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IEEE International Conference on Multimedia and Expo (ICME)

作者： Fei Tao Junbiao Pang Chunjie Zhang Liang Li Li Su Weigang Zhang Qingming Huang Guiping Su School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China College of Metropolitan Transportation Beijing University of Technology China Key Lab on Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9781467372596

In web topic detection, detecting “hot” topics from enormous User-Generated Content (UGC) on web data poses two main difficulties that conventional approaches can barely handle: 1) poor feature representations from noisy images and short texts; and 2) uncertain roles of modalities where visual content is either highly or weakly relevant to textual cues due to less-constrained data. In this paper, following the detection by ranking approach, we address the problem by learning a robust shared representation from multiple, noisy and complementary features, and integrating both textual and visual graphs into a k-Nearest Neighbor Similarity Graph (k-N 2 SG). Then Non-negative Matrix Factorization using Random walk (NMFR) is introduced to generate topic candidates. An efficient fusion of multiple graphs is then done by a Latent Poisson Deconvolution (LPD) which consists of a poisson deconvolution with sparse basis similarities for each edge. Experiments show significantly improved accuracy of the proposed approach in comparison with the state-of-the-art methods on two public data sets.

关键词： Visualization Deconvolution Robustness Feature extraction Optimization Correlation Noise measurement

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Event-based clustering for reducing labeling costs of incident-related microposts 2

Event-based clustering for reducing labeling costs of incide...

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2nd International Workshop on Mining Urban data, MUD 2015 - co-located with 32nd International Conference on Machine Learning, ICML 2015

作者： Schulz, Axel Ristoski, Petar Fürnkranz, Johannes Janssen, Frederik DB Mobility Logistics AG Telecooperation Lab Technische Universität Darmstadt Germany Data and Web Science Group University of Mannheim Germany Knowledge Engineering Group Technische Universität Darmstadt Germany

Automatically identifying the event type of event-related information in the sheer amount of social media data makes machine learning inevitable. However, this is highly dependent on (1) the number of correctly labeled instances and (2) labeling costs. Active learning has been proposed to reduce the number of instances to label. Though, current approaches focus on the thematic dimension, i.e., the event type, for selecting instances to label;other metadata such as spatial and temporal information that is helpful for achieving a more fine-grained clustering is currently not taken into account. Also, labeling quality is always assumed to be perfect as currently no qualitative information is present for manual event type labeling. In this paper, we present a novel event-based clustering strategy that makes use of temporal, spatial, and thematic metadata to determine instances to label. Furthermore, we also inspect the quality of the manual labeling in a crowdsourcing study by comparing experts and non-experts. An evaluation on incident-related tweets shows that (i) labels provided by crowdsourcing are of acceptable quality and (ii) our selection strategy for active learning outperforms current state-of-theart approaches even with few labeled instances. Copyright © 2015 for this paper by its authors.

关键词： Metadata

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Event-based clustering for reducing labeling costs of event-related microposts 9

Event-based clustering for reducing labeling costs of event-...

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9th International Conference on Web and Social Media, ICWSM 2015

作者： Schulz, Axel Janssen, Frederik Ristoski, Petar Fürnkranz, Johannes DB Mobility Logistics AG Germany Telecooperation Lab Technische Universität Darmstadt Germany Data and Web Science Group University of Mannheim Germany Knowledge Engineering Group Technische Universität Darmstadt Germany

ISBN: (纸本)9781577357339

Automatically identifying the event type of event-related information in the sheer amount of social media data makes machine learning inevitable. However, this is highly dependent on (1) the number of correctly labeled instances and (2) labeling costs. Active learning has been proposed to reduce the number of instances to label. Albeit the thematic dimension is already used, other metadata such as spatial and temporal information that is helpful for achieving a more fine-grained clustering is currently not taken into account. In this paper, we present a novel event-based clustering strategy that makes use of temporal, spatial, and thematic metadata to determine instances to label. An evaluation on incident-related tweets shows that our selection strategy for active learning outperforms current state-of-the-art approaches even with few labeled instances. © Copyright 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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Enhancing Student Engagement through Personalized Motivations

Enhancing Student Engagement through Personalized Motivation...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Athanasios Staikopoulos Ian OKeeffe Bilal Yousuf Owen Conlan Eddie Walsh Vincent Wade Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin University of Dublin Trinity College Dublin IE

A lack of appropriate engagement in online learning courses is a significant factor leading to poor performance and high dropout rates. This paper presents a framework that provides personalized motivations in the form of feedback, advices and reminders in order to enhance the engagement of students with online learning. It also describes the design, implementation and evaluation of the framework which has been used by over one hundred students. The paper focuses on how the framework by modelling and monitoring the level of engagement of students, it provides tailored motivations to stimulate and sustain their engagement. Finally, the paper compares the results from a previous student cohort (where no motivations were provided) to highlight how the framework has significantly increased the level of engagement of students.

关键词： Monitoring Engines Education Context Electronic mail Least squares approximations Portals

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Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019

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eClinicalMedicine 2023年 64卷 102193-102193页

作者： Wu, Dongze Jin, Yingzhao Xing, Yuhan Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abd-Allah, Foad Abdelmasseh, Michael Abdollahifar, Mohammad-Amin Abdulah, Deldar Morad Abedi, Aidin Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abuabara, Katrina Abyadeh, Morteza Addo, Isaac Yeboah Adeniji, Kayode Nelson Adepoju, Abiola Victor Adesina, Miracle Ayomikun Adnani, Qorinah Estiningtyas Sakilah Afarideh, Mohsen Aghamiri, Shahin Agodi, Antonella Agrawal, Anurag Arriagada, Constanza Elizabeth Aguilera Ahmad, Aqeel Ahmad, Danish Ahmad, Sohail Ahmadi, Ali Ahmed, Ayman Aithala, Janardhana P. Ajadi, Abdullateef Abiodun Ajami, Marjan Akbarzadeh-Khiavi, Mostafa Alahdab, Fares AlBataineh, Mohammad T. Alemi, Sharifullah Al-Gheethi, Adel Ali Saeed Ali, Liaqat Alif, Sheikh Mohammad Almazan, Joseph Uy Almustanyir, Sami Alqahtani, Jaber S. Alqasmi, Ibrahim Altaf, Ihsan Ullah Khan Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Al-Worafi, Yaser Mohammed Aly, Hany Amani, Reza Amu, Hubert Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Ansar, Adnan Ansariniya, Hossein Anyasodor, Anayochukwu Edward Arabloo, Jalal Arefnezhad, Reza Arulappan, Judie Asghari-Jafarabadi, Mohammad Ashraf, Tahira Atata, Jamila Abdulhamid Athari, Seyyed Shamsadin Atlaw, Daniel Atout, Maha Moh'd Wahbi Aujayeb, Avinash Awan, Asma Tahir Ayatollahi, Haleh Azadnajafabad, Sina Azzam, Ahmed Y. Badawi, Alaa Badiye, Ashish D. Bagherieh, Sara Baig, Atif Amin Bantie, Berihun Bantie Barchitta, Martina Bardhan, Mainak Barker-Collo, Suzanne Lyn Barone-Adesi, Francesco Batra, Kavita Bayileyegn, Nebiyou Simegnew Behnoush, Amir Hossein Belgaumi, Uzma Iqbal Bemanalizadeh, Maryam Bensenor, Isabela M. Beyene, Kebede A. Bhagavathula, Akshaya Srikanth Bhardwaj, Pankaj Bhaskar, Sonu Bhat, Ajay Nagesh Bitaraf, Saeid Bitra, Veera R. Boloor, Archith Bora, Kaustubh Botelho, João Silva Buchbinder, Rachelle Calina, Daniela Cámera, Luis Alberto Carvalho, Andre F. Chan, Jeffrey Shi Kai Chattu, Vijay Kumar Abebe, Endeshaw Chekol Department of Rheumatology and Immunology Sichuan Provincial People's Hospital School of Medicine University of Electronic Science and Technology of China China Department of Medicine & Therapeutics The Chinese University of Hong Kong Hong Kong Department of Paediatrics The Chinese University of Hong Kong Hong Kong School of Pharmacy The Chinese University of Hong Kong Hong Kong Jockey Club School of Public Health and Primary Care The Chinese University of Hong Kong Hong Kong Department of Nursing Woldia University Woldia Ethiopia Department of Orthopedic Surgery Harvard University Boston MA United States Postgraduate Medical Education Harvard University Boston MA United States Maternal Fetal Care Center Harvard University Boston MA United States Department of Health Policy and Oral Epidemiology Harvard University Boston MA United States Division of General Internal Medicine Harvard University Boston MA United States Department of Orthopaedic Surgery Shahid Beheshti University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Biotechnology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran National Nutrition and Food Technology Research Institute Shahid Beheshti University of Medical Sciences Tehran Iran Department of Immunology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Medical Genetics Shahid Beheshti University of Medical Sciences Tehran Iran Center for Comprehensive Genetic Services Shahid Beheshti University of Medical Sciences Tehran Iran Cancer Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Functional Neuro

Background: The causes for immune-mediated inflammatory diseases (IMIDs) are diverse and the incidence trends of IMIDs from specific causes are rarely studied. The study aims to investigate the pattern and trend of IMIDs from 1990 to 2019. Methods: We collected detailed information on six major causes of IMIDs, including asthma, inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, psoriasis, and atopic dermatitis, between 1990 and 2019, derived from the Global Burden of Disease study in 2019. The average annual percent change (AAPC) in number of incidents and age standardized incidence rate (ASR) on IMIDs, by sex, age, region, and causes, were calculated to quantify the temporal trends. Findings: In 2019, rheumatoid arthritis, atopic dermatitis, asthma, multiple sclerosis, psoriasis, inflammatory bowel disease accounted 1.59%, 36.17%, 54.71%, 0.09%, 6.84%, 0.60% of overall new IMIDs cases, respectively. The ASR of IMIDs showed substantial regional and global variation with the highest in High SDI region, High-income North America, and United States of America. Throughout human lifespan, the age distribution of incident cases from six IMIDs was quite different. Globally, incident cases of IMIDs increased with an AAPC of 0.68 and the ASR decreased with an AAPC of −0.34 from 1990 to 2019. The incident cases increased across six IMIDs, the ASR of rheumatoid arthritis increased (0.21, 95% CI 0.18, 0.25), while the ASR of asthma (AAPC = −0.41), inflammatory bowel disease (AAPC = −0.72), multiple sclerosis (AAPC = −0.26), psoriasis (AAPC = −0.77), and atopic dermatitis (AAPC = −0.15) decreased. The ASR of overall and six individual IMID increased with SDI at regional and global level. Countries with higher ASR in 1990 experienced a more rapid decrease in ASR. Interpretation: The incidence patterns of IMIDs varied considerably across the world. Innovative prevention and integrative management strategy are urgently needed to mitigate the increasing ASR of rh

关键词： Global burden of disease study Immune-mediated inflammatory disease Incidence Trend

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A Secure Collusion-Aware and Probability-Aware Range Query Processing in Tiered Sensor Networks

A Secure Collusion-Aware and Probability-Aware Range Query P...

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Reliable Distributed Systems

作者： Lei Dong Xuan Chen Jianxiang Zhu Hong Chen Ke Wang Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing China School of Information Renmin University of China Beijing China School of Computing Science Simon Fraser University Burnaby BC Canada Key Lab. of Data Eng. & Knowledge Eng. of Minist. of Educ. Beijing China

With high expansibility and efficient power usage, tiered wireless sensor networks are widely deployed in many fields as an important part of Internet of Things (IoTs). It is challenging to process range query while protecting sensitive data from adversaries. Moreover, most existing work focuses on privacy-preserving range query neglecting collusion attacks and probability attacks, which are more threatening to network security. In this paper, we first propose a secure range query protocol called secRQ, which not only protects data privacy, but also resists collusion attacks and probability attacks in tiered wireless sensor networks. Generalized inverse matrices and distance-based range query mechanism are used to guarantee security as well as high efficiency and accuracy. Besides, we propose the mutual verification scheme to verify the integrity of query results. Finally, both theoretical analysis and experimental results confirm the security, efficiency and accuracy of secRQ.

关键词： Wireless sensor networks Protocols data privacy Biomedical monitoring Query processing Monitoring Heart rate

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Simulation, comparison and analysis of Wireless Sensor Networks protocols: LEACH, LEACH-C, LEACH-1R, and HEED

Simulation, comparison and analysis of Wireless Sensor Netwo...

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International Conference on Electrical engineering (ICEE)

作者： Mohammed Omari Soumia Laroui Department of Computer Science and Engineering Shahid Beheshti University Tehran Iran Knowledge and Data Engineering Group Trinity College Dublin LDDI Laboratory Computer Science Department University of Colonel Ahmed Draya Adrar 01000 Algeria

Wireless Sensor Networks (WSNs) are made up of tiny sensor nodes which sense the data and communicate to the base station via other nodes. These sensor nodes are inexpensive portable devices with limited processing power and energy resources which make them in need of smart clustering protocols. Many clustering and routing protocols were proposed in the literature to serve large networks of such tiny devices. In this paper, we have implemented and analyzed different clustering protocols, namely LEACH, LEACH-C, LEACH-1R, and HEED using MATLAB environment. These clustering protocols are compared in different terms such as residual energy, data delivery to the base station, maximum number of rounds and the number of live nodes. Experimental results showed a better performance of the LEACH protocols when compared to the different versions of HEED. Moreover, LEACH-1R proved to be efficient in terms of network lifetime.

关键词： Wireless sensor networks Base stations Mathematical model Energy consumption Batteries Routing protocols

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Temporal reasoning on Twitter streams using semantic web technologies

Temporal reasoning on Twitter streams using semantic web tec...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Meng Cui Wei Tai Declan O'Sullivan Trinity College Dublin School of Computer Science & Statistics Dublin Ireland Knowledge and Data Engineering Group School of Computer Science & Statistics Dublin Ireland University of Dublin Trinity College Dublin IE

There has been a significant increase in recent years in the volume and diversity of streams of data, data streams from sensors, data streams arising from the analysis of content or data mining, right through to user generated Twitter streams. There has been a corresponding increase in demand for more real-time analysis of these streams in order to spot significant events and trends of interest to an individual or business. This has resulted in an increased need to achieve efficient temporal reasoning upon the streams. In this paper, we present a novel approach to perform temporal reasoning on real time streams of data using Semantic Web Technologies so that we could derive more valuable information by taking account of the time dimension. Moreover, in order to deal with such high-frequency data, several filter mechanisms have been implemented to, significantly, improve the performance of the reasoning process. In order to illustrate and evaluate the approach, the real-time analysis of Twitter data is taken as a concrete use case for such data streams.

关键词： Cognition Resource description framework Twitter Semantics Real-time systems Filtering theory

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