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36th AAAI Conference on Artificial intelligence / 34th Conference on Innovative Applications of Artificial intelligence / 12th Symposium on Educational Advances in Artificial intelligence

作者： Sun, Ling Rao, Yuan Zhang, Xiangbo Lan, Yuqian Yu, Shuanghe Xi An Jiao Tong Univ Sch Software Engn Xian Key Lab Social Intelligence & Complex Data P Xian Peoples R China Shaanxi Joint Key Lab Artifact Intelligence Xian Peoples R China

ISBN: (纸本)9781577358763

Predicting the diffusion cascades is a critical task to understand information spread on social networks. Previous methods usually focus on the order or structure of the infected users in a single cascade, thus ignoring the global dependencies of users and cascades, limiting the performance of prediction. Current strategies to introduce social networks only learn the social homogeneity among users, which is not enough to describe their interaction preferences, let alone the dynamic changes. To address the above issues, we propose a novel information diffusion prediction model named Memory-enhanced Sequential Hypergraph Attention Networks (MS-HGAT). Specifically, to introduce the global dependencies of users, we not only take advantages of their friendships, but also consider their interactions at the cascade level. Furthermore, to dynamically capture users' preferences, we divide the diffusion hypergraph into several sub graphs based on timestamps, develop Hypergraph Attention Networks to learn the sequential hypergraphs, and connect them with gated fusion strategy. In addition, a memoryenhanced embedding lookup module is proposed to capture the learned user representations into the cascade-specific embedding space, thus highlighting the feature interaction within the cascade. The experimental results over four realistic datasets demonstrate that MS-HGAT significantly outperforms the state-of-the-art diffusion prediction models in both Hits@K and MAP@k metrics.

关键词： Embeddings

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Category-Controlled Encoder-Decoder for Fake News Detection

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第2期35卷 1242-1257页

作者： Wu, Lianwei Rao, Yuan Zhang, Cong Zhao, Yongqiang Nazir, Ambreen Xi An Jiao Tong Univ Sch Software Engn Xian Key Lab Social Intelligence & Complex Data Pr Xian 710054 Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Joint Key Lab Artifact Intelligence Sublab Xian 710054 Shaanxi Peoples R China Xian Huanyu Satellite Control & Data Applicat Co L Xian 710065 Shaanxi Peoples R China

The existing data-driven approaches typically capture credibility-indicative representations from relevant articles for fake news detection, such as skeptical and conflicting opinions. However, these methods still have several drawbacks: 1) Due to the difficulty of collecting fake news, the capacity of the existing datasets is relatively small;and 2) there is considerable unverified news that lacks conflicting voices in relevant articles, which makes it difficult for the existing methods to identify their credibility. Especially, the differences between true and fake news are not limited to whether there are conflict features in their relevant articles, but also include more extensive hidden differences at the linguistic level, such as the perspectives of emotional expression (like extreme emotion in fake news), writing style (like the shocking title in clickbait), etc., the existing methods are difficult to fully capture these differences. To capture more general and wide-ranging differences between true and fake news, in this paper, directly from the different categories of news itself, we propose a Category-controlled Encoder-Decoder model (CED) to generate examples with category-differentiated features and extend the dataset capacity to achieve data enhancement effect, thus enhancing fake news detection. Specifically, to make the generated examples enrich more news features, we develop news-guided encoder to guide relevant articles to generate news-semantic context representations. To drive the generated examples to contain more category-differentiated features, we devise category-controlled decoder which relies on pattern-shared unit to respectively capture intra-category shared features within true or fake news, and employs restriction unit to force the two types of shared features to be more different for highlighting inter-category differentiated features. The experimental results on three datasets demonstrate the superiority of CED.

关键词： Feature extraction Semantics Decoding social networking (online) Task analysis data models Recurrent neural networks Encoder decoder fake news detection social media analysis natural language processing

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Pre-trained Language Embedding-based Contextual Summary and Multi-scale Transmission Network for Aspect Extraction 8

Pre-trained Language Embedding-based Contextual Summary and ...

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8th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019

作者： Feng, Cong Rao, Yuan Nazir, Ambreen Wu, Lianwei He, Long Research Institute of Xi’an Jiaotong University Shenzhen China Lab of Social Intelligence and Complex Data Processing School of Software Engineering Xi’an Jiao Tong University Xi’an China Shaanxi Joint Key Laboratory for Artifact Intelligence Xi’an Jiao Tong University Xi’an China

With the development of IOT and 5G technology, people’s demand for information acquisition is more inclined to accuracy, intelligence and timeliness. How to help designer obtain the real-time information of specific product reviews from the massive online consumers and upgrade the new design strategy has become a hot topic for research. In this paper, we define the problem as an aspect extraction task, and propose a novel deep learning model that comprises of three modules: pre-training language model embedding, multi-scale transmission network and contextual summary, which aims to provide an end-to-end solution without any additional supervision. To this end, we adopt BERT to overcome the disadvantage of traditional embedding methods, which cannot combine contextual information. Multi-scale transmission network is proposed to integrate the Bi-GRU and a group of CNN networks to extract sequential and local features of words respectively. Contextual summary is a tailor-made representation distilled from the input sentence, conditioned on each current word, and thus can assist aspect prediction. Experimental results over three benchmark SemEval datasets clearly illustrate that our model can achieve the state-of-the-art performance. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/)

关键词： Extraction

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Semantic Enhancement and Multi-level label Embedding for Chinese News Headline Classification

Semantic Enhancement and Multi-level Label Embedding for Chi...

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International Joint Symposium on Artificial intelligence and Natural Language processing (iSAI-NLP)

作者： Jiangnan Qi Yuan Rao Ling Sun Xiong Yang Lab of Social Intelligence and Complex Data Processing School of Software Engineering xi’an Jiao Tong University xi’an China

ISBN: (数字)9781728156316

ISBN: (纸本)9781728156323

News headline classification is a specific example of short text classification, which aims to extract semantic information from the short text and classify it accurately. It can provide a fast classification method for data of various kinds of news media, thus arousing the common concern of academia and industry. Most short text classification methods are based on the semantic expansion of external knowledge, which is unable to expansion dynamically in real time and make full use of label information. To overcome these problems, we propose a novel method which consists of three parts: semantic enhancement, multi-dimensional feature fusion network and multi-level label embedding. Firstly, the word-level semantic information are embedded into the character encoding from pre-train model to enhance semantic features. Secondly, both of Bi-GRU and multi-scale CNN are used to extract sequence and local features of text to enhance the semantic representation of the sentence. Furthermore, the multi-level label embedding is used to filter textual vector and assist classification in the word and sentence level respectively. Experimental results on NLPCC 2017 Chinese news headline classification task show that our model achieves 84.74% of accuracy and 84.75% of F1, improves over the best baseline model by 1.5% and 1.6%, respectively, and reaches the state-of-the-art performance.

关键词： Semantics Feature extraction Task analysis Convolution data mining Bit error rate social intelligence

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DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification 58

DTCA: Decision Tree-based Co-Attention Networks for Explaina...

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58th Annual Meeting of the Association-for-Computational-Linguistics (ACL)

作者： Wu, Lianwei Rao, Yuan Zhao, Yongqiang Liang, Hao Nazir, Ambreen Xi An Jiao Tong Univ Sch Software Engn Lab Social Intelligence & Complex Data Proc Xian Peoples R China Xi An Jiao Tong Univ Sub Lab Shannxi Joint Key Lab Artifact Intelligence Xian Peoples R China Xi An Jiao Tong Univ Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781952148255

Recently, many methods discover effective evidence from reliable sources by appropriate neural networks for explainable claim verification, which has been widely recognized. However, in these methods, the discovery process of evidence is nontransparent and unexplained. Simultaneously, the discovered evidence only roughly aims at the interpretability of the whole sequence of claims but insufficient to focus on the false parts of claims. In this paper, we propose a Decision Tree-based Co-Attention model (DTCA) to discover evidence for explainable claim verification. Specifically, we first construct Decision Tree-based Evidence model (DTE) to select comments with high credibility as evidence in a transparent and interpretable way. Then we design Co-attention Self-attention networks (CaSa) to make the selected evidence interact with claims, which is for 1) training DTE to determine the optimal decision thresholds and obtain more powerful evidence;and 2) utilizing the evidence to find the false parts in the claim. Experiments on two public datasets, RumourEval and PHEME, demonstrate that DTCA not only provides explanations for the results of claim verification but also achieves the state-of-the-art performance, boosting the F1-score by 3.11%, 2.41%, respectively.

关键词： Decision trees

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HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection 37

HG-SL: Jointly Learning of Global and Local User Spreading B...

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37th AAAI Conference on Artificial intelligence (AAAI) / 35th Conference on Innovative Applications of Artificial intelligence / 13th Symposium on Educational Advances in Artificial intelligence

作者： Sun, Ling Rao, Yuan Lan, Yuqian Xia, Bingcan Li, Yangyang Xi An Jiao Tong Univ Sch Software Engn Xian Key Lab Social Intelligence & Complex Data P Xian Peoples R China Natl Engn Lab Risk Percept & Prevent Beijing Peoples R China

ISBN: (纸本)9781577358800

Recently, fake news forgery technology has become more and more sophisticated, and even the profiles of participants may be faked, which challenges the robustness and effectiveness of traditional detection methods involving text or user identity. Most propagation-only approaches mainly rely on neural networks to learn the diffusion pattern of individual news, but this is insufficient to describe the differences in news spread ability, and also ignores the valuable global connections of news and users, limiting the performance of detection. Therefore, we propose a joint learning model named HG-SL, which is blind to news content and user identity, but capable of catching the differences between true and fake news in the early stages of propagation through global and local user spreading behavior. Specifically, we innovatively design a Hypergraph-based Global interaction learning module to capture the global preferences of users from their co-spreading behaviors, and introduce node centrality encoding to complement user influence in hypergraph learning. Moreover, the designed Self-attention-based Local context learning module first introduce spread status in behavior learning process to highlight the propagation ability of news and users, thus providing additional signals for verifying news authenticity. Experiments on real-world datasets indicate that our HG-SL, which solely relies on user behavior, outperforms SOTA baselines utilizing multidimensional features in both fake news detection and early detection task.

关键词： Fake detection

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Unified Dual-view Cognitive Model for Interpretable Claim Verification 59

Unified Dual-view Cognitive Model for Interpretable Claim Ve...

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Joint Conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint Conference on Natural Language processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Wu, Lianwei Rao, Yuan Lan, Yuqian Sun, Ling Qi, Zhaoyin Xi An Jiao Tong Univ Sch Software Engn Xian Key Lab Social Intelligence & Complex Data P Xian Shaanxi Peoples R China Shaanxi Joint Key Lab Artifact Intelligence Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781954085527

Recent studies constructing direct interactions between the claim and each single user response to capture evidence have shown remarkable success in interpretable claim verification. Owing to different single responses convey different cognition of individual users, the captured evidence belongs to the perspective of individual cognition. However, individuals' cognition of social things is not always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a claim. The captured evidence correspondingly contains some unobjective and biased information. In this paper, we propose a Dual-viewmodel based on the views of Collective and Individual Cognition (CICD) for interpretable claim verification. For collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentence-level semantics (i.e., the overall responses) among all users to generate global evidence. For individual cognition, we select the top-k articles with high degree of difference and interact with the claim to explore the local key evidence fragments. To weaken the bias of individual cognition-view evidence, we devise an inconsistent loss to suppress the divergence between global and local evidence for strengthening the consistent shared evidence between the both. Experiments on three benchmark datasets confirm the effectiveness of CICD.

关键词： Semantics

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Evidence-aware hierarchical interactive attention networks for explainable claim verification 29

Evidence-aware hierarchical interactive attention networks f...

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29th International Joint Conference on Artificial intelligence, IJCAI 2020

作者： Wu, Lianwei Rao, Yuan Yang, Xiong Wang, Wanzhen Nazir, Ambreen Lab of Social Intelligence and Complexity Data Processing School of Software Engineering Xi'an Jiaotong University China Shannxi Joint Key Laboratory for Artifact Intelligence Sub-Lab of Xi'an Jiaotong University China Research Institute of Xi'an Jiaotong University Shenzhen China

ISBN: (纸本)9780999241165

Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evidence, but this evidence is difficult to focus on the questionable parts of unverified claims. Besides, they utilize relevant articles indiscriminately, ignoring the source credibility of these articles, which may cause quiet a few unreliable articles to interfere with the assessment results. In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification. EHIAN first designs internal interaction layer (IIL) to strengthen deep interaction and matching between claims and relevant articles for obtaining key evidence fragments, and then proposes global inference layer (GIL) that fuses source features of articles and interacts globally with the average semantics of all articles and finally earns the more credible evidence semantics discussing the questionable parts of claims. Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Semantics

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Graph Explicit Attention Network Based on Predefined Strategy 12

Graph Explicit Attention Network Based on Predefined Strateg...

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Zhang, Xiangbo Rao, Yuan Sun, Ling Lab of Social Intelligence and Complexity Data Processing School of Software Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China Shannxi Joint Key Laboratory for Artifact Intelligence Sub-Lab of Xi'an Jiaotong University China Research Institute of Xi'an Jiaotong University Shenzhen China

Graph attention network (GAT) has achieved great success in graph representation learning in recent years. However, the lower Physical properties of GAT in training process severely affects the application of attention mechanism in graph domain. Particularly, the attention coefficients and the multi-head mechanism are calculated or introduced in the process of model training, which obviously increases the storage complexity. In this paper, we present a graph explicit attention network (GEAT), a novel graph attention architecture that leverage predefined strategy to calculate attention coefficients, which combine global structural and node feature information. In this way, GEAT can effectively reduce the storage complexity, improve the training efficiency and alleviate the over-smoothing problem of attention scores. Experiments on benchmark datasets — Core, Citeseer and Pubmed — demonstrate that our model outperforms the state-of-the-art methods with a clear margin in challenging classifcation tasks, while being computationally efficient. © 2022 The Authors. Published by Elsevier B.V.

关键词： Attention mechanism Deep Learning Graph neural network

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Joint learning of video scene detection and annotation via multi-modal adaptive context network

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期249卷

作者： Xu, Yifei Pan, Litong Sang, Weiguang Luo, Hailun Li, Li Wei, Pingping Zhu, Li Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Alltuu Inc Hangzhou 310000 Zhejiang Peoples R China Shaanxi Joint Key Lab Artifact Intelligence Xian 710054 Shaanxi Peoples R China Xian Key Lab Social Intelligence & Complex Data Pr Xian 710054 Shaanxi Peoples R China

The tasks of scene detection and annotation have gained impressive attention for understanding video content. The main challenges lie in mitigating the error propagation of shot detection, recognizing cuts and gradual transitions, fusing hierarchical multi -modal cues, and solving these two tasks simultaneously. To address these challenges, we propose the Multi -modal Adaptive Context Network (MACN) to jointly learn scene detection and annotation from a window partitioning perspective. As a shared task -agnostic part, we perform Windowbased Cross -modal Representation (WCR) to distill complex semantic correlations from multi -modal sources for each window. Considering the long-term temporal dependency of variable -length scenes, we further develop Adaptive Context -aware Representation (ACR) to improve the performance for specific tasks. Different from previous works, scene detection is formulated as locating the starting window and its associated location offset and transition duration. Meanwhile, we assemble two multi -label sub -classifiers in different levels to predict the labels for each scene candidate. Experimental comparisons to state-of-the-art algorithms on the TAVS and ClipShots indicate that the proposed method yields promising performance in both tasks. Our code and test sample videos are released at MACN.

关键词： Scene detection Scene annotation Cross-modal representation Video understanding

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