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arXiv 2023年

作者： Kou, Zhiqiang Xie, Yucheng Wang, Jing Jia, Yuheng Shi, Boyu Geng, Xin MOE Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering Southeast University Nanjing China

This paper introduces RankMatch, an innovative approach for Semi-Supervised Label Distribution Learning (SSLDL). Addressing the challenge of limited labeled data, RankMatch effectively utilizes a small number of labeled examples in conjunction with a larger quantity of unlabeled data, reducing the need for extensive manual labeling in Deep Neural network (DNN) applications. Specifically, RankMatch introduces an ensemble learning-inspired averaging strategy that creates a pseudo-label distribution from multiple weakly augmented images. This not only stabilizes predictions but also enhances the model’s robustness. Beyond this, RankMatch integrates a pairwise relevance ranking (PRR) loss, capturing the complex inter-label correlations and ensuring that the predicted label distributions align with the ground truth. We establish a theoretical generalization bound for RankMatch, and through extensive experiments, demonstrate its superiority in performance against existing SSLDL methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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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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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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RoKEPG: RoBERTa and Knowledge Enhancement for Prescription Generation of Traditional Chinese Medicine

RoKEPG: RoBERTa and Knowledge Enhancement for Prescription G...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Pu, Hua Mi, Jiacong Lu, Shan He, Jieyue Southeast University School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Moe Jiangsu Nanjing210018 China Nanjing Fenghuo Tiandi Communication Technology Co. Ltd Jiangsu Nanjing China

ISBN: (纸本)9798350337488

Traditional Chinese medicine (TCM) prescription is the most critical form of TCM treatment, and uncovering the complex nonlinear relationship between symptoms and TCM is of great significance for clinical practice and assisting physicians in diagnosis and treatment. Although there have been some studies on TCM prescription generation, these studies consider a single factor and directly model the symptom-prescription generation problem mainly based on symptom descriptions, lacking guidance from TCM knowledge. To this end, we propose a RoBERTa and Knowledge Enhancement model for Prescription Generation of Traditional Chinese Medicine (RoKEPG). RoKEPG is firstly pre-trained by our constructed TCM corpus, followed by fine-tuning the pre-trained model, and the model is guided to generate TCM prescriptions by introducing four classes of knowledge of TCM through the attention mask matrix. Experimental results on the publicly available TCM prescription dataset show that RoKEPG improves the F1 metric by about 2% over the baseline model with the best results. © 2023 IEEE.

关键词： Deep learning Pre-trained language models Traditional Chinese medicine prescription generation

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Code Generation From Flowcharts with Texts: A Benchmark Dataset and An Approach

Code Generation From Flowcharts with Texts: A Benchmark Data...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Liu, Zejie Hu, Xiaoyu Zhou, Deyu Li, Lin Zhang, Xu Xiang, Yanzheng Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Nanjing Origin Information Technology Company China

Currently, researchers focus on generating codes from the requirement documents. However, current approaches still perform poorly on some requirements needing complex problem-solving skills. In reality, to tackle such complex requirements, instead of directly translating requirement documents into codes, software engineers write codes via unified modeling language diagrams, such as flowcharts, an intermediate tool to analyze and visualize the system. Therefore, we propose a new source code generation task, that is, to generate source code from flowcharts with texts. We manually construct a benchmark dataset containing 320 flowcharts with their corresponding source codes. Obviously, it is not straightforward to employ the current approaches for the new source code generation task since (1) the flowchart is a graph that contains various structures, including loop, selection, and others which is different from texts;(2) the connections between nodes in the flowchart are abundant and diverse which need to be carefully handled. To solve the above problems, we propose a two-stage code generation model. In the first stage, a structure recognition model is employed to transform the flowchart into pseudo-code containing the structural conventions of a typical programming language such as while, if. In the second stage, a code generation model is employed to convert the pseudo-code into code. Experimental results show that the proposed approach can achieve some improvement over the baselines. © 2022 Association for Computational Linguistics.

关键词： Flowcharting

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Universal and General Quantum Simultaneous Secret Distribution with Dense Coding by Using One-Dimensional High-Level Cluster States

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Journal of computer Science & Technology 2021年第1期36卷 221-230页

作者： Zhi-Hao Liu Han-Wu Chen School of Computer Science and Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Nanjing 211189China

A universal and general quantum simultaneous secret distribution(QSSD)protocol is put forward based on the properties of the one-dimensional high-level cluster states,in which one sender dispatches different high-level classical secret messages to many users at the same *** to the idea of quantum dense coding,the sender can send different two-dit classical messages(two d-level classical numbers)to different receivers simultaneously by using a one-dimensional d-level cluster state,which means that the information capacity is up to the *** estimate the security of quantum channels,a new eavesdropping check strategy is put ***,a new attack model,the general individual attack is proposed and *** is shown that the new eavesdropping check strategy can effectively prevent the traditional attacks including the general individual *** addition,multiparty quantum secret report(MQSR,the same as quantum simultaneous secret submission(QSSS))in which different users submit their different messages to one user simultaneously can be gotten if the QSSD protocol is changed a little.

关键词： quantum simultaneous secret distribution(QSSD) multiparty quantum secret report(MQSR) quantum simultaneous secret submission(QSSS) quantum secret sharing quantum broadcast communication

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Persuasion Should be Double-Blind: A Multi-Domain Dialogue Dataset With Faithfulness Based on Causal Theory of Mind

arXiv

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arXiv 2025年

作者： Zhang, Dingyi Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Persuasive dialogue plays a pivotal role in human communication, influencing various domains. Recent persuasive dialogue datasets often fail to align with real-world interpersonal interactions, leading to unfaithful representations. For instance, unrealistic scenarios may arise, such as when the persuadee explicitly instructs the persuader on which persuasion strategies to employ, with each of the persuadee’s questions corresponding to a specific strategy for the persuader to follow. This issue can be attributed to a violation of the "Double Blind" condition, where critical information is fully shared between participants. In actual human interactions, however, key information—such as the mental state of the persuadee and the persuasion strategies of the persuader—is not directly accessible. The persuader must infer the persuadee’s mental state using Theory of Mind capabilities and construct arguments that align with the persuadee’s motivations. To address this gap, we introduce ToMMA, a novel multi-agent framework for dialogue generation that is guided by causal Theory of Mind. This framework ensures that information remains undisclosed between agents, preserving "double-blind" conditions, while causal ToM directs the persuader’s reasoning, enhancing alignment with human-like persuasion dynamics. Consequently, we present CToMPersu, a multi-domain, multi-turn persuasive dialogue dataset that tackles both double-blind and logical coherence issues, demonstrating superior performance across multiple metrics and achieving better alignment with real human dialogues. Our dataset and prompts are available at https://***/DingyiZhang/ToMMA-CToMPersu. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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Decomposition-Based Classifier Chains for Multi-Dimensional Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 176-191页

作者： Jia, Bin-Bin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou730050 China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing210096 China

In multi-dimensional classification, the semantics of objects are characterized by multiple class variables from different dimensions. To model the dependencies among class variables, one natural strategy is to build a number of multiclass classifiers in a chaining structure, one per dimension, where the subsequent classifiers on the chain augment the feature space with all labeling information used by the preceding classifiers. However, it is shown that this strategy cannot compete with existing state-of-the-art approaches via comparative studies. One possible reason is that inaccurate predictions of preceding classifiers would degenerate the performance of subsequent ones. Besides, it is more difficult to learn a multiclass classifier than a binary one with the same accuracy, and better performance can be expected if the multi-dimensional classification problem can be solved by building multiple binary classifiers in a chaining structure. Based on these conjectures, this article proposes an approach, which builds a chain of binary classifiers to solve the multi-dimensional classification problem with the help of one-versus-one decomposition. To address the issue that different one-versus-one decomposed problems involve different training examples, the feature space is augmented with the binary predictions of preceding classifiers on the chain to train the subsequent ones. To alleviate the effect of the specified chaining order, the ensemble version of the proposed approach is further investigated. Comparative studies over 20 benchmark datasets clearly show the superiority of the proposed approach against the state-of-the-art multi-dimensional classification baselines. © 2020 IEEE.

关键词： Machine learning

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Exploiting Multi-Label Correlation in Label Distribution Learning

arXiv

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arXiv 2023年

作者： Kou, Zhiqiang Wang, Jing Jia, Yuheng Geng, Xin School of Computer Science and Engineering Southeast University Nanjing210096 China MOE Key Laboratory of Computer Network and Information Integration China

Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance. Many LDL methods proposed to leverage label correlation in the learning process to solve the exponential-sized output space;among these, many exploited the low-rank structure of label distribution to capture label correlation. However, recent studies disclosed that label distribution matrices are typically full-rank, posing challenges to those works exploiting low-rank label correlation. Note that multi-label is generally low-rank;low-rank label correlation is widely adopted in multi-label learning (MLL) literature. Inspired by that, we introduce an auxiliary MLL process in LDL and capture low-rank label correlation on that MLL rather than LDL. In such a way, low-rank label correlation is appropriately exploited in our LDL methods. We conduct comprehensive experiments and demonstrate that our methods are superior to existing LDL methods. Besides, the ablation studies justify the advantages of exploiting low-rank label correlation in the auxiliary MLL. © 2023, CC BY.

关键词： Learning systems

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