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In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第2期27卷 711-721页

作者： Baumgartl, T. Petzold, M. Wunderlich, M. Hohn, M. Archambault, D. Lieser, M. Dalpke, A. Scheithauer, S. Marschollek, M. Eichel, V. M. Mutters, N. T. Von Landesberger, T. Tech Univ Darmstadt Darmstadt Germany Univ Rostock Rostock Germany Swansea Univ Swansea W Glam Wales Univ Hosp Heidelberg Heidelbeig Germany Tech Univ Dresden Dresden Germany Univ Gottingen Univ Med Gottingen Gottingen Germany L Reichertz Inst Med Informat Hannover Germany HiGHined Infect Control Study Grp Hong Kong Peoples R China

Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak - the patient zero or index patient - requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks.

关键词： dynamic networks visualization applications health medicine outbreak Klebsiella infection control

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Threat Intelligence in IoMTs with Federated Learning using Non-IID data: An Experimental analysis

Threat Intelligence in IoMTs with Federated Learning using N...

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ieee International symposium on Telecommunication Technologies (ISTT)

作者： Syed Hussain Ali Kazmi Rosilah Hassan Faizan Qamar Kashif Nisar Dahlila Putri Dahnil Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia Swinburne University of Technology Sydney New South Wales Australia Software Technology & Management Research Center (Softam) Universiti Kebangsaan Malaysia Selangor Malaysia

ISBN: (数字)9798350392074

ISBN: (纸本)9798350392081

In the rapidly evolving field of Artificial Intelligence (AI) empowered cyberspace, securing the Internet of Medical Things (IoMTs) demands innovative strategies. Intrusion Detection Systems (IDS) integrated with Deep Learning (DL) have become a transformative approach for threat intelligence in IoMTs. However, centralized data processing in DL-based IDS raises privacy concerns. Therefore, Federated Learning (FL) has gained attention for potentially enhanced intrusion detection with privacy preservation. This study investigates the effectiveness of FL-enabled intrusion detection for IoMTs, particularly focusing on the challenge of Non-IID (Non-Independently and Identically Distributed) data. Utilizing the WUSTL-EHMS-2020 dataset of IoMTs, which includes various attacks such as man-in-the-middle (MitM), spoofing, and data injection attacks, random distribution based Non-IID data have been created and visualized using Principle Components analysis (PCA). The experimentation is performed using Python based FLOWER federated learning framework. The comparative IDS simulations on centralized data and non-IID data reveal significant variations in the performance of IDS for both centralized learning and FL. Thereby, this study highlights the implications of these findings for future research directions.

关键词： data privacy Privacy Medical devices Federated learning Intrusion detection data visualization data processing Security Artificial intelligence Principal component analysis

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Insole-based Real-time Gait analysis: Feature Extraction and Classification 8

Insole-based Real-time Gait Analysis: Feature Extraction and...

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8th ieee International symposium on Inertial Sensors and Systems (INERTIAL)

作者： Anwary, Arif Reza Arifoglu, Damla Jones, Michael Vassallo, Michael Bouchachia, Hamid Swansea Univ Swansea Univ Med Sch Swansea W Glam Wales UCL Dept Comp Sci London England Bournemouth Univ Dept Comp Bournemouth Dorset England Royal Bournemouth Hosp Bournemouth Dorset England

ISBN: (纸本)9781728150994

Gait assessment relies on clinical tools based on observation by trained staffs who give a subjective opinion. Objective gait analysis via motion capture systems (e.g. Qualisys) have limited availability as they are laboratory based and require complex equipment. A low-cost user-friendly Inertial Measurement Units (IMUs) embedded insole and an Android App based personalized gait analysis system is developed for uses in home or clinics. Accelerometer and gyroscope synchronous data are collected from both right and left legs for 10 young and 10 older adults a period of 100 consecutive days. We propose an automatic gait features extraction method, real-time visualization and age-groups classification. Accuracy of stride detection method is 100% for young. Accuracy for older adults is 91% for right and 88% for left leg. Convolutional neural networks (CNNs) are used to extract features from gait data and are combined with long short-term memory (LSTM) to exploit the time information between features. This is evaluated empirically using traditional classification and deep learning techniques (CNN+LS TM RNN) regardless of feature engineering. Accuracy to classify young and older adults with CNN-LSTM, NB, SVM and J48 is 100%. Our insole-based gait analysis automatically interprets the gait features and users can monitor their gait at home using our simple visualization tool that allows widespread home-based diagnosis and management of gait abnormalities and rehabilitation.

关键词： Gait Assessment Gait Feature Engineering Inertial Measurement Unit Machine Learning Deep learning

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Hotspot and development trend of democratic education in China : CiteSpace visualization and analysis based on CNKI data

Hotspot and development trend of democratic education in Chi...

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ieee International symposium on Information (IT) in Medicine and Education, ITME

作者： Can Xu Zuai Liu Shu Zhou Military and Political Basic Education College National University of Defense Technology Hunan China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the development of science, technology and social economy, the importance of democratic education in modern society has become more and more prominent. This paper takes the data, analysed from democratic education literature published in CNKI journals between 2009 and 2024, as the research sample. Using the Citespace tool to draw the visualization graphs of keyword co-occurrence mapping, keyword clustering, keyword co-occurrence area and outbreak words, so as to make a scientific econometric analysis of the hot spots and development trends of democratic education in China. It can be seen that the researches of democratic education in China have become more and more in-depth. In China, most of the current researches focus on the democratization of educational methods and the content of democratic thought education. However, in modern intelligent era, the research on strengthening the communication of democratic consciousness is still rare. For using big data and information technology analysis to promote the development of democratic education, this study puts forward innovative suggestions and valuable insights in the aspects of educational content, educational mode, and professional development of democratic ideology and culture. Benefiting from these, it is possible to provide new ideas for exploring the modern and innovative democratic education of Chinese style.

关键词： Training Neuroscience Education Focusing Psychology Big data Media Market research Regulation Information and communication technology

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large Scene Micro-Doppler analysis on SAR Images

Large Scene Micro-Doppler Analysis on SAR Images

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Adrian Focsa Andrei Anghel Giovanni Nico Jolanda Patruno Mihai Datcu POLITEHNICA National University of Science and Technology Bucharest Romania Military Technical Academy “Ferdinand I” Bucharest Romania Dian S.R.L. Matera Italy Institute for Applied Mathematics National Research Council of Italy Bari Italy RHEA Group Frascati Italy

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In this work, the perspective of micro-Doppler analysis from synthetic aperture radar (SAR) images is assessed for large-scale areas. We propose a processing chain that implies a coarse vibrometry estimation based on azimuth sub-aperture decomposition and local Doppler centroid computations, followed by an in-depth analysis based on conventional SAR micro-Doppler algorithms. The methdology is assessed quantitatively using simulated data in keeping with Sentinel-1 IW imaging parameters.

关键词： Vibrations Poles and towers Sentinel-1 Imaging Geoscience and remote sensing Estimation Radar polarimetry

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Augmenting Human Perception: Mediation of Extrasensory Signals in Head-Worn Augmented Reality 20

Augmenting Human Perception: Mediation of Extrasensory Signa...

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20th ieee International symposium on Mixed and Augmented Reality (ISMAR)

作者： Erickson, Austin Reiners, Dirk Bruder, Gerd Welch, Gregory F. Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9781665412988

Mediated perception systems are systems in which sensory signals from the user's environment are mediated to the user's sensory channels. This type of system has great potential for enhancing the perception of the user via augmenting and/or diminishing incoming sensory signals according to the user's context, preferences, and perceptual capability. They also allow for extending the perception of the user to enable them to sense signals typically imperceivable to human senses, such as regions of the electromagnetic spectrum beyond visible light. In this paper, we present a prototype mediated perception system that maps extrasensory spatial data into visible light displayed within an augmented reality (AR) optical see-through head-mounted display (OST-HMD). Although the system is generalized such that it could support any spatial sensor data with minor modification, we chose to test the system using thermal infrared sensors. This system improves upon previous extended perception augmented reality prototypes in that it is capable of projecting registered egocentric sensor data in real time onto a 3D mesh generated by the OST-HMD that is representative of the user's environment. We present the lessons learned through iterative improvements to the system, as well as a performance analysis of the system and recommendations for future work.

关键词： Computing methodologies Computer graphics Graphics systems and interfaces Mixed / augmented reality Human-centered computing visualization visualization systems and tools

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Exploring Foundation Models Fine-Tuning for Cytology Classification

Exploring Foundation Models Fine-Tuning for Cytology Classif...

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ieee International symposium on Biomedical Imaging

作者： Manon Dausort Tiffanie Godelaine Maxime Zanella Karim El Khoury Isabelle Salmon Benoît Macq ICTEAM Université Catholique de Louvain Belgium ILIA Université de Mons Belgium CMMI Université Libre de Bruxelles Belgium

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Cytology slides are essential tools in diagnosing and staging cancer, but their analysis is time-consuming and costly. Foundation models have shown great potential to assist in these tasks. In this paper, we explore how existing foundation models can be applied to cytological classification. More particularly, we focus on low-rank adaptation, a parameter-efficient fine-tuning method suited to few-shot learning. We evaluated five foundation models across four cytological classification datasets. Our results demonstrate that fine-tuning the pre-trained backbones with low-rank adaptation significantly improves model performance compared to fine-tuning only the classifier head, achieving state-of-the-art results on both simple and complex classification tasks while requiring fewer data samples. Our source code is available on GitHub https://***/mdausort/Cytology-fine-tuning.

关键词： Adaptation models visualization Head Foundation models Histopathology Source coding Transfer learning data models Few shot learning Software development management

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Roadmap for Electrical Impedance Spectroscopy for Sensing: A Tutorial

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ieee SENSORS JOURNAL 2021年第20期21卷 22246-22257页

作者： Buscaglia, Lorenzo A. Oliveira, Osvaldo N. Carmo, Joao Paulo Univ Sao Paulo Sao Carlos Inst Phys Dept Phys & Mat Sci Bernhard Gross Polymers Grp BR-13560970 Sao Carlos Brazil Univ Sao Paulo Sao Carlos Sch Engn Dept Elect Engn BR-13560970 Sao Carlos Brazil

Electrical impedance spectroscopy has been used extensively for sensing and biosensing due to the multiple electrical properties that can be interrogated through varying the frequency of the electrical excitation. In this paper, we review the basic concepts and key issues for applying impedance spectroscopy in sensing and biosensing, with emphasis on the development of precise, low-cost and portable instruments. An impedance spectroscopy system can be divided into three parts: the signal processing unit, the sensing unit and the data analysis unit. Herein, we focus on the signal processing unit, responsible for generating the excitation signal and performing the impedance readout. Special attention is given to small and low-cost signal processing circuits, which are essential for portability and point-of-care diagnosis. We also elaborate upon the various methods to fabricate the sensing units, including the choice of nanomaterials and biomolecules in controlled molecular architectures. From an instrumentation perspective, we discuss possible sources of interference in the measurement protocols. When impedance spectroscopy measurements are performed with arrays of sensing units, as with electronic tongues, large amounts of data are generated. This has motivated an increasing use of statistical and computational methods for data analysis. We present some of these methods, with examples of information visualization and machine learning techniques, which have been employed in analyzing impedance spectroscopy data in recent years.

关键词： Impedance Spectroscopy Oscillators Sensors Resonant frequency Biosensors Phase locked loops Impedance spectroscopy review signal processing signal generation impedance readout biosensors nanomaterials information visualization machine learning

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Dynamic Dashboard for Malaria Control: Integrating Climate Factors for Comprehensive Monitoring

Dynamic Dashboard for Malaria Control: Integrating Climate F...

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ieee International Workshop on Medical Measurement and Applications (MEMEA)

作者： Saad Abouzahir Sharim Jamal Abdulmotaleb El Saddik Mohammad Yaqub Hosni Ghedira Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi UAE School of Electrical Engineering and Computer Science University of Ottawa Canada

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

The burden of malaria continues to exert a pro-found impact on global health, consistently straining healthcare systems in endemic regions. Malaria is a vector-borne disease transmitted by infected female mosquitoes during the blood meal. The incidence of malaria is closely linked to environmental factors that affect the mosquito population and thereby influence transmission rates. Climate is a key element, as it can significantly increase mosquito populations, increasing the risk of malaria spread. Recognizing the pivotal role of climate in malaria transmission, numerous studies have trying to understand the climate-malaria relationship to establish a weather-based early warning system. However, many of these studies lack broad applicability, being confined to the specific characteristics of the study area. In response to this limitation, we introduce in this work an open-source dashboard designed for the global generalization of malaria research. This tool incorporates various data analysis and visualization technologies, offering a dynamic and interactive platform that helps researchers, epidemiologists, and health professionals in making informed decisions. Our dashboard allows for comprehensive monitoring and enables users to draw nuanced conclusions tailored to the specifics of their data. This dashboard was evaluated using real-world data from Indonesia, demonstrating its capability in providing detailed analysis at a granular level. The dashboard effectively enhances the understanding of climatic determinants that precipitate malaria outbreaks, thereby supporting the fight against this disease. It stands as a testament to how technology can be harnessed to tackle global health challenges, offering a scalable solution for malaria-prone regions worldwide.

关键词： data analysis Satellites Malaria Sociology Weather forecasting data visualization Predictive models

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JobViewer: Graph-based visualization for Monitoring High-Performance Computing System

JobViewer: Graph-based Visualization for Monitoring High-Per...

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ieee/ACM International symposium on Big data Computing (BDC)

作者： Tommy Dang Ngan V.T. Nguyen Jie Li Alan Sill Jon Hass Yong Chen Department of Computer Science Texas Tech University Lubbock Texas USA High Performance Computing Center Texas Tech University Lubbock Texas USA Dell Inc Austin Texas USA

visualization aims to strengthen data exploration and analysis, especially for complex and high-dimensional data. High-performance computing (HPC) systems are typically large and complicated instruments that generate massive performance and operation time series. Monitoring HPC systems’ performance is a daunting task for HPC admins and researchers due to their dynamic natures. This work proposes a visual design using the bipartite graph’s idea to visualize HPC clusters’ structure, metrics, and job scheduling data. We built a web-based prototype, called JobViewer, that integrates advanced methods in visualization and human-computer interaction (HCI) to demonstrate the benefits of visualization in real-time monitoring HPC centers. We also showed real use cases and a user study to validate the efficiency and highlight the current approach’s drawbacks.

关键词： Human computer interaction visualization High performance computing Source coding Time series analysis data visualization Bipartite graph

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