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学者姓名：廖祥文
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The budgeted influence maximization (BIM) problem aims to select an optimal subset of nodes, each with a unique selection cost, to maximize the influence in a network under a fixed budget. Most existing methods address static networks with constant node infection states, but usually dismiss the dynamic interaction of competing emotions within the network. This work extends the BIM problem to account for dynamic emotional competitions. Specifically, we first formally formulate the BIM problem in a competitive environment where emotions dynamically influence and transform each other over time. Then, we introduce a local structure-sensitive heuristic function designed to evaluate a node's influence potential in a competitive environment. Furthermore, we propose a heuristic mutation particle swarm optimization (HMPSO) algorithm to identify a set of high-impact nodes, thereby maximizing desirable information spread. Experimental results conducted on two real-world networks demonstrate that the HMPSO algorithm outperforms existing advanced methods for the BIM problem.
Keyword ：
competitive environment competitive environment emotional propagation emotional propagation influence maximization influence maximization particle swarm optimization particle swarm optimization
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| GB/T 7714 | Chen, Zhihao , Chen, Chao , Cai, Tiecheng et al. A Heuristic Mutation Particle Swarm Optimization Algorithm for Budgeted Influence Maximization with Emotional Competition [J]. | ELECTRONICS , 2025 , 14 (7) . |
| MLA | Chen, Zhihao et al. "A Heuristic Mutation Particle Swarm Optimization Algorithm for Budgeted Influence Maximization with Emotional Competition" . | ELECTRONICS 14 . 7 (2025) . |
| APA | Chen, Zhihao , Chen, Chao , Cai, Tiecheng , Wei, Jingjing , Liao, Xiangwen . A Heuristic Mutation Particle Swarm Optimization Algorithm for Budgeted Influence Maximization with Emotional Competition . | ELECTRONICS , 2025 , 14 (7) . |
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The microscopic cascade prediction task has wide applications in downstream areas like "rumor detection". Its goal is to forecast the diffusion routines of information cascade within networks. Existing works typically formulate it as a classification task, which fails to well align with the Social Homophily assumption, as it just use the features of "infected" users while neglecting those of "uninfected" users in representation learning. Moreover, these methods focus primarily on social relationships, thereby dismissing other vital dimensions like users' historical behavior and the underlying preferences behind it. To address these challenges, we introduce the MSR (Multifaceted Self-Retrieval) framework. During encoding, in addition to the existing social graph, we construct a preference graph to represent "behavioral preferences" and further propose a modified multi-channel GRAU for multi-view analysis of cascade phenomenon. For decoding, our approach diverges from classification-based methods by reformulating the task as an information retrieval problem that predicts the target user with similarity measures. Empirical evaluations on public datasets demonstrate that this framework significantly outperforms baselines on Hits@kappa and MAP@kappa, affirming its enhanced ability.
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| GB/T 7714 | Hong, Dongsheng , Chen, Chao , Li, Xujia et al. MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction [J]. | THIRTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-25, VOL 39 NO 11 , 2025 : 11781-11789 . |
| MLA | Hong, Dongsheng et al. "MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction" . | THIRTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-25, VOL 39 NO 11 (2025) : 11781-11789 . |
| APA | Hong, Dongsheng , Chen, Chao , Li, Xujia , Wang, Shuhui , Lin, Wen , Liao, Xiangwen . MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction . | THIRTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-25, VOL 39 NO 11 , 2025 , 11781-11789 . |
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Abstract ：
The microscopic cascade prediction task has wide applications in downstream areas like 'rumor detection'. Its goal is to forecast the diffusion routines of information cascade within networks. Existing works typically formulate it as a classification task, which fails to well align with the Social Homophily assumption, as it just use the features of 'infected' users while neglecting those of 'uninfected' users in representation learning. Moreover, these methods focus primarily on social relationships, thereby dismissing other vital dimensions like users' historical behavior and the underlying preferences behind it. To address these challenges, we introduce the MSR (Multifaceted Self-Retrieval) framework. During encoding, in addition to the existing social graph, we construct a preference graph to represent 'behavioral preferences' and further propose a modified multi-channel GRAU for multi-view analysis of cascade phenomenon. For decoding, our approach diverges from classification-based methods by reformulating the task as an information retrieval problem that predicts the target user with similarity measures. Empirical evaluations on public datasets demonstrate that this framework significantly outperforms baselines on Hits@κ and MAP@κ, affirming its enhanced ability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
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Online searching Online searching Taxonomies Taxonomies
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| GB/T 7714 | Hong, Dongsheng , Chen, Chao , Li, Xujia et al. MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction [C] . 2025 : 11781-11789 . |
| MLA | Hong, Dongsheng et al. "MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction" . (2025) : 11781-11789 . |
| APA | Hong, Dongsheng , Chen, Chao , Li, Xujia , Wang, Shuhui , Lin, Wen , Liao, Xiangwen . MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction . (2025) : 11781-11789 . |
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| GB/T 7714 | Fu, Zhou , Zhang, Yanchun , Chen, Qingcai et al. Preface [J]. | Communications in Computer and Information Science , 2025 , 2433 CCIS : v-vii . |
| MLA | Fu, Zhou et al. "Preface" . | Communications in Computer and Information Science 2433 CCIS (2025) : v-vii . |
| APA | Fu, Zhou , Zhang, Yanchun , Chen, Qingcai , Lin, Hongfei , Liu, Lei , Liao, Xiangwen et al. Preface . | Communications in Computer and Information Science , 2025 , 2433 CCIS , v-vii . |
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Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.
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| GB/T 7714 | Guo, Lei , Xie, Chengyi , Miao, Rui et al. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging [J]. | ANALYTICAL CHEMISTRY , 2024 , 96 (9) : 3829-3836 . |
| MLA | Guo, Lei et al. "DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging" . | ANALYTICAL CHEMISTRY 96 . 9 (2024) : 3829-3836 . |
| APA | Guo, Lei , Xie, Chengyi , Miao, Rui , Xu, Jingjing , Xu, Xiangnan , Fang, Jiacheng et al. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging . | ANALYTICAL CHEMISTRY , 2024 , 96 (9) , 3829-3836 . |
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The advancement of generative artificial intelligence technology has significantly contributed to the progress in various fields. However, this technological development has also inadvertently facilitated the creation and widespread dissemination of misinformation. Prior research has concentrated on addressing grammatical issues, inflammatory content, and other pertinent features by employing deep learning models to characterize and model deceptive elements within fake news content. These approaches not only are lack of the capability to assess the content itself, but also fall short in elucidating the reasons behind the model's classification. Based on the above problems, we propose a fine-grained fake news detection method with implicit semantic enhancement. This method fully utilizes the summarization and reasoning capabilities of the existing generative large language model. The method employs inference based on major events, fine-grained minor events, and implicit information to systematically evaluate the authenticity of news content. This method strategically leverages the full potential of the model by decomposing tasks, thereby not only optimizing its proficiency but also significantly enhancing its prowess in capturing instances of fake news. Simultaneously, it is designed to be interpretable, providing a solid foundation for detection. With its inherent ability, this method not only ensures reliable identification but also holds vast potential for diverse applications. © 2024 Science Press. All rights reserved.
Keyword ：
Computational linguistics Computational linguistics Deep learning Deep learning Fake detection Fake detection Semantics Semantics Social networking (online) Social networking (online)
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| GB/T 7714 | Jing, Ke , Zheyong, Xie , Tong, Xu et al. An Implicit Semantic Enhanced Fine-Grained Fake News Detection Method Based on Large Language Models [J]. | Computer Research and Development , 2024 , 61 (5) : 1250-1260 . |
| MLA | Jing, Ke et al. "An Implicit Semantic Enhanced Fine-Grained Fake News Detection Method Based on Large Language Models" . | Computer Research and Development 61 . 5 (2024) : 1250-1260 . |
| APA | Jing, Ke , Zheyong, Xie , Tong, Xu , Yuhao, Chen , Xiangwen, Liao , Enhong, Chen . An Implicit Semantic Enhanced Fine-Grained Fake News Detection Method Based on Large Language Models . | Computer Research and Development , 2024 , 61 (5) , 1250-1260 . |
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Empathetic response generation endeavors to empower dialogue systems to perceive speakers' emotions and generate empathetic responses accordingly. Psychological research demonstrates that emotion, as an essential factor in empathy, encompasses trait emotions, which are static and context-independent, and state emotions, which are dynamic and context-dependent. However, previous studies treat them in isolation, leading to insufficient emotional perception of the context, and subsequently, less effective empathetic expression. To address this problem, we propose Combining Trait and State emotions for Empathetic Response Model (CTSM). Specifically, to sufficiently perceive emotions in dialogue, we first construct and encode trait and state emotion embeddings, and then we further enhance emotional perception capability through an emotion guidance module that guides emotion representation. In addition, we propose a cross-contrastive learning decoder to enhance the model's empathetic expression capability by aligning trait and state emotions between generated responses and contexts. Both automatic and manual evaluation results demonstrate that CTSM outperforms state-of-the-art baselines and can generate more empathetic responses. Our code is available at https://github.com/wangyufeng-empty/CTSM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.
Keyword ：
Behavioral research Behavioral research Emotion Recognition Emotion Recognition Learning systems Learning systems Speech processing Speech processing Speech recognition Speech recognition
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| GB/T 7714 | Wang, Yufeng , Chen, Chao , Yang, Zhou et al. CTSM: Combining Trait and State Emotions for Empathetic Response Model [C] . 2024 : 4214-4225 . |
| MLA | Wang, Yufeng et al. "CTSM: Combining Trait and State Emotions for Empathetic Response Model" . (2024) : 4214-4225 . |
| APA | Wang, Yufeng , Chen, Chao , Yang, Zhou , Wang, Shuhui , Liao, Xiangwen . CTSM: Combining Trait and State Emotions for Empathetic Response Model . (2024) : 4214-4225 . |
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多模态生成式摘要往往采用序列到序列(Seq2Seq)框架,目标函数在字符级别优化模型,根据局部最优解生成单词,忽略了摘要样本全局语义信息,使得摘要与多模态信息产生语义偏差,容易造成事实性错误.针对上述问题,提出一种基于语义相关性分析的多模态摘要模型.首先,在Seq2Seq框架基础上对多模态摘要进行训练,生成语义多样性的候选摘要;其次,构建基于语义相关性分析的摘要评估器,从全局的角度学习候选摘要之间的语义差异性和真实评价指标ROUGE(Recall-Oriented Understudy for Gisting Evaluation)的排序模式,从而在摘要样本层面优化模型;最后,不依赖参考摘要,利用摘要评估器对候选摘要进行评价,使得选出的摘要与源文本在语义空间中尽可能相似.实验结果表明,在公开数据集MMSS上,相较于MPMSE(Multimodal Pointer-generator via Multimodal Selective Encoding)模型,所提模型在ROUGE-1、ROUGE-2、ROUGE-L评价指标上分别提升了3.17、1.21和2.24个百分点.
Keyword ：
事实性错误 事实性错误 多模态 多模态 序列到序列 序列到序列 生成式摘要 生成式摘要 语义相关性 语义相关性
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| GB/T 7714 | 林于翔 , 吴运兵 , 阴爱英 et al. 基于语义相关性分析的多模态摘要模型 [J]. | 计算机应用 , 2024 , 44 (1) : 65-72 . |
| MLA | 林于翔 et al. "基于语义相关性分析的多模态摘要模型" . | 计算机应用 44 . 1 (2024) : 65-72 . |
| APA | 林于翔 , 吴运兵 , 阴爱英 , 廖祥文 . 基于语义相关性分析的多模态摘要模型 . | 计算机应用 , 2024 , 44 (1) , 65-72 . |
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Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses. Psychological theories posit that comprehending emotional and cognitive states necessitates iteratively capturing and understanding associated words across dialogue utterances. However, existing approaches regard dialogue utterances as either a long sequence or independent utterances for comprehension, which are prone to overlook the associated words between them. To address this issue, we propose an Iterative Associative Memory Model (IAMM) for empathetic response generation. Specifically, we employ a novel second-order interaction attention mechanism to iteratively capture vital associated words between dialogue utterances and situations, dialogue history, and a memory module (for storing associated words), thereby accurately and nuancedly comprehending the utterances. We conduct experiments on the Empathetic-Dialogue dataset. Both automatic and human evaluations validate the efficacy of the model. Variant experiments on LLMs also demonstrate that attending to associated words improves empathetic comprehension and expression. © 2024 Association for Computational Linguistics.
Keyword ：
Associative processing Associative processing Associative storage Associative storage Computational linguistics Computational linguistics Speech recognition Speech recognition
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| GB/T 7714 | Yang, Zhou , Ren, Zhaochun , Wang, Yufeng et al. An Iterative Associative Memory Model for Empathetic Response Generation [C] . 2024 : 3081-3092 . |
| MLA | Yang, Zhou et al. "An Iterative Associative Memory Model for Empathetic Response Generation" . (2024) : 3081-3092 . |
| APA | Yang, Zhou , Ren, Zhaochun , Wang, Yufeng , Sun, Haizhou , Chen, Chao , Zhu, Xiaofei et al. An Iterative Associative Memory Model for Empathetic Response Generation . (2024) : 3081-3092 . |
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Empathetic response generation endeavours to perceive the interlocutor's emotional and cognitive states in the dialogue and express proper responses. Previous studies detect the interlocutor's states by understanding the immediate context of the dialogue. However, these methods are at an elementary/intermediate level of empathetic understanding due to the neglect of the broader context (i.e., the situation) and its associations with the dialogue, leading to inaccurate comprehension of the interlocutor's states. In this paper, we utilize the EMPATHETIC-DIALOGUES dataset consisting of 25k dialogues, and on this basis, we propose a Situation-Dialogue Association Model (SDAM). SDAM focuses on the broader context, i.e., the situation, and enhances the understanding of empathy from explicit and implicit associations. Regarding explicit associations, we propose a bidirectional filtering encoder. It selects relevant keywords between the situation and dialogue, learning their direct lexical relevance. For implicit associations, we use a knowledge-based hypergraph network grounded to learn convoluted connections between the situation and the dialogue. Moreover, we also introduce a simple finetuning approach that combines SDAM with large language models to further strengthen the empathetic understanding capability. Compared to the baseline, SDAM demonstrates superior empathetic ability. In terms of emotion accuracy, fluency, and response diversity (Distinct1/Distinct-2), SDAM achieves improvements of 12.25 (a 30.47% increase), 0.3 (a 0.85% increase), and 0.86/1.23 (116.22% and 30.67% increases), respectively. Additionally, our variant model based on large language models exhibits better emotion recognition capability without compromising response quality, specifically achieving an improvement of 0.23 (a 0.37% increase) in emotion accuracy.
Keyword ：
Emotional detection Emotional detection Empathetic response generation Empathetic response generation Natural language processing Natural language processing Text generation Text generation
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| GB/T 7714 | Yang, Zhou , Ren, Zhaochun , Wang, Yufeng et al. Situation-aware empathetic response generation [J]. | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (6) . |
| MLA | Yang, Zhou et al. "Situation-aware empathetic response generation" . | INFORMATION PROCESSING & MANAGEMENT 61 . 6 (2024) . |
| APA | Yang, Zhou , Ren, Zhaochun , Wang, Yufeng , Sun, Haizhou , Zhu, Xiaofei , Liao, Xiangwen . Situation-aware empathetic response generation . | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (6) . |
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