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description Publicationkeyboard_double_arrow_right Article 2024Publisher:Elsevier BV Authors: Han, Xiao; Wang, Leye; Wu, Junjie; Fang, Xiao;Han, Xiao; Wang, Leye; Wu, Junjie; Fang, Xiao;Vertical Federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collaborations with multiple data suppliers (i.e., data parties) in a decentralized and privacy-preserving way. Despite the fast-growing interest in VFL, the lack of effective and secure tools for assessing the value of data owned by data parties hinders the application of VFL in business contexts. In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method. Specifically, we first introduce a novel data valuation metric, namely MShapley-CMI. The metric evaluates a data party's contribution to a predictive analytics task without the need of executing a machine learning model, making it well-suited for real-world applications of VFL. Next, we develop an innovative federated computation method that calculates the MShapley-CMI value for each data party in a privacy-preserving manner. Extensive experiments conducted on six public datasets validate the efficacy of FedValue for data valuation in the context of VFL. In addition, we illustrate the practical utility of FedValue with a case study involving federated movie recommendations.
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.2139/ssrn.4682439&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.2139/ssrn.4682439&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024 FinlandPublisher:Institute of Electrical and Electronics Engineers (IEEE) Zhilin Lu; Rongpeng Li; Kun Lu; Xianfu Chen; Ekram Hossain; Zhifeng Zhao; Honggang Zhang;Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present the ecosystems of SemCom, including history, theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content & channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., conventional communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities. This paper has been accepted for publication in the IEEE Communications Surveys and Tutorials
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archivehttps://doi.org/10.1109/comst....Article . 2024 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/comst.2023.3333342&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archivehttps://doi.org/10.1109/comst....Article . 2024 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/comst.2023.3333342&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2023 Germany, SwitzerlandPublisher:Association for Computing Machinery (ACM) Hu, Xuke; Zhou, Zhiyong; Li, Hao; Hu, Yingjie; Gu, Fuqiang; Kersten, Jens; Fan, Hongchao; Klan, Friederike;A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to the process of recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of the specific applications is still missing. Further, there lacks a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and a core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching-based, statistical learning-based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references across the world. Results from this thorough evaluation can help inform future methodological developments for location reference recognition, and can help guide the selection of proper approaches based on application needs. 35 pages, 11 figures
Zurich Open Reposito... arrow_drop_down Zurich Open Repository and ArchiveArticle . 2024License: CC BYData sources: Zurich Open Repository and ArchiveDLR publication server; ACM Computing SurveysArticle . 2022 . 2023 . Peer-reviewedarXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archiveadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3625819&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!more_vert Zurich Open Reposito... arrow_drop_down Zurich Open Repository and ArchiveArticle . 2024License: CC BYData sources: Zurich Open Repository and ArchiveDLR publication server; ACM Computing SurveysArticle . 2022 . 2023 . Peer-reviewedarXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archiveadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3625819&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 NetherlandsPublisher:Institute of Electrical and Electronics Engineers (IEEE) Yunshi Lan; Gaole He; Jinhao Jiang; Jing Jiang; Wayne Xin Zhao; Ji-Rong Wen;Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performance on complex questions is still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances on KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and relevant background. Then, we describe benchmark datasets for complex KBQA task and introduce the construction process of these datasets. Next, we present two mainstream categories of methods for complex KBQA, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods. Specifically, we illustrate their procedures with flow designs and discuss their major differences and similarities. After that, we summarize the challenges that these two categories of methods encounter when answering complex questions, and explicate advanced solutions and techniques used in existing work. Finally, we conclude and discuss several promising directions related to complex KBQA for future research. Comment: 20 pages, 4 tables, 7 figures. arXiv admin note: text overlap with arXiv:2105.11644
TU Delft Repository arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Knowledge and Data EngineeringArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tkde.2022.3223858&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert TU Delft Repository arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Knowledge and Data EngineeringArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tkde.2022.3223858&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Institute of Electrical and Electronics Engineers (IEEE) Authors: Yunqi Zhu; Xuebing Yang; Yuanyuan Wu; Wensheng Zhang;Yunqi Zhu; Xuebing Yang; Yuanyuan Wu; Wensheng Zhang;pmid: 37566507
This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50 K, 16 K and 378 K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the created datasets with pre-trained encoder-decoder language models, including BERT2BERT, BERTShare, RoBERTaShare, Pegasus, ProphetNet, T5-large, BART and GSUM. Further, based on the BART model, we leverage the sampled summaries from the training set as prior knowledge guidance, for encoding additional contextual representations of the guidance with the encoder and enhancing the decoding representations in the decoder. The experimental results confirm the improvement of ROUGE scores and BERTScore made by the proposed method.
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveIEEE Journal of Biomedical and Health InformaticsArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jbhi.2023.3304376&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveIEEE Journal of Biomedical and Health InformaticsArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jbhi.2023.3304376&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:IOP Publishing Authors: Wu, Longyan; Tang, Lisheng; Huang, Ran;Wu, Longyan; Tang, Lisheng; Huang, Ran;An in-situ Dynamic Mechanical Analysis (DMA) method for soft matter developed by our group [Wu. et.al. 2022] encounters the problem of irregular samples, which significantly vary in shape and size in practice, therefore a standard sample "large enough" to ignore the boundary and size effects is necessary to determine the baseline of test and build the correspondence between this new method to classical mechanical tests. In this work, we use finite element analysis to approach the optimal size of a brick sample where the stress on the boundaries in three spatial directions are ignorable, and certified the results by testing a series of silicone gel samples on the in-situ DMA device. The stress-strain of tensile and compression are characterized. The material properties of gel are chosen to be close to the biological soft tissue. The size of 40mm(L)*40mm(W)*20mm(H) is determined to be the optimal result. Comment: 7 pages, 7 figures
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveJournal of Physics : Conference SeriesArticle . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/1742-6596/2612/1/012014&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveJournal of Physics : Conference SeriesArticle . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/1742-6596/2612/1/012014&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 NetherlandsPublisher:Elsevier BV Tianyang Liu; Chong Wang; Kun Huang; Peng Liang; Beiqi Zhang; Maya Daneva; Marten van Sinderen;$\textbf{Context}$: The release planning of mobile apps has become an area of active research, with most studies centering on app analysis through release notes in the Apple App Store and tracking user reviews via issue trackers. However, the correlation between these release notes and user reviews in App Store remains understudied. $\textbf{Objective}$: In this paper, we introduce $\textit{RoseMatcher}$, a novel automatic approach to match relevant user reviews with app release notes and identify matched pairs with high confidence. $\textbf{Methods}$: We collected 944 release notes and 1,046,862 user reviews from 5 mobile apps in the Apple App Store as research data to evaluate the effectiveness and accuracy of $\textit{RoseMatcher}$, and conducted deep content analysis on matched pairs. $\textbf{Results}$: Our evaluation shows that $\textit{RoseMatcher}$ can reach a hit ratio of 0.718 for identifying relevant matched pairs, and with the manual labeling and content analysis of 984 relevant pairs, we identify 8 roles that user reviews play in app updates according to the relationship between release notes and user reviews in the relevant matched pairs. $\textbf{Conclusions}$: Our findings indicate that both app development teams and users pay close attention to release notes and user reviews, with release notes typically addressing feature requests, bug reports, and complaints, and user reviews offering positive, negative, and constructive feedback. Overall, the study highlights the importance of the communication between app development teams and users in the release planning of mobile apps, with relevant reviews tending to be posed within a short period before and after the release of release notes, with the average time interval between the post time of release notes and user reviews being approximately one year. Comment: Accepted by Information and Software Technology 2023
Information and Soft... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.infsof.2023.107261&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 2 citations 2 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Information and Soft... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.infsof.2023.107261&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Elsevier BV Zhaoshan Liu; Qiujie Lv; Ziduo Yang; Yifan Li; Chau Hung Lee; Lei Shen;The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community. Comment: Computers in Biology and Medicine Accepted
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print ArchiveComputers in Biology and MedicineArticle . 2023 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.compbiomed.2023.107268&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 13 citations 13 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print ArchiveComputers in Biology and MedicineArticle . 2023 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.compbiomed.2023.107268&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Institute of Electrical and Electronics Engineers (IEEE) Authors: Xinyan Zhao; Liangwei Chen; Huanhuan Chen;Xinyan Zhao; Liangwei Chen; Huanhuan Chen;pmid: 34793308
Knowledge based dialogue systems have attracted increasing research interest in diverse applications. However, for disease diagnosis, the widely used knowledge graph is hard to represent the symptom-symptom relations and symptom-disease relations since the edges of traditional knowledge graph are unweighted. Most research on disease diagnosis dialogue systems highly rely on data-driven methods and statistical features, lacking profound comprehension of symptom-disease relations and symptom-symptom relations. To tackle this issue, this work presents a weighted heterogeneous graph based dialogue system for disease diagnosis. Specifically, we build a weighted heterogeneous graph based on symptom co-occurrence and a proposed symptom frequency-inverse disease frequency. Then this work proposes a graph based deep Q-network (Graph-DQN) for dialogue management. By combining Graph Convolutional Network (GCN) with DQN to learn the embeddings of diseases and symptoms from both the structural and attribute information in the weighted heterogeneous graph, Graph-DQN could capture the symptom-disease relations and symptom-symptom relations better. Experimental results show that the proposed dialogue system rivals the state-of-the-art models. More importantly, the proposed dialogue system can complete the task with less dialogue turns and possess a better distinguishing capability on diseases with similar symptoms.
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2020Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Neural Networks and Learning SystemsArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefIEEE Transactions on Neural Networks and Learning SystemsArticle . 2021Data sources: Europe PubMed Centralhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2020Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Neural Networks and Learning SystemsArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefIEEE Transactions on Neural Networks and Learning SystemsArticle . 2021Data sources: Europe PubMed Centralhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tnnls.2021.3124640&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 NetherlandsPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:UKRI | Maze Theory - AI Driven N...UKRI| Maze Theory - AI Driven Narrative Characters & Immersive EnvironmentsZengjie Zhang; Fangzhou Liu; Tong Liu; Jianbin Qiu; Martin Buss;Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may lead to poor estimation precision when PE is not ensured, for instance, when the disturbance gain of the system is close to the singularity. In this paper, we propose a novel disturbance observer based on concurrent learning (CL) with time-variant history stacks, which ensures high estimation precision even in PE-free cases. The disturbance observer is designed in both continuous and discrete time. The estimation errors of the proposed method are proved to converge to a bounded set using the Lyapunov method. A history-sample-selection procedure is proposed to reduce the estimation error caused by the accumulation of old history samples. A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied. This justifies the correctness of the proposed CL-based disturbance observer and verifies its applicability to solving practical problems.
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Circuits and Systems I Regular PapersArticle . 2023Data sources: Eindhoven University of Technology Research PortalIEEE Transactions on Circuits and Systems I Regular PapersArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tcsi.2023.3274558&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Circuits and Systems I Regular PapersArticle . 2023Data sources: Eindhoven University of Technology Research PortalIEEE Transactions on Circuits and Systems I Regular PapersArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tcsi.2023.3274558&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Article 2024Publisher:Elsevier BV Authors: Han, Xiao; Wang, Leye; Wu, Junjie; Fang, Xiao;Han, Xiao; Wang, Leye; Wu, Junjie; Fang, Xiao;Vertical Federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collaborations with multiple data suppliers (i.e., data parties) in a decentralized and privacy-preserving way. Despite the fast-growing interest in VFL, the lack of effective and secure tools for assessing the value of data owned by data parties hinders the application of VFL in business contexts. In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method. Specifically, we first introduce a novel data valuation metric, namely MShapley-CMI. The metric evaluates a data party's contribution to a predictive analytics task without the need of executing a machine learning model, making it well-suited for real-world applications of VFL. Next, we develop an innovative federated computation method that calculates the MShapley-CMI value for each data party in a privacy-preserving manner. Extensive experiments conducted on six public datasets validate the efficacy of FedValue for data valuation in the context of VFL. In addition, we illustrate the practical utility of FedValue with a case study involving federated movie recommendations.
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.2139/ssrn.4682439&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.2139/ssrn.4682439&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024 FinlandPublisher:Institute of Electrical and Electronics Engineers (IEEE) Zhilin Lu; Rongpeng Li; Kun Lu; Xianfu Chen; Ekram Hossain; Zhifeng Zhao; Honggang Zhang;Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present the ecosystems of SemCom, including history, theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content & channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., conventional communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities. This paper has been accepted for publication in the IEEE Communications Surveys and Tutorials
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archivehttps://doi.org/10.1109/comst....Article . 2024 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/comst.2023.3333342&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archivehttps://doi.org/10.1109/comst....Article . 2024 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/comst.2023.3333342&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2023 Germany, SwitzerlandPublisher:Association for Computing Machinery (ACM) Hu, Xuke; Zhou, Zhiyong; Li, Hao; Hu, Yingjie; Gu, Fuqiang; Kersten, Jens; Fan, Hongchao; Klan, Friederike;A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to the process of recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of the specific applications is still missing. Further, there lacks a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and a core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching-based, statistical learning-based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references across the world. Results from this thorough evaluation can help inform future methodological developments for location reference recognition, and can help guide the selection of proper approaches based on application needs. 35 pages, 11 figures
Zurich Open Reposito... arrow_drop_down Zurich Open Repository and ArchiveArticle . 2024License: CC BYData sources: Zurich Open Repository and ArchiveDLR publication server; ACM Computing SurveysArticle . 2022 . 2023 . Peer-reviewedarXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archiveadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3625819&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!more_vert Zurich Open Reposito... arrow_drop_down Zurich Open Repository and ArchiveArticle . 2024License: CC BYData sources: Zurich Open Repository and ArchiveDLR publication server; ACM Computing SurveysArticle . 2022 . 2023 . Peer-reviewedarXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archiveadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1145/3625819&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 NetherlandsPublisher:Institute of Electrical and Electronics Engineers (IEEE) Yunshi Lan; Gaole He; Jinhao Jiang; Jing Jiang; Wayne Xin Zhao; Ji-Rong Wen;Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performance on complex questions is still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances on KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and relevant background. Then, we describe benchmark datasets for complex KBQA task and introduce the construction process of these datasets. Next, we present two mainstream categories of methods for complex KBQA, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods. Specifically, we illustrate their procedures with flow designs and discuss their major differences and similarities. After that, we summarize the challenges that these two categories of methods encounter when answering complex questions, and explicate advanced solutions and techniques used in existing work. Finally, we conclude and discuss several promising directions related to complex KBQA for future research. Comment: 20 pages, 4 tables, 7 figures. arXiv admin note: text overlap with arXiv:2105.11644
TU Delft Repository arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Knowledge and Data EngineeringArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tkde.2022.3223858&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert TU Delft Repository arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2021Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Knowledge and Data EngineeringArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2021License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tkde.2022.3223858&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Institute of Electrical and Electronics Engineers (IEEE) Authors: Yunqi Zhu; Xuebing Yang; Yuanyuan Wu; Wensheng Zhang;Yunqi Zhu; Xuebing Yang; Yuanyuan Wu; Wensheng Zhang;pmid: 37566507
This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50 K, 16 K and 378 K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the created datasets with pre-trained encoder-decoder language models, including BERT2BERT, BERTShare, RoBERTaShare, Pegasus, ProphetNet, T5-large, BART and GSUM. Further, based on the BART model, we leverage the sampled summaries from the training set as prior knowledge guidance, for encoding additional contextual representations of the guidance with the encoder and enhancing the decoding representations in the decoder. The experimental results confirm the improvement of ROUGE scores and BERTScore made by the proposed method.
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveIEEE Journal of Biomedical and Health InformaticsArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jbhi.2023.3304376&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveIEEE Journal of Biomedical and Health InformaticsArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/jbhi.2023.3304376&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:IOP Publishing Authors: Wu, Longyan; Tang, Lisheng; Huang, Ran;Wu, Longyan; Tang, Lisheng; Huang, Ran;An in-situ Dynamic Mechanical Analysis (DMA) method for soft matter developed by our group [Wu. et.al. 2022] encounters the problem of irregular samples, which significantly vary in shape and size in practice, therefore a standard sample "large enough" to ignore the boundary and size effects is necessary to determine the baseline of test and build the correspondence between this new method to classical mechanical tests. In this work, we use finite element analysis to approach the optimal size of a brick sample where the stress on the boundaries in three spatial directions are ignorable, and certified the results by testing a series of silicone gel samples on the in-situ DMA device. The stress-strain of tensile and compression are characterized. The material properties of gel are chosen to be close to the biological soft tissue. The size of 40mm(L)*40mm(W)*20mm(H) is determined to be the optimal result. Comment: 7 pages, 7 figures
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveJournal of Physics : Conference SeriesArticle . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/1742-6596/2612/1/012014&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveJournal of Physics : Conference SeriesArticle . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1088/1742-6596/2612/1/012014&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 NetherlandsPublisher:Elsevier BV Tianyang Liu; Chong Wang; Kun Huang; Peng Liang; Beiqi Zhang; Maya Daneva; Marten van Sinderen;$\textbf{Context}$: The release planning of mobile apps has become an area of active research, with most studies centering on app analysis through release notes in the Apple App Store and tracking user reviews via issue trackers. However, the correlation between these release notes and user reviews in App Store remains understudied. $\textbf{Objective}$: In this paper, we introduce $\textit{RoseMatcher}$, a novel automatic approach to match relevant user reviews with app release notes and identify matched pairs with high confidence. $\textbf{Methods}$: We collected 944 release notes and 1,046,862 user reviews from 5 mobile apps in the Apple App Store as research data to evaluate the effectiveness and accuracy of $\textit{RoseMatcher}$, and conducted deep content analysis on matched pairs. $\textbf{Results}$: Our evaluation shows that $\textit{RoseMatcher}$ can reach a hit ratio of 0.718 for identifying relevant matched pairs, and with the manual labeling and content analysis of 984 relevant pairs, we identify 8 roles that user reviews play in app updates according to the relationship between release notes and user reviews in the relevant matched pairs. $\textbf{Conclusions}$: Our findings indicate that both app development teams and users pay close attention to release notes and user reviews, with release notes typically addressing feature requests, bug reports, and complaints, and user reviews offering positive, negative, and constructive feedback. Overall, the study highlights the importance of the communication between app development teams and users in the release planning of mobile apps, with relevant reviews tending to be posed within a short period before and after the release of release notes, with the average time interval between the post time of release notes and user reviews being approximately one year. Comment: Accepted by Information and Software Technology 2023
Information and Soft... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.infsof.2023.107261&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 2 citations 2 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Information and Soft... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print Archivehttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.infsof.2023.107261&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Elsevier BV Zhaoshan Liu; Qiujie Lv; Ziduo Yang; Yifan Li; Chau Hung Lee; Lei Shen;The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community. Comment: Computers in Biology and Medicine Accepted
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print ArchiveComputers in Biology and MedicineArticle . 2023 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.compbiomed.2023.107268&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 13 citations 13 popularity Top 10% influence Average impulse Top 10% Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2022Data sources: arXiv.org e-Print ArchiveComputers in Biology and MedicineArticle . 2023 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.compbiomed.2023.107268&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023Publisher:Institute of Electrical and Electronics Engineers (IEEE) Authors: Xinyan Zhao; Liangwei Chen; Huanhuan Chen;Xinyan Zhao; Liangwei Chen; Huanhuan Chen;pmid: 34793308
Knowledge based dialogue systems have attracted increasing research interest in diverse applications. However, for disease diagnosis, the widely used knowledge graph is hard to represent the symptom-symptom relations and symptom-disease relations since the edges of traditional knowledge graph are unweighted. Most research on disease diagnosis dialogue systems highly rely on data-driven methods and statistical features, lacking profound comprehension of symptom-disease relations and symptom-symptom relations. To tackle this issue, this work presents a weighted heterogeneous graph based dialogue system for disease diagnosis. Specifically, we build a weighted heterogeneous graph based on symptom co-occurrence and a proposed symptom frequency-inverse disease frequency. Then this work proposes a graph based deep Q-network (Graph-DQN) for dialogue management. By combining Graph Convolutional Network (GCN) with DQN to learn the embeddings of diseases and symptoms from both the structural and attribute information in the weighted heterogeneous graph, Graph-DQN could capture the symptom-disease relations and symptom-symptom relations better. Experimental results show that the proposed dialogue system rivals the state-of-the-art models. More importantly, the proposed dialogue system can complete the task with less dialogue turns and possess a better distinguishing capability on diseases with similar symptoms.
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2020Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Neural Networks and Learning SystemsArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefIEEE Transactions on Neural Networks and Learning SystemsArticle . 2021Data sources: Europe PubMed Centralhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tnnls.2021.3124640&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2020Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Neural Networks and Learning SystemsArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: CrossrefIEEE Transactions on Neural Networks and Learning SystemsArticle . 2021Data sources: Europe PubMed Centralhttps://doi.org/10.48550/arxiv...Article . 2020License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tnnls.2021.3124640&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 NetherlandsPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:UKRI | Maze Theory - AI Driven N...UKRI| Maze Theory - AI Driven Narrative Characters & Immersive EnvironmentsZengjie Zhang; Fangzhou Liu; Tong Liu; Jianbin Qiu; Martin Buss;Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may lead to poor estimation precision when PE is not ensured, for instance, when the disturbance gain of the system is close to the singularity. In this paper, we propose a novel disturbance observer based on concurrent learning (CL) with time-variant history stacks, which ensures high estimation precision even in PE-free cases. The disturbance observer is designed in both continuous and discrete time. The estimation errors of the proposed method are proved to converge to a bounded set using the Lyapunov method. A history-sample-selection procedure is proposed to reduce the estimation error caused by the accumulation of old history samples. A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied. This justifies the correctness of the proposed CL-based disturbance observer and verifies its applicability to solving practical problems.
arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Circuits and Systems I Regular PapersArticle . 2023Data sources: Eindhoven University of Technology Research PortalIEEE Transactions on Circuits and Systems I Regular PapersArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tcsi.2023.3274558&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down arXiv.org e-Print ArchiveOther literature type . Preprint . 2023Data sources: arXiv.org e-Print ArchiveIEEE Transactions on Circuits and Systems I Regular PapersArticle . 2023Data sources: Eindhoven University of Technology Research PortalIEEE Transactions on Circuits and Systems I Regular PapersArticle . 2023 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/tcsi.2023.3274558&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu