Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

Arslanovic Jasmina, Ajana Löw, Lovric Mario, Kern Roman

BODY MASS INDEX, BODY IMAGE DISSATISFACTION AND EATING DISORDER SYMPTOMS IN FEMALE AQUATIC SPORTS: COMPARISON OF ARTISTIC (SYNCHRONIZED) SWIMMERS AND FEMALE WATER-POLO PLAYER

Sports Healt, Sage publishing, 2019

Journal
Previous studies suggest that artistic swimming athletes may demonstrate a certain degree of eating disorders symptoms, which could be higher compared to non-leanness sports and non-aquatic sports. However, systematic research on eating disorders in artistic swimming is limited and the nature and antecedents of the development of eating disorders in this specific population of athletes is still scarce. Hence, the aim of our research was to investigate the eating disorder symptoms in artistic swimming athletes using the EAT-26 instrument, and to examine the relation of the incidence and severity of these symptoms to body mass index and body image dissatisfaction. Further, we wanted to compare artistic swimmers with athletes of a non-leanness (but also an aquatic) sport, therefore we also included a group of female water polo athletes of the same age. The sample consisted of 36 artistic swimmers and 34 female water polo players (both aged 13-16). To test the presence of the eating disorder symptoms the EAT-26 was used. The Mann-Whitney U Test (MWU) was used to test for the differences in EAT26 total score, so the EAT26 total score and the Dieting subscale showed significant differences between the two groups. The median value for EAT26 total score is higher in the artistic swimmers group (C = 11) than within the water polo players (C = 8). The decision tree was used to discriminate artistic swimmers and female water polo players. It is important to emphasize that every sport demands certain nutrition and most of them demand certain weight of athlete, but it should be achieved by expert team (coach, psychologist, nutritionist and physiotherapist).
2019

Maritsch Martin, Diana Suleimenova, Geiger Bernhard, Derek Groen

AI-Support for large-scale Refugee Movement Simulations

Computing Systems Week Spring 2019, HiPEAC, Edinburgh, 2019

Konferenz
2019

Lovric Mario, Fadljevic Leon, Kern Roman

Comparison of machine learning and physical modeling for predictive maintenance in an electro-galvanising proces

2019

2019

Lovric Mario, Žuvela Petar, Kern Roman

Improvement in QSRR of proteins through interpretable ensemble machine learning model

IAPC-8 Meeting, 2019

Konferenz
2019

Lovric Mario, Šimić Iva, Godec Ranka

Analysis of traffic related pollutants at a typical urban street canyon site in Zagreb, Croatia, using machine learning

Environmental Pollutio, Elsevier, 2019

Journal
Narrow city streets engulfed by tall buildings are favorable to a general effect of a “canyon” in which pollution accumulates strongly on a relatively small area because of weak or inexistent ventilation. The aim of this study was to determine and compare levels of nitrogen-oxide (NO2), elemental carbon (EC) and organic carbon (OC) mass concentrations in PM10 particles between seasons and different years collected during at one such street canyon location in the center of Zagreb in 2011, 2012 and 2013. Daily samples were collected and analyzed. Through employment of machine learning we showed seasonal and yearly variations of mass concentrations for carbon species in PM10 and NO2, as well as their covariations and relationships. Furthermore by using random forest regression we managed to unravel non-linear and complex relationships between the particles concentrations and achieve reasonable predictivity in descending order OC > PM10 > EC > NO2
2019

Malev Olga, Lovric Mario, Draženka Stipaničev, Siniša Repec, Dalma Martinović-Weigelt, Davor Zanella, Tomislav Ivanković, Valnea Sindičić, Mei Li, Goran Klobučar

Toxicity prediction and effect characterization of 90 pharmaceuticals and illicit drugs measured in plasma of fish from the River Sava, Croati

Water research, 2019

Journal
2019

Thalmann Stefan, Gursch Heimo, Suschnigg Josef, Gashi Milot, Ennsbrunner Helmut, Fuchs Anna Katharina, Schreck Tobias, Mutlu Belgin, Mangler Jürgen, Huemer Christian, Lindstaedt Stefanie

Cognitive Decision Support for Industrial Product Life Cycles: A Position Paper

Proceedings of the Eleventh International Conference on Advanced Cognitive Technologies and Applications (COGNITIVE 2019), Marta Franova, Charlotte Sennersten, Jayfus T. Doswell, IARIA, Venice, Italy, 2019

Konferenz
Current trends in manufacturing lead to more intelligent products, produced in global supply chains in shorter cycles, taking more and complex requirements into account. To manage this increasing complexity, cognitive decision support systems, building on data analytic approaches and focusing on the product life cycle, stages seem a promising approach. With two high-tech companies (world market leader in their domains) from Austria, we are approaching this challenge and jointly develop cognitive decision support systems for three real world industrial use cases. Within this position paper, we introduce our understanding of cognitive decision support and we introduce three industrial use cases, focusing on the requirements for cognitive decision support. Finally, we describe our preliminary solution approach for each use case and our next steps.
2019

Stepputat Kendra, Kienreich Wolfgang, Dick Christopher S.

Digital Methods in Intangible Cultural Heritage Research: A Case Study in Tango Argentino

Journal on Computing and Cultural Heritage (JOCCH), ACM, ACM, New York, NY, USA, 2019

Journal
With this article, we present the ongoing research project “Tango Danceability of Music in European Perspective” and the transdisciplinary research design it is built upon. Three main aspects of tango argentino are in focus—the music, the dance, and the people—in order to understand what is considered danceable in tango music. The study of all three parts involves computer-aided analysis approaches, and the results are examined within ethnochoreological and ethnomusicological frameworks. Two approaches are illustrated in detail to show initial results of the research model. Network analysis based on the collection of online tango event data and quantitative evaluation of data gathered by an online survey showed significant results, corroborating the hypothesis of gatekeeping effects in the shaping of musical preferences. The experiment design includes incorporation of motion capture technology into dance research. We demonstrate certain advantages of transdisciplinary approaches in the study of Intangible Cultural Heritage, in contrast to conventional studies based on methods from just one academic discipline.
2019

Pammer-Schindler Viktoria

alt.chi Commentary to: Homewood, Sarah: Inaction as a Design Decision: Reflections on Not Designing Self-Tracking Tools for Menopaus

2019 CHI Extended Abstracts on Human Factors in Computing System, ACM, 2019

Konferenz
This is a commentary of mine, created in the context of an open review process, selected for publication alongside the accepted original paper in a juried process, and published alongside the paper at the given DOI,
2019

Xie Benjamin, Harpstead Erik, DiSalvo Betsy, Slovak Petr, Kharuffa Ahmed, Lee Michael J., Pammer-Schindler Viktoria, Ogan Amy, Williams Joseph Jay

Learning, Education and HCI

Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing System, ACM, 2019

Konferenz
2019

Tiago Santos, Stefan Schrunner, Geiger Bernhard, Olivia Pfeiler, Anja Zernig, Andre Kaestner, Kern Roman

Feature Extraction From Analog Wafermaps: A Comparison of Classical Image Processing and a Deep Generative Mode

IEEE Transactions on Semiconductor Manufacturing, IEEE, 2019

Journal
Semiconductor manufacturing is a highly innovative branch of industry, where a high degree of automation has already been achieved. For example, devices tested to be outside of their specifications in electrical wafer test are automatically scrapped. In this paper, we go one step further and analyze test data of devices still within the limits of the specification, by exploiting the information contained in the analog wafermaps. To that end, we propose two feature extraction approaches with the aim to detect patterns in the wafer test dataset. Such patterns might indicate the onset of critical deviations in the production process. The studied approaches are: 1) classical image processing and restoration techniques in combination with sophisticated feature engineering and 2) a data-driven deep generative model. The two approaches are evaluated on both a synthetic and a real-world dataset. The synthetic dataset has been modeled based on real-world patterns and characteristics. We found both approaches to provide similar overall evaluation metrics. Our in-depth analysis helps to choose one approach over the other depending on data availability as a major aspect, as well as on available computing power and required interpretability of the results
2019

Winter Kevin, Kern Roman

Know-Center at SemEval-2019 Task 5: Multilingual Hate SpeechDetection on Twitter using CNNs

Proceedings of the Thirteenth International Workshop on Semantic Evaluation, 2019

Konferenz
This paper presents the Know-Center system submitted for task 5 of the SemEval-2019workshop. Given a Twitter message in either English or Spanish, the task is to first detect whether it contains hateful speech and second,to determine the target and level of aggression used. For this purpose our system utilizes word embeddings and a neural network architecture, consisting of both dilated and traditional convolution layers. We achieved aver-age F1-scores of 0.57 and 0.74 for English and Spanish respectively.
2019

Rauch Manuela, Gaal Alexander, Simic Ilija, Sabol Vedran

Evaluation of Visual Decision Support Systems used in Semiconductor Industry

2019

Konferenz
2019

Lovric Mario, Molero Perez Jose Manuel, Kern Roman

PySpark and RDKit: moving towards Big Data in QSAR

Molecular Informatics, Wiley, 2019

Journal
The authors present an implementation of the cheminformatics toolkit RDKit in a distributed computing environment, Apache Hadoop. Together with the Apache Spark analytics engine, wrapped by PySpark, resources from commodity scalable hardware can be employed for cheminformatic calculations and query operations with basic knowledge in Python programming and understanding of the resilient distributed datasets (RDD). Three use cases of cheminfomatical computing in Spark on the Hadoop cluster are presented; querying substructures, calculating fingerprint similarity and calculating molecular descriptors. The source code for the PySpark‐RDKit implementation is provided. The use cases showed that Spark provides a reasonable scalability depending on the use case and can be a suitable choice for datasets too big to be processed with current low‐end workstations
2019

Geiger Bernhard, Schrunner Stefan, Kern Roman

An Information-Theoretic Measure for Pattern Similarity in Analog Wafermap

European Advanced Process Control and Manufacturing Conf. (apc|m, Villach, 2019

Konferenz
Schrunner and Geiger have contributed equally to this work.
2019

Adolfo Ruiz Calleja, Dennerlein Sebastian, Kowald Dominik, Theiler Dieter, Lex Elisabeth, Tobias Ley

An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server

Journal of Learning Analytics, Society for Learning Analytics Research (SoLAR), UTS ePress , 2019

Journal
In this paper, we propose the Social Semantic Server (SSS) as a service-based infrastructure for workplace andprofessional Learning Analytics (LA). The design and development of the SSS has evolved over 8 years, startingwith an analysis of workplace learning inspired by knowledge creation theories and its application in differentcontexts. The SSS collects data from workplace learning tools, integrates it into a common data model based ona semantically-enriched Artifact-Actor Network and offers it back for LA applications to exploit the data. Further,the SSS design promotes its flexibility in order to be adapted to different workplace learning situations. Thispaper contributes by systematizing the derivation of requirements for the SSS according to the knowledge creationtheories, and the support offered across a number of different learning tools and LA applications integrated to it.It also shows evidence for the usefulness of the SSS extracted from four authentic workplace learning situationsinvolving 57 participants. The evaluation results indicate that the SSS satisfactorily supports decision making indiverse workplace learning situations and allow us to reflect on the importance of the knowledge creation theoriesfor such analysis.
2019

Kaiser Rene, Thalmann Stefan, Pammer-Schindler Viktoria, Fessl Angela

Collaborating in a Research and Development Project: Knowledge Protection Practices applied in a Co-opetitive Setting

10th Conference Professional Knowledge Management, Data-Driven Knowledge Management workshop, proWM’19, Potsdam, DE, 2019

Konferenz
Organisations participate in collaborative projects that include competitors for a number of strategic reasons, even whilst knowing that this requires them to consider both knowledge sharing and knowledge protection throughout collaboration. In this paper, we investigated which knowledge protection practices representatives of organizations employ in a collaborative research and innovation project that can be characterized as a co-opetitive setting. We conducted a series of 30 interviews and report the following seven practices in structured form: restrictive partner selection in operative project tasks, communication through a gatekeeper, to limit access to a central platform, to hide details of machine data dumps, to have data not leave a factory for analysis, a generic model enabling to hide usage parameters, and to apply legal measures. When connecting each practice to a priori literature, we find three practices focussing on collaborative data analytics tasks had not yet been covered so far.
2019

Renner Bettina, Wesiak Gudrun, Pammer-Schindler Viktoria, Prilla Michael, Müller Lars, Morosini Dalia, Mora Simone, Faltin Nils, Cress Ulrike

Computer-supported reflective learning: How apps can foster reflection at work.

Behaviour & Information Technology, Taylor & Francis, Taylor & Francis, 2019

Journal
2019

Fessl Angela, Simic Ilija, Barthold Sabine, Pammer-Schindler Viktoria

Concept and Development of an Information Literacy Curriculum Widget

Conference on Learning Information Literacy , Deutschland, 2019

Konferenz
Information literacy, the access to knowledge and use of it are becoming a precondition for individuals to actively take part in social,economic, cultural and political life. Information literacy must be considered as a fundamental competency like the ability to read, write and calculate. Therefore, we are working on automatic learning guidance with respect to three modules of the information literacy curriculum developed by the EU (DigComp 2.1 Framework). In prior work, we havelaid out the essential research questions from a technical side. In this work, we follow-up by specifying the concept to micro learning, and micro learning content units. This means, that the overall intervention that we design is concretized to: The widget is initialized by assessing the learners competence with the help of a knowledge test. This is the basis for recommending suitable micro learning content, adapted to the identifi ed competence level. After the learner has read/worked through the content, the widget asks a reflective question to the learner. The goal of the reflective question is to deepen the learning. In this paper we present the concept of the widget and its integration in a search platform.
2019

Fruhwirth Michael, Breitfuß Gert, Müller Christiana

Mit Daten Wert schaffen: Datengetriebene Geschäftsmodelle als Weg in die Zukunft

Österreichischer Verband der Wirtschaftsingenieure, Österreichischer Verband der Wirtschaftsingenieure, Graz, 2019

Journal
Die Nutzung von Daten in Unternehmen zur Analyse und Beantwortung vielfältiger Fragestellungen ist “daily business”. Es steckt aber noch viel mehr Potenzial in Daten abseits von Prozessoptimierungen und Business Intelligence Anwendungen. Der vorliegende Beitrag gibt einen Überblick über die wichtigsten Aspekte bei der Transformation von Daten in Wert bzw. bei der Entwicklung datengetriebener Geschäftsmodelle. Dabei werden die Charakteristika von datengetriebenen Geschäftsmodellen und die benötigten Kompetenzen näher beleuchtet. Vier Fallbeispiele österreichischer Unternehmen geben Einblicke in die Praxis und abschließend werden aktuelle Herausforderungen und Entwicklungen diskutiert.
2019

Luzhnica Granit, Veas Eduardo Enrique

Background Perception and Comprehen-sion of Symbols Conveyed through Vibrotactile Wearable Displays

ACM International Conference on Intelligent User Interfaces , Los Angelos, 2019

Konferenz
2019

Luzhnica Granit, Veas Eduardo Enrique

Optimising the Encoding for Vibrotactile Skin Reading

ACM CHI Conference on Human Factors in Computing Systems, 2019

Konferenz
2019

Lassnig Markus, Stabauer Petra, Breitfuß Gert, Müller Julian M.

Erfolgreiche Konzepte und Handlungsempfehlungen für digitale Geschäftsmodellinnovationen

HMD Edition, Springer Verlag, 2019

Buch
Zahlreiche Forschungsergebnisse im Bereich Geschäftsmodellinnovationen haben gezeigt, dass über 90 Prozent aller Geschäftsmodelle der letzten 50 Jahre aus einer Rekombination von bestehenden Konzepten entstanden sind. Grundsätzlich gilt das auch für digitale Geschäftsmodellinnovationen. Angesichts der Breite potenzieller digitaler Geschäftsmodellinnovationen wollten die Autoren wissen, welche Modellmuster in der wirtschaftlichen Praxis welche Bedeutung haben. Deshalb wurde die digitale Transformation mit neuen Geschäftsmodellen in einer empirischen Studie basierend auf qualitativen Interviews mit 68 Unternehmen untersucht. Dabei wurden sieben geeignete Geschäftsmodellmuster identifiziert, bezüglich ihres Disruptionspotenzials von evolutionär bis revolutionär klassifiziert und der Realisierungsgrad in den Unternehmen analysiert. Die stark komprimierte Conclusio lautet, dass das Thema Geschäftsmodellinnovationen durch Industrie 4.0 und digitale Transformation bei den Unternehmen angekommen ist. Es gibt jedoch sehr unterschiedliche Geschwindigkeiten in der Umsetzung und im Neuheitsgrad der Geschäftsmodellideen. Die schrittweise Weiterentwicklung von Geschäftsmodellen (evolutionär) wird von den meisten Unternehmen bevorzugt, da hier die grundsätzliche Art und Weise des Leistungsangebots bestehen bleibt. Im Gegensatz dazu gibt es aber auch Unternehmen, die bereits radikale Änderungen vornehmen, die die gesamte Geschäftslogik betreffen (revolutionäre Geschäftsmodellinnovationen). Entsprechend wird im vorliegenden Artikel ein Clustering von Geschäftsmodellinnovatoren vorgenommen – von Hesitator über Follower über Optimizer bis zu Leader in Geschäftsmodellinnovationen.
2019

Lovric Mario, Banic Ivana, Cuder Gerald, Kern Roman, Turkalj Mirjana, Matija Rijavec, Peter Korosec

Treatment outcome clustering patterns correspond to discrete asthma phenotypes in childre

European Respiratory Journa, European Respiratory Societ, 2019

Journal
Despite widely and regularly used therapy asthma in children is not fully controlled. Recognizing the complexity of asthma phenotypes and endotypes imposed the concept of precision medicine in asthma treatment. By applying machine learning algorithms assessed with respect to their accuracy in predicting treatment outcome, we have successfully identified 4 distinct clusters in a pediatric asthma cohort with specific treatment outcome patterns according to changes in lung function (FEV1 and MEF50), airway inflammation (FENO) and disease control likely affected by discrete phenotypes at initial disease presentation, differing in the type and level of inflammation, age of onset, comorbidities, certain genetic and other physiologic traits. The smallest and the largest of the 4 clusters- 1 (N= 58) and 3 (N= 138) seemed to have a more positive pattern of treatment outcomes and were characterized by more prominent atopic markers and a predominant allelic (A allele) effect for rs37973 in the GLCCI1 gene previously associated with positive treatment outcomes in asthmatics. These patients also had a relatively later onset of disease (6+ yrs). Clusters 2 (N= 87) and 4 (n= 64) had poorer treatment success and were characterized by higher levels of airway and systemic inflammation, but varied in the type of inflammation (predominantly neutrophilic for cluster 4 and likely mixed-type for cluster 2), comorbidities (obesity for cluster 2) and platelet count (lowest for cluster 4). The results of this study emphasize the issues in asthma management due to the overgeneralized approach to the disease, not taking into account specific disease phenotypes
2019

Clemens Bloechl, Rana Ali Amjad, Geiger Bernhard

Co-Clustering via Information-Theoretic Markov Aggregation

IEEE Transactions on Knowledge and Data Engineering, IEEE, 2019

Journal
We present an information-theoretic cost function for co-clustering, i.e., for simultaneous clustering of two sets based on similarities between their elements. By constructing a simple random walk on the corresponding bipartite graph, our cost function is derived from a recently proposed generalized framework for information-theoretic Markov chain aggregation. The goal of our cost function is to minimize relevant information loss, hence it connects to the information bottleneck formalism. Moreover, via the connection to Markov aggregation, our cost function is not ad hoc, but inherits its justification from the operational qualities associated with the corresponding Markov aggregation problem. We furthermore show that, for appropriate parameter settings, our cost function is identical to well-known approaches from the literature, such as “Information-Theoretic Co-Clustering” by Dhillon et al. Hence, understanding the influence of this parameter admits a deeper understanding of the relationship between previously proposed information-theoretic cost functions. We highlight some strengths and weaknesses of the cost function for different parameters. We also illustrate the performance of our cost function, optimized with a simple sequential heuristic, on several synthetic and real-world data sets, including the Newsgroup20 and the MovieLens100k data sets
2019

Lovric Mario, Petar Žuvela, Bono Lucic, J. Jay Liu, Kern Roman, Tomasz Bączek

Machine learning methods for cross-column prediction of retention time in reversed-phased liquid chromatography

8th World Conference on Physico Chemical Methods in Drug Discovery and Developmen, 2019

Konferenz
Quantitative structure-retention relationships (QSRR) were employed to build global models for prediction of chromatographic retention time of synthetic peptides across six RP-LC-MS/MS columns and varied experimental conditions. The global QSRR models were based on only three a priori selected molecular descriptors: sum of gradient retention times of 20 natural amino acids (logSumAA), van der Waals volume (logvdWvol.), and hydrophobicity (clogP) related to the retention mechanism of RP-LC separation of peptides. Three machine learning regression methods were compared: random forests (RF), partial least squares (PLS), and adaptive boosting (ADA). All the models were comprehensively optimized through 3-fold cross-validation (CV) and validated through an external validation set. The chemical domain of applicability was also defined. Percentage root mean square error of prediction (%RMSEP) was used as an external validation metric. Results have shown that RF exhibited a %RMSEP of 14.99 %; PLS exhibited a %RMSEP of 40.561 %; whereas ADA exhibited a %RMSEP of 26.35 %. The ensemble models considerably outperform the conventional PLS-based QSRR model. Novel methods of tree-based model explainability were employed to reveal mechanisms behind black-box global ensemble QSRR models. The models revelead the highest feature importance for sum of gradient retention times (logSumAA), followed by van der Waals volume (logvdWvol.), and hydrophobicity (clogP). The promising results of this study show the potential of machine learning for improved peptide identification, retention time standardization and integration into state-of-the-art LC-MS/MS proteomics workflows.
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