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Exploring the Feasibility and Challenges of Treating Follow-up Patients via a Mobile Platform in China

Proceedings of the ACM on Human-computer Interaction 2025年第2期9卷 1-31页

作者： Jiaojiao Fu Yangfan Zhou Xin Wang Yi Guo School of Information Science and Engineering East China University of Science and Technology Shanghai China and School of Computer Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China and Shanghai Key Lab. of Intelligent Information Processing Shanghai China School of Information Science and Engineering East China University of Science and Technology Shanghai China

Mobile technology is being increasingly adopted in teleconsultation for its convenience and mobility. Although widely accepted by physicians for informal online consultations, the effectiveness of mobile platforms in formal medical interventions, such as follow-up treatments, remains largely underexplored. This research presents a case study from a Chinese hospital, examining how physicians use mobile platforms to treat chronic patients, the challenges they encounter, and their overall experience. The study aims to determine whether mobile technology can enable physicians to fulfill their responsibilities in managing chronic disease follow-ups online. Through observations and interviews, we found that using mobile platforms introduces significant challenges. Physicians operate in complex and varying environments, such as workplaces, homes, and even public areas, making them struggle to access comprehensive information, communicate effectively, and maintain detailed medical records. As a result, physicians face poor working conditions, reduced efficiency, and struggle to make accurate treatment decisions. This study highlights the need for policy reforms and technological innovations to ensure sustainable teleconsultation practices on mobile platforms. It contributes to the HCI and CSCW communities by highlighting the feasibility of using mobile platforms in online chronic disease follow-ups.

关键词： boundary chinese hospital mhealth teleconsultation

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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

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Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine availab.e datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

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Automated Clinical Summary Generation via Integrating Structured and Unstructured Data 12th

Automated Clinical Summary Generation via Integrating Struct...

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12th CCF Conference on BigData, BigData 2024

作者： Fu, Jiaojiao Yang, Bowen Guo, Yi Zhou, Yangfan Wang, Xin School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Lab. of Intelligent Information Processing Fudan University Shanghai China School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9789819610235

Automatically generating clinical texts can significantly reduce the time physicians spend on clinical data recording, which is particularly important for developing countries where physicians are extremely busy due to a severe shortage. This work automatically generates discharge summaries as a case to explore the methods and feasibilities of automatic clinical text summarization. Existing work typically uses either structured or unstructured data alone to generate discharge summaries. However, the content generated often has issues such as being overly verbose, lacking focus, or omitting significant information, especially key indicators and medications. This work innovatively proposes a data integration-based clinical text generation approach, using content generated from unstructured clinical data as the basis and supplementing it with text generated from structured clinical data. This study utilizes advanced natural language processing algorithms and models to create clinical texts. It addresses the challenges of lacking datasets suitable for fine-tuning pre-trained models and combining the advantages rather than the disadvantages of both types to produce discharge summaries. Experimental results show that the structured supplementation approach can effectively improve the generation of clinical texts. This work demonstrates that clinical texts generated using existing natural language processing technologies still do not meet the demands of medical practice, pointing out the need to develop further text generation technologies tailored to the characteristics of clinical data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data assimilation

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第6期47卷 4652-4667页

作者： Ziyuan Luo Anderson Rocha Boxin Shi Qing Guo Haoliang Li Renjie Wan Department of Computer Science Hong Kong Baptist University Hong Kong SAR China Artificial Intelligence Lab. Recod.ai Institute of Computing University of Campinas Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China IHPC and CFAR Agency for Science Technology and Research (A*STAR) Singapore Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness.

关键词： Neural radiance field Watermarking Three-dimensional displays Training Robustness Pipelines Computational modeling Visualization Solid modeling Rendering (computer graphics)

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A parallel ant colonies approach to de novo prediction of protein backbone in CASP8/9

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Science China(information Sciences) 2013年第10期56卷 226-238页

作者： LV Qiang WU HongJie WU JinZhen HUANG Xu LUO XiaoHu QIAN PeiDe School of Computer Science and Technology Soochow University Jiangsu Provincial Key Lab for Information Processing Technologies School of Electronic and Information Engineering Suzhou University of Science and Technology

Predicting the three-dimensional structure of proteins from amino acid sequences with only a few remote homologs,or de novo prediction,remains a major challenge in computational *** modeling of the protein backbone represents the initial phase of a protein structure prediction *** a parallel ant colony optimization based on sharing one pheromone matrix,this report proposes a parallel approach to predict the structure of a protein *** parallel approach combines various sources of energy functions and generates protein backbones with the lowest energies jointly determined by the various energy *** free modeling targets in CASP8/9 are used to evaluate the performance of the *** 13 targets in CASP8,two out of the predicted model1s selected by our approach are the best of the published CASP8 results,and seven out of the model1s are ranked in the top *** 29 targets in CASP9,20 out of the best models from our predictions are ranked in the top 10,and 11 out of the model1s are ranked in the top 10.

关键词： protein backbone prediction parallel ant colony optimization

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Improve tree kernel-based event pronoun resolution with competitive information

Improve tree kernel-based event pronoun resolution with comp...

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22nd International Joint Conference on Artificial Intelligence, IJCAI 2011

作者： Kong, Fang Zhou, Guodong JiangSu Provincial Key Lab. for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

ISBN: (纸本)9781577355120

Event anaphora resolution plays a critical role in discourse analysis. This paper proposes a tree kernel- based framework for event pronoun resolution. In particular, a new tree expansion scheme is introduced to automatically determine a proper parse tree structure for event pronoun resolution by considering various kinds of competitive information related with the anaphor and the antecedent candidate. Evaluation on the OntoNotes English corpus shows the appropriateness of the tree kernel-based framework and the effectiveness of competitive information for event pronoun resolution.

关键词： Trees (mathematics)

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A tree kernel-based unified framework for Chinese zero anaphora resolution

A tree kernel-based unified framework for Chinese zero anaph...

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Conference on Empirical Methods in Natural Language processing, EMNLP 2010

作者： Kong, Fang Zhou, Guodong JiangSu Provincial Key Lab. for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

ISBN: (纸本)1932432868

This paper proposes a unified framework for zero anaphora resolution, which can be divided into three sub-tasks: zero anaphor detection, anaphoricity determination and antecedent identification. In particular, all the three sub-tasks are addressed using tree kernel-based methods with appropriate syntactic parse tree structures. Experimental results on a Chinese zero anaphora corpus show that the proposed tree kernel-based methods significantly outperform the feature-based ones. This indicates the critical role of the structural information in zero anaphora resolution and the necessity of tree kernel-based methods in modeling such structural information. To our best knowledge, this is the first systematic work dealing with all the three sub-tasks in Chinese zero anaphora resolution via a unified framework. Moreover, we release a Chinese zero anaphora corpus of 100 documents, which adds a layer of annotation to the manually-parsed sentences in the Chinese Treebank (CTB) 6.0. © 2010 Association for Computational Linguistics.

关键词： Trees (mathematics)

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A Fast Calculation of Metric Scores for Learning Bayesian Network

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International Journal of Automation and computing 2012年第1期9卷 37-44页

作者： Qiang Lv Xiao-Yan Xia Pei-De Qian School of Computer Science and Technology Soochow University Suzhou 215006 PRC Jiangsu Provincial Key Lab for Computer Information Processing Technology Suzhou 215006 PRC

Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.

关键词： Frequent counting radix-based calculation ADtree learning Bayesian network metric score

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Prof. Rudolf Emil Kalman OBITUARY

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IEEE CONTROL SYSTEMS MAGAZINE 2017年第1期37卷 151-152页

作者： Antoulas, Athanasios Georgiou, Tryphon T. Khargonekar, Pramod P. Ozguler, A. Bulent Sontag, Eduardo D. Yamamoto, Yutaka JiangSu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University Suzhou China

Recounts the career and contributions of Professor Rudolf Emil Kalman.

关键词： Obituaries Kalman, Rudolf Emil

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

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IEEE Transactions on Wireless Communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily availab.e training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2025 IEEE.

关键词： Green's function

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