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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge Computing Environment

IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge Computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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A Novel Transformer-based Multiscale Siamese Framework for High-resolution Remote Sensing Change Detection

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13087-13099页

作者： Wang, Liangjun Chen, Weitao Wang, Haoyi Chen, Zhengchao China University of Geosciences Faculty of Computer Science Wuhan430074 China Ministry of Education Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Wuhan430074 China Airborne Remote Sensing Center Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Change detection (CD) in remote sensing based on deep convolutional neural networks (CNNs) and transformers has played a crucial role in surface monitoring and resource development. However, the inadequate extraction of local and global features, along with the insufficient utilization of multiscale features, constrains the ability to identify change regions at multiple scales. To resolve the aforementioned issue, we propose a transformer-based multiscale Siamese framework (TMSF) for high-resolution remote sensing CD, aiming to improve detection accuracy. First, we propose a dual-attention token-based (DAT-based) transformer module, which embeds attention tokens into the transformer, effectively integrating local and global features and thereby enhancing the discriminative power of the extracted features. Second, we design a multiscale feature adaptive fusion module (MFAFM) that incorporates a channel attention mechanism to adaptively integrate multiscale information, enabling more precise identification of change regions at various scales. We verify the effectiveness of the proposed method in improving CD accuracy on three widely used benchmark datasets, LEVIR-CD, WHU-CD and CDD. Experimental results show that our method outperforms ten state-of-the-art CD methods in terms of F1-score and intersection over union (IoU) metrics. Notably, compared to that of five multiscale-based methods, our proposed TMSF achieves superior performance while requiring only half the number of parameters and computational cost. Thus, the proposed model demonstrates a marked advancement in remote sensing CD. © 2008-2012 IEEE.

关键词： Deep neural networks

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Cross-Modal Adapter: Parameter-Efficient Transfer Learning Approach for Vision-Language Models

Cross-Modal Adapter: Parameter-Efficient Transfer Learning A...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Juncheng Yang Zuchao Li Shuai Xie Weiping Zhu Wei Yu Shijun Li School of Computer Science Wuhan University Wuhan China School of Electronic Information Engineering Henan Polytechnic Institute Nanyang China National Engineering Research Center for Multimedia Software Wuhan China JD Explore Academy Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Adapter-based parameter-efficient transfer learning has achieved exciting results in vision-language models. Traditional adapter methods often require training or fine-tuning, facing challenges such as insufficient samples or resource limitations. While some methods overcome the need for training by leveraging image modality cache and retrieval, they overlook the text modality’s importance and cross-modal cues for the efficient adaptation of parameters in visual-language models. This work introduces a cross-modal parameter-efficient approach named XMAdapter. XMAdapter establishes cache models for both text and image modalities. It then leverages retrieval through visual-language bimodal information to gather clues for inference. By dynamically adjusting the affinity ratio, it achieves cross-modal fusion, decoupling different modal similarities to assess their respective contributions. Additionally, it explores hard samples based on differences in cross-modal affinity and enhances model performance through adaptive adjustment of sample learning intensity. Extensive experimental results on benchmark datasets demonstrate that XMAdapter outperforms previous adapter-based methods significantly regarding accuracy, generalization, and efficiency.

关键词： Training Adaptation models Analytical models Accuracy Transfer learning Benchmark testing Tuning

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TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models

arXiv

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arXiv 2023年

作者： Xiong, Weidan Zhang, Hongqian Peng, Botao Hu, Ziyu Wu, Yongli Guo, Jianwei Huang, Hui Shenzhen University China Shenzhen University China MAIS Institute of Automation Chinese Academy of Sciences China Shenzhen University College of Computer Science & Software Engineering China

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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FuseFPS: Accelerating Farthest Point Sampling with Fusing KD-tree Construction for Point Clouds

arXiv

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arXiv 2023年

作者： Han, Meng Wang, Liang Xiao, Limin Zhang, Hao Zhang, Chenhao Xie, Xilong Zheng, Shuai Dong, Jin State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Academy of Blockchain and Edge Computing China

Point cloud analytics has become a critical workload for embedded and mobile platforms across various applications. Farthest point sampling (FPS) is a fundamental and widely used kernel in point cloud processing. However, the heavy external memory access makes FPS a performance bottleneck for real-time point cloud processing. Although bucket-based farthest point sampling can significantly reduce unnecessary memory accesses during the point sampling stage, the KD-tree construction stage becomes the predominant contributor to execution time. In this paper, we present FuseFPS, an architecture and algorithm co-design for bucket-based farthest point sampling. We first propose a hardware-friendly sampling-driven KD-tree construction algorithm. The algorithm fuses the KD-tree construction stage into the point sampling stage, further reducing memory accesses. Then, we design an efficient accelerator for bucket-based point sampling. The accelerator can offload the entire bucket-based FPS kernel at a low hardware cost. Finally, we evaluate our approach on various point cloud datasets. The detailed experiments show that compared to the state-of-the-art accelerator QuickFPS, FuseFPS achieves about 4.3× and about 6.1× improvements on speed and power efficiency, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Trees (mathematics)

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Enhancing ASIC Technology Mapping via Parallel Supergate Computing

arXiv

引用

arXiv 2024年

作者： Cai, Ye Yang, Zonglin Ni, Liwei Xie, Biwei Li, Xingquan College of Computer Science and Software Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China

With the development of large-scale integrated circuits, electronic design automation (EDA) tools are increasingly emphasizing efficiency, with parallel algorithms becoming a trend. The optimization of delay reduction is a crucial factor for ASIC technology mapping, and supergate technology proves to be an effective method for achieving this in EDA tools flow. However, we have observed that increasing the number of generated supergates can reduce delay, but this comes at the cost of an exponential increase in computation time. In this paper, we propose a parallel supergate computing method that addresses the tradeoff between time-consuming and delay optimization. The proposed method utilizes the input-constrained supergate pattern to parallelly generate the supergate candidates, and then filter the valid supergates as the results. Experiment results show the efficiency of the proposed method, for example, it can attain the improvement of 4× speedup in computation time and 10.1 in delay reduction with 32 threads. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

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Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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Enhancing ASIC Technology Mapping via Parallel Supergate Computing

Enhancing ASIC Technology Mapping via Parallel Supergate Com...

引用

Electronics Design Automation (ISEDA), International Symposium of

作者： Ye Cai Zonglin Yang Liwei Ni Biwei Xie Xingquan Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China State Key Lab of Processors Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (数字)9798350352030

ISBN: (纸本)9798350352047

With the development of large-scale integrated circuits, electronic design automation (EDA) tools are increasingly emphasizing efficiency, with parallel algorithms becoming a trend. The optimization of delay reduction is a crucial factor for ASIC technology mapping, and supergate technology proves to be an effective method for achieving this in EDA tools flow. However, we have observed that increasing the number of generated supergates can reduce delay, but this comes at the cost of an exponential increase in computation time. In this paper, we propose a parallel supergate computing method that addresses the tradeoff between time-consuming and delay optimization. The proposed method utilizes the input-constrained supergate pattern to parallelly generate the supergate candidates, and then filter the valid supergates as the results. Experiment results show the efficiency of the proposed method, for example, it can attain the improvement of 4x speedup in computation time and 10.1 in delay reduction with 32 threads.

关键词： Integrated circuits Design automation Costs Instruction sets Aerospace electronics Market research Delays

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Improving Critical Node Detection Using Neural Network-based Initialization in a Genetic Algorithm

arXiv

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arXiv 2024年

作者： Liu, Chanjuan Ge, Shike Chen, Zhihan Pei, Wenbin Zhu, Enqiang Mei, Yi Ishibuchi, Hisao The School of Computer Science and Technology Dalian University of Technology 116024 China The Institute of Software Chinese Academy of Science Beijing100190 China The Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China The School of Engineering and Computer Science Victoria University of Wellington Wellington6012 New Zealand The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The Critical Node Problem (CNP) is concerned with identifying the critical nodes in a complex network. These nodes play a significant role in maintaining the connectivity of the network, and removing them can negatively impact network performance. CNP has been studied extensively due to its numerous real-world applications. Among the different versions of CNP, CNP-1a has gained the most popularity. The primary objective of CNP-1a is to minimize the pair-wise connectivity in the remaining network after deleting a limited number of nodes from a network. Due to the NP-hard nature of CNP-1a, many heuristic/metaheuristic algorithms have been proposed to solve this problem. However, most existing algorithms start with a random initialization, leading to a high cost of obtaining an optimal solution. To improve the efficiency of solving CNP-1a, a knowledge-guided genetic algorithm named K2GA has been proposed. Unlike the standard genetic algorithm framework, K2GA has two main components: a pretrained neural network to obtain prior knowledge on possible critical nodes, and a hybrid genetic algorithm with local search for finding an optimal set of critical nodes based on the knowledge given by the trained neural network. The local search process utilizes a cut node-based greedy strategy. The effectiveness of the proposed knowledge-guided genetic algorithm is verified by experiments on 26 real-world instances of complex networks. Experimental results show that K2GA outperforms the state-of-the-art algorithms regarding the best, median, and average objective values, and improves the best upper bounds on the best objective values for eight real-world instances. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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A framework for a foundational cyber counterintelligence awareness and skills training programme 19

A framework for a foundational cyber counterintelligence awa...

引用

19th European Conference on Cyber Warfare and Security, ECCWS 2020

作者： Sithole, Thenjiwe du Toit, Jaco Jaquire, Victor von Solms, Sebastian Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781912764617

Organisations from all sectors are confronted with cyber threats and cyberattacks of increasing sophistication and intensity. Now, even more than before, cyber attackers’ modus operandi includes intelligence collection, espionage and influencing. Unsurprisingly then, conventional defence-only cybersecurity is no longer effective in protecting the confidentiality, integrity and availability (CIA) of information and information systems from these rapidly evolving cyber threats. Consequently, organisations have to evolve the protection of their information and information systems’ CIA by implementing a proactive, approach that incorporates cyber counterintelligence (CCI) at its core. CCI includes both defensive and offensive activities. This approach is not only the purview state actors and their security apparatus, but (with certain qualifications) can significantly benefit the private sector and other non-state actors. Organisations’ successful adoption of CCI depends on the implementation of an effective CCI Awareness and Skills Training (CCI AST) programme. Such a programme’s utility and importance range from providing employees with the knowledge to proactively counter cyber threats;to CCI career pathing that involves the acquisition of more advanced CCI skillset. Despite its importance, there is limited published academic and industry literature on CCI awareness, education and training. This paper proposes a CCI AST framework that could assist organisations - irrespective of nature of business and size - to implement a foundational CCI awareness and skills programme. The CCI AST framework identifies and concisely describes the critical elements (fields of knowledge and skills) indispensable to a foundational CCI awareness and skills programme. The paper then proceeds with outlining the pitch and approach to be followed in transferring knowledge and skills in respect of each of the identified elements. The paper concludes with a summary of findings. © 2020 Curran

关键词： Information systems

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