Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

Schrunner Stefan, Jenul Anna, Scheider Michael, Zernig Anja, Kaestner Andre, Kern Roman

A Health Factor for Process Patterns - Enhancing Semiconductor Manufacturing by Pattern Recognition in Analog Wafermaps

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 2019

Konferenz
Electrical measurement data at the end of semi- conductor frontend production, so-called wafer test data, pro- vide deep insight into the preceding manufacturing process. Patterns in these datasets, such as spatial regularities on the wafer, frequently indicate that deviations occurred during production, potentially leading to failures in the produced devices. As such patterns of interest differ w.r.t. their shapes and equally important their intensities, pattern recognition is challenging, but crucial as a prerequisite for production environments in Industry 4.0. In this work, we propose an indicator for the presence and development of process patterns, a so-called ”Health Factor for Process Patterns”, embedded in a framework of statistical decision theory. We provide adequate machine learning components, focusing on the recognition and assessment of known patterns in analog wafer test data. Finally, we conduct experiments using simulated as well as real-world datasets to demonstrate that our method yields competitive results and can be extended to a decision support system for industrial usage.
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

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

Geiger Bernhard

On the Information Dimension of Random Variables and Stochastic Processes

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

Silva Nelson, Blascheck Tanja, Jianu Radu, Rodrigues Nils, Weiskopf Daniel, Raubal Martin, Schreck Tobias

Eye Tracking Support for Visual Analytics Systems: Foundations, Current Applications, and Research Challenges

ACM, ACM, Denver, Colorado, USA, 2019

Konferenz
Visual analytics (VA) research provides helpful solutions for interactive visual data analysis when exploring large and complexdatasets. Due to recent advances in eye tracking technology, promising opportunities arise to extend these traditional VA approaches.Therefore, we discuss foundations for eye tracking support in VAsystems. We first review and discuss the structure and range oftypical VA systems. Based on a widely used VA model, we presentfive comprehensive examples that cover a wide range of usage scenarios. Then, we demonstrate that the VA model can be used tosystematically explore how concrete VA systems could be extendedwith eye tracking, to create supportive and adaptive analytics systems. This allows us to identify general research and applicationopportunities, and classify them into research themes. In a call foraction, we map the road for future research to broaden the use ofeye tracking and advance visual analytics.
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

Lovric Mario, Žuvela Petar, Kern Roman, Lucic, Bono, J. Jay Liu, 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, IAPC, Split, Croatia, 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.
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

Kowald Dominik, Traub Matthias, Theiler Dieter, Gursch Heimo, Lacic Emanuel, Lindstaedt Stefanie , Kern Roman, Lex Elisabeth

Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets

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

Konferenz
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

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

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 Metrik

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

Konferenz
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

Duricic Tomislav, Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Exploiting weak ties in trust-based recommender systems using regular equivalence

EUROCSS'2019, 2019

Konferenz
User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction of available items. One solution is to incorporate additional information into the recommendation process such as explicit trust scores that are assigned by users to others or implicit trust relationships that result from social connections between users. Such relationships typically form a very sparse trust network, which can be utilized to generate recommendations for users based on people they trust. In our work, we explore the use of regular equivalence applied to a trust network to generate a similarity matrix that is used for selecting k-nearest neighbors used for item recommendation. Two vertices in a network are regularly equivalent if their neighbors are themselves equivalent and by using the iterative approach of calculating regular equivalence, we can study the impact of strong and weak ties on item recommendation. We evaluate our approach on cold start users on a dataset crawled from Epinions and find that by using weak ties in addition to strong ties, we can improve the performance of a trust-based recommender in terms of recommendation accuracy.
2019

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

Wolfbauer Irmtraud

Digitalisation of Apprenticeship Training

15th EATEL summer school on technology enhanced learning 2019, Bari, 2019

Konferenz
Presentation of PhDUse Case: An online learning platform for apprentices.Research opportunities: Target group is under-researched1. Computer usage & ICT self-efficacy2. Communities of practice, identities as learnersReflection guidance technologies3. Rebo, the reflection guidance chatbot
2019

Wolfbauer Irmtraud

Digitalisation of Apprenticeship Training

Partner Day - Know-Center GmbH, Graz, 2019

Konferenz
Use Case: An online learning platform for apprentices.Research opportunities: Target group is under-researched1. Computer usage & ICT self-efficacy2. Communities of practice, identities as learnersReflection guidance technologies3. Rebo, the reflection guidance chatbot
2019

Kowald Dominik, Lex Elisabeth, Schdel Markus

Modeling Artist Preferences for Personalized Music Recommendation

In Late-Breaking-Results Track of the 20th annual conference of the International Society for Music Information Retrieval (ISMIR'2019, 2019

Konferenz
2019

Dennerlein Sebastian, Pammer-Schindler Viktoria, Maitz Katharina, Ebner Markus, Getzinger Günter, Ebner Martin

TEL Marketplace – A Sandpit- and Co-Design-informed Innovation Process for Implementing TEL Research in Higher Education

International Conference on Human-Centered Digitalization - Workshop: Innovating Digital Education and Skills in Different Cultures, on a Global Scope and in an Interdisciplinary Context , 2019

Konferenz
Innovating digital education in a sustainable manner requires a human-centered approach. Perspectives of all relevant stakeholders must be respected in a responsible innovation process to address actual problems in teaching as well as learning and increase acceptance of a Technology-Enhanced-Learning (TEL) solution. In higher education, this requires an interdisciplinary iterative co-design process including researchers, teachers, students and all other university institutions and their representatives being affected by the digital teaching and/or learning innovation. We suggest an innovation process called TEL Marketplace following the idea of a sandpit or idealab. First, it offers a place for exchange in form of a f2f-marketplace, where researchers present their TEL innovations for discussion, problem-mapping and team formation with lecturers and students. Second, a two-step process takes place consisting of a competitive call for the distribution of funding among the submissions and a cooperative innovation phase for co-creation of the TEL solutions in the selected innovation-teams before implementing and evaluating them in university courses. For the cooperative innovation process, we developed a “University Innovation Canvas” that serves as a boundary object for the interdisciplinary innovation-teams and triggers reflection about important factors to be respected for a sustainable implementation. Thereby, the canvas represents a ‘living document’ evolving alongside the innovation process and allowing for targeted feedback and support. The end of the TEL Marketplace represents the beginning of the next iteration. Previously funded projects provide input for new projects and, at the same time, can apply for another round of funding with new goals. The maturity of the developed innovations then informs the decision for continuation or standardization. The TEL Marketplace, therefore, aims at leveraging existing research results and establishing a living community of practice, whilst preventing to fund “research for the sake of research” and reaching punctual impact, only.
2019

Al-Ubaidi Tarek, Khodachenko Maxim, Kern Roman, Granitzer Michael, Poedts Stefaan

Advanced Techniques for Signal Search and Automatic Classification of Observational Space Data

European Planetary Science Congress, 2019

Konferenz
The presentation will outline various approaches inmachine learning and content based searchinvestigated by members of the former IMPEx-FP7(http://impex-fp7.oeaw.ac.at/) project consortium, inclose cooperation with partners Know-Center, GrazUniversity of Technology, and University of Passauand discuss some of the numerous possibilities thatopen up, using these or equivalent techniques in theemerging field of e-Science in conjunction withspace science. In particular, the presentation willfocus on applications that allow systems toautomatically classify and pre-select scientific dataand hence speed up scientific workflows significantlyby supporting scientists with the cumbersome task ofgoing through vast amounts of data manually, lookingfor specific patterns, signals and phenomena ofinterest prior to selecting specific data for closerexamination and analysis.
2019

Iacopo Vagliano, Fessl Angela, Franziska Günther, Thomas Köhler, Vasileios Mezaris, Ahmed Saleh, Ansgar Scherp, Simic Ilija

Training Researchers with the MOVING Platform

In Proceedings of the International Conference on Multimedia Modeling , Springer International Publishing, 2019

Konferenz
The MOVING platform enables its users to improve their information literacy by training how to exploit data and text mining methods in their daily research tasks. In this paper, we show how it can support researchers in various tasks, and we introduce its main features, such as text and video retrieval and processing, advanced visualizations, and the technologies to assist the learning process.
2019

Fessl Angela, Apaolaza Aitor, Gledson Ann, Pammer-Schindler Viktoria, Vigo Markel

"Mirror, mirror on my search..." Data-driven Reflection and Experimentation with Search Behaviour

Proceedings of the European Conference on Technology Enhanced Learning: Transforming Learning wiht MEaningful Technologies, Springer International Publishing, Delft, 2019

Konferenz
Searching on the web is a key activity for working and learning purposes. In this work, we aimed to motivate users to reflect on their search behaviour, and to experiment with different search functionalities. We implemented a widget that logs user interactions within a search platform, mirrors back search behaviours to users, and prompts users to reflect about it. We carried out two studies to evaluate the impact of such widget on search behaviour: in Study 1 (N = 76), participants received screenshots of the widget including reflection prompts while in Study 2 (N = 15), a maximum of 10 search tasks were conducted by participants over a period of two weeks on a search platform that contained the widget. Study 1 shows that reflection prompts induce meaningful insights about search behaviour. Study 2 suggests that, when using a novel search platform for the first time, those participants who had the widget prioritised search behaviours over time. The incorporation of the widget into the search platform after users had become familiar with it, however, was not observed to impact search behaviour. While the potential to support un-learning of routines could not be shown, the two studies suggest the widget’s usability, perceived usefulness, potential to induce reflection and potential to impact search behaviour.
2019

Kopeinik Simone, Seitlinger Paul, Lex Elisabeth

A Study of Confirmation Bias and Polarization in Information Behavio

European Symposium on Computational Social Science (EuroCSS, Zurich, Switzerlan, 2019

Konferenz
2019

Kopeinik Simone, Lex Elisabeth, Kowald Dominik, Albert Dietrich, Seitlinger Paul

A Real-Life School Study of Confirmation Bias and Polarisation in Information Behaviou

Lecture Notes in Computer Science, Springer, 2019

Konferenz
When people engage in Social Networking Sites, they influence one another through their contributions. Prior research suggests that the interplay between individual differences and environmental variables, such as a person’s openness to conflicting information, can give rise to either public spheres or echo chambers. In this work, we aim to unravel critical processes of this interplay in the context of learning. In particular, we observe high school students’ information behavior (search and evaluation of Web resources) to better understand a potential coupling between confirmatory search and polarization and, in further consequence, improve learning analytics and information services for individual and collective search in learning scenarios. In an empirical study, we had 91 high school students performing an information search in a social bookmarking environment. Gathered log data was used to compute indices of confirmatory search and polarisation as well as to analyze the impact of social stimulation. We find confirmatory search and polarization to correlate positively and social stimulation to mitigate, i.e., reduce the two variables’ relationship. From these findings, we derive practical implications for future work that aims to refine our formalism to compute confirmatory search and polarisation indices and to apply it for depolarizing information services
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

Böhm Matthias , Alexandre V. Evfimievski, Berthold Reinwald

Efficient Data-Parallel Cumulative Aggregates for Large-Scale Machine Learning

2019

Konferenz
2019

Johanna Sommer, Böhm Matthias , Alexandre V. Evfimievski, Berthold Reinwald, Peter J. Haas

MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions

2019

Konferenz
2019

Monsberger Michael, Koppelhuber Daniela, Sabol Vedran, Gursch Heimo, Spataru Adrian, Prentner Oliver

An Innovative User Feedback System for Sustainable Buildings

Sustainable Built Environment D-A-CH Conference 2019 (SBE19), IOP Publishing Ltd, Bristol, UK, 2019

Konferenz
A lot of research is currently focused on studying user behavior indirectly by analyzing sensor data. However, only little attention has been given to the systematic acquisition of immediate user feedback to study user behavior in buildings. In this paper, we present a novel user feedback system which allows building users to provide feedback on the perceived sense of personal comfort in a room. To this end, a dedicated easy-to-use mobile app has been developed; it is complemented by a supporting infrastructure, including a web page for an at-a-glance overview. The obtained user feedback is compared with sensor data to assess whether building services (e.g., heating, ventilation and air-conditioning systems) are operated in accordance with user requirements. This serves as a basis to develop algorithms capable of optimizing building operation by providing recommendations to facility management staff or by automatic adjustment of operating points of building services. In this paper, we present the basic concept of the novel feedback system for building users and first results from an initial test phase. The results show that building users utilize the developed app to provide both, positive and negative feedback on room conditions. They also show that it is possible to identify rooms with non-ideal operating conditions and that reasonable measures to improve building operation can be derived from the gathered information. The results highlight the potential of the proposed system.
2019

Fuchs Alexandra, Geiger Bernhard, Hobisch Elisabeth, Koncar Philipp, Saric Sanja, Scholger Martina

Distant Spectators: Mining TEI-encoded periodicals of the Enlightenment

TEI Conf. and Member's Meeting, Graz, 2019

Konferenz
with contributions from Denis Helic and Jacqueline More
2019

Lindstaedt Stefanie , Geiger Bernhard, Pirker Gerhard

Big Data and Data Driven Modeling - A New Dawn for Engine Operation and Development

17th Symp. The Working Process of the Internal Combustion Engine, Graz, 2019

Konferenz
2019

Gursch Heimo, Cemernek David, Wuttei Andreas, Kern Roman

Cyber-Physical Systems as Enablers in Manufacturing Communication and Worker Support

Mensch und Computer 2019, Frank Steinicke und Katrin Wolf, Gesellschaft für Informatik e.V., Bonn, Germany, 2019

Konferenz
The increasing potential of Information and Communications Technology (ICT) drives higher degrees of digitisation in the manufacturing industry. Such catchphrases as “Industry 4.0” and “smart manufacturing” reflect this tendency. The implementation of these paradigms is not merely an end to itself, but a new way of collaboration across existing department and process boundaries. Converting the process input, internal and output data into digital twins offers the possibility to test and validate the parameter changes via simulations, whose results can be used to update guidelines for shop-floor workers. The result is a Cyber-Physical System (CPS) that brings together the physical shop-floor, the digital data created in the manufacturing process, the simulations, and the human workers. The CPS offers new ways of collaboration on a shared data basis: the workers can annotate manufacturing problems directly in the data, obtain updated process guidelines, and use knowledge from other experts to address issues. Although the CPS cannot replace manufacturing management since it is formalised through various approaches, e. g., Six-Sigma or Advanced Process Control (APC), it is a new tool for validating decisions in simulation before they are implemented, allowing to continuously improve the guidelines.
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
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

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.
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