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2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

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

ISBN: (纸本)9780738110837

Named entity disambiguation is an important task in the process of knowledge space construction and improvement, which builds a bridge between entities in text and entities in knowledge space. However, the existing entity disambiguation methods mainly rely on the similar or related entities in the context. For the short text widely appeared in the practical application scenarios, the performance of the existing methods drops sharply. In this paper, we propose an entity disambiguation method based on Uniform Content Label knowledge space (UCLKS-ED). This method uses UCL knowledge space to provide additional knowledge information for the matching task of candidate entities and the source entity, and extends the representation of the source entity by conceptualizing. The experimental results on a large manually annotated short-text dataset and another standard dataset demonstrate that the proposed method achieves significant improvement over the state-of-the-art baselines. © 2021 IEEE.

关键词： Large dataset

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Attention-Based Graph Clustering with Enhanced Delivery Operator

Attention-Based Graph Clustering with Enhanced Delivery Oper...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Xuanqi Liu Sen Xu Xiufang Xu Naixuan Guo Xuesheng Bian Chaofeng Chen Yancheng Institute of Technology (School of Information Engineering) Yancheng China Yancheng Institute of Technology (School of Information Engineering) Key Laboratory of Computer Network and Information Integration (Southeast University) Yancheng China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Recently, graph clustering methods that integrate structured information into deep clustering have become one of the important research directions. However, current methods still lack an efficient and flexible fusion strategy when combining auto encoders and Graph Convolutional network (GCN) to learn feature representation. Accordingly, we propose a network named Structural Attention-based Graph Clustering network (SAGC). Firstly, we use the attention coefficient as weighted values to dynamically allocate the contribution of node attribute features and structural features. Secondly, an enhanced delivery operator is designed to efficiently convey the node features learned by the autoencoder. Lastly, a dual self-supervised mechanism is employed to unify the optimization of the two types of node features, thereby enhancing the stability of the model. Experiments on various types of datasets validate that our method outperforms state-of-the-art methods.

关键词： Graph convolutional networks Clustering methods Autoencoders Big Data Stability analysis Optimization

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BWG: An IOC Identification Method for Imbalanced Threat Intelligence Datasets

BWG: An IOC Identification Method for Imbalanced Threat Inte...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Juncheng Lu Yiyang Zhao Yan Wang Jiyuan Cui Sanfeng Zhang School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

APT attacks are becoming increasingly complex and stealthy. To effectively counter APT attacks, modelling threat intelligence data based on graphs, identifying Indicators of Compromise (IOC) nodes, and providing early warnings have become new research hotspots. However, the problem of node category imbalance in such graph datasets restricts the identification capabilities of these methods. Therefore, this paper proposes a supervised graph data augmentation method. In the training phase, graph disentangled representation learning is utilized to perform feature embedding for minority class nodes, effectively alleviating the sparsity problem faced by traditional methods and effectively integrating neighbourhood information of minority class nodes at a higher semantic level. Additionally, two loss functions designed based on link prediction and prototype constraints enhance node type consistency and semantic consistency, respectively. Experimental results on the APT and PDNS datasets demonstrate that the proposed method outperforms other baseline models in identification performance; even in highly imbalanced scenarios, it surpasses the second-best model.

关键词： Training Data privacy Computational modeling Semantics Disentangled representation learning Prototypes Data augmentation Data models Security

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BWG: An IOC Identification Method for Imbalanced Threat Intelligence Datasets 23

BWG: An IOC Identification Method for Imbalanced Threat Inte...

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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Lu, Juncheng Zhao, Yiyang Wang, Yan Cui, Jiyuan Zhang, Sanfeng Southeast University School of Cyber Science and Engineering Nanjing China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

APT attacks are becoming increasingly complex and stealthy. To effectively counter APT attacks, modelling threat intelligence data based on graphs, identifying Indicators of Compromise (IOC) nodes, and providing early warnings have become new research hotspots. However, the problem of node category imbalance in such graph datasets restricts the identification capabilities of these methods. Therefore, this paper proposes a supervised graph data augmentation method. In the training phase, graph disentangled representation learning is utilized to perform feature embedding for minority class nodes, effectively alleviating the sparsity problem faced by traditional methods and effectively integrating neighbourhood information of minority class nodes at a higher semantic level. Additionally, two loss functions designed based on link prediction and prototype constraints enhance node type consistency and semantic consistency, respectively. Experimental results on the APT and PDNS datasets demonstrate that the proposed method outperforms other baseline models in identification performance;even in highly imbalanced scenarios, it surpasses the second-best model. © 2024 IEEE.

关键词： Supervised learning

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Improved NL2SQL based on Multi-layer Expert network

arXiv

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arXiv 2023年

作者： Hao, Chenduo Zhang, Xu School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

The Natural Language to SQL (NL2SQL) technique is used to convert natural language queries into executable SQL statements. Typically, slot-filling is employed as a classification method for multi-task cases to achieve this goal. However, slot-filling can result in inaccurate SQL statement generation due to negative migration issues arising from different classification tasks. To overcome this limitation, this study introduces a new approach called Multi-Layer Expert Generate SQL (MLEG-SQL), which utilizes a dedicated multi-task hierarchical network. The lower layer of the network extracts semantic features of natural language statements, while the upper layer builds a specialized expert system for handling specific classification tasks. This hierarchical approach mitigates performance degradation resulting from different task conflicts. The proposed method was evaluated on the WiKSQL dataset and was found to be effective in generating accurate SQL statements. © 2023, CC BY.

关键词： Filling

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Integrating Syntactic and Semantic Knowledge in AMR Parsing with Heterogeneous Graph Attention network

Integrating Syntactic and Semantic Knowledge in AMR Parsing ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yikemaiti Sataer Chuanqi Shi Miao Gao Yunlong Fan Bin Li Zhiqiang Gao School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China

Abstract Meaning Representation (AMR) parsing is the task of translating a sentence to an AMR semantic graph which captures the basic meaning of the sentence, and is empowered by pre-trained Transformer models recently. These models encode the syntactic and semantic knowledge implicitly through self-supervised pre-trained tasks. We argue that encoding the syntactic and semantic knowledge explicitly is beneficial to AMR parsing and can improve data efficiency. Specifically, syntactic dependency and semantic role labeling (SRL) have similar sub-structures with AMR. In this work, we propose a novel linguistic knowledge-enhanced AMR parsing model, which augments the pre-trained Transformer with syntactic dependency and semantic role labeling structures of sentences. By applying a heterogeneous graph attention network, we can obtain syntactically and semantically augmented word representation, which can be integrated using an attentive integration layer and gating mechanism. Experimental results show that our model achieves state-of-the-art performance on different benchmarks, especially in out-of-domain and low-resource scenarios.

关键词： Knowledge engineering Semantics Syntactics Linguistics Benchmark testing Signal processing Transformers

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Offensive Text Style Transfer based on Unsupervised Learning

Offensive Text Style Transfer based on Unsupervised Learning

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2022 International Conference on Artificial Intelligence and Intelligent information Processing, AIIIP 2022

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

ISBN: (纸本)9781510660175

As the current mainstream social networking method, online social networking brings convenience to people, but also generates negative problems such as language violence. Therefore, offensive text style transfer research has become an arduous task. Research has shown that the semantic loss is caused by the discreteness and timing of the text data, resulting in a low degree of content retention after style transferring, and the lack of relevant parallel corpus data and specific style tag keywords, therefore, the accuracy of text style transfer is limited. Aiming at the existing problems, this paper proposes an Offensive Text Style Transfer based on the Unsupervised Learning (OTST-UL) model. First, the input text is encoded through the bidirectional encoding attention mechanism to retain the core content of the text. Then the generator-discriminator is applied for adversarial training, and a reconstruction loss algorithm is constructed to ensure the accuracy of the offensive language style transformation and the integrity of the text content. Experimental results show that the OTST-UL model outperforms existing text style transfer models on offensive language datasets. © 2022 SPIE.

关键词： Unsupervised learning

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On accuracy rate of community detection and pairing in mobile social network

On accuracy rate of community detection and pairing in mobil...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Jinbin Tu Qing Li Yun Wang School of Computer Science and Engineering Southeast University Key Lab of Computer Network and Information Integration Nanjing MOE China

Mobile Social network(MSN) is an opportunity network that considers the social attributes of nodes, and also uses the ”store-carry-forward” model to carry out data transfer between nodes. The community nature of nodes is an important reference in MSN to select messaging path. In this paper, a new community cluster problem based on our observed pickup strategies for car-hailing is proposed, which is involving community detection and community pairing. Then we formulate it by using T-Sorokin mobility model. The Cluster-First algorithm and Pair-First algorithm are proposed based on the defined community dependency metric to maximize the accuracy rate. Simulation experimental results show that both CFA and PFA can effectively solve this problem with an accuracy rate of over 70%. And it is concluded that cluster-first is superior to pair-first for this considered problem.

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Proposal Feature Learning Using Proposal Relations for Weakly Supervised Object Detection

Proposal Feature Learning Using Proposal Relations for Weakl...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhaofei Wang Weijia Zhang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle NSW Australia

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Weakly Supervised Object Detection (WSOD) trains detectors using only image-level annotations. Most existing WSOD models are based on pre-computed proposals and do not fully explore the relations of proposals. In this work, we address this limitation by proposing two approaches of Proposal Feature Learning for WSOD (PFL-WSOD), which effectively capture intra-proposal relations and inter-proposal relations respectively, thus improving proposal representation. To extract intra-proposal relations, we propose to utilize Self-Attention on Single Proposal for capturing relations inside each proposal. For inter-proposal relations, we propose Salient Region Banks by capturing a unique type of inter-proposal relation called deep inclusion, which significantly improves proposal representation when used in synergy with contrastive learning. Experimental results on benchmarks demonstrate the effectiveness of our methods.

关键词： Representation learning Annotations Object detection Contrastive learning Detectors Benchmark testing Feature extraction

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Partial multi-label learning with probabilistic graphical disambiguation 23

Partial multi-label learning with probabilistic graphical di...

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Proceedings of the 37th International Conference on Neural information Processing Systems

作者： Jun-Yi Hang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China

In partial multi-label learning (PML), each training example is associated with a set of candidate labels, among which only some labels are valid. As a common strategy to tackle PML problem, disambiguation aims to recover the ground-truth labeling information from such inaccurate annotations. However, existing approaches mainly rely on heuristics or ad-hoc rules to disambiguate candidate labels, which may not be universal enough in complicated real-world scenarios. To provide a principled way for disambiguation, we make a first attempt to explore the probabilistic graphical model for PML problem, where a directed graph is tailored to infer latent ground-truth labeling information from the generative process of partial multi-label data. Under the framework of stochastic gradient variational Bayes, a unified variational lower bound is derived for this graphical model, which is further relaxed probabilistically so that the desired prediction model can be induced with simultaneously identified ground-truth labeling information. Comprehensive experiments on multiple synthetic and real-world data sets show that our approach outperforms the state-of-the-art counterparts.

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