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Using an online community of practice to explore the informal use of mobile phones by health workers

Oxford open digital health 2023年第1期1卷 oqac003页

作者： Claire Glenton Josephine Nabukenya Smisha Agarwal Michele Meltzer Elisabeth Mukendi Immaculate Nakityo Lwanga Josephine Namitala Sunanda Reddy Geoff Royston Tigest Tamrat Neil Pakenham-Walsh Department of Health and Functioning Western Norway University of Applied Sciences 5020 Bergen Norway. Department of Information Systems School of Computing & Informatics Technology Makerere University 7062 Kampala Uganda. Department of International Health Johns Hopkins University Bloomberg School of Public Health Baltimore MD 20205 USA. Department of Medicine Thomas Jefferson University Philadelphia PA 19107 USA. Department of Health Senghor University Alexandria Governorate 5361022 Egypt. School of Public Health Makerere University 7062 Kampala Uganda. Department of Adult and Community Education Makerere University 7062 Kampala Uganda. Centre for Applied Research and Education on Neurodevelopmental Impairments and Disability-related Health Initiatives New Delhi 110075 India. Healthcare Information for All Global Healthcare Information Network Oxfordshire UK. UNDP/UNFPA/UNICEF/World Bank Special Program of Research Development and Research Training in Human Reproduction (HRP) Department of Sexual and Reproductive Health and Research World Health Organization Geneva 1211 Switzerland.

Health workers are increasingly harnessing mobile phones to develop their own solutions to work challenges. The mHEALTH-INNOVATE project aims to explore this topic further. In 2022, Healthcare Information for All organized an online discussion among health workers and other stakeholders to inform the project. Twenty-five people joined the discussion. Contributors' descriptions of the varieties of mobile phone use tallied with previous research, including for communication with patients and colleagues. In addition, they described increased mobile phone use in response to the COVID-19 pandemic and the increased need for communication, monitoring and reporting, including during lockdowns. Some solutions were health worker-initiated, including the establishment of WhatsApp groups. The discussion has helped develop a definition of informal mobile phone use.

关键词： digital health digital innovation health workforce informal mhealth mobile device mobile health

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Automating the segmentation of necrotized regions in cassava root images

Automating the segmentation of necrotized regions in cassava...

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2018 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2018

作者： Ninsiima, Flavia Delmira Owomugisha, Godliver Mwebaze, Ernest Makerere University School of Computing and Informatics Technology P.O. Box 7062 Kampala Uganda University of Groningen Johann Bernoulli Institute for Mathematics and Computer Science P.O. Box 407 Groningen9700 AK Netherlands Busitema University Faculty of Engineering P.O. Box 236 Tororo Uganda

ISBN: (纸本)1601324855

The most common form of symptom measurement in assessment of crop health is visual inspection of the plants. In this paper we discuss a prototypical symptom assessment method for assessing the amount of rot or necrosis in the cross-section of a cassava root tuber. Necrosis is largely associated with the presence of the viral disease Cassava Brown Streak Disease (CBSD), the most dangerous viral cassava disease in Sub-Saharan Africa now. Subjective assessment of symptoms tends to be sub-optimal leading to inaccurate estimates. We propose to automate this task by use of computer vision techniques to standardise the assessment of necrosis. Our work extends previous work in this area in two ways;(i) through the development of algorithms that segment out roots from noisy backgrounds and (ii) we provide improved techniques for identifying necrotic regions in the root as well as evaluating how accurate these methods are. © 2018 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV *** right reserved.

关键词： Computer vision

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VV-NET: Voxel VAE net with group convolutions for point cloud segmentation

arXiv

引用

arXiv 2018年

作者： Meng, Hsien-Yu Gao, Lin Lai, Yu-Kun Manocha, Dinesh University of Maryland College Park United States Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences School of Computer Science & Informatics Cardiff University Tsinghua University

We present a novel algorithm for point cloud segmentation. Our approach transforms unstructured point clouds into regular voxel grids, and further uses a kernel-based interpolated variational autoencoder (VAE) architecture to encode the local geometry within each voxel. Traditionally, the voxel representation only comprises Boolean occupancy information which fails to capture the sparsely distributed points within voxels in a compact manner. In order to handle sparse distributions of points, we further employ radial basis functions (RBF) to compute a local, continuous representation within each voxel. Our approach results in a good volumetric representation that effectively tackles noisy point cloud datasets and is more robust for learning. Moreover, we further introduce group equivariant CNN to 3D, by defining the convolution operator on a symmetry group acting on Z3 and its isomorphic sets. This improves the expressive capacity without increasing parameters, leading to more robust segmentation results. We highlight the performance on standard benchmarks and show that our approach outperforms state-of-the-art segmentation algorithms on the ShapeNet and S3DIS datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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AAANE: Attention-based adversarial autoencoder for multi-scale network embedding

arXiv

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arXiv 2018年

作者： Sang, Lei Xu, Min Qian, Shengsheng Wu, Xindong School of Computer and Information Hefei University of Technology Faculty of Engineering and IT University of Technology Sydney National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Computing and Informatics University of Louisiana Lafayette United States

Network embedding represents nodes in a continuous vector space and preserves structure information from the Network. Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in the embedding learning. In this paper, we propose an Attention-based Adversarial Autoencoder Network Embedding(AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. The proposed AAANE consists of two components: 1) Attention-based autoencoder effectively capture the highly non-linear network structure, which can de-emphasize irrelevant scales during training. 2) An adversarial regularization guides the autoencoder learn robust representations by matching the posterior distribution of the latent embeddings to given prior distribution. This is the first attempt to introduce attention mechanisms to multi-scale network embedding. Experimental results on real-world networks show that our learned attention parameters are different for every network and the proposed approach outperforms existing state-of-the-art approaches for network embedding. Copyright © 2018, The Authors. All rights reserved.

关键词： Network embeddings

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Water demand management at rural area using Micro-Component Analysis: a case study at Kenyir Lake, Malaysia

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IOP Conference Series: Earth and Environmental Science 2022年第1期955卷

作者： N N I M Azlan N M Saad S Norhisham M A Malek N S M Shkuri M Zolkepli L W Ean A M Mohamad Department of Civil Engineering College of Engineering Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia Institute of Energy Infrastructures (IEI) Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia College of Computing & Informatics Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia College of Energy Economics & Social Sciences Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia 43400 UPM Serdang Selangor Malaysia Institute of Sustainable Energy Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia School of Law and Centre for Testing Measurement and Appraisal (CeTMA) Universiti Utara Malaysia 06010 Bukit Kayu Hitam Kedah

Water is one of the cornerstones in human survival and a necessity for socio-economic development. Due to that, effective management of water demand especially in the rural area becomes vital. The objective of this study is to conduct a water demand study at Kenyir Lake, Terengganu using Micro-Component Analysis (MCA). Water demand is also being assessed using National Water Services Commissions (SPAN) Guidelines, parallel to water guidelines applied for Malaysia's building and comparison purposes. MCA considers detailed parameters of water use activities of the respective study area, whereas SPAN Guidelines use the fixed values of average daily water demand according to the type of building. The available dataset was interpreted using MCA and SPAN Guidelines, resulting in total daily water demand at Kenyir Lake of 1249.8 m3 and 1254.1 m3 respectively. Estimated water demand using SPAN Guidelines is found to be higher than MCA. The value differences occur due to the average water demand value in the guideline that is limited to a specific type of premises and does not comply with premises there. The estimated water demand can be adopted as a baseline for water companies and the government to improve the demand and supply of water.

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Performance evaluation of energy autonomous sensors for air quality monitoring in Internet of Vehicles

Performance evaluation of energy autonomous sensors for air ...

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IEEE Vehicular technology Conference

作者： Shaik Shabana Anjum Ismail Ahmedy Nasrin Aghamohammadi Muhammad Ahsan Qureshi Rafidah Md Noor Mohammad Hossein Anisi Norazlina Binti Khamis Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Faculty of medicine University of Malaya Kuala Lumpur Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan

The technological advancements in the field of internet of things (IoT) have paved way for the evolution of internet of vehicles (IoV), where the things are replaced with vehicles through enabling technologies such as vehicular adhoc networks (VANETs) and intelligent transportation systems (ITS). The technology of simultaneous wireless information and power transfer (SWIPT) provides potential opportunity for increasing the lifetime of the energy-autonomous nodes in vehicular networks. Moreover, the integration of SWIPT with cognitive radio sensor networks (CRSN) has been proved as a vital technique for increasing throughput and effective utilization of the spectrum. In this paper, we consider a cooperative CRSN with near field SWIPT technique for autonomous vehicles (AUV) to monitor the air quality, where the secondary user (SU) forwards and sends the information to the destination through the energy harvested from the radio frequency (RF) signal, thereby focusing towards achieving maximum throughput and also address the trade-off between performance and energy consumption. The problem is formulated using Energy Harvesting and Information Transfer (EHIT) technique and the proof-of-concept is presented based upon a small-scale hardware model to test the performance of the sensors. The results indicate that the proposed SWIPT method for energy autonomous sensors provides optimal solution and has higher performance in comparison to the state-of-art solutions.

关键词： Radio frequency Wireless sensor networks Receivers Hardware Integrated circuit modeling Temperature sensors

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Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

arXiv

引用

arXiv 2022年

作者： Sudre, Carole H. Van Wijnen, Kimberlin Dubost, Florian Adams, Hieab Atkinson, David Barkhof, Frederik Birhanu, Mahlet A. Bron, Esther E. Camarasa, Robin Chaturvedi, Nish Chen, Yuan Chen, Zihao Chen, Shuai Dou, Qi Evans, Tavia Ezhov, Ivan Gao, Haojun Sanguesa, Marta Girones Gispert, Juan Domingo Anson, Beatriz Gomez Hughes, Alun D. Ikram, M. Arfan Ingala, Silvia Jaeger, H. Rolf Kofler, Florian Kuijf, Hugo J. Kutnar, Denis Li, Bo Lorenzini, Luigi Menze, Bjoern Molinuevo, Jose Luis Pan, Yiwei Puybareau, Elodie Rehwald, Rafael Su, Ruisheng Shi, Pengcheng Tillin, Therese Tochon, Guillaume Urien, Hélène van der Velden, Bas H.M. van der Velpen, Isabelle F. Wiestler, Benedikt Wolters, Frank J. Yilmaz, Pinar de Groot, Marius Vernooij, Meike W. de Bruijne, Marleen MRC Unit for Lifelong Health and Ageing at UCL University College London London United Kingdom Centre for Medical Image Computing University College London London United Kingdom School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Clinical Genetics and Radiology Erasmus MC Rotterdam Netherlands Centre for Medical Imaging University College London London United Kingdom Department of Radiology and Nuclear Medicine Amsterdam University Medical Centre Amsterdam Netherlands Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Radiology University of Massachusetts Medical School Worcester United States School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Chinese University of Hong Kong China Department of Informatics Technische Universitat Munchen Munich Germany Department of Radiology Zhejiang University Hangzhou China Image Sciences Institute University Medical Center Utrecht Utrecht Netherlands Pasqual Maragall Foundation Barcelona Spain Department of Radiology Hospital San Pau i santa Creu Barcelona Spain Department of Epidemiology Erasmus MC Rotterdam Netherlands Institute of Neurology University College London London United Kingdom Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen Shenzhen China LRDE EPITA Paris France ISEP-Institut Supérieur d’Électronique de Paris Issy-les-Moulineaux France Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Departme

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level. © 2022, CC BY-SA.

关键词： Brain

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Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals

arXiv

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arXiv 2020年

作者： Kinnunen, Tomi Delgado, Héctor Evans, Nicholas Lee, Kong Aik Vestman, Ville Nautsch, Andreas Todisco, Massimiliano Wang, Xin Sahidullah, Md Yamagishi, Junichi Reynolds, Douglas A. School of Computing University of Eastern Finland Länsikatu 15 JoensuuFI-80101 Finland Nuance Communications Inc. 39 Gran Vía Madrid28013 Spain EURECOM Campus SophiaTech 450 Route des Chappes Biot06410 France NEC Corporation 7-1 Shiba 5-chome Minato-ku Tokyo108-8001 Japan National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo Japan Université de Lorraine CNRS Inria LORIA NancyF-54000 France MIT Lincoln Laboratory Massachusetts Institute of Technology 244 Wood Street LexingtonMA02421-6426 United States

Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artificial inputs. The reliability of spoofing CMs is typically gauged using the equal error rate (EER) metric. The primitive EER fails to reflect application requirements and the impact of spoofing and CMs upon ASV and its use as a primary metric in traditional ASV research has long been abandoned in favour of risk-based approaches to assessment. This paper presents several new extensions to the tandem detection cost function (t-DCF), a recent risk-based approach to assess the reliability of spoofing CMs deployed in tandem with an ASV system. Extensions include a simplified version of the t-DCF with fewer parameters, an analysis of a special case for a fixed ASV system, simulations which give original insights into its interpretation and new analyses using the ASVspoof 2019 database. It is hoped that adoption of the t-DCF for the CM assessment will help to foster closer collaboration between the anti-spoofing and ASV research communities. Copyright © 2020, The Authors. All rights reserved.

关键词： Cost functions

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CPSA: A cyber-physical security assessment tool for situational awareness in smart grid 3

CPSA: A cyber-physical security assessment tool for situatio...

引用

3rd ACM Workshop on Cyber-Physical Systems Security and PrivaCy, CPS-SPC 2017

作者： Saxena, Neetesh Chukwuka, Victor Xiong, Leilei Grijalva, Santiago Department of Computing and Informatics Bournemouth University United Kingdom School of Electrical and Computer Engineering Georgia Institute of Technology United States

ISBN: (纸本)9781450353946

It has now become critical and important to understanding the nature of cyber-attacks and their impact on the physical operation of emerging smart electricity grids. Modeling and simulation provide a cost-effective means to develop frameworks and algorithms that address cyber-physical security challenges facing the smart grid. Existing simulation tools support either the communication network or the power system, but not both together. Thus, it is difficult to explore the effects of cyber-physical attacks on power system dynamics and operations. In order to bridge this gap, a cyber-physical co-simulator is required. In this paper, we present a novel integrated cyber-physical security co-simulator tool capable of cyber-physical security assessment (CPSA), which simulates the communication network and the power system together. The tool identifies future vulnerable states and bad measurements and guides the operator at the control center on taking appropriate action to minimize disruption of the physical power system operation due to cyber-attack. The developed tool can be used in understanding of power system monitoring, analyzing the nature of cyber-attacks, detecting bad measurement data, bad command, disabled devices and understand their impact on the operation of the power system. © 2017 ACM.

关键词： Cyber Physical System

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Tight bounds for popping algorithms

arXiv

引用

arXiv 2018年

作者： Guo, Heng He, Kun School of Informatics University of Edinburgh Informatics Forum EdinburghEH8 9AB United Kingdom Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Beijing100190 China

We sharpen run-time analysis for algorithms under the partial rejection sampling framework. Our method yields improved bounds for the cluster-popping algorithm for approximating all-terminal network reliability;the cycle-popping algorithm for sampling rooted spanning trees;the sink-popping algorithm for sampling sink-free orientations. In all three applications, our bounds are not only tight in order, but also optimal in constants. Copyright © 2018, The Authors. All rights reserved.

关键词： Trees (mathematics)

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