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Improving search strategies of auditors - A focus group on reflection interventions 7

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Improving search strategies of auditors - A focus group on r...

7th Workshop on Awareness and Reflection in technology Enhanced Learning, ARTEL 2017

作者： Fessl, Angela Pammer, Viktoria Wiese, Michael Thalmann, Stefan Know-Center Inffeldgasse 13 GrazA-8010 Austria Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 13 Graz8010 Austria Ernst and Young Wirtschaftsprufungsgesellschaft Wittekindstraße 1a Essen45131 Germany

Financial auditors routinely search internal as well as public knowledge bases as part of the auditing process. Efficient search strategies are crucial for knowledge workers in general and for auditors in particular. Modern search technology quickly evolves;and features beyond keyword search like fac-etted search or visual overview of knowledge bases like graph visualisations emerge. It is therefore desirable for auditors to learn about new innovations and to explore and experiment with such technologies. In this paper, we present a reflection intervention concept that intends to nudge auditors to reflect on their search behaviour and to trigger informal learning in terms of by trying out new or less frequently used search features. The reflection intervention concept has been tested in a focus group with six auditors using a mockup. Foremost, the discussion centred on the timing of reflection interventions and how to raise mo-tivation to achieve a change in search behaviour.

关键词： Search engines

iLCM - A virtual research infrastructure for large-scale qualitative data

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arXiv 2018年

作者： Niekler, Andreas Bleier, Arnim Kahmann, Christian Posch, Lisa Wiedemann, Gregor Erdogan, Kenan Heyer, Gerhard Strohmaier, Markus Faculty of Mathematics and Computer Science University Leipzig Augustusplatz 10 Leipzig04109 Germany Department of Computational Social Science GESIS Leibniz Institute for the Social Sciences Unter Sachsenhausen 6-8 Cologne50667 Germany Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 16c Graz8010 Austria HumTec Institute RWTH Aachen University Theaterplatz 14 Aachen52062 Germany

The iLCM project pursues the development of an integrated research environment for the analysis of structured and unstructured data in a "Software as a Service" architecture (SaaS). The research environment addresses requirements for the quantitative evaluation of large amounts of qualitative data with text mining methods as well as requirements for the reproducibility of data-driven research designs in the social sciences. For this, the iLCM research environment comprises two central components. First, the Leipzig Corpus Miner (LCM), a decentralized SaaS application for the analysis of large amounts of news texts developed in a previous Digital Humanities project. Second, the text mining tools implemented in the LCM are extended by an "Open Research Computing" (ORC) environment for executable script documents, so-called "notebooks". This novel integration allows to combine generic, high-performance methods to process large amounts of unstructured text data and with individual program scripts to address specific research requirements in computational social science and digital humanities. ***. Copyright © 2018, The Authors. All rights reserved.

关键词： Behavioral research

Stroke genetics informs drug discovery and risk prediction across ancestries (vol 611, pg 115, 2022)

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NATURE 2022年第7938期612卷 E7-E7页

作者： Mishra, Aniket Malik, Rainer Hachiya, Tsuyoshi Jurgenson, Tuuli Namba, Shinichi Posner, Daniel C. Kamanu, Frederick K. Koido, Masaru Le Grand, Quentin Shi, Mingyang He, Yunye Georgakis, Marios K. Caro, Ilana Krebs, Kristi Liaw, Yi-Ching Vaura, Felix C. Lin, Kuang Winsvold, Bendik Slagsvold Srinivasasainagendra, Vinodh Parodi, Livia Bae, Hee-Joon Chauhan, Ganesh Chong, Michael R. Tomppo, Liisa Akinyemi, Rufus Roshchupkin, Gennady V. Habib, Naomi Jee, Yon Ho Thomassen, Jesper Qvist Abedi, Vida Carcel-Marquez, Jara Nygaard, Marianne Leonard, Hampton L. Yang, Chaojie Yonova-Doing, Ekaterina Knol, Maria J. Lewis, Adam J. Judy, Renae L. Ago, Tetsuro Amouyel, Philippe Armstrong, Nicole D. Bakker, Mark K. Bartz, Traci M. Bennett, David A. Bis, Joshua C. Bordes, Constance Borte, Sigrid Cain, Anael Ridker, Paul M. Cho, Kelly Chen, Zhengming Cruchaga, Carlos Cole, John W. de Jager, Phil L. de Cid, Rafael Endres, Matthias Ferreira, Leslie E. Geerlings, Mirjam I. Gasca, Natalie C. Gudnason, Vilmundur Hata, Jun He, Jing Heath, Alicia K. Ho, Yuk-Lam Havulinna, Aki S. Hopewell, Jemma C. Hyacinth, I. Hyacinth Inouye, Michael Jacob, Mina A. Jeon, Christina E. Jern, Christina Kamouchi, Masahiro Keene, Keith L. Kitazono, Takanari Kittner, Steven J. Konuma, Takahiro Kumar, Amit Lacaze, Paul Launer, Lenore J. Lee, Keon-Joo Lepik, Kaido Li, Jiang Li, Liming Manichaikul, Ani Markus, Hugh S. Marston, Nicholas A. Meitinger, Thomas Mitchell, Braxton D. Montellano, Felipe A. Morisaki, Takayuki Mosley, Thomas H. Nalls, Mike A. Nordestgaard, Borge G. O'Donnell, Martin J. Okada, Yukinori Onland-Moret, N. Charlotte Ovbiagele, Bruce Peters, Annette Psaty, Bruce M. Rich, Stephen S. Rosand, Jonathan Sabatine, Marc S. Sacco, Ralph L. Saleheen, Danish Sandset, Else Charlotte Salomaa, Veikko Sargurupremraj, Muralidharan Sasaki, Makoto Satizabal, Claudia L. Schmidt, Carsten O. Shimizu, Atsushi Smith, Nicholas L. Sloane, Kelly L. Sutoh, Yoichi Sun, Yan V. Tanno, Kozo Tiedt, Steffen Tatlisumak, Turgut Torres-Aguila, Nuria P. Tiwari, Hemant K. Tregouet, Bordeaux Population Health Research Center University of Bordeaux Inserm UMR 1219 Bordeaux France Department of Public Health Bordeaux University Hospital Bordeaux France Department of Neurology Memory Clinic Bordeaux University Hospital Bordeaux France Department of Neurology Institute for Neurodegenerative Diseases CHU de Bordeaux Bordeaux France Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and the Massachusetts Institute of Technology Cambridge MA USA Munich Cluster for Systems Neurology Munich Germany German Center for Neurodegenerative Diseases (DZNE) Munich Germany Iwate Tohoku Medical Megabank Organization Iwate Medical University Iwate Japan Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia Institute of Mathematics and Statistics University of Tartu Tartu Estonia Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland University Center for Primary Care and Public Health Lausanne Switzerland Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Department of Genome Informatics Graduate School of Medicine The University of Tokyo Tokyo Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Integrated Frontier Research for Medical Science Division Institute for Open and Transdisciplinary Research Initiatives Osaka University Suita Japan Center for Infectious Disease Education and Research (CiDER) Osaka University Suita Japan Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA TIMI S

What if factories looked like forests? Redesigning the manufacturing industry 4.0 workplaces with an augmented reality inspired nature metaphor

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What if factories looked like forests? Redesigning the manuf...

2017 International Conference on Knowledge Technologies and data-Driven Business Workshop, i-Know-WS 2017

作者： Barreiros, Carla Pammer, Viktoria Oberbichler, Ulf Veas, Eduardo Graz University of Technology Interactive Systems and Data Science Austria Know Center GmbH Ubiquitous Personal Computing Austria Alphagate GmbH Austria Know Center GmbH Knowledge Visualization Austria

In the Industry 4.0 environments, machines are becoming increasingly more complex, and tasks like machine configuration and maintenance, demand higher human expertise, thus operators have to be able to deal with such complex systems. In this paper we introduce an AR nature inspired metaphor - BioIoT- to communicate information about complex, real-time processes in an engaging, interactive and apparent manner. In addition, we present an Industry 4.0 use case scenario where the BioIoT concept can be applied. We emphasize the contributions of such concept in the future manufacturing industrial workplaces.

关键词： Industry 4.0

Learning analytics to improve writing skills for young children – an holistic approach

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Journal of Research in Innovative Teaching and Learning 2017年第2期10卷 143-159页

作者： Steinhauer, Nina Gros, Michael Ebner, Martin Ebner, Markus Huppertz, Anneliese Cormann, Mike Biermeier, Susanne Burk, Lena Edtstadler, Konstanze Gabriel, Sonja Wintschnig, Martina Aspalter, Christian Martich, Susanne Landesinstitut für Pädagogik und Medien Saarbrücken Germany Department of Educational Technology Graz University of Technology Graz Austria Institute for Interactive Systems and Data Science Faculty of Computer Science and Biomedical Engineering Technische Universitat Graz Graz Austria School of Raeren Raeren Belgium Albert-Weisgerber School St Ingbert Germany Institute of Professionalisation in Early Childhood and Primary Teacher Education University College of Teacher Education Steiermark Graz Austria University College of Teacher Education Vienna/Krems Vienna Austria

Purpose: Due to the important role of orthography in society, the project called IDeRBlog presented in this paper created a web-based tool to motivate pupils to write text as well as to read and to comment on texts written by fellow students. In addition, IDeRBlog aims to improve student’s German orthography skills and supports teachers and parents with training materials for their students. The paper aims to discuss these issues. Design/methodology/approach: With the aid of learning analytics, the submitted text is analyzed and special feedback is given to the students so that they can try to correct the misspelled words themselves. The teachers as well as the parents are benefiting from the analysis and exercises suggested by the system. Findings: A recent study showed the efficiency of the system in form of an improvement of the students’ orthographic skills. Over a period of four months 70 percent of the students achieved a significant reduction of their spelling mistakes. Originality/value: IDeRBlog is an innovative approach to improving orthography skills combining blogging and new media with writing and practice. © 2017, Nina Steinhauer, Michael Gros, Martin Ebner, Markus Ebner, Anneliese Huppertz, Mike Cormann, Susanne Biermeier, Lena Burk, Konstanze Edtstadler, Sonja Gabriel, Martina Wintschnig, Christian Aspalter and Susanne Martich.

关键词： German orthography German spelling acquisition Learning analytics technology enhanced learning

No random transits in CHEOPS observations of HD 139139

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arXiv 2023年

作者： Alonso, R. Hoyer, S. Deleuil, M. Simon, A.E. Beck, M. Benz, W. Florén, H.-G. Guterman, P. Borsato, L. Brandeker, A. Gandolfi, D. Wilson, T.G. Zingales, T. Alibert, Y. Anglada, G. Bárczy, T. Navascues, D. Barrado Barros, S.C.C. Baumjohann, W. Beck, T. Billot, N. Bonfils, X. Broeg, Ch. Charnoz, S. Cameron, A. Collier van Damme, C. Corral Csizmadia, Sz. Cubillos, P.E. Davies, M.B. Deline, A. Delrez, L. Demangeon, O.D.S. Demory, B.-O. Ehrenreich, D. Erikson, A. Fortier, A. Fossati, L. Fridlund, M. Gillon, M. Güdel, M. Günther, M.N. Heitzmann, A. Helling, Ch. Isaak, K.G. Kiss, L.L. Kopp, E. Lam, K.W.F. Laskar, J. des Etangs, A. Lecavelier Lendl, M. Magrin, D. Maxted, P.F.L. Mordasini, Ch. Nascimbeni, V. Olofsson, G. Ottensamer, R. Pagano, I. Pallé, E. Peter, G. Piotto, G. Pollacco, D. Queloz, D. Ragazzoni, R. Rando, N. Rauer, H. Ribas, I. Santos, N.C. Scandariato, G. Ségransan, D. Smith, A.M.S. Sousa, S.G. Stalport, M. Szabó, Gy.M. Thomas, N. Udry, S. Ulmer, B. Van Grootel, V. Venturini, J. Verrecchia, F. Walton, N.A. Instituto de Astrofísica de Canarias Vía Láctea s/n Tenerife La Laguna38200 Spain Departamento de Astrofísica Universidad de La Laguna As-trofísico Francisco Sanchez s/n Tenerife La Laguna38206 Spain Aix Marseille Univ CNRS CNES LAM 38 rue Frédéric Joliot-Curie Marseille13388 France Physikalisches Institut University of Bern Gesellschaftsstrasse 6 Bern3012 Switzerland Observatoire Astronomique de l’Université de Genève Chemin Pegasi 51 VersoixCH-1290 Switzerland Center for Space and Habitability University of Bern Gesellschaftsstrasse 6 Bern3012 Switzerland Department of Astronomy Stockholm University AlbaNova University Center Stockholm10691 Sweden Division Technique INSU CS20330 La Seyne sur Mer83507 cedex France INAF Osservatorio Astronomico di Padova Vicolo dell’Osservatorio 5 Padova35122 Italy Dipartimento di Fisica Universita degli Studi di Torino via Pietro Giuria 1 TorinoI-10125 Italy Centre for Exoplanet Science SUPA School of Physics and Astronomy University of St Andrews North Haugh St AndrewsKY16 9SS United Kingdom Dipartimento di Fisica e Astronomia "Galileo Galilei" Universita degli Studi di Padova Vicolo dell’Osservatorio 3 Padova35122 Italy Campus UAB Can Magrans s/n Bellaterra08193 Spain Gran Capità 2-4 Barcelona08034 Spain Admatis 5. Kandó Kálmán Street Miskolc3534 Hungary ESAC campus Villanueva de la Cañada Madrid28692 Spain Instituto de Astrofisica e Ciencias do Espaco Universidade do Porto CAUP Rua das Estrelas Porto4150-762 Portugal Departamento de Fisica e Astronomia Faculdade de Ciencias Universidade do Porto Rua do Campo Alegre Porto4169-007 Portugal Space Research Institute Austrian Academy of Sciences Schmiedlstrasse 6 GrazA-8042 Austria Observatoire astronomique de l’Université de Genève Chemin Pegasi 51 Versoix1290 Switzerland Université Grenoble Alpes CNRS IPAG Grenoble38000 France Université de Paris Cité Institut de physique du globe de Paris CNRS 1 Rue Jussieu ParisF-7

Context. The star HD 139139 (a.k.a. ‘the Random Transiter’) is a star that exhibited enigmatic transit-like features with no apparent periodicity in K2 data. The shallow depth of the events (∼200 ppm – equivalent to transiting objects with radii of ∼1.5 R in front of a Sun-like star) and their non-periodicity constitute a challenge for the photometric follow-up of this star. Aims. The goal of this study is to confirm with independent measurements the presence of shallow, non-periodic transit-like features on this object. Methods. We performed observations with CHEOPS for a total accumulated time of 12.75 d, distributed in visits of roughly 20 h in two observing campaigns in years 2021 and 2022. The precision of the data is sufficient to detect 150 ppm features with durations longer than 1.5 h. We used the duration and times of the events seen in the K2 curve to estimate how many events should have been detected in our campaigns, under the assumption that their behaviour during the CHEOPS observations would be the same as in the K2 data of 2017. Results. We do not detect events with depths larger than 150 ppm in our data set. If the frequency, depth, and duration of the events were the same as in the K2 campaign, we estimate the probability of having missed all events due to our limited observing window would be 4.8%. Conclusions. We suggest three different scenarios to explain our results: 1) Our observing window was not long enough, and the events were missed with the estimated 4.8% probability. 2) The events recorded in the K2 observations were time critical, and the mechanism producing them was either not active in the 2021 and 2022 campaigns or created shallower events under our detectability level. 3) The enigmatic events in the K2 data are the result of an unidentified and infrequent instrumental noise in the original data set or its data treatment. © 2023, CC BY.

关键词： Stars

A cross-country comparison of crowdworker motivations

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arXiv 2017年

作者： Posch, Lisa Bleier, Arnim Flöck, Fabian Strohmaier, Markus Department of Computational Social Science GESIS - Leibniz Institute for the Social Sciences Germany Institute of Interactive Systems and Data Science Graz University of Technology Austria HumTec Institute RWTH Aachen University Germany

Crowd employment is a new form of short term employment that has been rapidly becoming a source of income for a vast number of people around the globe. It differs considerably from more traditional forms of work, yet similar ethical and optimization issues arise. One key to tackle such challenges is to understand what motivates the international crowd workforce. In this regard, we study one particularly prevalent type of crowd employment: micro-tasks. So far, the motivation of micro-task crowdworkers has mainly been investigated on Amazon Mechanical Turk (AMT) (e.g. [6, 7, 5]). Consequently, research has focused on workers from the USA and India, which constitute the main population on AMT [4]. This work, in contrast, addresses crowd employment as an increasingly international phenomenon by exploring the more workforce-diverse micro-task platform CrowdFlower1. Furthermore, questionnaires for measuring motivations should be carefully adapted and tested if they are to be used in these new and culturally diverse work environments, in order to produce interpretable results. To this end, we developed a novel scale that marks a major step in reliably measuring the motivations driving micro-task participation around the world, and we report on the results of applying this scale, which unveil significant international differences. Copyright © 2017, The Authors. All rights reserved.

关键词： Motivation

Author Correction: The power of genetic diversity in genome-wide association studies of lipids

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Nature 2023年第7965期618卷 E19-E20页

作者： Sarah E Graham Shoa L Clarke Kuan-Han H Wu Stavroula Kanoni Greg J M Zajac Shweta Ramdas Ida Surakka Ioanna Ntalla Sailaja Vedantam Thomas W Winkler Adam E Locke Eirini Marouli Mi Yeong Hwang Sohee Han Akira Narita Ananyo Choudhury Amy R Bentley Kenneth Ekoru Anurag Verma Bhavi Trivedi Hilary C Martin Karen A Hunt Qin Hui Derek Klarin Xiang Zhu Gudmar Thorleifsson Anna Helgadottir Daniel F Gudbjartsson Hilma Holm Isleifur Olafsson Masato Akiyama Saori Sakaue Chikashi Terao Masahiro Kanai Wei Zhou Ben M Brumpton Humaira Rasheed Sanni E Ruotsalainen Aki S Havulinna Yogasudha Veturi QiPing Feng Elisabeth A Rosenthal Todd Lingren Jennifer Allen Pacheco Sarah A Pendergrass Jeffrey Haessler Franco Giulianini Yuki Bradford Jason E Miller Archie Campbell Kuang Lin Iona Y Millwood George Hindy Asif Rasheed Jessica D Faul Wei Zhao David R Weir Constance Turman Hongyan Huang Mariaelisa Graff Anubha Mahajan Michael R Brown Weihua Zhang Ketian Yu Ellen M Schmidt Anita Pandit Stefan Gustafsson Xianyong Yin Jian'an Luan Jing-Hua Zhao Fumihiko Matsuda Hye-Mi Jang Kyungheon Yoon Carolina Medina-Gomez Achilleas Pitsillides Jouke Jan Hottenga Gonneke Willemsen Andrew R Wood Yingji Ji Zishan Gao Simon Haworth Ruth E Mitchell Jin Fang Chai Mette Aadahl Jie Yao Ani Manichaikul Helen R Warren Julia Ramirez Jette Bork-Jensen Line L Kårhus Anuj Goel Maria Sabater-Lleal Raymond Noordam Carlo Sidore Edoardo Fiorillo Aaron F McDaid Pedro Marques-Vidal Matthias Wielscher Stella Trompet Naveed Sattar Line T Møllehave Betina H Thuesen Matthias Munz Lingyao Zeng Jianfeng Huang Bin Yang Alaitz Poveda Azra Kurbasic Claudia Lamina Lukas Forer Markus Scholz Tessel E Galesloot Jonathan P Bradfield E Warwick Daw Joseph M Zmuda Jonathan S Mitchell Christian Fuchsberger Henry Christensen Jennifer A Brody Mary F Feitosa Mary K Wojczynski Michael Preuss Massimo Mangino Paraskevi Christofidou Niek Verweij Jan W Benjamins Jorgen Engmann Rachel L Kember Roderick C Slieker Ken Sin Lo Nuno R Zilhao Phuong Le Marcus E Kleber Graciela E Delgado Shaofeng Huo Dai Department of Internal Medicine Division of Cardiology University of Michigan Ann Arbor MI USA. VA Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Department of Biostatistics and Center for Statistics Genetics University of Michigan Ann Arbor MI USA. Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Clinical Pharmacology William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK. Endocrinology Boston Children's Hospital Boston MA USA. Medical and Population Genetics Broad Institute Cambridge MA USA. Department of Genetic Epidemiology University of Regensburg Regensburg Germany. McDonnell Genome Institute and Department of Medicine Washington University St Louis MO USA. Department of Precision Medicine Division of Genome Science National Institute of Health Cheongju-si South Korea. Tohoku Medical Megabank Organization Tohoku University Sendai Japan. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa. Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA. Blizard Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London London UK. Wellcome Sanger Institute Hinxton UK. Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA. Atlanta VA Health Care System Decatur GA USA. Malcolm Randall VA Medical Center Gainesville FL USA. Division of Vascular Sur

Review of machine learning algorithms in differential expression analysis

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arXiv 2017年

作者： Kuznetsova, Irina Karpievitch, Yuliya V. Filipovska, Aleksandra Lugmayr, Artur Holzinger, Andreas Graz University of Technology Graz Austria Inst. of Interactive Systems and Data Science Harry Perkins Inst. of Medical Research University of Western Australia Perth Australia Plant Energy Biology Centre of Excellence Harry Perkins Inst. of Medical Research University of Western Australia Perth Australia Harry Perkins Inst. of Medical Research University of Western Australia Perth Australia Curtin University Perth Australia Inst. of Interactive Systems and Data Science Graz University of Technology Graz Austria

In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNASeq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments. Copyright © 2017, The Authors. All rights reserved.

关键词： data mining