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arXiv 2025年

作者： Cao, Biwei Wu, Qihang Cao, Jiuxin Liu, Bo Gui, Jie School of Cyber Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Ministry of Education of China Nanjing211189 China Purple Mountain Laboratories Nanjing210000 China School of Computer Science and Engineering Southeast University Nanjing211189 China Engineering Research Center of Blockchain Application Supervision And Management Southeast University Ministry of Education China

With the rapid development of the Internet, the information dissemination paradigm has changed and the efficiency has been improved greatly. While this also brings the quick spread of fake news and leads to negative impacts on cyberspace. Currently, the information presentation formats have evolved gradually, with the news formats shifting from texts to multimodal contents. As a result, detecting multimodal fake news has become one of the research hotspots. However, multimodal fake news detection research field still faces two main challenges: the inability to fully and effectively utilize multimodal information for detection, and the low credibility or static nature of the introduced external information, which limits dynamic updates. To bridge the gaps, we propose ERIC-FND, an external reliable information-enhanced multimodal contrastive learning framework for fake news detection. ERIC-FND strengthens the representation of news contents by entity-enriched external information enhancement method. It also enriches the multimodal news information via multimodal semantic interaction method where the multimodal constrative learning is employed to make different modality representations learn from each other. Moreover, an adaptive fusion method is taken to integrate the news representations from different dimensions for the eventual classification. Experiments are done on two commonly used datasets in different languages, X (Twitter) and Weibo. Experiment results demonstrate that our proposed model ERIC-FND outperforms existing state-of-the-art fake news detection methods under the same settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Semi-Supervised Deep network Embedding Approach Based on the Neighborhood Structure

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Big Data Mining and Analytics 2019年第3期2卷 205-216页

作者： Wenmao Wu Zhizhou Yu Jieyue He School of Computer Science and Engineering Southeast UniversityNanjing 211100China MOE Key Laboratory of Computer Network and Information Integration Southeast UniversityNanjing 211100China

network embedding is a very important task to represent the high-dimensional network in a lowdimensional vector space,which aims to capture and preserve the network *** existing network embedding methods are based on shallow ***,actual network structures are complicated which means shallow models cannot obtain the high-dimensional nonlinear features of the network *** recently proposed unsupervised deep learning models ignore the labels *** address these challenges,in this paper,we propose an effective network embedding method of Structural Labeled Locally Deep Nonlinear Embedding(SLLDNE).SLLDNE is designed to obtain highly nonlinear features through utilizing deep neural network while preserving the label information of the nodes by using a semi-supervised classifier component to improve the ability of ***,we exploit linear reconstruction of neighborhood nodes to enable the model to get more structural *** experimental results of vertex classification on two real-world network datasets demonstrate that SLLDNE outperforms the other state-of-the-art methods.

关键词： network embedding deep learning network analysis

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Partial Offloading Strategy Based on Deep Reinforcement Learning in the Internet of Vehicles

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IEEE Transactions on Mobile Computing 2025年

作者： Tian, Shujuan Zhu, Xinjie Feng, Bochao Zheng, Zhirun Liu, Haolin Li, Zhetao Xiangtan University School of Computer Science School of Cyberspace Security Xiangtan411105 China Xiangtan University Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan411105 China Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan411105 China Xiangtan University School of Mathematics and Compulational Science Xiangtan China Jinan University College of Information Science and Technology Guangzhou510632 China

Driven by the increasing demands of vehicular tasks, edge offloading has emerged as a promising paradigm to enhance quality of experience (QoE) in Internet of Vehicles (IoV) networks. This approach enables vehicles to offload computation-intensive tasks to edge servers, resulting in reduced computation delays and lower energy consumption. However, traditional binary offloading limits the efficiency of edge offloading. To address this gap, we propose a partial offloading strategy that jointly optimizes the offloading ratio, computation, and communication resources in IoV. Recognizing the varying priorities of vehicular tasks regarding task delay and energy consumption, we formulate two distinct scenarios: one focused on minimizing delay and the other on minimizing energy consumption. Furthermore, we employ a reinforcement learning approach to establish a multi-dimensional joint optimization function by setting different objectives for each scenario. Based on this framework, we introduce a multi-state iteration deep deterministic policy gradient algorithm (SIDDPG), which effectively determines task partitioning and resource allocation. Simulation results demonstrate that the proposed algorithm outperforms benchmark schemes in terms of task delay and energy consumption. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Packet-level adaptive sampling on multi-fluctuation scale traffic

Packet-level adaptive sampling on multi-fluctuation scale tr...

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2005 International Conference on Communications, Circuits and Systems

作者： Xu, Li-Bo Wu, Guo-Xin Li, Jian-Fei Key Laboratory of Computer Network and Information Integration Southeast University Nanjing 210096 China

ISBN: (纸本)0780390156

In high-speed Internet links, effective and efficient packet sampling technology in traffic measurement becomes more and more indispensable for its benefits of reducing data quantity and saving various resources. We address the Issue of how to keep or improve sampling accuracy during burst or fluctuation periods. In the paper, an adaptive packet-level sampling method on different traffic fluctuation and burst scales is proposed. The method can dynamically adjust each packet sampling probability dependent on the magnitude of traffic fluctuation. The adjustment granularity is fine because it reaches single packet. By contrast to static random sampling method in experiments using real traffic trace, we show that the proposed adaptive sampling has great capability to trace traffic fluctuations and Improve sampling accuracy through capturing more fluctuation or burst details. © 2005 IEEE.

关键词： Telecommunication traffic

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PBNA: An Improved Probabilistic Biological network Alignment Method

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Tsinghua Science and Technology 2014年第6期19卷 658-667页

作者： Muwei Zhao Wei Zhong Jieyue He School of Computer Science and Engineering and also with MOE Key Laboratory of Computer Network and Information IntegrationSoutheast University Division of Math and Computer Science University of South Carolina Upstate Spartanburg SC 29303USA

Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment ***, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.

关键词： probabilistic biological network network alignment protein interaction network

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Wireless sensor network-enabled intravenous infusion monitoring

Wireless sensor network-enabled intravenous infusion monitor...

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作者： Zhang, Y. Zhang, S.F. Ji, Y. Wu, G.X. Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Considering that intravenous in blood infusion, usually applied without a proper control of the progress and velocity under unreliable manual monitoring practice, is the most popular and frequent clinical activity throughout the world, which can potentially endanger a patient's life, leads to a huge burden as patients, relatives and nursing staff can cause serious cases of medical negligence. In order to resolve this problem and provide a networkable solution the authors propose a novel radio frequency identification-based wireless sensor networkable intravenous infusion monitoring system. The proposed system is a wireless sensor system based on taking a fork-type light barrier as a sensor, a micro-control unit (MCU) as a ZigBee-based radio frequency device. The system's performance is evaluated through some laboratory experiments using tuning algorithm to demonstrate that the results show the required accuracy of the system to meet practical needs. © 2011 The Institution of Engineering and Technology.

关键词： Zigbee

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Efficient Asynchronous Uplink Sensing in Perceptive Mobile networks via Accurate Delay Estimation

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Li, Hang Teng, Fuxian Guo, Qinghua Zhang, J. Andrew Huang, Xiaojing Cheng, Zhiqun Hangzhou Dianzi University School of Electronics and Information Hangzhou310018 China Academy of Network and Communications of CETC National Key Laboratory of Advanced Communication Networks Hebei Shijiazhuang050081 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia University of Technology Sydney Global Big Data Technologies Centre UltimoNSW2007 Australia

In perceptive mobile networks (PMNs) with integrated sensing and communication (ISAC), sensing ambiguity in ranging and velocity measurements can be caused by timing offsets (TOs) and carrier frequency offsets (CFOs) between asynchronous sensing transmitter and receiver. In this paper, we propose an efficient method for uplink sensing with asynchronous transceivers, via accurately estimating path delays including TOs. Firstly, we develop a sparse signal recovery model to decouple multiple sensing parameters, i.e., delay, angle of arrival (AoA) and Doppler for multipath, facilitating efficient and low-complexity sensing parameter estimation. Then, leveraging unitary approximate message passing (UAMP) and sparse Bayesian learning (SBL), we obtain initial delay and signal estimates, which are then refined through Gauss-Seidel iteration. Finally, we conduct estimation and association of the remaining sensing parameters by coherently processing cross-correlation of received signals across the spatial and temporal domains. This effectively removes the sensing ambiguity incurred by time-varying TOs and CFOs, enabling accurate estimation of range and velocity for multiple targets. Simulation results verify the effectiveness of the proposed method, demonstrating its superior sensing performance, compared to existing methods such as cross-antenna cross correlation (CACC). © 2015 IEEE.

关键词： Frequency allocation

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A structured P2P system with match path and probability balance tree

A structured P2P system with match path and probability bala...

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First International Multi- Symposiums on computer and Computational Sciences, IMSCCS'06

作者： Xu, L.B. Wu, G.X. You, F.Q. Key Laboratory of Computer Network and Information Integration Southeast University Nanjing 210096 China

ISBN: (纸本)0769525814

Excellent P2P network infrastructure should support scalability and efficient search simultaneity. The intelligent routing mechanisms have flexible query expression but hold low scalability and recall rate. Contrastively, DHTs are scalable but either difficult to organize or too expensive for maintenance. A new structured P2P model is proposed in which resource storages are organized by match path and random access method. When an approximately balance distributed character is obtained, nodes will keep high search efficiency and low maintenance cost. The system supports various query models, has load balancing capability and high scalability, and ensures search range of each node can cover entire network. The system runs without any center service while all nodes simultaneously take index storage, data storage and routing task to drive network by only maintaining a little local information. © 2006 IEEE.

关键词： Distributed computer systems

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Label distribution learning for scene text detection

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Frontiers of computer Science 2023年第6期17卷 5-12页

作者： Haoyu MA Ningning LU Junjun MEI Tao GUAN Yu ZHANG Xin GENG School of Computer Science and Engineering and the Key Lab of Computer Network and Information Integration(Ministry of Education)Southeast UniversityNanjing 211189China ZTE Corporation Nanjing 210012China

Recently,segmentation-based scene text detection has drawn a wide research interest due to its flexibility in describing scene text instance of arbitrary shapes such as curved ***,existing methods usually need complex post-processing stages to process ambiguous labels,i.e.,the labels of the pixels near the text boundary,which may belong to the text or *** this paper,we present a framework for segmentation-based scene text detection by learning from ambiguous *** use the label distribution learning method to process the label ambiguity of text annotation,which achieves a good performance without using additional post-processing *** on benchmark datasets demonstrate that our method produces better results than state-of-the-art methods for segmentation-based scene text detection.

关键词： scene text detection multi-task learning label distribution learning

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Design and Analysis of a Multiscale Active Queue Management Scheme

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Journal of computer Science & Technology 2006年第6期21卷 1022-1030页

作者：纪其进董永强 School of Computer Science and Engineering Southeast University Nanjing 210096 P.R. China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing 210096 P.R China

Since Internet is dominated by TCP-based applications, active queue management （AQM） is considered as an effective way for congestion control. However, most AQM schemes suffer obvious performance degradation with dynamic traffic. Extensive measurements found that Internet traffic is extremely bursty and possibly self-similar. We propose in this paper a new AQM scheme called multiscale controller （MSC） based on the understanding of traffic burstiness in multiple time scale. Different from most of other AQM schemes, MSC combines rate-based and queue-based control in two time scales. While the rate-based dropping on burst level （large time scales） determines the packet drop aggressiveness and is responsible for low and stable queuing delay, good robustness and responsiveness, the queue-based modulation of the packet drop probability on packet level （small time scales） will bring low loss and high throughput. Stability analysis is performed based on a fluid-flow model of the TCP/MSC congestion control system and simulation results show that MSC outperforms many of the current AQM schemes.

关键词： active queue management multiscale traffic burstiness fluid-flow model stability analysis

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