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TD3-Based Collaborative Computation Offloading and Charging Scheduling in Multi-UAV-Assisted MEC Networks

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TD3-Based Collaborative Computation Offloading and Charging ...

IEEE Conference on Wireless Communications and Networking

作者： Liang Zhao Yujun Yao Huan Zhou Hao Wang Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Computation offloading, resource allocation, and endurance issues in unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks have always been a research focus. UAV-aided MEC allows mobile users (MUs)' tasks to be offloaded to drones for processing in special scenarios, such as natural disasters or military attacks. However, as the number and size of offloaded tasks increase, a single UAV is difficult to meet all computational demands, result in the decline of QoS. To address this issue, this paper presents a collaborative computation offloading scheme where multiple UAVs can cooperate to handle massive tasks. Firstly, considering that battery-limited UAVs cannot complete all tasks and sustain flight without charging, we incorporate charging stations (CS) into multi-UAV-assisted MEC networks. Subsequently, we design a price-based incentive mechanism to maximize the total revenue obtained from UAVs' collaborative computation. Then, we formulate the joint optimization problem of computation offloading, resource allocation and charging scheduling as a Markov Decision Process (MDP), and propose a Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to find optimal strategies. Finally, extensive simulations demonstrate that the proposed TD3 algorithm outperforms other benchmark methods, achieving the highest overall system utility under different scenarios.

关键词： Processor scheduling Computational modeling Inductive charging Collaboration Charging stations Scheduling Resource management

Federated Distributed Deep Reinforcement Learning for Recommendation-enabled Edge Caching

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Federated Distributed Deep Reinforcement Learning for Recomm...

IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Hao Wang Huan Zhou Mingze Lit Liang Zhao Victor C.M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

This paper investigates a content recommendation-based edge caching method in multi-tier edge-cloud networks while considering content delivery and cache replacement decisions as well as bandwidth allocation strategies. First, we formu-late the optimization problem with the goal of minimizing long-term content delivery delay. Second, we model the optimization problem as a Partially Observable Markov Decision Process, and propose a Federated Distributed Deep Deterministic Policy Gradient-based method (FD3PG) to solve the corresponding problem. In conclusion, simulation results demonstrate that the proposed FD3PG achieves not only a much lower content delivery delay but also a higher cache hit rate compared with other baselines in various scenarios.

关键词： Simulation Markov decision processes Conferences Channel allocation Deep reinforcement learning Delays Optimization

Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation

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arXiv 2022年

作者： Jin, Yueming Yu, Yang Chen, Cheng Zhao, Zixu Heng, Pheng-Ann Stoyanov, Danail The Department of Computer Science University College London United Kingdom The Department of Computer Science and Engineering The Chinese University of Hong Kong HKSAR China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre. Previous works rely on conventional aggregation modules (e.g., dilated convolution, convolutional LSTM), which only make use of the local context. In this paper, we propose a novel framework STswinCL that explores the complementary intra- and inter-video relations to boost segmentation performance, by progressively capturing the global context. We firstly develop a hierarchy Transformer to capture intra-video relation that includes richer spatial and temporal cues from neighbor pixels and previous frames. A joint space-time window shift scheme is proposed to efficiently aggregate these two cues into each pixel embedding. Then, we explore inter-video relation via pixel-to-pixel contrastive learning, which well structures the global embedding space. A multi-source contrast training objective is developed to group the pixel embeddings across videos with the ground-truth guidance, which is crucial for learning the global property of the whole data. We extensively validate our approach on two public surgical video benchmarks, including EndoVis18 Challenge and CaDIS dataset. Experimental results demonstrate the promising performance of our method, which consistently exceeds previous state-of-the-art approaches. Code is available at https://***/YuemingJin/STswinCL. Copyright © 2022, The Authors. All rights reserved.

关键词： Pixels

Aesthetically Relevant Image Captioning

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arXiv 2022年

作者： Zhong, Zhipeng Zhou, Fei Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Peng Cheng National Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Institute for Artificial Intelligence and Robotics for Society China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China School of Computer Science The University of Nottingham United Kingdom

Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). Based on the observation that most textual comments of an image are about objects and their interactions rather than aspects of aesthetics, we first introduce the concept of Aesthetic Relevance Score (ARS) of a sentence and have developed a model to automatically label a sentence with its ARS. We then use the ARS to design the ARIC model which includes an ARS weighted IAC loss function and an ARS based diverse aesthetic caption selector (DACS). We present extensive experimental results to show the soundness of the ARS concept and the effectiveness of the ARIC model by demonstrating that texts with higher ARS’s can predict the aesthetic ratings more accurately and that the new ARIC model can generate more accurate, aesthetically more relevant and more diverse image captions. Furthermore, a large new research database containing 510K images with over 5 million comments and 350K aesthetic scores, and code for implementing ARIC are available at https://***/PengZai/ARIC. © 2022, CC BY-NC-ND.

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Learning from large-scale noisy web data with ubiquitous reweighting for image classification

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arXiv 2018年

作者： Li, Jia Song, Yafei Zhu, Jianfeng Cheng, Lele Su, Ying Ye, Lin Yuan, Pengcheng Han, Shumin State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Shenzhen Cyberspace Laboratory Shenzhen China National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer Science Peking University Beijing100871 China Computer Vision Technology Department of Baidu Beijing100871 China

Many advances of deep learning techniques originate from the efforts of addressing the image classification task on large-scale datasets. However, the construction of such clean datasets is costly and time-consuming since the Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In this paper, we propose a Ubiquitous Reweighting Network (URNet) that learns an image classification model from large-scale noisy data. By observing the web data, we find that there are five key challenges, i.e., imbalanced class sizes, high intra-classes diversity and inter-class similarity, imprecise instances, insufficient representative instances, and ambiguous class labels. To alleviate these challenges, we assume that every training instance has the potential to contribute positively by alleviating the data bias and noise via reweighting the influence of each instance according to different class sizes, large instance clusters, its confidence, small instance bags and the labels. In this manner, the influence of bias and noise in the web data can be gradually alleviated, leading to the steadily improving performance of URNet. Experimental results in the Webvision 2018 challenge with 16 million noisy training images from 5000 classes show that our approach outperforms state-of-the-art models and ranks the first place in the image classification task. [***] 3 Apr 2019. Copyright © 2018, The Authors. All rights reserved.

关键词： Image classification

F2A2: flexible fully-decentralized approximate actor-critic for cooperative multi-agent reinforcement learning

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2023年第1期24卷 8447-8521页

作者： Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan Hongyuan Zha School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.

关键词： cooperative MARL decentralized actor-critic primal-dual method

Masked Image Training for Generalizable Deep Image Denoising

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Masked Image Training for Generalizable Deep Image Denoising

Conference on computer vision and Pattern Recognition (CVPR)

作者： Haoyu Chen Jinjin Gu Yihao Liu Salma Abdel Magid Chao Dong Qiong Wang Hanspeter Pfister Lei Zhu The Hong Kong University of Science and Technology (Guangzhou) Shanghai AI Lab The University of Sydney ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Harvard University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences The Hong Kong University of Science and Technology

When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method for image denoising, especially with the emergence of Transformer-based models that have achieved notable state-of-the-art results on various image tasks. However, deep learning-based methods often suffer from a lack of generalization ability. For example, deep models trained on Gaussian noise may perform poorly when tested on other noise distributions. To address this issue, we present a novel approach to enhance the generalization performance of denoising networks, known as masked training. Our method involves masking random pixels of the input image and reconstructing the missing information during training. We also mask out the features in the self-attention layers to avoid the impact of training-testing inconsistency. Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios. Additionally, our interpretability analysis demonstrates the superiority of our method.

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RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation

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arXiv 2023年

作者： Li, Lijun Tian, Linrui Zhang, Xindi Wang, Qi Zhang, Bang Liu, Mengyuan Chen, Chen Alibaba Group China Shanghai Artificial Intelligence Laboratory China Key Laboratory of Machine Perception Shenzhen Graduate School Peking University China Center for Research in Computer Vision University of Central Florida United States

The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited. However, the variability of background, pose distribution, and texture can greatly influence the generalization ability. Therefore, we present a large-scale synthetic dataset –RenderIH– for interacting hands with accurate and diverse pose annotations. The dataset contains 1M photo-realistic images with varied backgrounds, perspectives, and hand textures. To generate natural and diverse interacting poses, we propose a new pose optimization algorithm. Additionally, for better pose estimation accuracy, we introduce a transformer-based pose estimation network, TransHand, to leverage the correlation between interacting hands and verify the effectiveness of RenderIH in improving results. Our dataset is model-agnostic and can improve more accuracy of any hand pose estimation method in comparison to other real or synthetic datasets. Experiments have shown that pretraining on our synthetic data can significantly decrease the error from 6.76mm to 5.79mm, and our Transhand surpasses contemporary methods. Our dataset and code are available at https://***/adwardlee/RenderIH. © 2023, CC BY.

关键词： Textures

Detection of privacy-sensitive situations for social robots in smart homes

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Detection of privacy-sensitive situations for social robots ...

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Francisco Erivaldo Fernandes Guanci Yang Ha Manh Do Weihua Sheng Oklahoma State University System Stillwater OK US Key Laboratory of Advanced Manufacturing Technology of Ministry of Education Guizhou University Guiyang China School of Electrical and Computer Engineering Shenzhen Academy of Robotics Stillwater Shenzhen OK USA

ISBN: (纸本)9781509024100

The ubiquitous use of cameras in a home environment raises privacy concerns, which is one of the major barriers to the deployment of smart home systems for elderly and disabled care. Social robots are equipped with cameras that can witness embarrassing situations of their owners, such as nakedness during a morning bath. In this paper, our proposed solution is to let the robot detect such privacy-sensitive moments and then turn away to avoid observing the person. We proposed a method based on the Convolutional Neural Networks (CNN) to detect nakedness in a daily living scenario. In this method, two CNNs are used: one detects whether a person is present or not in the current scene, and the other detects privacy-sensitive situations. We implemented the method on the ASCC home service robot developed in our lab. The CNNs were trained with a database composed of more than 2900 pictures collected from the Internet and from our ASCC Smart Home. In our experiments, we considered three human poses: sitting, standing, and lying. If the robot detects a privacy-sensitive situation, it will turn around and verbally announce its intention, which ensures the person that he or she is not being watched. Our experiments validate the proposed privacy preserving method. We also conducted a user study and find that people prefer that the robot takes some actions to avoid directly watching them in privacy-sensitive situations.

关键词： Privacy Cameras Robot vision systems Service robots Smart homes Security