAMO Academy Alibaba Group China

Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are still plagued by the hallucination problem, which limits the practicality in many scenarios. Hallucination refers to the information of LVLMs’ responses that does not exist in the visual input, which poses potential risks of substantial consequences. There has been limited work studying hallucination evaluation in LVLMs. In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework. HaELM achieves an approximate 95% performance comparable to ChatGPT and has additional advantages including low cost, reproducibility, privacy preservation and local deployment. Leveraging the HaELM, we evaluate the hallucination in current LVLMs. Furthermore, we analyze the factors contributing to hallucination in LVLMs and offer helpful suggestions to mitigate the hallucination problem. Our data and code are available at https://***/junyangwang0410/HaELM. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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Low-Quality Ultrasound Image Super-Resolution Enhancement Using Spatially Variable Neural Implicit Network

SSRN

引用

SSRN 2024年

作者： Wang, Xiaojie Xia, Jing Wu, Mei Gao, Shanshan Zhou, Yuanfeng The School of Software Shandong University Jinan250101 China The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore The Second Hospital of Shandong University Jinan250033 China The School of Computer Science and Technology Shandong University of Finance and Economics Jinan250100 China

Ultrasound images are widely used due to their ease of use and harmlessness to the human body. However, differences in acquisition equipment and frequency settings result in varying qualities of ultrasound images. Low-quality ultrasound images exhibit coarse grain and more impurities in the lumen, while high-quality ultrasound images display clear tissue structures, fine graininess, and clearly visible lumens. To address this issue, we propose an ultrasound image enhancement method based on spatially variable neural implicit networks. Our approach involves several steps: first, the feature encoding network operates on low-quality ultrasound images to produce a feature matrix. Simultaneously, the parameter generation network decomposes the low-quality image into a parameter matrix. The generated parameter matrix is then fed into the pixel prediction module, which utilizes the encoded features to predict pixel values. To align the predicted pixel values more closely with the distribution of high-quality ultrasound images and filter out interference from artifact information, we design a spectrum discriminator network based on the fast Fourier transform. This network demonstrates significant advantages in capturing high-quality information and reducing the impact of redundant information. In a comprehensive comparison, both qualitative and quantitative, with current state-of-the-art image enhancement and arbitrary magnification methods, our approach outperforms other methods. © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Fuzzy Neural Network Sliding-Mode Control with Actor-Critic for a Class of Robot Systems

Fuzzy Neural Network Sliding-Mode Control with Actor-Critic ...

引用

IEEE International Conference on Orange Technologies (ICOT)

作者： Lon-Chen Hung Chun-Cheng Wei Hung-Jen Chen Chih-Fu Yang Wei-Min Cheng Department of Electronic Engineering Lunghwa University of Science and Technology School of Software and Big Data Changzhou College of Information Technology Department of Computer Information and Network Engineering Lunghwa University of Science and Technology

ISBN: (纸本)9781665454209

In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. The fuzzy RBF neural network sliding-mode and actor-critic method is handled to compensate the uncertainty and disturbance of system. The stability analysis is based on for the proposed adaptive and robust control method. The simulation results show the effectiveness under the uncertainties.

关键词： Uncertainty Target tracking Trajectory tracking Simulation Neural networks Manipulators Stability analysis

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Invertible Residual Rescaling Models

arXiv

引用

arXiv 2024年

作者： Li, Jinmin Dai, Tao Zha, Yaohua Luo, Yilu Lu, Longfei Chen, Bin Wang, Zhi Xia, Shu-Tao Zhang, Jingyun Tsinghua Shenzhen International Graduate School Tsinghua University China College of Computer Science and Software Engineering Shenzhen University China Research Center of Artificial Intelligence Peng Cheng Laboratory China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China WeChat Pay Lab33 Tencent China

Invertible Rescaling Networks (IRNs) and their variants have witnessed remarkable achievements in various image processing tasks like image rescaling. However, we observe that IRNs with deeper networks are difficult to train, thus hindering the representational ability of IRNs. To address this issue, we propose Invertible Residual Rescaling Models (IRRM) for image rescaling by learning a bijection between a high-resolution image and its low-resolution counterpart with a specific distribution. Specifically, we propose IRRM to build a deep network, which contains several Residual Downscaling Modules (RDMs) with long skip connections. Each RDM consists of several Invertible Residual Blocks (IRBs) with short connections. In this way, RDM allows rich low-frequency information to be bypassed by skip connections and forces models to focus on extracting high-frequency information from the image. Extensive experiments show that our IRRM performs significantly better than other state-of-the-art methods with much fewer parameters and complexity. Particularly, our IRRM has respectively PSNR gains of at least 0.3 dB over HCFlow and IRN in the ×4 rescaling while only using 60% parameters and 50% FLOPs. The code will be available at https://***/THUKingmin/IRRM. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Language-driven Grasp Detection with Mask-guided Attention

Language-driven Grasp Detection with Mask-guided Attention

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tuan Van Vo Minh Nhat Vu Baoru Huang An Vuong Ngan Le Thieu Vo Anh Nguyen FPT Software AI Center Vietnam Automation & Control Institute TU Wien Austria Austrian Institute of Technology (AIT) GmbH Austria Imperial College London UK Department of Computer Science & Computer Engineering University of Arkansas USA National University of Singapore Singapore Department of Computer Science University of Liverpool UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Grasp detection is an essential task in robotics with various industrial applications. However, traditional methods often struggle with occlusions and do not utilize language for grasping. Incorporating natural language into grasp detection remains a challenging task and largely unexplored. To address this gap, we propose a new method for language-driven grasp detection with mask-guided attention by utilizing the transformer attention mechanism with semantic segmentation features. Our approach integrates visual data, segmentation mask features, and natural language instructions, significantly improving grasp detection accuracy. Our work introduces a new framework for language-driven grasp detection, paving the way for language-driven robotic applications. Intensive experiments show that our method outperforms other recent baselines by a clear margin, with a 10.0% success score improvement. We further validate our method in real-world robotic experiments, confirming the effectiveness of our approach.

关键词： Visualization Attention mechanisms Accuracy Service robots Source coding Semantic segmentation Natural languages Grasping Transformers Feature extraction

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Playing the video games during COVID-19 pandemic and its effects on player's well-being

Playing the video games during COVID-19 pandemic and its eff...

引用

Business Analytics for technology and Security (ICBATS), International Conference on

作者： Samar Billi Noaman Amer Ibrahim Liaqat Ali M. W. Iqbal Asma Ashraf Usama Haseeb Salman Muneer Rasah Almajed K. Hamid Faculty of Management Canadian University Dubai Dubai UAE College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Department of Software Engineering Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan Department of Computer Science Superior University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Computer and Information Technology American University in the Dubai United Arab Emirates

COVID-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COVID-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COVID-19. The carried out survey is based on the social video games results on players’ well-being and additionally on the effects of gamers’ health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COVID-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COVID-19 practical life 1 .

关键词： COVID-19 Video games Pandemics Games Machine learning Behavioral sciences Security

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Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength

arXiv

引用

arXiv 2023年

作者： Zheng, Tongya Zhang, Tianli Guan, Qingzheng Huang, Wenjie Feng, Zunlei Song, Mingli Chen, Chun Big Graph Center College of Computer Science Hangzhou City University China College of Computer Science Zhejiang University China College of Software Technology Zhejiang University China Department of Civil Engineering Zhejiang University China

Graph Neural Networks have emerged as an effective machine learning tool for multi-disciplinary tasks such as pharmaceutical molecule classification and chemical reaction prediction, because they can model non-euclidean relationships between different entities. Particle crushing, as a significant field of civil engineering, describes the breakage of granular materials caused by the breakage of particle fragment bonds under the modeling of numerical simulations, which motivates us to characterize the mechanical behaviors of particle crushing through the connectivity of particle fragments with Graph Neural Networks (GNNs). However, there lacks an open-source large-scale particle crushing dataset for research due to the expensive costs of laboratory tests or numerical simulations. Therefore, we firstly generate a dataset with 45,000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing. Secondly, we devise a hybrid framework based on GNNs to predict particle crushing strength in a particle fragment view with the advances of state-of-the-art GNNs. Finally, we compare our hybrid framework against traditional machine learning methods and the plain MLP to verify its effectiveness. The usefulness of different features is further discussed through the gradient attribution explanation w.r.t the predictions. Our data and code are released at https://***/doujiang-zheng/GNN-For-Particle-Crushing. Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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CodePhys: Robust Video-Based Remote Physiological Measurement Through Latent Codebook Querying

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang Xi'an Jiaotong University School of Software Engineering Xi'an China Tencent Ai Lab Shenzhen China Lappeenranta-Lahti University of Technology Lut Computer Vision and Pattern Recognition Laboratory Lappeenranta53850 Finland University of Oulu Center for Machine Vision and Signal Analysis Finland

Remote photoplethysmography (rPPG) aims to measure non-contact physiological signals from facial videos, which has shown great potential in many applications. Most existing methods directly extract video-based rPPG features by designing neural networks for heart rate estimation. Although they can achieve acceptable results, the recovery of rPPG signal faces intractable challenges when interference from real-world scenarios takes place on facial video. Specifically, facial videos are inevitably affected by non-physiological factors (e.g., camera device noise, defocus, and motion blur), leading to the distortion of extracted rPPG signals. Recent rPPG extraction methods are easily affected by interference and degradation, resulting in noisy rPPG signals. In this paper, we propose a novel method named CodePhys, which innovatively treats rPPG measurement as a code query task in a noise-free proxy space (i.e., codebook) constructed by ground-truth PPG signals. We consider noisy rPPG features as queries and generate high-fidelity rPPG features by matching them with noise-free PPG features from the codebook. Our approach also incorporates a spatial-aware encoder network with a spatial attention mechanism to highlight physiologically active areas and uses a distillation loss to reduce the influence of non-periodic visual interference. Experimental results on four benchmark datasets demonstrate that CodePhys outperforms state-of-the-art methods in both intra-dataset and cross-dataset settings. © 2025 IEEE.

关键词： Heart

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