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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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A dual-branch residual network for inhomogeneous dehazing

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2024年 102卷

作者： Xu, Yifei Li, Jingjing Wei, Pingping Wang, Aichen Rao, Yuan Xi An Jiao Tong Univ Sch Software Xian 710054 Shaanxi Peoples R China Shaanxi Joint Key Lab Artifact Intelligence Shaanxi 710054 Peoples R China Xi An Jiao Tong Univ State Key Lab Precis Micronano Mfg Technol Xian 710054 Shaanxi Peoples R China Jiangsu Univ Key Lab Modern Agr Equipment & Technol Zhenjiang 212013 Peoples R China Xian Key Lab Social Intelligence & Complex Data Pr Shaanxi 710054 Peoples R China

Image dehazing has now gained the dominant popularity in the field of image processing, particularly in inhomogeneous scene. Recent years have witnessed great progress in handling homogeneous dehazing problems. Due to the unknown haze distribution of the real world, it is extremely intractable to offer a clear view of the observed scene in limited inhomogeneous datasets. Furthermore, it is impossible to completely avoid artifacts of color distortion, over -enhancement, halo, and blur errors in order to provide reliable and stable results. To this end, we propose an end -to -end Dual -branch Residual Network (DBRN) for inhomogeneous dehazing that is composed of Hybrid Feature Subnet (HFS) and Attention Feature Fusion Subnet (AFFS). HFS explores high -quality global and local hazy features with long-range spatial fusion at different scales in an encoder-decoder manner, while AFFS makes artifact removal possible with a stack of Residual Convolution Attention Module (RCAM). Besides, the joint loss function aims to ensure that the recovered image is close to the ground -truth in the aspects of texture, color, structure index, and so on. Through this design, the model exhibits robustness in inhomogeneous hazy scenes, enabling high -quality visual restoration in scenes with varying haze densities. Extensive experimental results demonstrate that the proposed model performs favorably against the state-of-the-art methods on synthetic datasets and real -world hazy images. In addition, ablation studies are carried out to demonstrate the effectiveness of each component. The source code of the proposed method is available at https://***/jing-1196/DBRN/.

关键词： Single image dehazing Dual-branch Residual network

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Characterizing and Understanding Development of social Computing Through DBLP: A data-Driven Analysis

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Journal of social Computing 2022年第4期3卷 287-302页

作者： Jiaqi Wu Bodian Ye Qingyuan Gong Atte Oksanen Cong Li Jingjing Qu Felicia F.Tian Xiang Li Yang Chen the Shanghai Key Lab of Intelligent Information Processing the School of Computer ScienceFudan UniversityShanghai 200438China the Faculty of Social Sciences Tampere UniversityTampere 33100Finland School of Information Science and Technology Fudan UniversityShanghai 200438China Shanghai Artificial Intelligence Laboratory Shanghai 200232China. School of Social Development and Public Policy Fudan UniversityShanghai 200433China. Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 200092China.

During the past decades,the term“social computing”has become a promising interdisciplinary area in the intersection of computer science and social *** this work,we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project(DBLP),a representative computer science bibliography *** have observed a series of trends in the development of social computing,including the evolution of the number of publications,popular keywords,top venues,international collaborations,and research *** findings will be helpful for researchers and practitioners working in relevant fields.

关键词： social computing Digital Bibliography and Library Project(DBLP) bibliometric evolution visualization

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Hybrid Siamese Masked Autoencoders as Unsupervised Video Summarizer

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Xu, Yifei Wu, Zaiqiang Li, Li Li, Siqi Li, Wenlong Li, Mingqi Rao, Yuan Deng, Shuiguang Xi’an Jiao-tong University School of Software Shaanxi Xi’an710054 China Xi’an Jiaotong University Suzhou Institute Jiangsu Suzhou215000 China Shaanxi Joint Key Laboratory for Artifact Intelligence Shaanxi Xi’an710054 China Xi’an Key Laboratory for Social Intelligence and Complex Data Processing Shaanxi Xi’an710054 China Alltuu Inc Zhejiang Hangzhou310000 China College of Computer Science and Technology in Zhejiang University Zhejiang Hangzhou310027 China

Video summarization aims to seek the most important information from a source video while still retaining its primary content. In practical application, unsupervised video summarizers are acknowledged for their flexibility and superiority without requiring annotated data. However, they are looking for the determined rules on how much each frame is essential enough to be selected as a summary. Unlike conventional frame-based scoring methods, we propose a shot-level unsupervised video summarizer termed Hybrid Siamese Masked Autoencoders (H-SMAE) from a higher semantic perspective. Specifically, our method consists of Multi-view Siamese Masked Autoencoders (MV-SMAE) and Shot Diversity Enhancer (SDE). MV-SMAE tries to recover the masked shots from original frame feature and three unmasked shot subsets with elaborate Siamese masked autoencoders. Inspired by the masking idea in MAE, MV-SMAE introduces a Siamese architecture to model prior references to guide the reconstruction of masked shots. Besides, SDE improves the diversity of generated summary by minimizing the repelling loss among selected shots. Afterward, these two modules are fused followed by 0-1 knapsack algorithm to produce a video summary. Experiments on two challenging and diverse datasets demonstrate that our approach outperforms other state-of- the-art unsupervised and weakly-supervised methods, and even generates comparable results with several excellent supervised methods. The source code of H-SMAE is available at https://***/wzq0214/H-SMAE. © 1991-2012 IEEE.

关键词： Signal encoding

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Graph Explicit Attention Network Based on Predefined Strategy

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Procedia Computer Science 2022年 202卷 422-429页

作者： Xiangbo Zhang Yuan Rao Ling Sun Lab of Social Intelligence and Complexity Data Processing School of Software Engineering Xi’an Jiaotong University Xi’an Shaanxi 710049 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.

关键词： Deep Learning Graph neural network Attention mechanism

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Evidence Inference Networks for Interpretable Claim Verification 35

Evidence Inference Networks for Interpretable Claim Verifica...

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35th AAAI Conference on Artificial intelligence / 33rd Conference on Innovative Applications of Artificial intelligence / 11th Symposium on Educational Advances in Artificial intelligence

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

ISBN: (纸本)9781577358664

Existing approaches construct appropriate interaction models to explore semantic conflicts between claims and relevant articles, which provides practical solutions for interpretable claim verification. However, these conflicts are not necessarily all about questioning the false part of claims, which makes considerable semantic conflicts difficult to be used as evidence to explain the results of claim verification, especially those that cannot identify the core semantics of claims. In this paper, we propose evidence inference networks (EVIN), which focus on the conflicts questioning the core semantics of claims and serve as evidence for interpretable claim verification. Specifically, EVIN first captures the core semantic segments of claims and the users' principal opinions in relevant articles. Then, it finely-grained identifies the semantic conflicts contained in each relevant article from these opinions. Finally, EVIN constructs coherence modeling to match the conflicts that queries the core semantic fragments of claims as explainable evidence. Experiments on two widely used datasets demonstrate that EVIN not only achieves satisfactory performance but also provides explainable evidence for end-users.

关键词： Semantics

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Content-Based Hybrid Deep Neural Network Citation Recommendation Method 6th

Content-Based Hybrid Deep Neural Network Citation Recommenda...

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6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020

作者： Wang, Leipeng Rao, Yuan Bian, Qinyu Wang, Shuo Shenzhen Research Institute of Xi’an Jiaotong University Shenzhen518057 China Lab of Social Intelligence and Complex Data Processing Software School Xi’an Jiaotong University Xi’an710049 China Xi’an710049 China

ISBN: (纸本)9789811579837

The rapid growth of scientific papers makes it difficult to query related papers efficiently, accurately and with high coverage. Traditional citation recommendation algorithms rely heavily on the metadata of query documents, which leads to the low quality of recommendation results. In this paper, DeepCite, a content-based hybrid neural network citation recommendation method is proposed. First, the BERT model was used to extract the high-level semantic representation vectors in the text, then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence, and the text vectors were matched in depth to generate candidate sets. Further, the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multi-source features. In the reranking stage, a variety of Metapath features were extracted from the citation network, and added to the deep neural network to learn, and the ranking of recommendation results were optimized. Compared with PWFC, ClusCite, BM25, RW, NNRank models, the results of the Deepcite algorithm presented in the ANN datasets show that the precision (P@20), recall rate (R@20), MRR and MAP indexesrise by 2.3%, 3.9%, 2.4% and 2.1% respectively. Experimental results on DBLP datasets show that the improvement is 2.4%, 4.3%, 1.8% and 1.2% respectively. Therefore, the algorithm proposed in this paper effectively improves the quality of citation recommendation. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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The impact of quality-adjusted life years on evaluating COVID-19 mitigation strategies: lessons from age-specific vaccination roll-out and variants of concern in Belgium (2020-2022)

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BMC PUBLIC HEALTH 2024年第1期24卷 1171页

作者： Willem, Lander Abrams, Steven Franco, Nicolas Coletti, Pietro Libin, Pieter J. K. Wambua, James Couvreur, Simon Andre, Emmanuel Wenseleers, Tom Mao, Zhuxin Torneri, Andrea Faes, Christel Beutels, Philippe Hens, Niel Dept Family Med & Populat Hlth Antwerp Belgium Univ Antwerp Ctr Hlth Econ Res & Modelling Infect Dis Antwerp Belgium Hasselt Univ Data Sci Inst Hasselt Belgium Univ Namur Namur Inst Complex Syst naXys Namur Belgium Univ Namur Dept Math Namur Belgium Vrije Univ Brussel Artificial Intelligence Lab Brussels Belgium Univ Leuven Rega Inst Med Res Clin & Epidemiol Virol Leuven Belgium Sciensano Dept Epidemiol & Publ Hlth Brussels Belgium Univ Hosp Leuven Natl Reference Ctr Resp Pathogens Leuven Belgium Univ Leuven Dept Microbiol Immunol & Transplantat Leuven Belgium Univ Leuven Lab Socioecol & Social Evolut Leuven Belgium Univ New South Wales Sch Publ Hlth & Community Med Sydney NSW Australia

Background When formulating and evaluating COVID-19 vaccination strategies, an emphasis has been placed on preventing severe disease that overburdens healthcare systems and leads to mortality. However, more conventional outcomes such as quality-adjusted life years (QALYs) and inequality indicators are warranted as additional information for policymakers. Methods We adopted a mathematical transmission model to describe the infectious disease dynamics of SARS-COV-2, including disease mortality and morbidity, and to evaluate (non)pharmaceutical interventions. Therefore, we considered temporal immunity levels, together with the distinct transmissibility of variants of concern (VOCs) and their corresponding vaccine effectiveness. We included both general and age-specific characteristics related to SARS-CoV-2 vaccination. Our scenario study is informed by data from Belgium, focusing on the period from August 2021 until February 2022, when vaccination for children aged 5-11 years was initially not yet licensed and first booster doses were administered to adults. More specifically, we investigated the potential impact of an earlier vaccination programme for children and increased or reduced historical adult booster dose uptake. Results Through simulations, we demonstrate that increasing vaccine uptake in children aged 5-11 years in August-September 2021 could have led to reduced disease incidence and ICU occupancy, which was an essential indicator for implementing non-pharmaceutical interventions and maintaining healthcare system functionality. However, an enhanced booster dose regimen for adults from November 2021 onward could have resulted in more substantial cumulative QALY gains, particularly through the prevention of elevated levels of infection and disease incidence associated with the emergence of Omicron VOC. In both scenarios, the need for non-pharmaceutical interventions could have decreased, potentially boosting economic activity and mental well-being. Conclusion

关键词： COVID-19 Model QALY Vaccine Non-pharmaceutical intervention SARS-CoV-2

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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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