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Kun Ma

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8 papers

1 author row

EAAI Journal 2026 Journal Article

ESEN: Evidence-aware Semantic Enhancement Network for Fact-checking Fake News Detection

Yanfang Qiu
Kun Ma
Xiaoyun Liu
Ke Ji
Zhenxiang Chen
Bo Yang

Fact-checking fake news detection is a challenging task that requires retrieving multiple pieces of evidence to verify the truthfulness of a claim. Despite the effectiveness of current methods, they still suffer from the following problems: (1) neglecting attentional constraints on important words related to the claim; (2) insufficiently exploring the relationships between different pieces of evidence. To address these issues, we propose an Evidence-aware Semantic Enhancement Network for Fact-checking Fake News Detection (abbreviated as ESEN). First, we model the claim and evidence as a graph structure to extract semantic information, putting the attentive guidance to closely follow the syntactic information extraction. Then, we incorporate the extracted syntactic information into the semantic information. Finally, we propose an information interaction network for evidence perception to capture multi-level interactive information and enhance the interaction between evidences by constructing three types attention layer. Evaluation experiments are performed on the Snopes and PolitiFact datasets. Experimental results and ablation studies show that our ESEN outperforms other baseline models. Our source code is available at https: //github. com/makunjida/ESEN.

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AAMAS Conference 2025 Conference Paper

Multi-Ship Future Interaction Trajectory Prediction via Pre-Initializer Diffusion Model

Kun Ma
Qilong Han
Jingzheng Yao

Real-time stochastic multi-ship trajectory modeling is crucial for maritime safety. However, it remains challenging due to the uncertainty of dynamic vessel intentions and their complex interactions. Most existing studies rely on deterministic social data from historical time steps for modeling, which often fail to capture the future states of interacting ships, leading to unrealistic trajectory overlaps. Recent research has demonstrated that diffusion models excel in trajectory prediction due to their high generation quality, training stability, and diversity. However, their slow sampling speed limits real-time perception in maritime environments, as generating high-quality trajectories typically requires hundreds of denoising steps. To address these challenges, we propose a Multi-Ship Future interaction trajectory prediction approach based on a Pre-initializer Diffusion model (MFPD). By training a parameterized pre-initializer to directly learn the joint distribution of multiple denoising steps in the reverse diffusion process, our method significantly reduces the time cost of denoising while retaining only a few steps for fine-tuning the distribution. Specifically, in addition to encoding historical trajectory information and social interactions as state embeddings, we also incorporate future trajectory and multimodal maritime environmental information as input condition embeddings to fully capture potential future interactions and environmental features. Experimental results demonstrate that the proposed model significantly improves performance on two real-world datasets while greatly accelerating the sampling speed, demonstrating the superiority in real-world maritime environments.

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TCS Journal 2024 Journal Article

A local search approach to protocol verification

Kun Ma
Yongjian Li
Shaowei Cai
Yongxin Zhao

Verification of parameterized protocols is of great interest in the area of formal verification, mainly due to the practical importance of such systems. Explicit-state model checking is an essential approach in which states for a system are maintained in explicit form, as are all state transitions. In order to reduce the search space to a minimum, we propose a local search strategy. In detail, first, heuristic functions are designed to guide the searching of bugs more effectively, which exploits information extracted from invariants and rulesets, to improve the calculation of a distance between two states. Second, candidate solutions with poor scores are dropped for further compression of the state space explored. Especially, we apply successfully our local search algorithm to the reachable analysis of a real-world implementation of TileLink protocol.

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EAAI Journal 2024 Journal Article

DIMN: Dual Integrated Matching Network for multi-choice reading comprehension

Qiang Wei
Kun Ma
Xinyu Liu
Ke Ji
Bo Yang
Ajith Abraham

Multi-choice reading comprehension is a task that involves selecting the correct option from a set of option choices. Recently, the attention mechanism has been widely used to acquire embedding representations. However, there are two significant challenges: (1) generating the contextualized representations, namely, drawing associated information, and (2) capturing the global interactive relationship, namely, drawing local semantics. To address these issues, we have proposed the Dual Integrated Matching Network (DIMN) for multi-choice reading comprehension. It consists of two major parts. Fusing Information from Passage and Question-option pair into Enhanced Embedding Representation (FEER) is proposed to draw associated information to enhance embedding representation, which incorporates the information that reflects the most salient supporting entities to answer the question into the contextualized representations; Linear Integration of Co-Attention and Convolution (LIAC) is proposed to capture the interactive information and local semantics to construct global interactive relationship, which incorporates local semantics of a single sequence into the question-option-aware passage and passage-aware question-option representation. The experiments are shown that our DIMN performs better accuracy on three datasets: RACE (69. 34%), DREAM (68. 45%) and MCTest (71. 81% on MCTest160 and 78. 83% on MCTest500). Our DIMN is beneficial for improving the ability of machines to understand natural language. The system we have developed has been applied to customer service support. Our source code is accessible at https: //github. com/vqiangv/DIMN.

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EAAI Journal 2024 Journal Article

G-HFIN: Graph-based Hierarchical Feature Integration Network for propaganda detection of We-media news articles

Xinyu Liu
Kun Ma
Qiang Wei
Ke Ji
Bo Yang
Ajith Abraham

In the era of We-media, articles are written by independently individuals that are not officially registered with the authorities. Propaganda hidden in the We-media articles have the potential to polarize public opinion and influence the mindset of the target audience. Currently, graph neural networks (GNNs) have been remarkably successful in Natural Language Processing (NLP). However, there are still some challenges to apply existing GNN-based for propaganda detection due to the limitation of extracting diverse word dependencies and capturing non-consecutive and long-range context. In this paper, we have proposed a Graph-based Hierarchical Feature Integration Network (abbreviated as G-HFIN) for Propaganda Detection. Specifically, semantic, syntactic, and sequential features are extracted to construct three heterogeneous graphs. Then, the Residual-connected Dual-layer Coarsening and Refining procedures (abbreviated as RDCR) are proposed to promote information interactions between distant nodes that are not directly connected, preserving both local and global node information during the intra-graph information propagation. Subsequently, an Attention-based Three-channel Feature Integration (abbreviated as ATFI) is proposed to harmonize sequence, semantic and syntactic information from three channels during the inter-graph information enhancement. Intra-graph and Inter-graph Joint Information Propagation is to implement homogeneous and heterogeneous information interaction respectively. Finally, these news representations are pooled and fed into the propaganda detection classifier. The experiments on three public datasets demonstrate that our model has outperformed state-of-the-art methods.

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EAAI Journal 2021 Journal Article

Attention-based learning of self-media data for marketing intention detection

Zhihao Hou
Kun Ma
Yufeng Wang
Jia Yu
Ke Ji
Zhenxiang Chen
Ajith Abraham

In the context of natural language processing, accuracy of intention detection is the basis for subsequent research on human-machine speech interaction. However, the problem of ambiguity in word vectors reduces the accuracy of intent detection. Meantime, there is a disconnection between local features and global features as well, resulting in text feature extraction that cannot fully reflect semantic information. These issues are all barriers of intention detection. Therefore, this paper proposes an attention-based convolutional neural network for self-media data learning (called A-CNN) for marketing intention. We cascade the traditional CNN with the self-attention model in the Attention networks to form a new network structure called A-CNN, and put forward a fast feature extraction method based on skip-gram-based learning called FSLText, to represent the high-dimension word vectors in the A-CNN. On the premise of maintaining the advantages of the CNN, A-CNN can not only solve the problem of local and global features disconnection caused by the CNN pooling layer, but also avoid the increase of algorithm complexity. The Self-Attention mechanism in the Attention model can effectively optimize the weight of local features of the information in global features, and retain local features that are more useful for intention detection. A fast feature extraction method which is based on Skip-gram can retain the semantic and word order information of the text. The method is beneficial to the marketing intention detection. According to the experiment, our A-CNN, compared with traditional machine learning methods, can improve 12. 32% accuracy. Contrast to the dual-channel CNN, the accuracy rate is improved by 9. 68%, and compared with the ATT-CNN, it is improved by 9. 97%. On the F1 score, the A-CNN can improve the F1 score by about 9. 37% in comparison with the traditional machine learning methods, the accuracy rate is increased by 9. 68% compared with the dual-channel CNN, and 9. 68% in contrast with ATT-CNN. It illustrates that our A-CNN can effectively address semantic and feature selection for marketing intention detection.

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EAAI Journal 2020 Journal Article

A CLSTM-TMN for marketing intention detection

Yufeng Wang
Kun Ma
Laura Garcia-Hernandez
Jing Chen
Zhihao Hou
Ke Ji
Zhenxiang Chen
Ajith Abraham

In recent years, neural network-based models such as machine learning and deep learning have achieved excellent results in text classification. On the research of marketing intention detection, classification measures are adopted to identify news with marketing intent. However, most of current news appears in the form of dialogs. There are some challenges to find potential relevance between news sentences to determine the latent semantics. In order to address this issue, this paper has proposed a CLSTM-based topic memory network (called CLSTM-TMN for short) for marketing intention detection. A ReLU-Neuro Topic Model (RNTM) is proposed. A hidden layer is constructed to efficiently capture the subject document representation, Potential variables are applied to enhance the granularity of subject model learning. We have changed the structure of current Neural Topic Model (NTM) to add CLSTM classifier. This method is a new combination ensemble both long and short term memory (LSTM) and convolution neural network (CNN). The CLSTM structure has the ability to find relationships from a sequence of text input, and the ability to extract local and dense features through convolution operations. The effectiveness of the method for marketing intention detection is illustrated in the experiments. Our detection model has a more significant improvement in F1 (7%) than other compared models.

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EAAI Journal 2019 Journal Article

Improving the effectiveness of keyword search in databases using query logs

Ziqiang Yu
Ajith Abraham
Xiaohui Yu
Yang Liu
Jing Zhou
Kun Ma

Using query logs to enhance user experience has been extensively studied in the Web IR literature. However, in the area of keyword search on structured data (relational databases in particular), most existing works have focused on improving search result quality via designing better scoring functions, without giving explicit consideration to query logs. However, query logs can reflect the user preferences, so our work taps into the wealth of information contained in query logs and aims to enhance the search effectiveness by explicitly taking into account the log information when ranking the query results. Different from existing approaches only relying on a schema graph or a data graph, our work designs a comprehensive solution based on both the schema graph and the data graph for discovering top-k results with two stages. First, we identify top-k candidate networks with a query-log-aware ranking strategy by employing the largest frequent subtrees mined from query logs. Since a candidate network usually corresponds to multiple joined tuple trees, we further rank these joined tuple trees with the PageRank principle based on the data graph in the second stage. Finally, user studies on a real dataset validate the effectiveness of the proposed ranking strategy.

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