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Node re-ordering as a means of anomaly detection in time-evolving graphs 1

15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in databases, ECML PKDD 2016

作者： Rashidi, Lida Kan, Andrey Bailey, James Chan, Jeffrey Leckie, Christopher Liu, Wei Rajasegarar, Sutharshan Ramamohanarao, Kotagiri Data61 Victoria Research Laboratory Melbourne Australia Department of Computing and Information Systems The University of Melbourne Melbourne Australia Department of Computer Science and Information Technology RMIT University Melbourne Australia Department of Engineering and IT University of Technology Sydney Sydney Australia School of Information Technology Deakin University Geelong Australia

ISBN: (数字)9783319462271

ISBN: (纸本)9783319462264

Anomaly detection is a vital task for maintaining and improving any dynamic system. In this paper, we address the problem of anomaly detection in time-evolving graphs, where graphs are a natural representation for data in many types of applications. A key challenge in this context is how to process large volumes of streaming graphs. We propose a pre-processing step before running any further analysis on the data, where we permute the rows and columns of the adjacency matrix. This pre-processing step expedites graph mining techniques such as anomaly detection, PageRank, or graph coloring. In this paper, we focus on detecting anomalies in a sequence of graphs based on rank correlations of the reordered nodes. The merits of our approach lie in its simplicity and resilience to challenges such as unsupervised input, large volumes and high velocities of data. We evaluate the scalability and accuracy of our method on real graphs, where our method facilitates graph processing while producing more deterministic orderings. We show that the proposed approach is capable of revealing anomalies in a more efficient manner based on node rankings. Furthermore, our method can produce visual representations of graphs that are useful for graph compression. © Springer International Publishing AG 2016.

关键词： Graph theory

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Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions

arXiv

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arXiv 2018年

作者： Maier-Hein, Lena Eisenmann, Matthias Reinke, Annika Onogur, Sinan Stankovic, Marko Scholz, Patrick Arbel, Tal Bogunovic, Hrvoje Bradley, Andrew P. Carass, Aaron Carolin Feldmann Frangi, Alejandro F. Ful, Peter M. van Ginneken, Bram Hanbury, Allan Honauer, Katrin Kozubek, Michal Landman, Bennett A. Marz, Keno Maier, Oskar Maier-Hein, Klaus Menze, Bjoern H. Müller, Henning Neher, Peter F. Niessen, Wiro Rajpoot, Nasir Sharp, Gregory C. Sirinukunwattana, Korsuk Speidel, Stefanie Stock, Christian Stoyanov, Danail Taha, Abdel Aziz van der Sommen, Fons Wang, Ching-Wei Weber, Marc-André Zheng, Guoyan Jannin, Pierre Kopp-Schneider, Annette Heidelberg Germany Centre for Intelligent Machines McGill University MontrealQC Canada Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna Vienna Austria Science and Engineering Faculty Queensland University of Technology BrisbaneQLD Australia Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore United States CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Sheffield Sheffield United Kingdom Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center Nijmegen Netherlands Institute of Information Systems Engineering TU Wien Vienna Austria Heidelberg University Heidelberg Germany Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Electrical Engineering Vanderbilt University NashvilleTN United States Institute of Medical Informatics Universität zu Lübeck Lübeck Germany Heidelberg Germany Institute for Advanced Studies Department of Informatics Technical University of Munich Munich Germany Information System Institute HES-SO Sierre Switzerland Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC Rotterdam Netherlands Department of Computer Science University of Warwick Coventry United Kingdom Department of Radiation Oncology Massachusetts General Hospital BostonMA United States Institute of Biomedical Engineering University of Oxford Oxford United Kingdom National Center for Tumor Diseases Dresden Dresden Germany Heidelberg Germany Department of Computer Science University College London London United Kingdom Data Science Studio Research Studios Austria FG Vienna Austria Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands AIExplore NTUST Center of Computer Vision and Medical Imaging Graduate Institute of Biomedical Engineering National Ta

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality control

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Named-entity recognition and data visualization techniques to communicate mission command to autonomous systems

Named-entity recognition and data visualization techniques t...

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IEEE Symposium on systems and information engineering Design, SIEDS

作者： Donald Chesworth Nathan Harmon Leslie Tanner Stephanie Guerlain Michael Balazs Data Science Institute University of Virginia Department of Systems and Information Engineering University of Virginia The MITRE Corporation

As robotic systems are integrated into mission operations, they provide key benefits over traditional manned systems such as increased endurance, versatility, and risk reduction for personnel. In order for robotic systems to become fully integrated into United States Army missions, they will need to gain a level of autonomy that is closer to that of human personnel. Under Mission Command, the current system of issuing orders, an Operational Order (OPORD) contains the information required to execute a mission. The structure allows for authors of OPORDs to leave many details of the operation open to the discretion of the reader, making task interpretation and execution difficult for an autonomous system. The goal of this project, and one step of many in the process of automating systems to read OPORDs, is to show how OPORDs can be annotated with relevant information (such as locations, coordinates, and organizations) using the natural language processing techniques of tokenization and named-entity recognition (NER). Using A-fold cross validation of a Conditional Random Field (CRF) sequence model on 9 OPORDs containing 38,551 tokens, we were able to extract entities with an overall precision of 0.702, recall of 0.478, and F-measure of 0.569.

关键词： Organizations Robot kinematics Hidden Markov models data visualization Conferences Natural language processing

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VISNET: A Study Case for Visualize Sensor Ad-Hoc Networks Behavior

VISNET: A Study Case for Visualize Sensor Ad-Hoc Networks Be...

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International Conference on Virtual Reality and Visualization (ICVRV)

作者： Jorge Gonzalez Xun Luo Gustavo Alomia Claudia Zúñiga Yi Chen School of Computer Science and Engineering Tianjin University of Technology Tianjin China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Research Group COMBA RD University of Santiago de Cali Cali Colombia Beijing Key Laboratory of Big Data Technology for Food Safety School of Computer and Information Engineering Beijing Technology and Business University Beijing China

ISBN: (纸本)9781538626375;9781538626368

Ad-hoc sensor networks are difficult to maintain and follow, due to the facts that networks are autonomous, and nodes are usually located in a remote place. The need to monitor and keep records of the operation of these networks motivates us to develop and implement a tool that allows not only monitoring the network but also using the data circulating in it to provide useful information to a community, either about the conditions of the environment, climate or other type of information that can be captured through sensors. This paper proposes a way to implement a visualization tool and shows the experiments performed over a wireless Ad-hoc network using temperature and humidity sensors.

关键词： data visualization Humidity Ad hoc networks Temperature sensors Temperature distribution Virtual reality Monitoring

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Optimal Estimation of Sensor Biases for Asynchronous Multi-Sensor data Fusion

arXiv

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arXiv 2017年

作者： Pu, Wenqiang Liu, Ya-Feng Yan, Junkun Liu, Hongwei Luo, Zhi-Quan National Laboratory of Radar Signal Processing Collaborative Innovation Center of Information Sensing and Understanding Xidian University Xi'an710071 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen518100 China

An important step in a multi-sensor surveillance system is to estimate sensor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares (LS) formulation for the problem by assuming the existence of a reference target moving with an (unknown) constant velocity. We also propose an efficient block coordinate decent (BCD) optimization algorithm, with a judicious initialization, to solve the problem. The proposed BCD algorithm alternately updates the range and azimuth bias estimates by solving linear least squares problems and semidefinite programs (SDPs). In the absence of measurement noise, the proposed algorithm is guaranteed to find the global solution of the problem and the true biases. Simulation results show that the proposed algorithm significantly outperforms the existing approaches in terms of the root mean square error (RMSE). Copyright © 2017, The Authors. All rights reserved.

关键词： Mean square error

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Extracting Addresses from News Reports Using Conditional Random Fields

Extracting Addresses from News Reports Using Conditional Ran...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Donald E. Brown Xiaoqian Liu Department of Systems and Information Engineering University of Virginia Charlottesville VA Data Science Institute University of Virginia Charlottesville VA

ISBN: (纸本)9781509061686

Spatial analysis in many fields requires effective address extraction from text reports. This problem is of particular importance in social science where news reports contain information about socially relevant incidents. Previous address extraction work focuses on web pages where addresses are separated from other text, however news reports contain addresses embedded in text. Hence, the need for different methods. This paper describes and compares three supervised learning approaches and one semi-supervised learning approach to automatically extract street addresses from news reports. Experimental results with actual news reports show performance close to that achieved for web pages and some lift in accuracy from the semi-supervised approach. These results also show that different news sources produce different outcomes.

关键词： data mining Web pages Training Decision trees Boosting Semisupervised learning Labeling

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A Spatial-Cue-Based Probabilistic Model for Bird Song Scene Analysis

A Spatial-Cue-Based Probabilistic Model for Bird Song Scene ...

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International Conference on data science and Advanced Analytics (DSAA)

作者： Ryosuke Kojima Osamu Sugiyama Kotaro Hoshiba Reiji Suzuki Kazuhiro Nakadai Department of Biomedical Data Intelligence Kyoto University Kyoto Japan Preemptive Medicine & Lifestyle-Related Disease Research Center Kyoto University Hospital Kyoto Japan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo JAPAN Graduate School of Information Science Nagoya University Aichi Japan Honda Research Institute Japan Co. Ltd. Saitama JAPAN

This paper addresses bird song scene analysis focusing on location of birds and acoustic features of bird songs. Such a research area usually requires manual annotation related to positions and/or vocalization types of the target animals for a large amount of observed data. However, this manual annotation has two problems. One is that it is tough to annotate data observed in real environments because environmental noise exist and sound is reflected by trees and the ground, and also several birds at different locations may sing at the same time. The other is that it is inevitable that manual annotation produces inaccurate and inconsistent labels due to human errors and annotators' individual differences. For the first problem, we propose a Spatial-Cue-Based Probabilistic Model (SCBPM), which is a probabilistic model to estimate the maximum likelihood result for a bird song scene analysis by integrating sound source detection, localization, separation and identification based on spatial information of sound sources. For the second problem, we employ a semiautomatic annotation approach, in which a semi-supervised training method is deduced for SCBPM. This method decreases the amount of manual annotation. Preliminary experiments using recorded bird song data from the wild revealed that our system outperformed a conventional bird song scene analysis system by simply connecting sound source detection, localization, separation and identification in a cascade way in terms of identification accuracy.

关键词： Birds Acoustics Computational modeling Microphone arrays Multiple signal classification Spectrogram

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Survival analysis of carbapenemase-producing enterobacteriaceae infections in hospital patients

Survival analysis of carbapenemase-producing enterobacteriac...

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IEEE Symposium on systems and information engineering Design, SIEDS

作者： Ian Clark Antoine Rigaut Amy Mathers Donald Brown Laura E. Barnes Data Science Institute University of Virginia Division of Infectious Diseases and International Health University of Virginia Department of Systems and Information Engineering University of Virginia

The Centers for Disease Control and Prevention (CDC) estimate that more than two million people are sickened every year with antibiotic-resistant bacterial infections, and at least 23,000 die as a result of these infections in the U.S. alone. Carbapenem-resistant Enterobacteriaceae (CRE) are a particularly important subgroup with the ability to share genetic material, including the gene responsible for the production of carbapenemase enzyme. As a major threat to public health, CRE primarily affects sick patients in acute long-term health care facilities, such as Intensive Care Units. This study uses clinical data from a major U.S. hospital, as well as survival analysis methods to estimate time-to-colonization, patient risk of CRE colonization, and risk factors. A survival model was developed using Cox proportional-hazards regression to estimate time of infection. Additionally, we also used Kaplan-Meier estimation to approximate survival time. Our results support CDC's understanding of CRE infection. Sick patients with a history trauma and of staying in an ICU, which undergo certain procedures such as dialysis, fluoroscopy, respiratory intubation and mechanical ventilation, are more likely to get colonized by CRE. The fit was evaluated using the likelihood ratio test and we have found all our models to be significant.

关键词： Hospitals Antibiotics Sociology Statistics Strain Conferences Estimation

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Optimization of sepsis risk assessment for ward patients

Optimization of sepsis risk assessment for ward patients

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IEEE Symposium on systems and information engineering Design, SIEDS

作者： Shannon Mitchell Katherine Schinkel Yifeng Song Yuanbo Wang John Ainsworth Travis Halbert Stephen Strong Jinghe Zhang Christopher C. Moore Laura E. Barnes Data Science Institute University of Virginia University of Virginia Health System School of Medicine University of Virginia Systems & Information Engineering University of Virginia Division of Infectious Diseases and International Health University of Virginia Department of Systems and Information Engineering University of Virginia

Sepsis is the systemic response to infection complicated by organ dysfunction. It is a leading cause of in-hospital mortality, with an observed mortality of 20-40%, and is associated with significantly high costs. Furthermore, patients who survive sepsis are more likely to have permanent organ damage, cognitive impairment and physical disability. Survival from sepsis is dependent on early and targeted treatment. Accepted methods for identifying and predicting sepsis have typically been based on intensive care unit (ICU) patients and founded upon a limited and outdated definition of sepsis based on systemic inflammatory response syndrome (SIRS) criteria - one which is not clinically validated and shown to miss at least 1 out of 8 cases. Recently updated consensus guidelines on sepsis have been published and offer the possibility of improved EWS and predictive models. Our goal was to identify non-ICU ward patients with sepsis earlier and more accurately using the newly established sepsis definition and improved predictive models. Using multivariate logistic regression and routinely available physiological and laboratory data from electronic health records (EHRs), we derived an EWS that identifies at-risk ward patients 12-24 hours prior to sepsis onset with an area under the receiver operating characteristic curve (AUC) result of 74%. This model, when applied on a separate ICU population, achieved an AUC curve result of 56%, indicating the model has worse performance in this setting.

关键词： Blood Sociology Statistics Antibiotics Biomedical monitoring Predictive models databases

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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