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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Yin, Chen Sun, Tongda Dong, Yining Centre for Systems Informatics Engineering School of Data Science Hong Kong Institute for Data Science City University of Hong Kong Kowloon Hong Kong School of Data Science City University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9798350354010

Remaining useful life (RUL) prediction plays an essential role in the prognostic health management of cutting tools, which aims to ensure workpiece surface quality and extend tool life. Although deep learning methods have exhibited great performance in the field of tool RUL prediction, establishing an effective deep learning model usually requires extensive training data, and collecting substantial tool failure data in the actual milling process is time-consuming and expensive. Alternatively, constructing robust tool degradation features and developing a lightweight prediction model might be more practical and deployment-friendly. Therefore, this paper introduces an optimal tool degradation feature extraction method with a tailored dimension reduction algorithm named dynamic-inner canonical correlation analysis (DiCCA), where the temporal dependency and dynamic information hidden in the sequential tool degradation process are captured for the RUL prediction of cutting tools. Experimental studies are performed with a milling dataset, and the results indicate that the optimal tool degradation feature extracted by the proposed DiCCA can explicitly reflect the degradation process of cutting tools, and accurate RUL prediction can be achieved through a lightweight BP model. © 2024 IEEE.

关键词： Prediction models

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Feature-preserving color pencil drawings from photographs

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Computational Visual Media 2023年第4期9卷 807-825页

作者： Dong Wang Guiqing Li Chengying Gao Shengwu Fu Yun Liang College of Mathematics and Informatics South China Agricultural UniversityGuangzhouChina School of Computer Science&Engineering South China University of TechnologyGuangzhouChina School of Data and Computer Science Sun Yat-sen UniversityGuangzhouChina

Color pencil drawing is well-loved due to its rich *** paper proposes an approach for generating feature-preserving color pencil drawings from *** mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction *** lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph *** saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious ***,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are *** experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.

关键词： non-photorealistic rendering pencil drawings image editing feature preservation

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Knowledge Pyramid Perspective of the Political data Ecosystem: A Case Study of Bhutan 16

Knowledge Pyramid Perspective of the Political Data Ecosyste...

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16th International Conference on Knowledge Management and Information Systems, KMIS 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Namgay, Phub Wangdi, Pema Department of Informatics and Media Uppsala University Sweden School of Data Science and Data Analytics Sherubtse College Royal University of Bhutan Bhutan

ISBN: (纸本)9789897587160

This study examines the dynamics of data management and knowledge flow in the political data ecosystem through the lens of the Knowledge Pyramid. We used open-government electoral documents and polling data for granular insights into how data, information, knowledge, and wisdom (DIKW) are managed in Bhutan’s political data ecosystem. Bhutan’s electoral stakeholders and political parties manage and use DIKW of varying types, sizes, and complexities. In particular, political parties use information systems, websites, and social media to manage data and construct and use knowledge for political activities. Democracy is still young and gaining a foothold in Bhutan. The political parties do not employ complex data technologies and rich human resources to manage DIKW emanating from the political data ecosystem. Thus, scope exists for electoral stakeholders and political actors to explore and adopt effective and efficient knowledge management infrastructures to deal with DIKW elements in the political arena, namely the complex dynamics of turning raw data into higher elements of the Knowledge Pyramid. In addition to contributing to the knowledge management literature through an in-depth account of the DIKW aspects in the political space, this paper demonstrated the analytical and explanatory power of the Knowledge Pyramid for discourse on the political data ecosystem. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Bhutan Democratic Electoral Process Knowledge Management Knowledge Pyramid Political data Ecosystem

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The Generalized Fixed Point Iteration Method for AVE

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Journal of Mathematical Research with Applications 2024年第6期44卷 837-849页

作者： Changfeng MA Jing KANG School of Data and Computer Science Xiamen Institute of Technology College of Mathematics and Informatics Fujian Normal University

In this paper, based on the previous published work by Ke et al.(2019) and Li et al.(2022), by using the matrix splitting technique, generalized fixed point iteration method(GFPI) is established to solve the absolute value equation(AVE). The proposed method not only includes SOR-like method, FPI method, MFPI method and so on, but also generates some special versions. Some convergence conditions of the proposed method with different iteration error norms are presented. Furthermore, methods corresponding to other splitting methods are studied in detail. The effectiveness and feasibility of the proposed method are confirmed by some numerical experiments.

关键词： absolute value equations fixed point iteration convergence

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Secure smart contract-based digital twins for the Internet of Things

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Blockchain(Research and Applications) 2024年第1期5卷 39-49页

作者： Iakovos Pittaras Nikos Fotiou Christos Karapapas Vasilios A.Siris George C.Polyzos Mobile Multimedia Laboratory Department of InformaticsSchool of Information Sciences and TechnologyAthens University of Economics and BusinessAthens10434Greece School of Data Science The Chinese University of Hong KongShenzhen518172China

The proliferation of Internet of Things(IoT)devices that operate unattended providing a multitude of important and often sensitive services highlights the need for seamless interoperability and increased *** argue that digital twins of IoT devices,with the right design,can enhance the security,reliability,auditability,and interoperability of IoT *** salient features of digital twins have made them key elements for the IoT and Industry *** this paper,we leverage advances in W3C’s Web of Things(WoT)standards and distributed ledger technologies(DLTs)to present a novel design of the smart contract-based digital twins with enhanced security,transparency,interoperability,and *** provide two different variations of that general design using two different blockchains(one public and one private,permissioned blockchain),and we present design ***,we introduce an architecture for accessing and controlling IoT devices securely and reliably,providing full auditability,while at the same time using the proposed digital twins as an indirection mechanism(proxy).The proposed architecture leverages the blockchain to offer notable properties,namely,decentralization,immutability,auditability,non-repudiation,availability,and ***,it introduces mass actuation,easier management of IoT devices,and enhanced security to the IoT gateways,enables new business models,and makes consumer devices(vendor-)agnostic.

关键词： Digital twins Internet of Things Web of Things Distributed ledger technologies Hyperledger Fabric Ethereum

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JAMIA at 30: looking back and forward

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Journal of the American Medical informatics Association 2024年第1期31卷 1-9页

作者： Stead, William W. Miller, Randolph A. Ohno-Machado, Lucila Bakken, Suzanne Department of Biomedical Informatics Department of Medicine Vanderbilt University Medical Center Nashville United States Department of Biomedical Informatics Vanderbilt University School of Medicine Alexandria United States Section of Biomedical Informatics & Data Science Yale University School of Medicine New Haven CT 06520 United States School of Nursing Department of Biomedical Informatics Data Science Institute Columbia University NY 10032 NY United States

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data-Efficient Radiology Report Generation via Similar Report Features Enhancement 3rd

Data-Efficient Radiology Report Generation via Similar Repo...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Li, Yanfeng Sun, Jinghan Wang, Liansheng Department of Computer Science at School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

ISBN: (纸本)9783031820069

The utilization of Artificial Intelligence in automatically generating radiology reports presents a promising solution for enhancing the efficiency of the diagnostic process and reducing human error. However, existing methods require training on large datasets of image-report pairs, which are often scarce. Moreover, the accuracy of reports generated with limited paired data significantly diminishes. To address these challenges, this study introduces a data-efficient method that integrates the retrieval of similar reports with text fusion enhancements to tackle the scarcity of image-report pairs and generate accurate radiology reports. Our method is compared with several state-of-the-art approaches, showing advancements on the MIMIC-CXR and IU X-ray benchmarks with the same limited data pairs. It achieves near-optimal results on MIMIC-CXR and comparable results on IU-Xray, highlighting not only its effectiveness and potential to improve radiological diagnosis with fewer image reports but also its ability to generate more accurate reports. By enhancing cross-modal feature interaction and demonstrating higher diagnostic accuracy, this work contributes to the fields of clinical medicine and artificial intelligence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiology

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Evaluating and Incorporating Generative AI in Nursing informatics and data science Graduate Courses 16

Evaluating and Incorporating Generative AI in Nursing Inform...

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16th International Congress on Nursing informatics, NI 2024

作者： Reeder, Blaine Lee, Knoo Sinclair School of Nursing University of Missouri United States Institute of Data Science & Informatics University of Missouri United States

ISBN: (纸本)9781643685274

The disruptive effect and lasting implications of generative artificial intelligence ('Gen AI') tools for higher education are yet unknown. ChatGPT 3.5 was released in November 2022 and soon had over 100 million users. This public software release triggered a global Gen AI race among technology industry giants. Educators in higher education were unprepared for this seismic shift. For the Fall 2023 term, we revised two graduate courses in nursing informatics and data science using generative AI to anticipate broad student awareness of these tools. The aim of this report is to present two different examples of course component evaluation using publicly available Gen AI tools to inform course component revisions. This effort was undertaken to 'proof' assignments against inappropriate use of Gen AI, revise assignments to incorporate use of Gen AI tools, and inform course policies about appropriate use of Gen AI for course work. © 2024 The Authors.

关键词： Students

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Developing a Visual Dashboard to Improve Research Activity Assessment 11

Developing a Visual Dashboard to Improve Research Activity A...

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11th International Conference on Computer, Control, informatics and its Applications, IC3INA 2024

作者： Nur Rahman, Muhammad Zaki Prakasa, Esa Bijaksana, Moch Arif Bachelor of Data Science Faculty of Informatics Telkom University Bandung Indonesia Bandung Indonesia School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331542313

In research institutions, the ability to monitor and analyze publication output is crucial for assessing productivity and achieving strategic goals. This paper presents a dashboard designed to visualize and analyze publication data from a research center of the Indonesian public institution. The dashboard addresses several key questions: (1) whether the current number of publications is on track to meet the institution’s targets, (2) the comparison between journal and proceeding publications, (3) individual researcher productivity, (4) research group productivity, and (5) the collaborative efforts among researchers and groups. By leveraging this tool, stakeholders can gain insights into publication trends and identify areas for improvement. The findings reveal significant trends in publication types and collaboration patterns, offering a comprehensive overview of the institution’s research output. This study contributes to the understanding of research productivity metrics and their implications for strategic planning in academic research. © 2024 IEEE.

关键词： Collaboration Metrics Dashboard Visualization Publication data Research Productivity

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Differentiable Deformation Graph-Based Neural Non-rigid Registration

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Communications in Mathematics and Statistics 2023年第1期11卷 151-167页

作者： Wanquan Feng Hongrui Cai Junhui Hou Bailin Deng Juyong Zhang School of Mathematical Sciences University of Science and Technology of ChinaHefei 230026People’s Republic of China School of Data Science University of Science and Technology of ChinaHefei 230026People’s Republic of China Department of Computer Science City University of Hong KongHong KongPeople’s Republic of China School of Computer Science and Informatics Cardiff UniversityCardiffUK

The traditional pipeline for non-rigid registration is to iteratively update the correspondence and alignment such that the transformed source surface aligns well with the target *** the pipeline,the correspondence construction and iterative manner are key to the results,while existing strategies might result in local *** this paper,we adopt the widely used deformation graph-based representation,while replacing some key modules with neural learning-based ***,we design a neural network to predict the correspondence and its reliability confidence rather than the strategies like nearest neighbor search and pair ***,we adopt the GRU-based recurrent network for iterative refinement,which is more robust than the traditional *** model is trained in a self-supervised manner and thus can be used for arbitrary datasets without *** experiments demonstrate that our proposed method outperforms the state-of-the-art methods by a large margin.

关键词： Differentiable deformation graph Non-rigid registration

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