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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Qin, Huayu Zhang, Yi Sun, Jin Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798331509712

By deploying computing resources and services on network edge, mobile edge computing (MEC) can effectively address the energy limitations of mobile devices (MDs) through the computation offloading mechanism. This paper studies the problem of joint optimization of computation offloading, power assignment, and resource allocation, with the objective of reducing the total energy consumption in an MEC environment. We decompose the joint optimization problem into two sub-problems and suggest a composite method that integrates an analytical approach into the decision-making procedure of a deep reinforcement learning (DRL)-based algorithm. We first use the actor network of the twin delayed deep deterministic gradient (TD3), which is an advanced DRL algorithm, to determine the computation offloading rate for each MD as well as resource allocation on the MEC server. To facilitate the training process of DRL, we provide a rigorous analysis to derive the closed-form solution to the optimal assignment of the MD's operating frequency for local computation as well as transmission power for computation offloading. Distinct from other DRL-based offloading methods that solely rely on DRL techniques to determine the values of all decision variables, our composite method effectively enhances the DRL algorithm's decision-making capability in producing high-quality solutions. The evaluation results on a multi-user MEC system justify the superiority of the proposed method over baseline algorithms in producing more energy-efficient offloading solutions. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Faster Heuristic Scheduling Search for Robotic Cellular Manufacturing Systems with Generalized and Timed Petri Nets

A Faster Heuristic Scheduling Search for Robotic Cellular Ma...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Xiao, YuanZheng Wu, HaoRan Gao, YangQing Huang, Bo School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9781665410205

The design of the heuristic function in Petri-net-based A* search significantly impacts the efficiency and result quality of scheduling robotic cellular manufacturing (RCM) systems. Previous work in such designs lacks consideration of some key features such as token remaining time, alternative routes, weighted arcs, multiple resource copies, and batch-processing ability. This paper proposes a novel admissible heuristic function tailored to address these challenges. It is designed from the perspective of maximal time usage of each part token. In addition, it is admissible, which guarantees that its obtained schedules are optimal. Most importantly, it is highly informative, enabling efficient scheduling of generalized Petri nets (PNs) of RCM systems. Experimental simulations on benchmark PN models demonstrate the efficacy and efficiency of our method. Codes are available at https://***/PNOptimizer/NewHDesign. © 2024 IEEE.

关键词： Cellular manufacturing

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Deep Learning Models for Small Sample Hyperspectral Image Classification

Deep Learning Models for Small Sample Hyperspectral Image Cl...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Amoako, Prince Yaw Owusu Kyei, Emmanuel Yeboah School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9798350376838

A detailed background view of Hyperspectral remote sensing shows that hyperspectral image classification (HSIC) is very useful in many real-life applications. The existing deep learning models' capacity to learn new task-specific knowledge for generalization from a limited data sample that encourages less training is lacking in hyperspectral image classification. Development in deep learning has shown that dense models have better classification performance. However, this rule is not suitable for small data sample problems, especially in an unsupervised learning HSIC. The computational cost required by the state-of-the-art complex deep learning models renders them infeasible in constraint resource application environments. Consequently, this paper presents an analytical review of deep learning models for small sample HSIC. The focus is placed on supervised, semi-supervised, and unsupervised learning. Meta-learning, Meta-reinforcement Learning, and Capsule Networks are realized to be promising for Small Sample Hyperspectral Image Classification. © 2024 IEEE.

关键词： Deep reinforcement learning

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Boosting Adversarial Training with Learnable Distribution

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computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： Adversarial training feature space learnable distribution distribution centroid

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Virtual Assembly Collision Detection Algorithm Using Backpropagation Neural Network

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computers, Materials & Continua 2024年第10期81卷 1085-1100页

作者： Baowei Wang Wen You School of Computer Nanjing University of Information Science and TechnologyCollaborative Innovation Center of Jiangsu Atmospheric Environment and Equipment TechnologyDigital Forensics Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

As computer graphics technology continues to advance,Collision Detection(CD)has emerged as a critical element in fields such as virtual reality,computer graphics,and interactive *** is indispensable for ensuring the fidelity of physical interactions and the realism of virtual environments,particularly within complex scenarios like virtual assembly,where both high precision and real-time responsiveness are *** ongoing developments,current CD techniques often fall short in meeting these stringent requirements,resulting in inefficiencies and inaccuracies that impede the overall performance of virtual assembly *** address these limitations,this study introduces a novel algorithm that leverages the capabilities of a Backpropagation Neural Network(BPNN)to optimize the structural composition of the Hybrid Bounding Volume Tree(HBVT).Through this optimization,the research proposes a refined Hybrid Hierarchical Bounding Box(HHBB)framework,which is specifically designed to enhance the computational efficiency and precision of CD *** HHBB framework strategically reduces the complexity of collision detection computations,thereby enabling more rapid and accurate responses to collision *** experimental validation within virtual assembly environments reveals that the proposed algorithm markedly improves the performance of CD,particularly in handling complex *** optimized HBVT architecture not only accelerates the speed of collision detection but also significantly diminishes error rates,presenting a robust and scalable solution for real-time applications in intricate virtual *** findings suggest that the proposed approach offers a substantial advancement in CD technology,with broad implications for its application in virtual reality,computer graphics,and related fields.

关键词： Collision detection virtual assembly backpropagation neural network real-time interactivity

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LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

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computers, Materials & Continua 2023年第4期75卷 1-17页

作者： Xiaorui Zhang Rui Jiang Wei Sun Aiguo Song Xindong Wei Ruohan Meng Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Instrument Science and Engineering Southeast UniversityNanjing211189China School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

Robust watermarking requires finding invariant features under multiple attacks to ensure correct *** learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread *** existing methods use 3×3 small kernel convolution to extract image features and embed the ***,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the *** address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss *** uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel ***,the modification of the embedded watermarking on the cover image is extended to more *** the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight ***,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image *** experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

关键词： Robust watermarking large kernel convolution adaptive loss weights high-frequency wavelet loss deep learning

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FDSR-INT: A Flexible On-Demand In-Band Telemetry Approach for Aerial Computing Networks

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IEEE Internet of Things Journal 2025年第13期12卷 23257-23274页

作者： Xu, Jinghan Xu, Xiaolong Zhao, Juan Gao, Honghao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing The School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Gachon University College of Future Industry Gyeonggi-Do 461-701 Korea Republic of

In-band network telemetry (INT) is a new network measurement technique that provides real-time, fine-grained packet-level network measurements. However, standard INT lacks the flexibility to perform configurable on-demand network measurements. In this work, we propose a flexible on-demand network measurement mechanism (FDSR-INT) based on a dual bitmap by combining the programmability of segment routing based on IPv6 (SRv6) and the telemetry efficiency of the INT to achieve customizable network measurements. By designing the dual bitmap, the telemetry information of personalized probe nodes is supported, and the telemetry efficiency is improved. SRv6 is employed to direct measurement probe packets, ensuring coverage of a specified set of network targets. Its inherent programmability enables INT to conduct customized per-hop network measurements. The designed flexible INT field structure, which appends traceability information along with telemetry information, reduces the information traceability overhead in the control plane and improves the network compatibility of the mechanism. We solve the optimal probing path by solving a traveler problem in an auxiliary graph and design a greedy pathcutting algorithm to maximize the number of nodes for single packet probing while satisfying the packet length constraints to improve the success rate of the probing task. Finally, we implemented FDSR-INT using P4 and verified its performance experimentally in the constructed *** integrated simulation dynamic environment. FDSR-INT saves 30% of the data plane bandwidth and 54% of the data plane bandwidth on *** interfaces compared with SONM-SR-INT, etc. Furthermore, it has low control plane processing overhead and probe path transmission overhead. © 2014 IEEE.

关键词： Bandwidth

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Core-Competitiveness in Partially Observable Networked Market 24

Core-Competitiveness in Partially Observable Networked Marke...

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33rd ACM Web Conference, WWW 2024

作者： Li, Bin Hao, Dong School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798400701719

In auction theory, a core is a stable outcome where no subgroup of participants can achieve better results for themselves. Core-competitive auctions aim to generate revenue that is achievable in a core. They are particularly important because they not only generate optimized revenue for the seller, but also provide an efficient and stable environment for participants. We generalize the design of core-competitive auctions to encompass partially observable networked markets (PONM). Unlike traditional auctions, which often deal with scenarios of limited trading activity, our approach to core-competitive auctions for PONM captures the nature of real-world transaction markets, which is a large linking world for the economic entities and commodities circulate among the entities in the market. Our generalizing the auction market to PONM can much improve the liquidity of the auction, and is especially meaningful for the web economics. Specifically, we quantify the upper and lower bounds of the minimum core revenue in PONM, and further prove that there does not exist any truthful auction for PONM which is efficient and core-competitive. Governed by this impossible result, we identify the criteria that the allocation rule for PONM should meet. Based on these criteria, we propose a new class of auction mechanisms for PONM that is individually rational, incentive-compatible, and core-competitive. © 2024 ACM.

关键词： Competition

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CBIR: a novel identification approach for college students in need based on consumer behavior psychology theory

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Neural Computing and Applications 2025年第6期37卷 4663-4677页

作者： Liu, Xinze Liu, Shixi Hu, Xiaojing Zhang, Yudong Fang, Xianwen School of Computer and Information Engineering Chuzhou University Chuzhou239000 China School of Computer Science and Engineering Southeast University Nanjing210096 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

The accurate identification of students in need is crucial for governments and colleges to allocate resources more effectively and enhance social equity and educational fairness. Existing approaches to identifying students in need rely on manual operations that include manually extracting consumption behavior information, statistical consumption characteristics and principal component analysis. However, this issue may lead to low prediction accuracy and inefficiency in identifying students in need. We design a three-stage framework to accurately identify college students in need from the perspective of consumer behavior psychology. The consumption behavior information is first obtained from the student consumption records using the consumption behavior clustering approach. The consumption behavior matrix is then built by extracting consumption and spatiotemporal information in different periods. Finally, a novel consumption behavior identification ResNeSt (CBIR) model is proposed to identify college students in need accurately. The experimental results on real datasets show that the CBIR model has higher prediction accuracy than the baseline models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Social psychology

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Visual Prompting Unsupervised Domain Adaptation for Medical Image Segmentation 22

Visual Prompting Unsupervised Domain Adaptation for Medical ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Lu, Ziru Zhang, Yizhe Zhou, Yi Chen, Geng Zhou, Tao School of Computer Science and Engineering Nanjing University of Science and Technology China School of Computer Science and Engineering Southeast University China School of Computer Science and Engineering Northwestern Polytechnical University China

ISBN: (纸本)9798331520526

Unsupervised domain adaptation (UDA), aimed at improving the segmentation performance of deep models on un-labeled data, has attracted considerable attention. Recently, the Segment Anything Model (SAM) has gained widespread attention in various scenarios. In this work, we propose a Visual Prompting UDA (VP-UDA) framework for medical image segmentation, which leverages SAM's ability to improve overall generalization performance. Specifically, we first present a Hybrid Prompting Strategy (HPS) to empower SAM with profound downstream task-specific knowledge. Moreover, a SAM-based Guidance Learning (SGL) scheme is proposed to enhance the learning process of the segmentation model. Then, we propose a Consistency-based Pseudo-label Selection (CPS) strategy to discern and exclude the outlier points in the target feature space. Furthermore, a Frequency Prior-induced Fusion (FPF) module is proposed to effectively integrate the results from SAM and the segmentation model. Experimental results show the effectiveness and superiority of our model over other state-of-the-art UDA methods. © 2025 IEEE.

关键词： Semantic Segmentation

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