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14th IEEE International Conference on Cyber technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Zheng, Yu Liu, Yong Che, Xun School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Xiaolingwei Nanjing210094 China School of Computer Science and Engineering Nanjing University of Science and Technology Xiaolingwei Jiangsu Nanjing210094 China

ISBN: (纸本)9798331506056

These trackers based on the space-time memory network locate the target object in the search image employing contextual information from multiple memory frames and their corresponding foreground-background features. It is conceivable that these trackers are susceptible to the memory frame quality as well as the accuracy of the corresponding foreground labels. In the previous work (STMTrack), a fixed and experienced extraction strategy for memory frames was adopted, which hinders the improvement of performance and generalization. To address these limitations, we propose a dynamic and adaptive extraction strategy for memory frames to ensure that the most representative historical frames are selected into the set of memory frames to improve the accuracy of localization and reduce failures due to error accumulation. Specifically, we propose an extraction network to evaluate historical frames, where historical frames with the highest score are labeled as the memory frame and conversely dropped. Qualitative and quantitative analyses were implemented on multiple datasets (LaSOT and GOT-10K), and our method achieves significant performance gains (LaSOT: 60.6→61.4, GOT-10K: 64.2→65.2) over STMTrack especially for challenging scenarios (e.g., occlusion, vanishing, and reappearance, etc.). while bringing only a negligible inference speed degradation (Fps: 37 → 34), otherwise, the proposed method obtains competitive results compared to other state-of-the-art (SOTA) methods (CNN-based). © 2024 IEEE.

关键词： Space time adaptive processing

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On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

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computer Modeling in engineering & sciences 2024年第5期139卷 1755-1773页

作者： Jinyu Chen Long Shi Qisheng Huang Taotao Wang Daojing He School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing210094China School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen518055China College of Electronics and Information Engineering Shenzhen UniversityShenzhen518060China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault ***,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal *** a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain *** address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional *** paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided ***,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart ***,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing *** particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation ***,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s ***,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

关键词： Blockchain reputation management PoA throughput security decentralization

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Mutual A Priori based Completion for Low Overlapping Point Clouds Registration

Mutual A Priori based Completion for Low Overlapping Point C...

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

作者： Liu, Zhiyong Liu, Yazhou School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781665410205

This work presents a new registration method that especially designed for low-overlapping partial point clouds. Based on the assumption that the partial point clouds to be registered belong to the same target, the proposed Mutual a Priori based Completion (MPC) method uses the partial point clouds to be registered as the a priori for each other to expend each individual point clouds. The main challenge of this mutual reference approach is that the partial clouds without spatial alignment cannot provide reliable completion reference for each. Based on the mutual information maximization, a progressive completion structure is developed to make the alignment in the spatial, feature and completion space respectively. Experiments on public datasets show encouraging results. Especially for the ultra-low overlap cases, compared with the state-of-the-art (SOTA) models, the size of overlapping regions can be increased by about 14.0%, and the rotation and translation error can be reduced by 30.8% and 57.7% respectively. © 2024 IEEE.

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Joint Optimization of Service Caching and Task Scheduling in Energy-Efficient Mobile Edge Computing 4

Joint Optimization of Service Caching and Task Scheduling in...

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4th International Conference on Intelligent technology and Embedded Systems, ICITES 2024

作者： Chen, Bin Huang, Wenxuan Wang, Linhui Zhang, Yi Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798331506292

This paper studies an energy-efficient task scheduling problem that takes into account the cooperation among service caching-enabled mobile edge computing (MEC) servers. We consider a MEC system consisting of multiple sub-networks of MEC nodes and formulate the studied problem as a constrained optimization model that makes decisions on energy-efficient assignment of task requests. To cope with the formulated optimization problem, we propose a metaheuristic approach that is built upon the searching mechanism of the squirrel search algorithm (SSA) but involves improved operators and name the metaheuristic approach ISSA. By using an offloading sequence (i.e., a permutation of all computation tasks) to represent the scheduling solution, ISSA uses a cooperative task assignment heuristic for evaluating the fitness value of each candidate solution. This heuristic is responsible for dispatching each task in the sequence to a certain MEC node within the sub-network or outsourcing the task to the cloud in case of insufficient resources, such that the total energy consumption can be calculated. In addition, when updating the squirrel positions to facilitate the metaheuristic search, we employ a new updating rule to enhance ISSA's capability in exploring the solution space. We perform extensive simulations to demonstrate the advantage of our proposed ISSA over other metaheuristic approaches in reducing the total energy consumption. © 2024 IEEE.

关键词： Constrained optimization

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Cost-Effective Reliability-Constrained Multi-Domain Service Function Chain Orchestration: A Metaheuristic Approach 22

Cost-Effective Reliability-Constrained Multi-Domain Service ...

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

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

ISBN: (纸本)9798331509712

The orchestration of a service function chain (SFC) is responsible for the coordination of multiple virtual network functions (VNFs) in a sequence and the deployment of VNF nodes and links onto physical resources, for the purpose of delivering high-quality network services. This paper studies a multi-domain SFC orchestration problem that aims at minimizing the cost of deploying SFCs across multiple domains while ensuring the overall reliability of SFC requests. We establish a centralized multi-domain framework to formulate the SFC orchestration problem into a combinatorial optimization problem and propose a firefly algorithm (FA)-based metaheuristic approach, named as FA-SFCO, to solve the formulated problem. The proposed FA-SFCO uses a sequence of VNFs in the SFC to represent the orchestration solution, and converts all firefly individuals into valid VNF sequences to facilitate the evolutionary process of FA-SFCO. For each converted VNF sequence, FA-SFCO employs a series of operations, including backup instance allocation, SFC partitioning, and intra-domain mapping, to determine the deployment of all VNFs in the sequence, such that the fitness value of the orchestration in terms of the total deployment cost can be evaluated. Evaluation results on a multi-domain substrate network justify that FA-SFCO outperforms competing algorithms with a significant reduction in deployment cost while meeting the reliability requirement. © 2024 IEEE.

关键词： Combinatorial optimization

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Pairwise tagging framework for end-to-end emotion-cause pair extraction

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Frontiers of computer science 2023年第2期17卷 111-120页

作者： Zhen WU Xinyu DAI Rui XIA National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210023China

Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a *** generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and *** works adopt pipelined approaches or multi-task learning to address the ECPE ***,the pipelined approaches easily suffer from error propagation in real-world *** multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction *** address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging *** prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging *** this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause *** validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark *** experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.

关键词： emotion-cause pair extraction pairwise tagging framework end-to-end neural network

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Self-corrected unsupervised domain adaptation

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Frontiers of computer science 2022年第5期16卷 35-43页

作者： Yunyun WANG Chao WANG Hui XUE Songcan CHEN School of Computer Science and Engineering Nanjing University of Posts&TelecommunicationsNanjing 210046China School of Computer Science and Engineering Southeast UniversityNanjing 210096China School of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210023China

Unsupervised domain adaptation(UDA),which aims to use knowledge from a label-rich source domain to help learn unlabeled target domain,has recently attracted much *** methods mainly concentrate on source classification and distribution alignment between domains to expect the correct target *** in this paper,we attempt to learn the target prediction end to end directly,and develop a Self-corrected unsupervised domain adaptation(SCUDA)method with probabilistic label *** adopts a probabilistic label corrector to learn and correct the target labels ***,besides model parameters,those target pseudo-labels are also updated in learning and corrected by the anchor-variable,which preserves the class candidates for *** on real datasets show the competitiveness of SCUDA.

关键词： unsupervised domain adaptation adversarial Learning deep neural network pseudo-labels label corrector

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A Learning-Based Stochastic Game for Energy Efficient Optimization of UAV Trajectory and Task Offloading in Space/Aerial Edge Computing

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9717-9733页

作者： Li, Jialiuyuan Shi, You Dai, Chen Yi, Changyan Yang, Yuxiao Zhai, Xiangping Zhu, Kun Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing211106 China

In this paper, we study the energy-efficient unmanned aerial vehicle (UAV) and low earth orbital (LEO) satellite assisted mobile edge computing (MEC) in space-air-ground integrated networks (SAGINs). The key challenge involves how to efficiently execute tasks offloaded from IoT devices, given limited on-board computation capabilities of UAVs and delay constraints of tasks. To address it, UAVs are first allowed to adjust their trajectories to accommodate as many tasks as possible from IoT devices. Subsequently, UAVs compute a portion of these tasks while delegating the rest to LEO satellites for further execution. Accordingly, we formulate a joint optimization problem including UAV trajectory planning, task offloading, and bandwidth allocations, aiming to maximize the long-term energy efficiency of UAVs and LEO satellites (i.e., the total data size in computation offloading divided by energy consumption of UAVs and LEO satellites). Considering the interactions among intelligent UAVs, this problem is reformulated as a set of interconnected multi-agent stochastic games, and the existence of corresponding Nash Equilibrium (NE) is theoretically proved. To obtain the NE, a multi-agent reinforcement learning-based algorithm, namely UTPTR, is developed concerning system dynamics and uncertainties. Simulations evaluate the performance of the UTPTR and demonstrate its superiority compared to existing approaches. © 1967-2012 IEEE.

关键词： Stochastic systems

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Research on Low Earth Orbit Satellite Orbit Prediction Based on a Two-stage Integrated Model 24

Research on Low Earth Orbit Satellite Orbit Prediction Based...

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3rd International Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Yuan, Shuai Zhang, Kun School of Cyberspace Security Nanjing University of Science and Technology Jiangsu Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710834

With the number of low Earth orbit satellites and the expansion of application demands, satellite trajectory prediction is crucial for efficiently operating satellite systems. Traditional trajectory prediction methods rely on classical physical models such as Newtonian mechanics and celestial mechanics. Still, these methods have insufficient accuracy and real-time performance when facing low-orbit satellites’ complex in-orbit environments. Compared with traditional physical models, neural networks can better capture complex changes in trajectories and compensate for the shortcomings of traditional methods. This article analyzes the effects of atmospheric drag, solar radiation pressure, and other factors on the orbital dynamics of low Earth orbit satellites, proposes a trajectory prediction model based on deep neural networks, and uses Long Short-term Memory networks (LSTM) to improve prediction accuracy. To further improve prediction accuracy, this study adopted an ensemble learning strategy, integrating LSTM, GRU, and Transformer models to construct a two-stage ensemble model. The experimental results show that the fusion model significantly surpasses the single model in both short-term and long-term prediction tasks. In short-term forecasting, the combination of LSTM and GRU can efficiently capture rapid changes in trajectory data. In long-term forecasting, Transformer significantly reduces prediction errors through its powerful modeling capabilities. The two-stage integrated model performs excellently in satellite trajectory prediction, with RMSE and MAE reduced by 21.8% and 14.3%, respectively in short-term prediction compared to the LSTM model, and RMSE and MAE reduced by 4.5% and 10%, respectively in long-term prediction. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Enabling Secure Data Aggregation Scheme for Smart Grids based on Local Differential Privacy 24

Enabling Secure Data Aggregation Scheme for Smart Grids base...

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2nd International Conference on Electronics, computers and Communication technology, CECCT 2024

作者： He, Yixuan Mei, Lin Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology Jiangsu Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710193

Existing data aggregation schemes for privacy protection in smart grids face limitations in efficiency and application, often relying on trusted third parties or incurring significant overhead. To address these issues, this paper introduces a multidimensional and multisubset data aggregation scheme leveraging local differential privacy, aimed at enabling fine-grained data analysis while safeguarding privacy. The scheme effectively protects user privacy by locally perturbing data from randomly selected dimensions while utilizing the Chinese Remainder Theorem to compress multidimensional data, thereby optimizing the transmission. Theoretical and experimental results demonstrate that our scheme offers strong privacy, flexibility, and utility, achieving a relative error of aggregation results no more than 0.05 under optimal conditions. Additionally, it outperforms existing approaches in terms of both computational and communication overhead. © 2024 Copyright held by the owner/author(s).

关键词： Differential privacy

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