Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

Breitfuß Gert, Fruhwirth Michael, Pammer-Schindler Viktoria, Stern Hermann, Dennerlein Sebastian

The Data-Driven Business Value Matrix - A Classification Scheme for Data-Driven Business Models

32nd Bled eConference, University of Maribor, Faculty of Organizational Sciences, HUMANIZING TECHNOLOGY FOR A SUSTAINABLE SOCIETY JUNE 16 – 19, 2019, BLED, SLOVENIA,, Andreja Pucihar, PhD, et al., University of Maribor Press, Bled, Slovenia, 2019

Konferenz
Increasing digitization is generating more and more data in all areas ofbusiness. Modern analytical methods open up these large amounts of data forbusiness value creation. Expected business value ranges from process optimizationsuch as reduction of maintenance work and strategic decision support to businessmodel innovation. In the development of a data-driven business model, it is usefulto conceptualise elements of data-driven business models in order to differentiateand compare between examples of a data-driven business model and to think ofopportunities for using data to innovate an existing or design a new businessmodel. The goal of this paper is to identify a conceptual tool that supports datadrivenbusiness model innovation in a similar manner: We applied three existingclassification schemes to differentiate between data-driven business models basedon 30 examples for data-driven business model innovations. Subsequently, wepresent the strength and weaknesses of every scheme to identify possible blindspots for gaining business value out of data-driven activities. Following thisdiscussion, we outline a new classification scheme. The newly developed schemecombines all positive aspects from the three analysed classification models andresolves the identified weaknesses.
2019

Geiger Bernhard

On the Information Dimension of Random Variables and Stochastic Processe

Workshop on Casualty and Dynamics in Brain Networks @ Int. Joint Conf. on Neural Networks, Budapest, 2019

Konferenz
joint work with Tobias Koch, Universidad Carlos III de Madrid
2019

Jorge Guerra Torres, Veas Eduardo Enrique, Carlos Catania

A Study on Labeling Network Hostile Behavior with Intelligent Interactive Tools

IEEE Symposium on Visualization for Cyber Security , IEEE, 2019

Konferenz
Labeling a real network dataset is specially expensive in computer security, as an expert has to ponder several factors before assigning each label. This paper describes an interactive intelligent system to support the task of identifying hostile behavior in network logs. The RiskID application uses visualizations to graphically encode features of network connections and promote visual comparison. In the background, two algorithms are used to actively organize connections and predict potential labels: a recommendation algorithm and a semi-supervised learning strategy. These algorithms together with interactive adaptions to the user interface constitute a behavior recommendation. A study is carried out to analyze how the algo-rithms for recommendation and prediction influence the workflow of labeling a dataset. The results of a study with 16 participants indicate that the behaviour recommendation significantly improves the quality of labels. Analyzing interaction patterns, we identify a more intuitive workflow used when behaviour recommendation isavailable.
2019

Luzhnica Granit, Veas Eduardo Enrique

Boosting Word Recognition for Vibrotactile Skin Reading

ACM International Symposium on Wearable Computing, 2019

Konferenz
Proficiency in any form of reading requires a considerable amount of practice. With exposure, people get better at recognising words, because they develop strategies that enable them to read faster. This paper describes a study investigating recognition of words encoded with a 6-channel vibrotactile display. We train 22 users to recognise ten letters of the English alphabet. Additionally, we repeatedly expose users to 12 words in the form of training and reinforcement testing.Then, we test participants on exposed and unexposed words to observe the effects of exposure to words. Our study shows that, with exposure to words, participants did significantly improve on recognition of exposed words. The findings suggest that such a word exposure technique could be used during the training of novice users in order to boost the word recognition of a particular dictionary of words.
2019

Remonda Adrian, Krebs Sarah, Luzhnica Granit, Kern Roman, Veas Eduardo Enrique

Formula RL: Deep Reinforcement Learning for Autonomous Racing usingTelemetry Data

Workshop on Scaling-Up Reinforcement Learning (SURL) @ Int. Joint Conf. on Artificial Intelligence, 2019

Konferenz
This paper explores the use of reinforcement learning (RL) models for autonomous racing. In contrast to passenger cars, where safety is the top priority, a racing car aims to minimize the lap-time. We frame the problem as a reinforcement learning task witha multidimensional input consisting of the vehicle telemetry, and a continuous action space. To findout which RL methods better solve the problem and whether the obtained models generalize to drivingon unknown tracks, we put 10 variants of deep deterministic policy gradient (DDPG) to race in two experiments: i) studying how RL methods learn to drive a racing car and ii) studying how the learning scenario influences the capability of the models to generalize. Our studies show that models trained with RL are not only able to drive faster than the baseline open source handcrafted bots but also generalize to unknown tracks.
2019

Kowald Dominik, Lacic Emanuel, Theiler Dieter, Traub Matthias, Kuffer Lucky, Lindstaedt Stefanie , Lex Elisabeth

Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metri

REVEAL Workshop co-located with RecSys'2019, ACM, Kopenhagen, Denmark, 2019

Konferenz
2019

Kowald Dominik, Lex Elisabeth, Schedl Markus

Modeling Artist Preferences of Users with Different Music Consumption Patterns for Fair Music Recommendation

European Symposium on Computational Social Science (EuroCSS), Zurich, Switzerland, 2019

Konferenz
2019

Lex Elisabeth, Kowald Dominik

The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approac

49th GI Annual Conference (INFORMATIK'2019), Kassel, Germany, 2019

Konferenz
2019

Toller Maximilian, Geiger Bernhard, Kern Roman

A Formally Robust Time Series Distance Metric

Mile'TS @ SIGKDD, Anchorage, Alaska USA, 2019

Konferenz
Distance-based classification is among the most competitive classification methods for time series data. The most critical componentof distance-based classification is the selected distance function.Past research has proposed various different distance metrics ormeasures dedicated to particular aspects of real-world time seriesdata, yet there is an important aspect that has not been considered so far: Robustness against arbitrary data contamination. In thiswork, we propose a novel distance metric that is robust against arbitrarily “bad” contamination and has a worst-case computationalcomplexity of O(n logn). We formally argue why our proposedmetric is robust, and demonstrate in an empirical evaluation thatthe metric yields competitive classification accuracy when appliedin k-Nearest Neighbor time series classification.
2019

Breitfuß Gert, Berger Martin, Doerrzapf Linda

Towards Sustainable Business Models for Living Labs - A long-term Business Model Study of Austrian Urban Mobility Labs

4th New Business Model Conference 2019, Berlin, Florian Lüdeke-Freund et al., ESCP Europe Business School, Berlin, 2019

Konferenz
The Austrian Federal Ministry for Transport, Innovation and Technology created an initiative to fund the setup and operation of Living Labs to provide a vital innovation ecosystem for mobility and transport. Five Urban Mobility Labs (UML) located in four urban areas have been selected for funding (duration 4 years) and started operation in 2017. In order to cover the risk of a high dependency of public funding (which is mostly limited in time), the lab management teams face the challenge to develop a viable and future-proof UML Business Model. The overall research goal of this paper is to get empirical insights on how a UML Business Model evolves on a long-term perspective and which success factors play a role. To answer the research question, a method mix of desk research and qualitative methods have been selected. In order to get an insight into the UML Business Model, two circles of 10 semi-structured interviews (two responsible persons of each UML) are planned. The first circle of the interviews took place between July 2018 and January 2019. The second circle of interviews is planned for 2020. Between the two rounds of the survey, a Business Model workshop is planned to share and create ideas for future Business Model developments. Based on the gained research insights a comprehensive list of success factors and hands-on recommendations will be derived. This should help UML organizations in developing a viable Business Model in order to support sustainable innovations in transport and mobility.
2019

Kaiser Rene

The Virtual Director Concept: Data-Driven Adaptation and Personalization for Live Video Streams

Proceedings of the 1st International Workshop on Data-Driven Personalisation of Television (DataTV 2019), co-located with the ACM International Conference on Interactive Experiences for Television and Online Video (TVX 2019), CEUR-WS.org , Manchester, UK, 2019

Konferenz
This paper gives a comprehensive overview of the Virtual Director concept. A Virtual Director is a software component automating the key decision making tasks of a TV broadcast director. It decides how to mix and present the available content streams on a particular playout device, most essentially deciding which camera view to show and when to switch to another. A Virtual Director allows to take decisions respecting individual user preferences and playout device characteristics. In order to take meaningful decisions, a Virtual Director must be continuously informed by real-time sensors which emit information about what is happening in the scene. From such (low-level) 'cues', the Virtual Director infers higher-level events, actions, facts and states which in turn trigger the real-time processes deciding on the presentation of the content. The behaviour of a Virtual Director, the 'production grammar', defines how decisions are taken, generally encompassing two main aspects: selecting what is most relevant, and deciding how to show it, applying cinematographic principles.
2019

Luzhnica Granit, Veas Eduardo Enrique

Optimising the Encoding for Vibrotactile Skin Reading

ACM CHI Conference on Human Factors in Computing Systems, 2019

Konferenz
This paper proposes methods of optimising alphabet encoding for skin reading in order to avoid perception errors. First, a user study with 16 participants using two body locations serves to identify issues in recognition of both individual letters and words. To avoid such issues, a two-step optimisation method of the symbol encoding is proposed and validated in a second user study with eight participants using the optimised encoding with a seven vibromotor wearable layout on the back of the hand. The results show significant improvements in the recognition accuracy of letters (97%) and words (97%) when compared to the non-optimised encoding.
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

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

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

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

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

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

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

Luzhnica Granit, Veas Eduardo Enrique

Background Perception and Comprehension of Symbols Conveyed through Vibrotactile Wearable Displays

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

Konferenz
2019

Schweimer Christoph, Geiger Bernhard, Suleimenova Diana, Groen Derek, Gfrerer Christine, Pape David, Elsaesser Robert, Kocsis Albert Tihamér, Liszkai B., Horváth Zoltan

Model Reduction in HiDALGO - Initial Plans and Ideas

Workshop on Model Reduction of Complex Dynamical Systems (MODRED), Graz, 2019

Konferenz
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