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Automated Classification of Airborne Pollen using Neural Networks

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Automated Classification of Airborne Pollen using Neural Net...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Julian Schiele Fabian Rabe Maximilian Schmitt Manuel Glaser Franziska Häring Jens O. Brunner Bernhard Bauer Björn Schuller Claudia Traidl-Hoffmann Athanasios Damialis Chair of Health Care Operations / Health Information Management Faculty of Business and Economics University Center of Health Sciences at Klinikum Augsburg (UNIKA-T) Chair of Software Methodologies for Distributed Systems Faculty of Computer Science University of Augsburg ZD.B Chair of Embedded Intelligence for Health Care and Wellbeing Faculty of Computer Science University of Augsburg Chair and Institute of Environmental Medicine UNIKA-T Technical University of Munich and Helmholtz Zentrum München

Pollen allergies are considered as a global epidemic nowadays, as they influence more than a quarter of the worldwide population, with this percentage expected to rapidly increase because of ongoing climate change. To date, alerts on high-risk allergenic pollen exposure have been provided only via forecasting models and conventional monitoring methods that are laborious. The aim of this study is to develop and evaluate our own pollen classification model based on deep neural networks. Airborne allergenic pollen have been monitored in Augsburg, Bavaria, Germany, since 2015, using a novel automatic Bio-Aerosol Analyzer (BAA 500, Hund GmbH). The automatic classification system is compared and evaluated against our own, newly developed algorithm. Our model achieves an unweighted average precision of 83.0 % and an unweighted average recall of 77.1 % across 15 classes of pollen taxa. Automatic, real-time information on concentrations of airborne allergenic pollen will significantly contribute to the implementation of timely, personalized management of allergies in the future. It is already clear that new methods and sophisticated models have to be developed so as to successfully switch to novel operational pollen monitoring techniques serving the above need.

关键词： Monitoring Training Neural networks Atmospheric modeling Biological system modeling Respiratory system Neurons

Possibilities about the design Lean canvas model and its adaptation in the agile testing

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Possibilities about the design Lean canvas model and its ada...

2017 Symposium for Young Scientists in Technology, Engineering and Mathematics, SYSTEM 2017

作者： Nidagundi, Padmaraj Novickis, Leonids Faculty of Computer Science and Information Technology Institute of Applied Computer Systems Riga Technical University Riga Latvia Head of the Division of Software Engineering Faculty of Computer Science and Information Technology Riga Technical University Riga Latvia

software development industry is inventing new development methodologies in last 10 years for improving the existing developing tools, process and delivering a high-quality software to end customer in short time. In recent year's waterfall development becomes less attractive in the software industry and shifted its attention towards the new software development methodologies such as agile. Agile brought the new dimension to software development and improved the development, testing process and added new challenges. There are different agile development methodologies in practice such as Scrum, FDD, Kanban, DSDM, XP, ASD, Lean, and Crystal, all these have different approaches in software development but share the same principle of the agile. Nowadays software development process adopted a new number of software tools in the overall process of development and software testing also moved towards the more automation to deploy, validate and verify the software code as early possible. In recent years, lean canvas is used for the planning and validating the business model and this paper will bring more focus on the lean canvas model design adaptation in agile software test strategy building, test planning and guiding the test team to work fast. Copyright © 2017 held by the authors.

关键词： software testing

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

A fine-grained data set and analysis of tangling in bug fixing commits

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arXiv 2020年

作者： Herbold, Steffen Trautsch, Alexander Ledel, Benjamin Aghamohammadi, Alireza Ghaleb, Taher Ahmed Chahal, Kuljit Kaur Bossenmaier, Tim Nagaria, Bhaveet Makedonski, Philip Ahmadabadi, Matin Nili Szabados, Kristof Spieker, Helge Madeja, Matej Hoy, Nathaniel G. Lenarduzzi, Valentina Wang, Shangwen Rodríguez-Pérez, Gema Colomo-Palacios, Ricardo Verdecchia, Roberto Singh, Paramvir Qin, Yihao Chakroborti, Debasish Davis, Willard Walunj, Vijay Wu, Hongjun Marcilio, Diego Alam, Omar Aldaeej, Abdullah Amit, Idan Turhan, Burak Eismann, Simon Wickert, Anna-Katharina Malavolta, Ivano Sulír, Matúš Fard, Fatemeh Henley, Austin Z. Kourtzanidis, Stratos Tuzun, Eray Treude, Christoph Shamasbi, Simin Maleki Pashchenko, Ivan Wyrich, Marvin Davis, James C. Serebrenik, Alexander Albrecht, Ella Aktas, Ethem Utku Strüber, Daniel Erbel, Johannes Institute for Software and Systems Engineering TU Clausthal Clausthal-Zellefeld Germany Institute of Computer Science University of Goettingen Goettingen Germany Department of Computer Engineering Sharif University of Technology Tehran Iran School of Computing Queen's University Kingston Canada Department of Computer Science Guru Nanak Dev University Amritsar India Karlsruhe Germany Brunel University London Uxbridge United Kingdom Institute of Computer Science University of Goettingen Germany University of Tehran Tehran Iran Ericsson Hungary Ltd. Budapest Hungary Simula Research Laboratory Fornebu Norway Technical University of Košice Košice Slovakia LUT University Finland National University of Defense Technology Changsha China University of British Columbia Kelowna Canada Østfold University College Halden Norway Vrije Universiteit Amsterdam Amsterdam Netherlands University of Auckland Auckland New Zealand University of Saskatchewan Saskatoon Canada IBM Boulder United States University of Missouri-Kansas City Kansas City United States Università della Svizzera italiana Lugano Switzerland Trent University Peterborough Canada University of Maryland Baltimore County United States Imam Abdulrahman Bin Faisal University Saudi Arabia The Hebrew University Acumen Jerusalem Israel University of Oulu Oulu Finland Monash University Melbourne Australia University of Würzburg Würzburg Germany Technische Universität Darmstadt Darmstadt Germany University of Tennessee Knoxville United States University of Macedonia Thessaloniki Greece Department of Computer Engineering Bilkent University Ankara Turkey University of Melbourne Melbourne Australia Independent Researcher Tehran Iran University of Trento Trento Italy University of Stuttgart Stuttgart Germany Purdue University West LafayetteIN United States Eindhoven University of Technology Eindhoven Netherlands Softtech Inc. Research and Development Center Istanbul34947 Turkey Radboud Universi

Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

Inferring Network Connectivity from Event Timing Patterns

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Physical Review Letters 2018年第5期121卷 054101-054101页

作者： Jose Casadiego Dimitra Maoutsa Marc Timme Chair for Network Dynamics Institute of Theoretical Physics and Center for Advancing Electronics Dresden (cfaed) Technical University of Dresden 01062 Dresden Germany Network Dynamics Max Planck Institute for Dynamics and Self-Organization (MPIDS) 37077 Göttingen Germany Artificial Intelligence Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin 10587 Berlin Germany Bernstein Center for Computational Neuroscience (BCCN) 37077 Göttingen Germany Max Planck Institute for the Physics of Complex Systems 01069 Dresden Germany

Reconstructing network connectivity from the collective dynamics of a system typically requires access to its complete continuous-time evolution, although these are often experimentally inaccessible. Here we propose a theory for revealing physical connectivity of networked systems only from the event time series their intrinsic collective dynamics generate. Representing the patterns of event timings in an event space spanned by interevent and cross-event intervals, we reveal which other units directly influence the interevent times of any given unit. For illustration, we linearize an event-space mapping constructed from the spiking patterns in model neural circuits to reveal the presence or absence of synapses between any pair of neurons, as well as whether the coupling acts in an inhibiting or activating (excitatory) manner. The proposed model-independent reconstruction theory is scalable to larger networks and may thus play an important role in the reconstruction of networks from biology to social science and engineering.

关键词： Collective behavior in networks Collective dynamics Network structure Neural information Neuronal dynamics Neuronal networks Patterns in complex systems Synapses Topological tools

Testing cyber-physical systems through Bayesian optimization

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Testing cyber-physical systems through Bayesian optimization

作者： Deshmukh, Jyotirmoy Horvat, Marko Jin, Xiaoqing Majumdar, Rupak Prabhu, Vinayak S. Department of computer science University of southern California 941 Bloom Walk Los AngelesCA90089 United States Max Planck Institute for Software Systems Paul-Ehrlich Strasse G 26 Kaiserslautern67663 Germany Toyota Technical Center United States

Many problems in the design and analysis of cyber-physical systems (CPS) reduce to the following optimization problem: given a CPS which transforms continuous-time input traces in m to continuous-time output traces in n and a cost function over output traces, find an input trace which minimizes the cost. Cyber-physical systems are typically so complex that solving the optimization problem analytically by examining the system dynamics is not feasible. We consider a black-box approach, where the optimization is performed by testing the input-output behaviour of the CPS. We provide a unified, tool-supported methodology for CPS testing and optimization. Our tool is the first CPS testing tool that supports Bayesian optimization. It is also the first to employ fully automated dimensionality reduction techniques. We demonstrate the potential of our tool by running experiments on multiple industrial case studies. We compare the effectiveness of Bayesian optimization to state-of-the-art testing techniques based on CMA-ES and Simulated Annealing. © 2017 ACM.

关键词： Cyber Physical System

Supporting defect causal analysis in practice with cross-company data on causes of requirements engineering problems

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arXiv 2017年

作者： Kalinowski, Marcos Curty, Pablo Paes, Aline Spínola, Rodrigo Felderer, Michael Ferreira, Alexandre Fernández, Daniel Méndez Wagner, Stefan Niterói Brazil Fraunhofer Project Center at UFBA Salvador Brazil Institute of Computer Science University of Innsbruck Innsbruck Austria Rio de Janeiro Brazil Software & Systems Engineering Technical University of Munich Munich Germany Institute of Software Technology University of Stuttgart Stuttgart Germany

[Context] Defect Causal Analysis (DCA) represents an efficient practice to improve software processes. While knowledge on cause-effect relations is helpful to support DCA, collecting cause-effect data may require significant effort and time. [Goal] We propose and evaluate a new DCA approach that uses cross-company data to support the practical application of DCA. [Method] We collected cross-company data on causes of requirements engineering problems from 74 Brazilian organizations and built a Bayesian network. Our DCA approach uses the diagnostic inference of the Bayesian network to support DCA sessions. We evaluated our approach by applying a model for technology transfer to industry and conducted three consecutive evaluations: (i) in academia, (ii) with industry representatives of the Fraunhofer Project Center at UFBA, and (iii) in an industrial case study at the Brazilian National Development Bank (BNDES). [Results] We received positive feedback in all three evaluations and the cross-company data was considered helpful for determining main causes. [Conclusions] Our results strengthen our confidence in that supporting DCA with cross-company data is promising and should be further investigated. Copyright © 2017, The Authors. All rights reserved.

关键词： Requirements engineering

Supporting defect causal analysis in practice with cross-company data on causes of requirements engineering problems

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Supporting defect causal analysis in practice with cross-com...

IEEE/ACM International Conference on software Engineering: software Engineering in Practice Track (ICSE-SEIP)

作者： Marcos Kalinowski Pablo Curty Aline Paes Alexandre Ferreira Rodrigo Spinola Daniel Mendez Fernandez Michael Felderer Stefan Wagner Computing Institute Fluminense Federal University (UFF) Niterói Brazil Institute of Computer Science University of Innsbruck Innsbruck Austria Information Technology Area Brazilian National Development Bank (BNDES) Rio de Janeiro Brazil Software & Systems Engineering Technical University of Munich Munich Germany Institute of Software Technology University of Stuttgart Stuttgart Germany

关键词： Bayes methods Organizations Systematics Context software Guidelines Industries

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

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arXiv 2018年

作者： Binder, Alexander Bockmayr, Michael Hägele, Miriam Wienert, Stephan Heim, Daniel Hellweg, Katharina Stenzinger, Albrecht Parlow, Laura Budczies, Jan Goeppert, Benjamin Treue, Denise Kotani, Manato Ishii, Masaru Dietel, Manfred Hocke, Andreas Denkert, Carsten Müller, Klaus-Robert Klauschen, Frederick Machine-Learning Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin Marchstr. 23 Berlin10587 Germany Systems Pathology Lab Institute of Pathology Charité University Hospital Charitéplatz 1 Berlin10117 Germany Department of Internal Medicine Infectious Diseases and Pulmology Charitéplatz 1 Berlin10117 Germany Institute of Pathology University of Heidelberg Im Neuenheimer Feld 224 Heidelberg Germany Laboratory of Cellular Dynamics Immunology Frontier Research Center Osaka University 2-2 Yamada-oka Suita Osaka565-0871 Japan ISTD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Charité Berlin Charitéplatz 1 Berlin10117 Germany Department of Brain and Cognitive Engineering Korea University 145 Anam-ro Seongbukgu Seoul136-713 Korea Republic of Max-Planck-Institute for Informatics Saarbrücken Germany Department of Pediatric Hematology and Oncology University Medical Center Hamburg-Eppendorf Hamburg Germany

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment. Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy