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3rd International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Chen, Jianyong Liang, Lingyu Xiao, Wocheng South China University of Technology Guangzhou China Guangzhou China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9781510671485

Arbitrary-shaped text detection is a challenging task where a text instance representation should be well designed to cover diverse text geometry variances. Most of current methods represent text instances as image mask or contour point sequences, but they might lead to expensive post-processing or restricted capability of shape modeling. Most recently, Zhu et al. proposed Fourier Contour Embedding (FCE) method to represent arbitrary shaped text contours in the Fourier domain, and constructed FCENet to obtain state-of-the-art (SOTA) performance. However, even the FCE signature and FCENet may suffer significant detection performance degradation on slender-shaped text instances like most other text detectors because of the mismatch between the square-shaped receptive fields and the large aspect ratio of arbitrary-shaped slender text}. To tackle this problem, we designed Structured Fourier Contour Embedding (Structured-FCE) to encode both instance-level features and local geometric features of the boundaries. Then, we constructed Structured-FCENet to predict and merge Structured-FCE signatures to construct the boundaries of arbitrary-shaped slender texts. Qualitative and quantitative results illustrate that Structured-FCENet obtain SOTA performance on CTW1500, Total-Text and DAST1500 benchmark datasets, especially for the challenging slender text instances of various shape. © 2023 SPIE.

关键词： computer vision

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Resource Cooperative Scheduling Optimization Considering Security in Edge Mobile networks 19th

Resource Cooperative Scheduling Optimization Considering Sec...

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19th EAI International Conference on Collaborative Computing: networking, Applications and Worksharing, CollaborateCom 2023

作者： Fang, Cheng Yang, Peng Yi, Meng Du, Miao Li, Bing Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9783031545207

With the rapid development of technologies such as the Internet of Things and artificial intelligence, the contradiction between limited user computing resources and real-time, fast, and safe processing of large amounts of data has become an urgent issue. The emergence of edge computing provides IoT applications with a low-latency, high-bandwidth, and high-performance computing service. Due to the complexity and dynamics of the edge computing environment itself, and the limited resources of the terminal, the security issue of resource collaborative scheduling in the edge mobile network has become an important research topic. Different from existing work, this paper proposes an efficient and secure multi-user resource cooperative scheduling model, which comprehensively considers resource allocation, task offloading, QoE requirements, and data security. In the model, ChaCha20 encryption technology is introduced as a security mechanism to prevent data from being maliciously stolen by attackers during the offloading process, and computing speed is used as an indicator to quantify QoE requirements. A resource collaborative scheduling algorithm that integrates security mechanisms and computing acceleration is also proposed to minimize the total cost of optimizing the edge computing system. Finally, the effectiveness and superiority of the model and algorithm are verified by simulation experiments. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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Opinions Are Not Always Positive: Debiasing Opinion Summarization With Model-Specific and Model-Agnostic Methods 30

Opinions Are Not Always Positive: Debiasing Opinion Summariz...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Yanyue Lai, Yilong Wang, Zhenglin Li, Pengfei Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9782493814104

As in the existing opinion summary data set, more than 70% are positive texts, the current opinion summarization approaches are reluctant to generate the negative opinion summary given the input of negative opinions. To address such sentiment bias, two approaches are proposed through two perspectives: model-specific and model-agnostic. For the model-specific approach, a variational autoencoder is proposed to disentangle the input representation into sentiment-relevant and sentiment-irrelevant components through adversarial loss. Therefore, the sentiment information in the input is kept and employed for the following decoding which avoids interference of content information with emotional signals. To further avoid relying on some specific opinion summarization frameworks, a model-agnostic approach based on counterfactual data augmentation is proposed. A dataset with a more balanced emotional polarity distribution is constructed using a large pre-trained language model based on some pairwise and mini-edited principles. Experimental results show that the sentiment consistency of the generated summaries is significantly improved using the proposed approaches, while their semantics quality is unaffected. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Topgformer: Topological-Based Graph Transformer for Mapping Brain Structural Connectivity to Functional Connectivity 48

Topgformer: Topological-Based Graph Transformer for Mapping ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Guo, Dalu Zhang, Ke Li, Jiaxing Kong, Youyong Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Nanjing China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

ISBN: (纸本)9781728163277

Exploring the mapping between structural connectivity (SC) and functional connectivity (FC) is of essential importance to understanding the working mechanism of the human brain. Traditional methods are difficult to represent the complex relationship of high-order interaction between SC and FC. Recent learning-based methods can not well capture the important long-range interactions and edge information of brain connectivity. To address the issue, we propose a novel Topological-based Graph Transformer (TopGFormer) to generate functional connectivity from the structure connectivity with sufficient consideration of topological properties of brain connectivity. We propose a Topological Multi-Head Attention block that simultaneously uses centrality encoding and adjacency matrix to capture node and edge importance respectively. The centrality encoding reflects the importance of brain regions while the adjacency matrix captures the linking relationship of different regions. Experiments on HCP resting and emotion task datasets demonstrate the performance of the proposed method. © 2023 IEEE.

关键词： functional connectivity structural connectivity Structure-function mapping transformer

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Topology Uncertainty Modeling For Imbalanced Node Classification on Graphs 48

Topology Uncertainty Modeling For Imbalanced Node Classifica...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gao, Jiayi Li, Jiaxing Zhang, Ke Kong, Youyong Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9781728163277

Most existing graph neural networks work under a class-balanced assumption, while ignoring class-imbalanced scenarios that widely exist in real-world graphs. Although there are many methods in other fields that can alleviate this issue, they do not consider the special topology of the non-Euclidean graph. Hence, we propose Graph Topology Uncertainty (GraphTU), a novel probabilistic class-imbalanced solution specifically for graphs. Firstly, an invisible "uncertain gap"between under-represented minorities in training set and authentic minorities in unseen set is modeled by estimating statistical variances in topology. We extend the training distribution for minorities by sampling in this gap through a non-parametric way. Moreover, a gradient-guided mask is introduced to prevent biased statistics. Extensive experiments demonstrate the superior performance of GraphTU. © 2023 IEEE.

关键词： Class Imbalance Node Classification

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Multi-class Bitcoin mixing service identification based on graph classification

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Digital Communications and networks 2024年第6期10卷 1881-1893页

作者： Xiaoyan Hu Meiqun Gui Guang Cheng Ruidong Li Hua Wu School of Cyber Science&Engineering Southeast UniversityNanjing 211189China Purple Mountain Laboratories for Network and Communication Security Nanjing 211111China Jiangsu Provincial Engineering Research Center of Security for Ubiquitous Network Nanjing 211189China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina Research Base of International Cyberspace Governance Southeast UniversityNanjing 211189China Institute of Science and Engineering Kanazawa UniversityKakumaKanazawa920-1192Japan

Due to its anonymity and decentralization,Bitcoin has long been a haven for various illegal *** generally legalize illicit funds by Bitcoin mixing ***,it is critical to investigate the mixing services in cryptocurrency anti-money *** studies treat different mixing services as a class of suspicious Bitcoin ***,they are limited by relying on expert experience or needing to deal with large-scale *** far,multi-class mixing service identification has not been explored *** is challenging since mixing services share a similar procedure,presenting no sharp ***,mixing service identification facilitates the healthy development of Bitcoin,supports financial forensics for cryptocurrency regulation and legislation,and provides technical means for fine-grained blockchain *** paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification(BMSI-GC)***,BMSI-GC constructs 2-hop ego networks(2-egonets)of mixing services based on their historical ***,it applies graph2vec,a graph classification model mainly used to calculate the similarity between graphs,to automatically extract address features from the constructed ***,it trains a multilayer perceptron classifier to perform classification based on the extracted ***-GC is flexible without handling the full-size network and handcrafting address ***,the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for *** experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification,achieving an average identification F1-score of 95.08%.

关键词： Bitcoin Anti-money laundering Mixing service Ego network Graph classification

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keyphrase generation algorithm based on co-occurrence graph

Keyphrase generation algorithm based on co-occurrence graph

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2022 International Conference on Applied Mathematics and Computional Mechanics, AMCM 2022

作者： Fang, Haisheng Yang, Peng School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

In the keyphrase prediction task, it is usually difficult for a single corpus information to help the model learn enough core information, so this paper improves the current keyphrase generation method based on the corpus. From the developments of the co-occurrence, the use of the word conformity is mentioned in the original selection of the relevant text, and the purpose of achieving the maximum utilization of the spending information is achieved. At the same time, this paper proposes the CG-Net model, which uses the GAT network to learn co-present diagrams and implements a replication mechanism, which ultimately surpasses the current optimal method on the NUS test set. © Published under licence by IOP Publishing Ltd.

关键词： Co-Occurrence Relationship GAT keyphrase Generation

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Qubit-Based Distributed Frame Synchronization for Quantum key Distribution

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Journal of Lightwave Technology 2024年第11期43卷 5032-5039页

作者： Chen, Ye Huang, Chunfeng Lin, Guangshen Huang, Shuyi Zhang, Zhenrong Wei, Kejin Guangxi University Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Nanning530004 China

Quantum key distribution (QKD) is a method that enables two remote parties to share a secure key string. Clock synchronization between two parties is a crucial step in the normal operation of QKD. Qubit-based synchronization can achieve clock synchronization by transmitting quantum states between two remote parties, eliminating the necessity for hardware synchronization and thereby greatly reducing the hardware requirements of a QKD system. Nonetheless, classical qubit-based synchronization exhibits poor performance in continuous and high-loss systems, hindering its wide applicability in various scenarios. Here, we propose a qubit-based distributed frame synchronization method that can achieve time recovery in a continuously running system and resist higher losses. Experimental results show that the proposed method outperforms the advanced qubit-based synchronization method Qubit4Sync in a continuously running system. Particularly, the results demonstrate that our method surpasses all previous works in key parameters, including frequency and the synchronization length. We believe our method is applicable to a broad range of QKD scenarios, including drone-based QKD and quantum network construction. © 1983-2012 IEEE.

关键词： Qubits

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Generalization Analysis for Label-Specific Representation Learning 38

Generalization Analysis for Label-Specific Representation Le...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Zhang, Yi-Fan Zhang, Min-Ling School of Cyber Science and Engineering Southeast University Nanjing210096 China School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Label-specific representation learning (LSRL), i.e., constructing the representation with specific discriminative properties for each class label, is an effective strategy to improve the performance of multi-label learning. However, the generalization analysis of LSRL is still in its infancy. The existing theory bounds for multilabel learning, which preserve the coupling among different components, are invalid for LSRL. In an attempt to overcome this challenge and make up for the gap in the generalization theory of LSRL, we develop a novel vector-contraction inequality and derive the generalization bound for general function class of LSRL with a weaker dependency on the number of labels than the state of the art. In addition, we derive generalization bounds for typical LSRL methods, and these theoretical results reveal the impact of different label-specific representations on generalization analysis. The mild bounds without strong assumptions explain the good generalization ability of LSRL. © 2024 Neural information processing systems foundation. All rights reserved.

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Improved sparrow search algorithm for RFID network planning

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The Journal of China Universities of Posts and Telecommunications 2023年第1期30卷 93-102页

作者： Zhang Jiangbo Zheng Jiali Quan Yixuan Lin Zihan Xie Xiaode School of Computer Electrics and InformationGuangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China

To solve the problem that the performance of the coverage,interference rate,load balance andweak power in the radio frequency identification(RFID)network *** paper proposes an elite opposition-based learning and Lévy flight sparrow search algorithm(SSA),which is named elite opposition-based learning and Levy flight SSA(ELSSA).First,the algorithm initializes the population by an elite opposed-based learning strategy to enhance the diversity of the ***,Lévy flight is introduced into the scrounger’s position update formula to solve the situation that the algorithm falls into the local optimal *** has a probability that the current position is changed by Lévy *** method can jump out of the local optimal *** the end,the proposed method is compared with particle swarm optimization(PSO)algorithm,grey wolf optimzer(GWO)algorithm and SSA in the multiple simulation *** simulated results showed that,under the same number of readers,the average fitness of the ELSSA is improved respectively by 3.36%,5.67%and 18.45%.By setting the different number of readers,ELSSA uses fewer readers than other *** conclusion shows that the proposed method can ensure a satisfying coverage by using fewer readers and achieving higher comprehensive performance.

关键词： radio frequency identification network planning sparrow search algorithm elite opposition-based learning Levy flight

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