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UAV-Assisted Joint Mobile Edge computing and Data Collection via Matching-Enabled Deep Reinforcement Learning

IEEE Internet of Things Journal 2025年

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a non-convex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the UAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the data collection volume compared with other benchmark algorithms. © 2025 IEEE. All rights reserved,

关键词： Unmanned aerial vehicles (UAV)

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Influence of Different Cement Matrix on the Static and Dynamic Mechanical Properties of Ultra-High Performance Concrete (Uhpc)

SSRN

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SSRN 2024年

作者： Yu, Peng Zhong, Li Chen, Zheng Yun, Weijing Li, Wei School of Civil Engineering and Architecture State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures Key Laboratory of Disaster Prevention and Structural Safety Ministry of Education Guangxi Key Laboratory of Disaster Prevention and Structural Safety Scientific Research Center of Engineering Mechanics Guangxi University Nanning530004 China

The development of environmentally friendly and sustainable Ultra-High Performance Concrete (UHPC) with reduced carbon footprint has garnered significant attention in response to the challenges posed by global warming and the intricate international landscape. Portland cement (PC) is mainly utilized in the production of UHPC. Nevertheless, it is plagued with drawbacks such as long setting time, low early strength, high shrinkage, and poor toughness, particularly, its significant negative impact on the environment. In contrast, Sulphoaluminate cement (SAC) and aluminate cement (AC) have the advantages of lower CO2 emission, fast early strength development and excellent durability properties compared to PC. While SAC-UHPC and AC-UHPC perform better in terms of dynamic performance, there are fewer studies in this area. Therefore, this paper investigates the carbon footprint, workability, static and dynamic mechanical properties, porosity, physical phase composition and microstructure of UHPC prepared from PC, SAC, AC. The results show that aluminate cement-based UHPC (AC-UHPC) and Sulphoaluminate cement-based UHPC (SAC-UHPC) can reduce CO2 emissions by 204.92 kg and 199.09 kg per unit volume respectively, compared to portland cement-based UHPC (PC-UHPC);the workability of AC-UHPC is better than that of SAC-UHPC and PC-UHPC. SAC-UHPC and AC-UHPC have excellent early static mechanical properties, the compressive strength of SAC-UHPC reaches 76.3 MPa at the age of 3 days, while PC-UHPC is 54.8 MPa, showing a difference of 21.5 MPa, which is approximately 39% higher. SAC-UHPC is weaker than PC-UHPC and AC-UHPC in the terms of dynamic compression resistance, but it shows good deformation capacity and impact toughness under different strain rates, respectively. The hydration products of PC-UHPC are dominated by Ca(OH)2 and C-S-H, while SAC-UHPC are dominated by ettringite (AFt), and AC-UHPC are dominated by CAH10, C2AH8, and C3AH6. Hence, employing SAC and AC in the producti

关键词： Microstructure

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ViSpeak: Visual Instruction Feedback in Streaming Videos

arXiv

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arXiv 2025年

作者： Fu, Shenghao Yang, Qize Li, Yuan-Ming Peng, Yi-Xing Lin, Kun-Yu Wei, Xihan Hu, Jian-Fang Xie, Xiaohua Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Tongyi Lab Alibaba Group China Peng Cheng Laboratory China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology China China

Recent advances in Large Multi-modal Models (LMMs) are primarily focused on offline video understanding. Instead, streaming video understanding poses great challenges to recent models due to its time-sensitive, omni-modal and interactive characteristics. In this work, we aim to extend the streaming video understanding from a new perspective and propose a novel task named Visual Instruction Feedback in which models should be aware of visual contents and learn to extract instructions from them. For example, when users wave their hands to agents, agents should recognize the gesture and start conversations with welcome information. Thus, following instructions in visual modality greatly enhances user-agent interactions. To facilitate research, we define seven key subtasks highly relevant to visual modality and collect the ViSpeak-Instruct dataset for training and the ViSpeak-Bench for evaluation. Further, we propose the ViSpeak model, which is a SOTA streaming video understanding LMM with GPT-4o-level performance on various streaming video understanding benchmarks. After finetuning on our ViSpeak-Instruct dataset, ViSpeak is equipped with basic visual instruction feedback ability, serving as a solid baseline for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Video streaming

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DiffuRec: A Diffusion Model for Sequential Recommendation

arXiv

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arXiv 2023年

作者： Li, Zihao Li, Chenliang Sun, Aixin Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Nanyang Technological University Singapore

Mainstream solutions to Sequential Recommendation (SR) represent items with fixed vectors. These vectors have limited capability in capturing items’ latent aspects and users’ diverse preferences. As a new generative paradigm, Diffusion models have achieved excellent performance in areas like computer vision and natural language processing. To our understanding, its unique merit in representation generation well fits the problem setting of sequential recommendation. In this paper, we make the very first attempt to adapt Diffusion model to SR and propose DiffuRec, for item representation construction and uncertainty injection. Rather than modeling item representations as fixed vectors, we represent them as distributions in DiffuRec, which reflect user’s multiple interests and item’s various aspects adaptively. In diffusion phase, DiffuRec corrupts the target item embedding into a Gaussian distribution via noise adding, which is further applied for sequential item distribution representation generation and uncertainty injection. Afterward, the item representation is fed into an Approximator for target item representation reconstruction. In reverse phase, based on user’s historical interaction behaviors, we reverse a Gaussian noise into the target item representation, then apply a rounding operation for target item prediction. Experiments over four datasets show that DiffuRec outperforms strong baselines by a large margin1 Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian distribution

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AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection

AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domai...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yipeng Gao Kun-Yu Lin Junkai Yan Yaowei Wang Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Pengcheng Lab

In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a few target labeled images are available for training in addition to sufficient source labeled images. Critically, in FSDAOD, the data scarcity in the target domain leads to an extreme data imbalance between the source and target domains, which potentially causes over-adaptation in traditional feature alignment. To address the data imbalance problem, we propose an asymmetric adaptation paradigm, namely AsyFOD, which leverages the source and target instances from different perspectives. Specifically, by using target distribution estimation, the AsyFOD first identifies the target-similar source instances, which serves to augment the limited target instances. Then, we conduct asynchronous alignment between target-dissimilar source instances and augmented target instances, which is simple yet effective for alleviating the over-adaptation. Extensive experiments demonstrate that the proposed AsyFOD outperforms all state-of-the-art methods on four FSDAOD benchmarks with various environmental variances, e.g., 3.1% mAP improvement on Cityscapes-to-FoggyCityscapes and 2.9% mAP increase on Sim10k-to-Cityscapes. The code is available at https://***/Hlings/AsyFPD.

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WBRP: Consensus Algorithm Based on Weak Proof of Work

WBRP: Consensus Algorithm Based on Weak Proof of Work

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IEEE International Conference on Blockchain (Blockchain)

作者： Jiarui Li Dongbin Wang Hui Zhi Wencheng Zhu Jianing Li Zedong Wang School of Cyberspace Engineering Beijing University of Post and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Beijing China TravelSky Technology Limited Beijing China Key Laboratory of Ministry of Education and Trustworthy Distributed Computing and Service Beijing China

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

Nakamoto Consensus (NC) is a classic Proof of Work (PoW) consensus algorithm where the probability of miners earning the right to a block is proportional to their computational power. To prevent blockchain forks, the interval between block outputs is restricted, which results in lower throughput for PoW. To address blockchain performance issues, a transaction ledger scheme based on weak PoW consensus has been proposed. This approach combines strong and weak blocks, allowing miners to submit weak blocks with lower mining difficulty. Nodes can then use these weak blocks to organize transaction information on the main chain and ensure strict linear ordering of blocks and transactions through sorting rules. Experimental results demon-strate that the consensus algorithm based on weak PoW can significantly enhance system performance, allowing transaction throughput to increase linearly with bandwidth. Specifically, with a node bandwidth of 1 Gb/s, the performance can exceed NC by more than 300 times.

关键词： System performance Consensus algorithm Bandwidth Throughput Proof of Work Extensibility Blockchains

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Complex-valued cross-domain few-shot learning network for PolSAR image classification

Complex-valued cross-domain few-shot learning network for Po...

引用

IET International Radar Conference (IRC 2023)

作者： Yice Cao Dayi Zhu Zhenhua Wu Jie Chen Zhixiang Huang Lixia Yang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei 230601 People's Republic of China

ISBN: (数字)9781837240982

Gathering reliable labeled samples for polarimetric synthetic aperture (PolSAR) image classification is laborious. Moreover, applying a trained classifier to new domains often leads to noticeable performance degradation due to domain disparities. Therefore, this paper proposes the novel complex-valued cross-domain (CD) few-shot learning classification (CCFSLC) method for PolSAR images to address these issues. Firstly, the transferrable knowledge learning module (TKLM) with a complex-valued feature encoder (CVFE) is trained using source data with sufficient labeled samples. Then, the deep few-shot learning module (DFSLM), constructed using the pre-trained CVFE, is trained by episodes in both source and target domains, with only minimal target labeled samples. Meanwhile, the adversarial domain adaptation module (ADAM) is employed to eliminate domain shift. The proposed CCFSLC mainly focuses on exploring discriminative information from raw PolSAR data, while reducing the domain gap to recognize novel categories in unseen domains with only a few annotated samples. Experiments on typical PolSAR datasets validate the effectiveness of the proposed method.

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Human affinity for rivers

River

引用

River 2022年第1期1卷 4-14页

作者： Yichu Wang Alistair G.L.Borthwick Jinren Ni College of Water Sciences Beijing Normal UniversityBeijingChina The Key Laboratory of Water and Sediment Sciences Ministry of EducationPeking UniversityBeijingChina Eco-environment and Resource Efficiency Research Laboratory School of Environment and EnergyPeking University Shenzhen Graduate SchoolShenzhenChina School of Engineering Computing and MathematicsUniversity of PlymouthPlymouthUK State Environmental Protection Key Laboratory of All Materials Fluxes in River Ecosystems Ministry of Ecology and EnvironmentPeking UniversityBeijingChina Center for World's Large Rivers Southern University of Science and TechnologyShenzhenChina

Human civilization prospers along rivers ***,we investigated human–river relations by revealing the linkage between water and habitability for human settlements,and socioeconomic and cultural development across *** found that human–water co-occurrence relationships are self-similar over sub-basins for different scales of stream-order in the river *** to the earlier complete demographic census conducted during the reign of the Qing Dynasty(1776),there has been a general tendency for inhabitants to live close to rivers,characterized by population density associated with habitability cored by water under a near-natural state,which still remains to date(2019)even after long-term population growth and human *** history,we have extended the linkage of human settlements to humanistic attributes with river networks,derived four different modes of human aggregation towards rivers,and elucidated the geographical diversity of river density,population density,cultural prosperity,and clusters of ethnicity,particularly the Western and the Northeast culture established in the arid(desert)areas,the Huaxia culture in the alluvial plains,the Loess/Nomadic/Southwestern Ethnic culture in the plateaus,and the Qi-Lu/Wu-Yue/Linnan culture in coastlands across the whole *** work is also of significance to understanding long-term human–water relationships at a global scale.

关键词： cultural prosperity geographical diversity habitability human settlement population density river density river network

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A RIS-Aided MIMO Secure Communication Scheme Based on Subarray Partition

A RIS-Aided MIMO Secure Communication Scheme Based on Subarr...

引用

International Conference on Computer and Communications (ICCC)

作者： Suzhen Wang Hui Zhi Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Electronic and Information Engineering Anhui University Hefei China

Subarray partition of reconfigurable intelligent surface (RIS) can significantly reduce the computational complexity of solving optimal reflection coefficients. However, there is no research about the RIS subarray partition of RIS-aided multiple-input multiple-output (MIMO) communication for the eavesdropper scenario. In this paper, we intend to solve the optimization subarray partition problem for the eavesdropper scenario, design a RIS-aided MIMO secure communication scheme based on subarray partition (RSC-SP). We consider minimizing the number of subarrays while satisfying secrecy rate requirements. First, this problem is described as a nonconvex combinatorial optimization problem, then we solve it by combining alternating optimization and bisection. In this scheme, we derive the closed expressions of the optimal transmit covariance matrix and the optimal reflection coefficients of RIS. The alternating optimization algorithm is used to jointly optimize the transmit covariance matrix and the reflection coefficients of RIS, the bisection method is used to calculate the minimum number of subarrays. Simulation results show that compared with the traditional scheme without subarray partition, RSC-SP can significantly reduce the computational complexity while meeting secrecy rate requirements.

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