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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.
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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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| 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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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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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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Liver cancer manifests as a profoundly heterogeneous malignancy, posing significant challenges in terms of both therapeutic intervention and prognostic evaluation. Given that the liver is the largest metabolic organ, a prognostic risk model grounded in single-cell transcriptome analysis and a metabolic perspective can facilitate precise prevention and treatment strategies for liver cancer. Hence, we identified 11 cell types in a scRNA-seq profile comprising 105,829 cells and found that the metabolic activity of malignant cells increased significantly. Subsequently, a prognostic risk model incorporating tumor heterogeneity, cell interactions, tumor cell metabolism, and differentially expressed genes was established based on eight genes; this model can accurately distinguish the survival outcomes of liver cancer patients and predict the response to immunotherapy. Analyzing the immune status and drug sensitivity of the high- and low-risk groups identified by the model revealed that the high-risk group had more active immune cell status and greater expression of immune checkpoints, indicating potential risks associated with liver cancer-targeted drugs. In summary, this study provides direct evidence for the stratification and precise treatment of liver cancer patients, and is an important step in establishing reliable predictors of treatment efficacy in liver cancer patients.
Keyword ：
liver cancer liver cancer metabolic reprogramming metabolic reprogramming prognostic risk model prognostic risk model single-cell RNA-seq single-cell RNA-seq
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| GB/T 7714 | Xiong, Zhuang , Li, Lizhi , Wang, Guoliang et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model [J]. | GENES , 2024 , 15 (6) . |
| MLA | Xiong, Zhuang et al. "Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model" . | GENES 15 . 6 (2024) . |
| APA | Xiong, Zhuang , Li, Lizhi , Wang, Guoliang , Guo, Lei , Luo, Shangyi , Liao, Xiangwen et al. Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model . | GENES , 2024 , 15 (6) . |
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针对现有多模态讽刺解释模型在融合过程中仅关注图像中的细粒度特征信息,使得模型存在解释效果不佳、多模态特征难以融合等问题,本文设计了一种基于双流残差注意力的多模态融合机制.首先,本文采用了 BART和VGG19模型分别提取文本和图像两种模态特征.其次,模型经过两路多头注意力引导,分别关注图像和文本的细粒度信息,考虑到单纯的多头自注意力不能很好学习图文间的关联信息,采用二次注意力模块(AOA)合理分配特征权重.最后,本文将多模态特征拼接融合后输入BART解码器中进行讽刺解释.模型在公开的数据集MORE上的实验结果表明,相较于ExMore模型,本文模型在METEOR和ROUGE-L评价指标上分别提升了 4.35％、3.39％.实验结果表明本文模型能更好融合模态特征,从而显著地提升模型解释的效果.
Keyword ：
多模态 多模态 注意力机制 注意力机制 深度学习 深度学习 自然语言处理 自然语言处理 讽刺解释 讽刺解释
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| GB/T 7714 | 吴运兵 , 曾炜森 , 高航 et al. 基于双流残差融合的多模态讽刺解释研究 [J]. | 小型微型计算机系统 , 2024 , 45 (11) : 2628-2635 . |
| MLA | 吴运兵 et al. "基于双流残差融合的多模态讽刺解释研究" . | 小型微型计算机系统 45 . 11 (2024) : 2628-2635 . |
| APA | 吴运兵 , 曾炜森 , 高航 , 阴爱英 , 廖祥文 . 基于双流残差融合的多模态讽刺解释研究 . | 小型微型计算机系统 , 2024 , 45 (11) , 2628-2635 . |
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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 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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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.
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| GB/T 7714 | Yang, Z. , Ren, Z. , Wang, Y. et al. An Iterative Associative Memory Model for Empathetic Response Generation [未知]. |
| MLA | Yang, Z. et al. "An Iterative Associative Memory Model for Empathetic Response Generation" [未知]. |
| APA | Yang, Z. , Ren, Z. , Wang, Y. , Sun, H. , Chen, C. , Zhu, X. et al. An Iterative Associative Memory Model for Empathetic Response Generation [未知]. |
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Abstract ：
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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