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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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Context Convolution Dehazing Network With Channel Attention 5

Context Convolution Dehazing Network With Channel Attention

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5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Li, Yuanyuan Meng, Jun Zhu, Zhiqin Huang, Xinghua Qi, Guanqiu Luo, Yaqin Chongqing University of Posts and Telecommunications Chongqin Key Laboratory of Image Cognition Chongqing400065 China Chongqing University College of Automation Chongqing400044 China Buffalo State College Computer Information Systems Department BuffaloNY14222 United States

ISBN: (纸本)9781665426305

Fog and haze weather conditions lead to deterioration of image visual quality. Under these conditions, advanced image processing tasks such as object detection and image segmentation are difficult to perform. To solve related problems, in this paper we propose an end-to-end context dilated convolution dehazing network with channel attention to return to a clear image from a haze image. This model uses context dilated module extracts the multi-scale scene information in the haze image, which can better maintain the original color of the image while removing the haze. The channel attention enables the model to separate the importance of features and boosts the model's power to adapt to different input scenarios. In the training phase, the model uses contrastive learning to distinguish the potential difference between the haze picture and the clear picture, helping the model to better renewal the clear image. The results of contrastive tests with existing methods indicate the proposed method has excellent dehazing performance. © 2021 IEEE.

关键词： Convolution

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NutFrame: Frame-based Conceptual Structure Induction with LLMs 30

NutFrame: Frame-based Conceptual Structure Induction with LL...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Guo, Shaoru Chen, Yubo Liu, Kang Li, Ru Zhao, Jun School of Computer and Information Technology Shanxi University Taiyuan China The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9782493814104

Conceptual structure is fundamental to human cognition and natural language understanding. It is significant to explore whether Large Language Models (LLMs) understand such knowledge. Since FrameNet serves as a well-defined conceptual structure knowledge resource, with meaningful frames, fine-grained frame elements, and rich frame relations, we construct a benchmark for coNceptual structure induction based on FrameNet, called NutFrame. It contains three sub-tasks: Frame Induction, Frame Element Induction, and Frame Relation Induction. In addition, we utilize prompts to induce conceptual structure of Framenet with LLMs. Furthermore, we conduct extensive experiments on NutFrame to evaluate various widely-used LLMs. Experimental results demonstrate that FrameNet induction remains a challenge for LLMs. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Frame Element Induction Frame Induction Frame Relation Induction FrameNet

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Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection

Low-rank Adaptation Method for Wav2vec2-based Fake Audio Det...

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2023 Workshop on Deepfake Audio Detection and Analysis, DADA 2023

作者： Wang, Chenglong Yi, Jiangyan Zhang, Xiaohui Tao, Jianhua Yan, Xinrui Xu, Le Fu, Ruibo University of Science and Technology of China Hefei China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijng China School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computer and Information Technology Beijing Jiaotong University China Department of Automation Tsinghua University China

Self-supervised speech models are a rapidly developing research topic in fake audio detection. Many pre-trained models can serve as feature extractors, learning richer and higher-level speech features. However,when fine-tuning pre-trained models, there is often a challenge of excessively long training times and high memory consumption, and complete fine-tuning is also very expensive. To alleviate this problem, we apply low-rank adaptation(LoRA) to the wav2vec2 model, freezing the pre-trained model weights and injecting a trainable rank-decomposition matrix into each layer of the transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared with fine-tuning with Adam on the wav2vec2 model containing 317M training parameters, LoRA achieved similar performance by reducing the number of trainable parameters by 198 times. © 2023 CEUR-WS. All rights reserved.

关键词： Fake detection

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An intelligent system for Aerial image Retrieval and Classification

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3rd Hellenic Conference on Artificial Intelligence, SETN 2004

作者： Gasteratos, Antonios Zafeiridis, Panagiotis Andreadis, Ioannis Laboratory of Robotics and Automation Section of Production Systems Department of Production and Management Engineering Democritus University of Thrace Building of University’s Library Kimmeria XanthiGR-671 00 Greece Laboratory of Electronics Section of Electronics and Information Systems Technology Department of Electrical and Computer Engineering Democritus University of Thrace Vassilisis Sophias 12 XanthiGR-671 00 Greece

ISBN: (纸本)3540219374

Content based image retrieval is an active research area of pattern recognition. A new method of extracting global texture energy descriptors is proposed and it is combined with features describing the color aspect of texture, suitable for image retrieval. The same features are also used for image classification, by its semantic content. An exemplar fuzzy system for aerial image retrieval and classification is proposed. The fuzzy system calculates the degree that a class, such as sea, clouds, desert, forests and plantations, participates in the input image. Target applications include remote sensing, computer vision, forestry, fishery, agricultures, oceanography and weather forecasting. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Fuzzy systems

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3D R Transform on Spatio-Temporal Interest Points for Action Recognition

3D R Transform on Spatio-Temporal Interest Points for Action...

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IEEE Conference on computer Vision and Pattern Recognition

作者： Chunfeng Yuan Xi Li Weiming Hu Haibin Ling Stephen Maybank National Laboratory of Pattern Recognition Institute of Automation School of Computer Science University of Adelaide Department of Computer and Information Sciences Temple University School of Computer Science and Information Systems Birkbeck College

ISBN: (纸本)9781467364102

Spatio-temporal interest points serve as an elementary building block in many modern action recognition algorithms, and most of them exploit the local spatio-temporal volume features using a Bag of Visual Words (BOVW) representation. Such representation, however, ignores potentially valuable information about the global spatio-temporal distribution of interest points. In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the R transform which is defined as an extended 3D discrete Radon transform, followed by applying a two-directional two-dimensional principal component analysis. Such R feature captures the geometrical information of the interest points and keeps invariant to geometry transformation and robust to noise. In addition, we propose a new fusion strategy to combine the R feature with the BOVW representation for further improving recognition accuracy. We utilize a context-aware fusion method to capture both the pairwise similarities and higher-order contextual interactions of the videos. Experimental results on several publicly available datasets demonstrate the effectiveness of the proposed approach for action recognition.

关键词： BOVW points keeps ROENTGEN Recognition (Psychology) fusion method Transform Bag device

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Deep learning for object detection and grasping: A survey

Deep learning for object detection and grasping: A survey

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2018 IEEE International Conference on information and automation, ICIA 2018

作者： Jia, Qun Cai, Jun Cao, Zhiqiang Wu, Yelan Zhao, Xionglei Yu, Junzhi State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9781538680698

Detecting and grasping objects in unstructured environments is an important yet difficult task. Fortunately, the breakthroughs from deep convolutional networks stimulate the development of object detection and grasping. The survey aims to serve as a comparison for region-based and region-free detection framework based on deep learning, and supplies the latest research results of object grasping with deep learning. Firstly, we briefly analyze the object detection and grasping. Then, the representative object detection methods based on deep learning are overviewed. Thirdly, we introduce the application of convolutional neural networks in object grasping. Finally, the potential trends in object detection and grasping based on deep learning are discussed. © 2018 IEEE.

关键词： Object detection

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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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Distributed Flow Shop Scheduling with Sequence-Dependent Setup Times Using an Improved Iterated Greedy Algorithm

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Complex System Modeling and Simulation 2021年第3期1卷 198-217页

作者： Xue Han Yuyan Han Qingda Chen Junqing Li Hongyan Sang Yiping Liu Quanke Pan Yusuke Nojima the School of Computer Science Liaocheng UniversityLiaocheng 252000China the State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China the School of Information Science and Engineering Shandong Normal UniversityJinan 252000China the College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China the School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200072China the Department of Computer Science and Intelligent Systems Osaka Prefecture UniversityOsaka 599-8531Japan

To meet the multi-cooperation production demand of enterprises,the distributed permutation flow shop scheduling problem(DPFSP)has become the frontier research in the field of manufacturing *** this paper,we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequence-dependent setup *** solve DPFSPs,significant developments of some metaheuristic algorithms are *** this context,a simple and effective improved iterated greedy(NIG)algorithm is proposed to minimize makespan in *** to the features of DPFSPs,a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed *** compare the proposed algorithm with state-of-the-art algorithms,including the iterative greedy algorithm(2019),iterative greedy proposed by Ruiz and Pan(2019),discrete differential evolution algorithm(2018),discrete artificial bee colony(2018),and artificial chemical reaction optimization(2017).Simulation results show that NIG outperforms the compared algorithms.

关键词： distributed permutation flow shop iterated greedy local search swapping strategy

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A Migration Path to IEC 61499 for the Batch Process Industry

A Migration Path to IEC 61499 for the Batch Process Industry

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IEEE International Conference on Industrial Informatics (INDIN)

作者： J. Peltola J. Christensen S. Sierla K. Koskinen Laboratory of Information and Computer Systems in Automation Helsinki University of Technology Finland Holobloc Inc. Cleveland Heights OH USA

Many recent contributions in the field of embedded, networked control and collaborative manufacturing, such as dynamic reconfiguration, are based on the IEC 61499 standard, which defines a general-purpose architecture for control applications. The industrial acceptance of the standard remains unclear at this time, largely because there is no obvious way to apply domain-specific, industrially accepted engineering methodologies and concepts in the context of IEC 61499. This paper proposes a way to support the established practices of the batch process domain with IEC 61499, and assesses the feasibility of the approach with an experiment involving professional designers.

关键词： IEC standards Manufacturing industries Software standards Standards development Investments Industrial control Application software Software tools Process control Instruction sets

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