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Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen
2018
Lovric Mario, Krebs Sarah, Cemernek David, Kern Roman
BIG DATA IN INDUSTRIAL APPLICATION
XII Meeting of Young Chemical Engineers, Zagreb, Kroatien, 2018
Konferenz
The use of big data technologies has a deep impact on today’s research (Tetko et al., 2016) and industry (Li et al., n.d.), but also on public health (Khoury and Ioannidis, 2014) and economy (Einav and Levin, 2014). These technologies are particularly important for manufacturing sites, where complex processes are coupled with large amounts of data, for example in chemical and steel industry. This data originates from sensors, processes. and quality-testing. Typical application of these technologies is related to predictive maintenance and optimisation of production processes. Media makes the term “big data” a hot buzzword without going to deep into the topic. We noted a lack in user’s understanding of the technologies and techniques behind it, making the application of such technologies challenging. In practice the data is often unstructured (Gandomi and Haider, 2015) and a lot of resources are devoted to cleaning and preparation, but also to understanding causalities and relevance among features. The latter one requires domain knowledge, making big data projects not only challenging from a technical perspective, but also from a communication perspective. Therefore, there is a need to rethink the big data concept among researchers and manufacturing experts including topics like data quality, knowledge exchange and technology required. The scope of this presentation is to present the main pitfalls in applying big data technologies amongst users from industry, explain scaling principles in big data projects, and demonstrate common challenges in an industrial big data project
2018
Andrusyak Bohdan, Kugi Thomas, Kern Roman
Daily Prediction of Foreign Exchange Rates Based on the Stock Marke
Proceedings of the PEFNet 2017 conference, Jana Stávková, Mendel University Press, Brno, 2018
Konferenz
The stock and foreign exchange markets are the two fundamental financial markets in the world and play acrucial role in international business. This paper examines the possibility of predicting the foreign exchangemarket via machine learning techniques, taking the stock market into account. We compare prediction modelsbased on algorithms from the fields of shallow and deep learning. Our models of foreign exchange marketsbased on information from the stock market have been shown to be able to predict the future of foreignexchange markets with an accuracy of over 60%. This can be seen as an indicator of a strong link between thetwo markets. Our insights offer a chance of a better understanding guiding the future of market predictions.We found the accuracy depends on the time frame of the forecast and the algorithms used, where deeplearning tends to perform better for farther-reaching forecasts
2018
d'Aquin Mathie , Kowald Dominik, Fessl Angela, Thalmann Stefan, Lex Elisabeth
AFEL - Analytics for Everyday Learning
Proceedings of the International Projects Track co-located with the 27th International World Wide Web Conference, ACM, Lyon, France, 2018
Konferenz
The goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) online social environments; 2) creating methods for the analysis of such informal learning data, based on combining feature engineering and visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported; 4) evaluate all the former items in real world large scale applications and platforms.
2018
Kowald Dominik, Seitlinger Paul , Ley Tobias , Lex Elisabeth
The Impact of Semantic Context Cues on the User Acceptance of Tag Recommendations: An Online Study
Companion Proceedings of the 27th International World Wide Web Conference, ACM, Lyon, France, 2018
Konferenz
In this paper, we present the results of an online study with the aim to shed light on the impact that semantic context cues have on the user acceptance of tag recommendations. Therefore, we conducted a work-integrated social bookmarking scenario with 17 university employees in order to compare the user acceptance of a context-aware tag recommendation algorithm called 3Layers with the user acceptance of a simple popularity-based baseline. In this scenario, we validated and verified the hypothesis that semantic context cues have a higher impact on the user acceptance of tag recommendations in a collaborative tagging setting than in an individual tagging setting. With this paper, we contribute to the sparse line of research presenting online recommendation studies.
2018
Lovric Mario, Stipaničev Draženka , Repec Siniša , Malev Olga , Klobučar Göran
Combined toxic unit: Moving towards a multipath risk assessment strategy of organic contaminants in river sediment
, 2018
Konferenz
2018
Lacic Emanuel, Kowald Dominik, Reter-Haas Markus, Slawicek Valentin, Lex Elisabeth
Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendation
In Proceedings of the International Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization (IFUP'2018) co-located with the 11th ACM International Conference on Web Search and Data Mining, WSDM'2018, ACM, Los Angeles, USA, 2018
Konferenz
In this work, we address the problem of recommending jobs touniversity students. For this, we explore the impact of using itemembeddings for a content-based job recommendation system. Fur-thermore, we utilize a model from human memory theory to integratethe factors of frequency and recency of job posting interactions forcombining item embeddings. We evaluate our job recommendationsystem on a dataset of the Austrian student job portal Studo usingprediction accuracy, diversity as well as adapted novelty, which isintroduced in this work. We find that utilizing frequency and recencyof interactions with job postings for combining item embeddingsresults in a robust model with respect to accuracy and diversity, butalso provides the best adapted novelty results
2018
Luzhnica Granit, Veas Eduardo Enrique
Investigating Interactions for Text Recognition using a Vibrotactile Wearable Display
ACM International Conference on Intelligent User Interfaces , Tokyo, 2018
Konferenz
Vibrotactile skin-reading uses wearable vibrotactile displays to convey dynamically generated textual information. Such wearable displays have potential to be used in a broad range of applications. Nevertheless, the reading process is passive, and users have no control over the reading flow. To compensate for such drawback, this paper investigates what kind of interactions are necessary for vibrotactile skin reading and the modalities of such interactions. An interaction concept for skin reading was designed by taking into account the reading as a process. We performed a formative study with 22 participants to assess reading behaviour in word and sentence reading using a six-channel wearable vibrotactile display. Our study shows that word based interactions in sentence reading are more often used and preferred by users compared to character-based interactions and that users prefer gesture-based interaction for skin reading. Finally, we discuss how such wearable vibrotactile displays could be extended with sensors that would enable recognition of such gesture-based interaction. This paper contributes a set of guidelines for the design of wearable haptic displays for text communication.
2018
Breitfuß Gert, Berger Martin, Doerrzapf Linda
Innovation Milieus for Mobility – Analysis of Innovation Lab Approaches for the Establishment of Urban Mobility Labs in Austria
TRA Vienna 2018 - Transport Research Arena, 2018
Konferenz
The initiative „Urban Mobility Labs“ (UML), driven by the Austrian Ministry of Transport, Innovation and Technology, was started to support the setup of innovative and experimental environments for research, testing, implementation and transfer of mobility solutions. This should happen by incorporating the scientific community, citizens and stakeholders in politics and administration as well as other groups. The emerging structural frame shall enhance the efficiency and effectivity of the innovation process. In this paper insights and in-depth analysis of the approaches and experiences gained in the eight UML exploratory projects will be outlined. These projects were analyzed, systematized and enriched with further considerations. Furthermore, their knowledge growth as user-centered innovation environments was documented during the exploratory phase.
2018
Santos Tiago, Kern Roman
Understanding semiconductor production with variational auto-encoders
European Symposium on Artificial Neural Network (ESANN) 2018, 2018
Konferenz
Semiconductor manufacturing processes critically depend on hundreds of highly complex process steps, which may cause critical deviations in the end-product.Hence, a better understanding of wafer test data patterns, which represent stress tests conducted on devices in semiconductor material slices, may lead to an improved production process.However, the shapes and types of these wafer patterns, as well as their relation to single process steps, are unknown.In a first step to address these issues, we tailor and apply a variational auto-encoder (VAE) to wafer pattern images.We find the VAE's generator allows for explorative wafer pattern analysis, andits encoder provides an effective dimensionality reduction algorithm, which, in a clustering application, performs better than several baselines such as t-SNE and yields interpretable clusters of wafer patterns.
2018
Urak Günter, Ziak Hermann, Kern Roman
Source Selection of Long Tail Sources for Federated Search in an Uncooperative Setting
SAC, 2018
Konferenz
The task of federated search is to combine results from multiple knowledge bases into a single, aggregated result list, where the items typically range from textual documents toimages. These knowledge bases are also called sources, and the process of choosing the actual subset of sources for a given query is called source selection. A scenario wherethese sources do not provide information about their content in a standardized way is called uncooperative setting. In our work we focus on knowledge bases providing long tail content, i.e., rather specialized sources offering a low number of relevant documents. These sources are often neglected in favor of more popular knowledge sources, both by today’s Web users as well as by most of the existing source selection techniques. We propose a system for source selection which i) could be utilized to automatically detect long tail knowledge bases and ii) generates aggregated search results that tend to incorporate results from these long tail sources. Starting from the current state-of-the-art we developed components that allowed to adjust the amount of contribution from long tail sources. Our evaluation is conducted on theTREC 2014 Federated WebSearch dataset. As this dataset also favors the most popular sources, systems that include many long tail knowledge bases will yield low performancemeasures. Here, we propose a system where just a few relevant long tail sources are integrated into the list of more popular knowledge bases. Additionally, we evaluated the implications of an uncooperative setting, where only minimal information of the sources is available to the federated search system. Here a severe drop in performance is observed once the share of long tail sources is higher than 40%. Our work is intended to steer the development of federated search systems that aim at increasing the diversity and coverage of the aggregated search result.
2017
Thalmann Stefan, Thiele Janna, Manhart Markus, Virnes Marjo
Application Scenarios of Mobile Learning in Vocational Training: A Case Study of Ach So! in the Construction Sector
EdMedia: World Conference on Educational Media and Technology, Association for the Advancement of Computing in Education (AACE), Washington, DC, United States, 2017
Konferenz
This study explored the application scenarios of a mobile app called Ach So! for
workplace learning of construction work apprentices. The mobile application was used for
piloting new technology-enhanced learning practices in vocational apprenticeship training
at construction sites in Finland and in a training center in Germany. Semi-structured focus
group interviews were conducted after the pilot test periods. The interview data served as
the data source for the concept-driven framework analysis that employed theoretical ...
2017
Stabauer Petra, Breitfuß Gert, Lassnig Markus
Changing business models arising from digitalization. A best practice case study based on two Austrian companies
KF Uni Graz, Institut of System Sciences, Innovation and Sustainability, Graz, 2017
Konferenz
Nowadays digitalization is on everyone’s mind and affecting all areas of life. The rapid development of information technology and the increasing pervasiveness of digitalization represent new challenges to the business world. The emergence of the so-called fourth industrial revolution and the Internet of Things (IoT) confronts existing firms with changes in numerous aspects of doing business. Not only information and communication technologies are changing production processes through increasing automation. Digitalization can affect products and services itself. This could lead to major changes in a company’s value chain and as a consequence affects the company’s business model. In the age of digitalization, it is no longer sufficient to change single aspects of a firm’s business strategy, the business model itself needs to be the subject of innovation. This paper presents how digitalization affects business models of well-established companies in Austria. The results are demonstrated by means of two best practice case studies. The case studies were identified within an empirical research study funded by the Austrian Ministry for Transport, Innovation and Technology (BMVIT). The selected best practice cases presents how digitalization affects a firm’s business model and demonstrates the transformation of the value creation process by simultaneously contributing to sustainable development.
2017
Thalmann Stefan, Larrazábal Jorge, Pammer-Schindler Viktoria, Kreuzthaler Armin, Fessl Angela
Fast Language Learning: Being Able to Manage Projects in a Foreign Language within Two Month
EdMedia: World Conference on Educational Media and Technology, Association for the Advancement of Computing in Education (AACE, Washington, DC, United States, 2017
Konferenz
n times of globalization, also workforce needs to be able to go global. This holds
true especially for technical experts holding an exclusive expertise. Together with a global
manufacturing company, we addressed the challenge of being able to send staff into foreign
countries for managing technical projects in the foreign language. We developed a
language learning concept that combines a language learning platform with conventional
individual but virtually conducted coaching sessions. In our use case, we developed this ...
2017
Dennerlein Sebastian, Pammer-Schindler_TU Viktoria, Ginthör Robert, Breitfuß Gert, Stern Hermann
Bringing Big Data to Adolescence - Specifying Business Models by Practice
Institute of Systems Sciences, Innovation and Sustainability Research , Merangasse 18 /I , A - 8010 Graz, Austria, Baumgartner, R.J., Fuellsack, M., Gelbmann, U., Rauter, R, Graz, Austria, 2017
Konferenz
To specify the current understanding of business models in the realm of Big Data, we used a qualitative approach analysing 25 Big Data projects spread over the domains of Retail, Energy, Production, and Life Sciences, and various company types (SME, group, start-up, etc.). All projects have been conducted in the last two years at Austria’s competence center for Data-driven Business and Big Data Analytics, the Know-Center.
2017
Geiger Manfred, Waizenegger Lena, Treasue-Jones Tamsin, Sarigianni Christina, Maier Ronald, Thalmann Stefan, Remus Ulrich
NOT JUST ANOTHER TYPE OF RESISTANCE–TOWARDS A DEEPER UNDERSTANDING OF SUPPORTIVE NON-USE
25th European Conference on Information Systems, AIS, Guimarães, Portugal, 2017
Konferenz
Research on information system (IS) adoption and resistance has accumulated
substantial theoretical and managerial knowledge. Surprisingly, the paradox that end users
support and at the same time resist use of an IS has received relatively little attention. The
investigation of this puzzle, however, is important to complement our understanding of
resistant behaviours and consequently to strengthen the explanatory power of extant
theoretical constructs on IS resistance. We investigate an IS project within the healthcare ...
2017
Hasitschka Peter, Sabol Vedran, Thalmann Stefan
Toward a Visual Analytics Framework for Learning Communities in Industry 4.0
9. Konferenz Professionelles Wissensmanagement (Professional Knowledge Management), York Sure-Vetter, Stefan Zander, Andreas Harth, Karlsruhe, Deutschland, 2017
Konferenz
Industry 4.0 describes the digitization and the interlinking
of companies working together in a supply chain [1]. Thereby,
the digitization and the interlinking does not only affects the
machines and IT infrastructure, rather also the employees are
affected [3]. The employees have to acquire more and more
complex knowledge within a shorter period of time. To cope
with this challenge, the learning needs to be integrated into the
daily work practices, while the learning communities should
map the organizational production networks [2]. Such learning
networks support the knowledge exchange and joint problem
solving together with all involved parties [4]. However, in
such communities not all involved actors are known and hence
support to find the right learning material and peers is needed.
Nowadays, many different learning environments are used
in the industry. Their complexity makes it hard to understand
whether the system provides an optimal learning environment.
The large number of learning resources, learners and their
activities makes it hard to identify potential problems inside a
learning environment. Since the human visual system provides
enormous power for discovering patterns from data displayed
using a suitable visual representation [5], visualizing such a
learning environment could provide deeper insights into its
structure and activities of the learners.
Our goal is to provide a visual framework supporting the
analysis of communities that arise in a learning environment.
Such analysis may lead to discovery of information that helps
to improve the learning environment and the users’ learning
success.
2017
Fessl Angela, Pammer-Schindler_TU Viktoria, Wiese Michael, Thalmann Stefan
Improving Search Strategies of Auditors – A Focus Group on Reflection Interventions
ARTEL Workshop at EC-TEL 2017, Tallin, Estland, 2017
Konferenz
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.
2017
Gursch Heimo, Cemernek David, Kern Roman
Multi-Loop Feedback Hierarchy Involving Human Workers in Manufacturing Processes
Mensch und Computer 2017 - Workshopband, Manuel Burghardt, Raphael Wimmer, Christian Wolff, Christa Womser-Hacker, Gesellschaft für Informatik e.V., Regensburg, 2017
Konferenz
In manufacturing environments today, automated machinery works alongside human workers. In many cases computers and humans oversee different aspects of the same manufacturing steps, sub-processes, and processes. This paper identifies and describes four feedback loops in manufacturing and organises them in terms of their time horizon and degree of automation versus human involvement. The data flow in the feedback loops is further characterised by features commonly associated with Big Data. Velocity, volume, variety, and veracity are used to establish, describe and compare differences in the data flows.
2017
de Reuver Mark, Tarkus Astrid, Haaker Timber, Breitfuß Gert, Roelfsema Melissa, Kosman Ruud, Heikkilä Marikka
Designing an ICT tooling platform to support the needs of SMEs in business model innovation
Unversity of Maribor, Faculty of Organizational Sciences, Andreja Pucihar, Ph.D., Mirjana Kljajić Borštnar, Ph.D., Christian Kittl, Ph.D., Pascal Ravesteijn, Ph.D., Roger Clarke, Ph.D., Roger Bons, Ph.D, University of Maribor Press, Maribor, 2017
Konferenz
In this paper, we present two design cycles for an online platform with ICT-enabled tooling that supports business model innovation by SMEs. The platform connects the needs of the SMEs regarding BMI with tools that can help to solve those needs and questions. The needs are derived from our earlier case study work (Heikkilä et al. 2016), showing typical BMI patterns of the SMEs needs - labelled as ‘I want to’s - about what an entrepreneur wants to achieve with business model innovation. The platform provides sets of integrated tools that can answer the typical ‘I want to’ questions that SMEs have with innovating their business models.
2017
Kraker Peter, Enkhbayar Asuraa, Schramm Maxi, Kittel Christopher, Chamberlain Scott, Skaug Mike , Brembs Björn
Open Knowledge Maps: A Visual Interface to the World’s Scientific Knowledge
Open Science Conference 2017, Berlin, 2017
Konferenz
2017
Reiter-Haas Markus, Slawicek Valentin, Lacic Emanuel
Studo Jobs: Enriching Data With Predicted Job Labels
ACM, Graz, 2017
Konferenz
2017
Ruiz Adolfo, Prieto Luis Pablo, Ley Tobias, Jesús Rodríguez Triana María , Dennerlein Sebastian
Learning Analytics for Professional and Workplace Learning: A Literature Review
EC-TEL 2017 EUROPEAN CONFERENCE ON TECHNOLOGY ENHANCED LEARNING, Springer, Tallinn, 2017
Konferenz
Despite the ubiquity of learning in the everyday life of most workplaces, the learning analytics community only has paid attention to such settings very recently. One probable reason for this oversight is the fact that learning in the workplace is often informal, hard to grasp and not univocally defined. This paper summarizes the state of the art of Workplace Learning Analytics (WPLA), extracted from a systematic literature review of five academic databases as well as other known sources in the WPLA community. Our analysis of existing proposals discusses particularly on the role of different conceptions of learning and their influence on the LA proposals’ design and technology choices. We end the paper by discussing opportunities for future work in this emergent field.
2017
Lacic Emanuel, Kowald Dominik, Lex Elisabeth
Tailoring Recommendations for a Multi-Domain Environment
ACM International Conference on Recommender Systems 2017, RecSys'2017, ACM, Como, Italy, 2017
Konferenz
Recommender systems are acknowledged as an essential instrumentto support users in finding relevant information. However,the adaptation of recommender systems to multiple domain-specificrequirements and data models still remains an open challenge. Inthe present paper, we contribute to this sparse line of research withguidance on how to design a customizable recommender systemthat accounts for multiple domains with heterogeneous data. Usingconcrete showcase examples, we demonstrate how to setup amulti-domain system on the item and system level, and we reportevaluation results for the domains of (i) LastFM, (ii) FourSquare,and (iii) MovieLens. We believe that our findings and guidelinescan support developers and researchers of recommender systemsto easily adapt and deploy a recommender system in distributedenvironments, as well as to develop and evaluate algorithms suitedfor multi-domain settings
2017
Kowald Dominik, Kopeinik Simone , Lex Elisabeth
The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems
International Conference on User Modeling, Adaptation and Personalization 2017, UMAP'2017, ACM, Bratislava, 2017
Konferenz
Recommender systems have become important tools to support
users in identifying relevant content in an overloaded information
space. To ease the development of recommender systems, a number
of recommender frameworks have been proposed that serve a wide
range of application domains. Our TagRec framework is one of the
few examples of an open-source framework tailored towards developing
and evaluating tag-based recommender systems. In this paper,
we present the current, updated state of TagRec, and we summarize
and reect on four use cases that have been implemented with
TagRec: (i) tag recommendations, (ii) resource recommendations,
(iii) recommendation evaluation, and (iv) hashtag recommendations.
To date, TagRec served the development and/or evaluation process
of tag-based recommender systems in two large scale European
research projects, which have been described in 17 research papers.
us, we believe that this work is of interest for both researchers
and practitioners of tag-based recommender systems.
2017
Görgöh Edit, Vignoli Michela, Gauch Stephan, Blümel Clemens, Kraker Peter, Hasani-Mavriqi Ilire, Luzi Daniela , Walker Mappet, Toli Eleni, Sifacaki Electra
Opening up new channels for scholarly review, dissemination, and assessment
OpenSym 2017 International Symposium on Open Collaboration, Lorraine Morgan, ACM, Galway, Ireland, 2017
Konferenz
The growing dissatisfaction with the traditional scholarly communication process and publishing practices as well as increasing usage and acceptance of ICT and Web 2.0 technologies in research have resulted in the proliferation of alternative review, publishing and bibliometric methods. The EU-funded project OpenUP addresses key aspects and challenges of the currently transforming science landscape and aspires to come up with a cohesive framework for the review-disseminate-assess phases of the research life cycle that is fit to support and promote open science. The objective of this paper is to present first results and conclusions of the landscape scan and analysis of alternative peer review, altmetrics and innovative dissemination methods done during the first project year.
2017
Shao Lin, Silva Nelson, Schreck Tobias, Eggeling Eva
Visual Exploration of Large Scatter Plot Matrices by Pattern Recommendation based on Eye Tracking
ESIDA 2017 - Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, co-located with IUI 2017 - International Conference on Intelligent User Interfaces, ACM, Limassol, Cyprus, 2017
Konferenz
The Scatter Plot Matrix (SPLOM) is a well-known technique for visual analysis of high-dimensional data. However, one problem of large SPLOMs is that typically not all views are potentially relevant to a given analysis task or user. The matrix itself may contain structured patterns across the dimensions, which could interfere with the investigation for unexplored views. We introduce a new concept and prototype implementation for an interactive recommender system supporting the exploration of large SPLOMs based on indirectly obtained user feedback from user eye tracking. Our system records the patterns that are currently under exploration based on gaze times, recommending areas of the SPLOM containing potentially new, unseen patterns for successive exploration. We use an image-based dissimilarity measure to recommend patterns that are visually dissimilar to previously seen ones, to guide the exploration in large SPLOMs. The dynamic exploration process is visualized by an analysis provenance heatmap, which captures the duration on explored and recommended SPLOM areas. We demonstrate our exploration process by a user experiment, showing the indirectly controlled recommender system achieves higher pattern recall as compared to fully interactive navigation using mouse operations.
2017
Görögh Edit, Toli Eleni, Kraker Peter
Opening up the research lifecycle: review, assessment and dissemination of scholarly publications
Open Science Conference 2017, Berlin, 2017
Konferenz
2017
Kopeinik Simone, Lex Elisabeth, Seitlinger Paul, Tschinkel, Ley Tobias
Supporting collaborative learning with tag recommendations: a real-world study in an inquiry-based classroom project
Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK 2017), ACM, Vancouver, 2017
Konferenz
In online social learning environments, tagging has demonstratedits potential to facilitate search, to improve recommendationsand to foster reflection and learning.Studieshave shown that shared understanding needs to be establishedin the group as a prerequisite for learning. We hypothesisethat this can be fostered through tag recommendationstrategies that contribute to semantic stabilization.In this study, we investigate the application of two tag recommendersthat are inspired by models of human memory:(i) the base-level learning equation BLL and (ii) Minerva.BLL models the frequency and recency of tag use while Minervais based on frequency of tag use and semantic context.We test the impact of both tag recommenders on semanticstabilization in an online study with 56 students completinga group-based inquiry learning project in school. Wefind that displaying tags from other group members contributessignificantly to semantic stabilization in the group,as compared to a strategy where tags from the students’individual vocabularies are used. Testing for the accuracyof the different recommenders revealed that algorithms usingfrequency counts such as BLL performed better whenindividual tags were recommended. When group tags wererecommended, the Minerva algorithm performed better. Weconclude that tag recommenders, exposing learners to eachother’s tag choices by simulating search processes on learners’semantic memory structures, show potential to supportsemantic stabilization and thus, inquiry-based learning ingroups.
2017
Kowald Dominik, Pujari Subash Chandra, Lex Elisabeth
Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach
Proceedings of the 26th International World Wide Web Conference, WWW'2017, ACM, Perth, Western Australia, 2017
Konferenz
Hashtags have become a powerful tool in social platforms
such as Twitter to categorize and search for content, and to
spread short messages across members of the social network.
In this paper, we study temporal hashtag usage practices in
Twitter with the aim of designing a cognitive-inspired hashtag
recommendation algorithm we call BLLI,S. Our main
idea is to incorporate the effect of time on (i) individual
hashtag reuse (i.e., reusing own hashtags), and (ii) social
hashtag reuse (i.e., reusing hashtags, which has been previously
used by a followee) into a predictive model. For this,
we turn to the Base-Level Learning (BLL) equation from the
cognitive architecture ACT-R, which accounts for the timedependent
decay of item exposure in human memory. We
validate BLLI,S using two crawled Twitter datasets in two
evaluation scenarios. Firstly, only temporal usage patterns
of past hashtag assignments are utilized and secondly, these
patterns are combined with a content-based analysis of the
current tweet. In both evaluation scenarios, we find not only
that temporal effects play an important role for both individual
and social hashtag reuse but also that our BLLI,S approach
provides significantly better prediction accuracy and
ranking results than current state-of-the-art hashtag recommendation
methods.
2017
Traub Matthias, Gursch Heimo, Lex Elisabeth, Kern Roman
Data Market Austria - Austria's First Digital Ecosystem for Data, Businesses, and Innovation
Exploring a changing view on organizing value creation: Developing New Business Models. Contributions to the 2nd International Conference on New Business Models, Institute of Systems Sciences, Innovation and Sustainability Research, Merangasse 18, 8010 Graz, Austria, Graz, 2017
Konferenz
New business opportunities in the digital economy are established when datasets describing a problem, data services solving the said problem, the required expertise and infrastructure come together. For most real-word problems finding the right data sources, services consulting expertise, and infrastructure is difficult, especially since the market players change often. The Data Market Austria (DMA) offers a platform to bring datasets, data services, consulting, and infrastructure offers to a common marketplace. The recommender systems included in DMA analyses all offerings, to derive suggestions for collaboration between them, like which dataset could be best processed by which data service. The suggestions should help the costumers on DMA to identify new collaborations reaching beyond traditional industry boundaries to get in touch with new clients or suppliers in the digital domain. Human brokers will work together with the recommender system to set up data value chains matching different offers to create a data value chain solving the problems in various domains. In its final expansion stage, DMA is intended to be a central hub for all actors participating in the Austrian data economy, regardless of their industrial and research domain to overcome traditional domain boundaries.
2017
Trattner Christoph, Elsweiler David
Investigating the Healthiness of Internet-Sourced Recipes
Proceedings of the 26th International Conference on World Wide Web, ACM, Perth, Australia, 2017
Konferenz
Food recommenders have the potential to positively in uence theeating habits of users. To achieve this, however, we need to understandhow healthy recommendations are and the factors whichin uence this. Focusing on two approaches from the literature(single item and daily meal plan recommendation) and utilizing alarge Internet sourced dataset from Allrecipes.com, we show howalgorithmic solutions relate to the healthiness of the underlyingrecipe collection. First, we analyze the healthiness of Allrecipes.comrecipes using nutritional standards from the World Health Organisationand the United Kingdom Food Standards Agency. Second,we investigate user interaction patterns and how these relate to thehealthiness of recipes. Third, we experiment with both recommendationapproaches. Our results indicate that overall the recipes inthe collection are quite unhealthy, but this varies across categorieson the website. Users in general tend to interact most often with theleast healthy recipes. Recommender algorithms tend to score popularitems highly and thus on average promote unhealthy items. Thiscan be tempered, however, with simple post- ltering approaches,which we show by experiment are better suited to some algorithmsthan others. Similarly, we show that the generation of meal planscan dramatically increase the number of healthy options open tousers. One of the main ndings is, nevertheless, that the utilityof both approaches is strongly restricted by the recipe collection.Based on our ndings we draw conclusions how researchers shouldattempt to make food recommendation systems promote healthynutrition.
2017
Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph
Tags, Titles or Q & A: Choosing Content Descriptors for Visual Recommender Systems
Proceedings of the 28th ACM Conference on Hypertext and Social Media, ACM, Prague, Czech Republic, 2017
Konferenz
In today's digital age with an increasing number of websites, social/learning platforms, and different computer-mediated communication systems, finding valuable information is a challenging and tedious task, regardless from which discipline a person is. However, visualizations have shown to be effective in dealing with huge datasets: because they are grounded on visual cognition, people understand them and can naturally perform visual operations such as clustering, filtering and comparing quantities. But, creating appropriate visual representations of data is also challenging: it requires domain knowledge, understanding of the data, and knowledge about task and user preferences. To tackle this issue, we have developed a recommender system that generates visualizations based on (i) a set of visual cognition rules/guidelines, and (ii) filters a subset considering user preferences. A user places interests on several aspects of a visualization, the task or problem it helps to solve, the operations it permits, or the features of the dataset it represents. This paper concentrates on characterizing user preferences, in particular: i) the sources of information used to describe the visualizations, the content descriptors respectively, and ii) the methods to produce the most suitable recommendations thereby. We consider three sources corresponding to different aspects of interest: a title that describes the chart, a question that can be answered with the chart (and the answer), and a collection of tags describing features of the chart. We investigate user-provided input based on these sources collected with a crowd-sourced study. Firstly, information-theoretic measures are applied to each source to determine the efficiency of the input in describing user preferences and visualization contents (user and item models). Secondly, the practicability of each input is evaluated with content-based recommender system. The overall methodology and results contribute methods for design and analysis of visual recommender systems. The findings in this paper highlight the inputs which can (i) effectively encode the content of the visualizations and user's visual preferences/interest, and (ii) are more valuable for recommending personalized visualizations.
2017
Cemernek David, Gursch Heimo, Kern Roman
Big Data as a Promoter of Industry 4.0: Lessons of the Semiconductor Industry
IEEE 15th International Conference of Industrial Informatics - INDIN'2017, IEEE, Emden, Germany, 2017
Konferenz
The catchphrase “Industry 4.0” is widely regarded as a methodology for succeeding in modern manufacturing. This paper provides an overview of the history, technologies and concepts of Industry 4.0. One of the biggest challenges to implementing the Industry 4.0 paradigms in manufacturing are the heterogeneity of system landscapes and integrating data from various sources, such as different suppliers and different data formats. These issues have been addressed in the semiconductor industry since the early 1980s and some solutions have become well-established standards. Hence, the semiconductor industry can provide guidelines for a transition towards Industry 4.0 in other manufacturing domains. In this work, the methodologies of Industry 4.0, cyber-physical systems and Big data processes are discussed. Based on a thorough literature review and experiences from the semiconductor industry, we offer implementation recommendations for Industry 4.0 using the manufacturing process of an electronics manufacturer as an example.
2017
Luzhnica Granit, Veas Eduardo Enrique, Stein Sebastian, Pammer-Schindler Viktoria, Williamson John, Murray Smith Roderick
Personalising Vibrotactile Displays through Perceptual Sensitivity Adjustment
Proceedings of the 2017 ACM International Symposium on Wearable Computing, ACM, Maui, Hawai, USA, 2017
Konferenz
Haptic displays are commonly limited to transmitting a dis- crete set of tactile motives. In this paper, we explore the transmission of real-valued information through vibrotactile displays. We simulate spatial continuity with three perceptual models commonly used to create phantom sensations: the lin- ear, logarithmic and power model. We show that these generic models lead to limited decoding precision, and propose a method for model personalization adjusting to idiosyncratic and spatial variations in perceptual sensitivity. We evaluate this approach using two haptic display layouts: circular, worn around the wrist and the upper arm, and straight, worn along the forearm. Results of a user study measuring continuous value decoding precision show that users were able to decode continuous values with relatively high accuracy (4.4% mean error), circular layouts performed particularly well, and per- sonalisation through sensitivity adjustment increased decoding precision.
2017
Guerra Jorge, Catania Carlos, Veas Eduardo Enrique
Visual exploration of network hostile behavior
Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, ACM, Limassol, Cyprus, 2017
Konferenz
This paper presents a graphical interface to identify hostilebehavior in network logs. The problem of identifying andlabeling hostile behavior is well known in the network securitycommunity. There is a lack of labeled datasets, which makeit difficult to deploy automated methods or to test the perfor-mance of manual ones. We describe the process of search-ing and identifying hostile behavior with a graphical tool de-rived from an open source Intrusion Prevention System, whichgraphically encodes features of network connections from alog-file. A design study with two network security expertsillustrates the workflow of searching for patterns descriptiveof unwanted behavior and labeling occurrences therewith.
2017
Veas Eduardo Enrique
From Search to Discovery with Visual Exploration Tools
Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, ACM, Limassol, Cyprus, 2017
Konferenz
In our goal to personalize the discovery of scientific information, we built systems using visual analytics principles for exploration of textual documents [1]. The concept was extended to explore information quality of user generated content [2]. Our interfaces build upon a cognitive model, where awareness is a key step of exploration [3]. In education-related circles, a frequent concern is that people increasingly need to know how to search, and that knowing how to search leads to finding information efficiently. The ever-growing information overabundance right at our fingertips needs a naturalskill to develop and refine search queries to get better search results, or does it?Exploratory search is an investigative behavior we adopt to build knowledge by iteratively selecting interesting features that lead to associations between representative items in the information space [4,5]. Formulating queries was proven more complicated for humans than recognizing information visually [6]. Visual analytics takes the form of an open ended dialog between the user and the underlying analytics algorithms operating on the data [7]. This talk describes studies on exploration and discovery with visual analytics interfaces that emphasize transparency and control featuresto trigger awareness. We will discuss the interface design and the studies of visual exploration behavior.
2017
Ziak Hermann, Kern Roman
Evaluation of Contextualization and Diversification Approaches in Aggregated Search
TIR @ DEXA International Conference on Database and Expert Systems Applications, 2017
Konferenz
The combination of different knowledge bases in thefield of information retrieval is called federated or aggregated search. It has several benefits over single source retrieval but poses some challenges as well. This work focuses on the challenge of result aggregation; especially in a setting where the final result list should include a certain degree of diversity and serendipity. Both concepts have been shown to have an impact on how user perceive an information retrieval system. In particular, we want to assess if common procedures for result list aggregation can be utilized to introduce diversity and serendipity. Furthermore, we study whether a blocking or interleaving for result aggregation yields better results. In a cross vertical aggregated search the so-called verticalscould be news, multimedia content or text. Block ranking is one approach to combine such heterogeneous result. It relies on the idea that these verticals are combined into a single result list as blocks of several adjacent items. An alternative approach for this is interleaving. Here the verticals are blended into one result list on an item by item basis, i.e. adjacent items in the result list may come from different verticals. To generate the diverse and serendipitous results we reliedon a query reformulation technique which we showed to be beneficial to generate diversified results in previous work. To conduct this evaluation we created a dedicated dataset. This dataset served as a basis for three different evaluation settings on a crowd sourcing platform, with over 300 participants. Our results show that query based diversification can be adapted to generate serendipitous results in a similar manner. Further, we discovered that both approaches, interleaving and block ranking, appear to be beneficial to introduce diversity and serendipity. Though it seems that queries either benefit from one approach or the other but not from both.
2017
di Sciascio Maria Cecilia, Mayr Lukas, Veas Eduardo Enrique
Exploring and Summarizing Document Colletions with Multiple Coordinated Views
Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics, ACM, Limassol, Cyprus, 2017
Konferenz
Knowledge work such as summarizing related research inpreparation for writing, typically requires the extraction ofuseful information from scientific literature. Nowadays theprimary source of information for researchers comes fromelectronic documents available on the Web, accessible throughgeneral and academic search engines such as Google Scholaror IEEE Xplore. Yet, the vast amount of resources makesretrieving only the most relevant results a difficult task. Asa consequence, researchers are often confronted with loadsof low-quality or irrelevant content. To address this issuewe introduce a novel system, which combines a rich, inter-active Web-based user interface and different visualizationapproaches. This system enables researchers to identify keyphrases matching current information needs and spot poten-tially relevant literature within hierarchical document collec-tions. The chosen context was the collection and summariza-tion of related work in preparation for scientific writing, thusthe system supports features such as bibliography and citationmanagement, document metadata extraction and a text editor.This paper introduces the design rationale and components ofthe PaperViz. Moreover, we report the insights gathered in aformative design study addressing usability
2017
Barreiros Carla, Veas Eduardo Enrique, Pammer-Schindler Viktoria
BioIoT: Communicating Sensory Information of a Coffee Machine Using a Nature Metaphor
CHI '17 CHI Conference on Human Factors in Computing Systems, ACM, ACM, Denver, Colorado, USA, 2017
Konferenz
In our research we explore representing the state of production machines using a new nature metaphor, called BioIoT. The underlying rationale is to represent relevant information in an agreeable manner and to increase machines’ appeal to operators. In this paper we describe a study with twelve participants in which sensory information of a coffee machine is encoded in a virtual tree. All participants considered the interaction with the BioIoT pleasant; and most reported to feel more inclined to perform machine maintenance, take “care” for the machine, than given classic state representation. The study highlights as directions for follow-up research personalization, intelligibility vs representational power, limits of the metaphor, and immersive visualization.
2017
Kowald Dominik, Lex Elisabeth
Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithm
European Symposium on Computational Social Sciences, ESCSS'2017, ACM, London, 2017
Konferenz
In this paper, we study the imbalance between current state-of-the-art tag recommendation algorithms and the folksonomy structures of real-world social tagging systems. While algorithms such as FolkRank are designed for dense folksonomy structures, most social tagging systems exhibit a sparse nature. To overcome this imbalance, we show that cognitive-inspired algorithms, which model the tag vocabulary of a user in a cognitive-plausible way, can be helpful. Our present approach does this via implementing the activation equation of the cognitive architecture ACT-R, which determines the usefulness of units in human memory (e.g., tags). In this sense, our long-term research goal is to design hybrid recommendation approaches, which combine the advantages of both worlds in order to adapt to the current setting (i.e., sparse vs. dense ones)
2017
Luzhnica Granit, Veas Eduardo Enrique
Vibrotactile Patterns using Sensitivity Prioritisation
Proceedings of the 2017 ACM International Symposium on Wearable Computers, ACM, Maui, Hawai, USA, 2017
Konferenz
This paper investigates sensitivity based prioritisation in the construction of tactile patterns. Our evidence is obtained by three studies using a wearable haptic display with vibrotactile motors (tactors). Haptic displays intended to transmit symbols often suffer the tradeoff between throughput and accuracy. For a symbol encoded with more than one tactor simultaneous onsets (spatial encoding) yields the highest throughput at the expense of the accuracy. Sequential onset increases accuracy at the expense of throughput. In the desire to overcome these issues, we investigate aspects of prioritisation based on sensitivity applied to the encoding of haptics patterns. First, we investigate an encoding method using mixed intensities, where different body locations are simultaneously stimulated with different vibration intensities. We investigate whether prioritising the intensity based on sensitivity improves identification accuracy when compared to simple spatial encoding. Second, we investigate whether prioritising onset based on sensitivity affects the identification of overlapped spatiotemporal patterns. A user study shows that this method significantly increases the accuracy. Furthermore, in a third study, we identify three locations on the hand that lead to an accurate recall. Thereby, we design the layout of a haptic display equipped with eight tactors, capable of encoding 36 symbols with only one or two locations per symbol.
2017
Kern Roman, Falk Stefan, Rexha Andi
Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator
Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), Isabelle Augenstein, Mrinal Das, Sebastian Riedel, Lakshmi Vikraman, Andrew McCallum, ACL, Vancouver, Canada, 2017
Konferenz
This paper describes our participation inSemEval-2017 Task 10, named ScienceIE(Machine Reading for Scientist). We competedin Subtask 1 and 2 which consist respectivelyin identifying all the key phrasesin scientific publications and label them withone of the three categories: Task, Process,and Material. These scientific publicationsare selected from Computer Science, MaterialSciences, and Physics domains. We followeda supervised approach for both subtasksby using a sequential classifier (CRF - ConditionalRandom Fields). For generating oursolution we used a web-based application implementedin the EU-funded research project,named CODE. Our system achieved an F1score of 0.39 for the Subtask 1 and 0.28 forthe Subtask 2.
2017
Müller-Putz G.R., Ofner P., Schwarz A., Pereira J., Luzhnica Granit, di Sciascio Maria Cecilia, Veas Eduardo Enrique, Stein S., Williamson J., Murray-Smith R., Escolano C., Montesano L., Hessing B., Schneiders M., Rupp R.
MoreGrasp: Restoration of upper limb function in individuals with high spinal cord injury by multimodal neuroprostheses for interaction in daily activities
7th Graz Brain-Computer Interface Conference 2017, Graz, 2017
Konferenz
The aim of the MoreGrasp project is to develop a non-invasive, multimodal user interface including a brain-computer interface(BCI)for intuitive control of a grasp neuroprosthesisto supportindividuals with high spinal cord injury(SCI)in everyday activities. We describe the current state of the project, including the EEG system, preliminary results of natural movements decoding in people with SCI, the new electrode concept for the grasp neuroprosthesis, the shared control architecture behind the system and the implementation ofa user-centered design.
2017
Rexha Andi, Kern Roman, Ziak Hermann, Dragoni Mauro
A semantic federated search engine for domain-specific document retrieval
SAC '17 Proceedings of the Symposium on Applied Computing, Sung Y. Shin, Dongwan Shin, Maria Lencastre, ACM, Marrakech, Morocco, 2017
Konferenz
Retrieval of domain-specific documents became attractive for theSemantic Web community due to the possibility of integrating classicInformation Retrieval (IR) techniques with semantic knowledge.Unfortunately, the gap between the construction of a full semanticsearch engine and the possibility of exploiting a repository ofontologies covering all possible domains is far from being filled.Recent solutions focused on the aggregation of different domain-specificrepositories managed by third-parties. In this paper, wepresent a semantic federated search engine developed in the contextof the EEXCESS EU project. Through the developed platform,users are able to perform federated queries over repositories in atransparent way, i.e. without knowing how their original queries aretransformed before being actually submitted. The platform implementsa facility for plugging new repositories and for creating, withthe support of general purpose knowledge bases, knowledge graphsdescribing the content of each connected repository. Such knowledgegraphs are then exploited for enriching queries performed byusers.
2017
Schrunner Stefan, Bluder Olivia, Zernig Anja, Kaestner Andre, Kern Roman
Markov Random Fields for Pattern Extraction in Analog Wafer Test Data
International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), IEEE, Montreal, Canada, 2017
Konferenz
In semiconductor industry it is of paramount im- portance to check whether a manufactured device fulfills all quality specifications and is therefore suitable for being sold to the customer. The occurrence of specific spatial patterns within the so-called wafer test data, i.e. analog electric measurements, might point out on production issues. However the shape of these critical patterns is unknown. In this paper different kinds of process patterns are extracted from wafer test data by an image processing approach using Markov Random Field models for image restoration. The goal is to develop an automated procedure to identify visible patterns in wafer test data to improve pattern matching. This step is a necessary precondition for a subsequent root-cause analysis of these patterns. The developed pattern ex- traction algorithm yields a more accurate discrimination between distinct patterns, resulting in an improved pattern comparison than in the original dataset. In a next step pattern classification will be applied to improve the production process control.
2017
Tschinkel Gerwald, Sabol Vedran
Evaluating the Memorability and Readability of Micro-Filter Visualisations
International Conference on Information Visualization Theory and Applications, Porto, Portugal, 2017
Konferenz
When using classical search engines, researchers are often confronted with a number of results far beyond what they can realistically manage to read; when this happens, recommender systems can help, by pointing users to the most valuable sources of information. In the course of a long-term research project, research into one area can extend over several days, weeks, or even months. Interruptions are unavoidable, and, when multiple team members have to discuss the status of a project, it’s important to be able to communicate the current research status easily and accurately. Multiple type-specific interactive views can help users identify the results most relevant to their focus of interest. Our recommendation dashboard uses micro-filter visualizations intended to improve the experience of working with multiple active filters, allowing researchers to maintain an overview of their progress. Within this paper, we carry out an evaluation of whether micro-visualizations help to increase the memorability and readability of active filters in comparison to textual filters. Five tasks, quantitative and qualitative questions, and the separate view on the different visualisation types enabled us to gain insights on how micro-visualisations behave and will be discussed throughout the paper.
2017
Toller Maximilian, Kern Roman
Robust Parameter-Free Season Length Detection in Time Series
MILETS 2017 @ International Conference on Knowledge Discovery and Data Mining, Halfiax, Nova Scotia Canada, 2017
Konferenz
The in-depth analysis of time series has gained a lot of re-search interest in recent years, with the identification of pe-riodic patterns being one important aspect. Many of themethods for identifying periodic patterns require time series’season length as input parameter. There exist only a few al-gorithms for automatic season length approximation. Manyof these rely on simplifications such as data discretization.This paper presents an algorithm for season length detec-tion that is designed to be sufficiently reliable to be used inpractical applications. The algorithm estimates a time series’season length by interpolating, filtering and detrending thedata. This is followed by analyzing the distances betweenzeros in the directly corresponding autocorrelation function.Our algorithm was tested against a comparable algorithmand outperformed it by passing 122 out of 165 tests, whilethe existing algorithm passed 83 tests. The robustness of ourmethod can be jointly attributed to both the algorithmic ap-proach and also to design decisions taken at the implemen-tational level.
2017
Rexha Andi, Kröll Mark, Ziak Hermann, Kern Roman
Extending Scientific Literature Search by Including the Author’s Writing Style
Fifth Workshop on Bibliometric-enhanced Information Retrieval, Atanassova, I.; Bertin, M.; Mayr, P., Springer, Aberdeen, UK, 2017
Konferenz
Our work is motivated by the idea to extend the retrieval of related scientific literature to cases, where the relatedness also incorporates the writing style of individual scientific authors. Therefore we conducted a pilot study to answer the question whether humans can identity authorship once the topological clues have been removed. As first result, we found out that this task is challenging, even for humans. We also found some agreement between the annotators. To gain a better understanding how humans tackle such a problem, we conducted an exploratory data analysis. Here, we compared the decisions against a number of topological and stylometric features. The outcome of our work should help to improve automatic authorship identificationalgorithms and to shape potential follow-up studies.
2017
Santos Tiago, Walk Simon, Helic Denis
Nonlinear Characterization of Activity Dynamics in Online Collaboration Websites
WWW '17 Companion Proceedings of the 26th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, Perth, Australia, 2017
Konferenz
Modeling activity in online collaboration websites, such asStackExchange Question and Answering portals, is becom-ing increasingly important, as the success of these websitescritically depends on the content contributed by its users. Inthis paper, we represent user activity as time series and per-form an initial analysis of these time series to obtain a bet-ter understanding of the underlying mechanisms that governtheir creation. In particular, we are interested in identifyinglatent nonlinear behavior in online user activity as opposedto a simpler linear operating mode. To that end, we applya set of statistical tests for nonlinearity as a means to char-acterize activity time series derived from 16 different onlinecollaboration websites. We validate our approach by com-paring activity forecast performance from linear and nonlin-ear models, and study the underlying dynamical systems wederive with nonlinear time series analysis. Our results showthat nonlinear characterizations of activity time series helpto (i) improve our understanding of activity dynamics in on-line collaboration websites, and (ii) increase the accuracy offorecasting experiments.
2017
Strohmaier David, di Sciascio Maria Cecilia, Errecalde Marcelo, Veas Eduardo Enrique
WikiLyzer: Interactive Information Quality Assessment in Wikipedia
ACM Intelligent User Interfaces, 2017
Konferenz
Innovations in digital libraries and services enable users to access large amounts of data on demand. Yet, quality assessment of information encountered on the Internet remains an elusive open issue. For example, Wikipedia, one of the most visited platforms on the Web, hosts thousands of user-generated articles and undergoes 12 million edits/contributions per month. User-generated content is undoubtedly one of the keys to its success, but also a hindrance to good quality: contributions can be of poor quality because everyone, even anonymous users, can participate. Though Wikipedia has defined guidelines as to what makes the perfect article, authors find it difficult to assert whether their contributions comply with them and reviewers cannot cope with the ever growing amount of articles pending review. Great efforts have been invested in algorith-mic methods for automatic classification of Wikipedia articles (as featured or non-featured) and for quality flaw detection. However, little has been done to support quality assessment of user-generated content through interactive tools that allow for combining automatic methods and human intelligence. We developed WikiLyzer, a toolkit comprising three Web-based interactive graphic tools designed to assist (i) knowledge discovery experts in creating and testing metrics for quality measurement , (ii) users searching for good articles, and (iii) users that need to identify weaknesses to improve a particular article. A case study suggests that experts are able to create complex quality metrics with our tool and a report in a user study on its usefulness to identify high-quality content.
2017
d'Aquin Mathieu , Adamou Alessandro , Dietze Stefan , Fetahu Besnik , Gadiraju Ujwal , Hasani-Mavriqi Ilire, Holz Peter, Kümmerle Joachim, Kowald Dominik, Lex Elisabeth, Lopez Sola Susana, Mataran Ricardo, Sabol Vedran, Troullinou Pinelopi, Veas Eduardo
AFEL: Towards Measuring Online Activities Contributions to Self-Directed Learning
7th Workshop on Awareness and Reflection in Technology Enhanced Learning (ARTEL 2017), Kravcik M., Mikroyannidis A., Pammer-Schindler V., Prilla M., CEUR-WS, Tallinn, Estonia, 2017
Konferenz
More and more learning activities take place online in a self-directed manner. Therefore, just as the idea of self-tracking activities for fitness purposes has gained momentum in the past few years, tools and methods for awareness and self-reflection on one's own online learning behavior appear as an emerging need for both formal and informal learners. Addressing this need is one of the key objectives of the AFEL (Analytics for Everyday Learning) project. In this paper, we discuss the different aspects of what needs to be put in place in order to enable awareness and self-reflection in online learning. We start by describing a scenario that guides the work done. We then investigate the theoretical, technical and support aspects that are required to enable this scenario, as well as the current state of the research in each aspect within the AFEL project. We conclude with a discussion of the ongoing plans from the project to develop learner-facing tools that enable awareness and self-reflection for online, self-directed learners. We also elucidate the need to establish further research programs on facets of self-tracking for learning that are necessarily going to emerge in the near future, especially regarding privacy and ethics.
2017
Mohr Peter, Mandl David, Tatzgern Markus, Veas Eduardo Enrique, Schmalstieg Dieter, Kalkofen Denis
Retargeting Video Tutorials Showing Tools With Surface Contact to Augmented Reality
Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ACM, Denver, CO, USA, 2017
Konferenz
A video tutorial effectively conveys complex motions, butmay be hard to follow precisely because of its restriction toa predetermined viewpoint. Augmented reality (AR) tutori-als have been demonstrated to be more effective. We bringthe advantages of both together by interactively retargetingconventional, two-dimensional videos into three-dimensionalAR tutorials. Unlike previous work, we do not simply overlayvideo, but synthesize 3D-registered motion from the video.Since the information in the resulting AR tutorial is registeredto 3D objects, the user can freely change the viewpoint with-out degrading the experience. This approach applies to manystyles of video tutorials. In this work, we concentrate on aclass of tutorials which alter the surface of an object
2016
Rexha Andi, Dragoni Mauro, Kern Roman, Kröll Mark
An Information Retrieval Based Approach for Multilingual Ontology Matching
International Conference on Applications of Natural Language to Information Systems, Métais E., Meziane F., Saraee M., Sugumaran V., Vadera S. , Springer , Salford, UK, 2016
Konferenz
Ontology matching in a multilingual environment consists of finding alignments between ontologies modeled by using more than one language. Such a research topic combines traditional ontology matching algorithms with the use of multilingual resources, services, and capabilities for easing multilingual matching. In this paper, we present a multilingual ontology matching approach based on Information Retrieval (IR) techniques: ontologies are indexed through an inverted index algorithm and candidate matches are found by querying such indexes. We also exploit the hierarchical structure of the ontologies by adopting the PageRank algorithm for our system. The approaches have been evaluated using a set of domain-specific ontologies belonging to the agricultural and medical domain. We compare our results with existing systems following an evaluation strategy closely resembling a recommendation scenario. The version of our system using PageRank showed an increase in performance in our evaluations.
2016
Rexha Andi, Kröll Mark, Kern Roman
Social Media Monitoring for Companies: A 4W Summarisation Approach
European Conference on Knowledge Management, Dr. Sandra Moffett and Dr. Brendan Galbraith, Academic Conferences and Publishing International Limited, Belfast, Northern Ireland, UK, 2016
Konferenz
Monitoring (social) media represents one means for companies to gain access to knowledge about, for instance, competitors, products as well as markets. As a consequence, social media monitoring tools have been gaining attention to handle amounts of data nowadays generated in social media. These tools also include summarisation services. However, most summarisation algorithms tend to focus on (i) first and last sentences respectively or (ii) sentences containing keywords.In this work we approach the task of summarisation by extracting 4W (who, when, where, what) information from (social)media texts. Presenting 4W information allows for a more compact content representation than traditional summaries. Inaddition, we depart from mere named entity recognition (NER) techniques to answer these four question types by includingnon-rigid designators, i.e. expressions which do not refer to the same thing in all possible worlds such as “at the main square”or “leaders of political parties”. To do that, we employ dependency parsing to identify grammatical characteristics for each question type. Every sentence is then represented as a 4W block. We perform two different preliminary studies: selecting sentences that better summarise texts by achieving an F1-measure of 0.343, as well as a 4W block extraction for which we achieve F1-measures of 0.932; 0.900; 0.803; 0.861 for “who”, “when”, “where” and “what” category respectively. In a next step the 4W blocks are ranked by relevance. The top three ranked blocks, for example, then constitute a summary of the entire textual passage. The relevance metric can be customised to the user’s needs, for instance, ranked by up-to-dateness where the sentences’ tense is taken into account. In a user study we evaluate different ranking strategies including (i) up-todateness,(ii) text sentence rank, (iii) selecting the firsts and lasts sentences or (iv) coverage of named entities, i.e. based on the number of named entities in the sentence. Our 4W summarisation method presents a valuable addition to a company’s(social) media monitoring toolkit, thus supporting decision making processes.
2016
Pimas Oliver, Rexha Andi, Kröll Mark, Kern Roman
Profiling microblog authors using concreteness and sentiment - Know-Center at PAN 2016 author profiling
PAN 2016, Krisztian Balog, Linda Cappellato, Nicola Ferro, Craig Macdonald, Springer, Evora, Portugal, 2016
Konferenz
The PAN 2016 author profiling task is a supervised classification problemon cross-genre documents (tweets, blog and social media posts). Our systemmakes use of concreteness, sentiment and syntactic information present in thedocuments. We train a random forest model to identify gender and age of a document’sauthor. We report the evaluation results received by the shared task.
2016
Trattner Christoph, Kuśmierczyk Tomasz, Rokicki Markus, Herder Eelco
Plate and Prejudice: Gender Differences in Online Cooking
UMAP 2016, ACM, Halifax, NS, Canada , 2016
Konferenz
Historically, there have always been differences in how men andwomen cook or eat. The reasons for this gender divide have mostlygone in Western culture, but still there is qualitative and anecdotalevidence that men prefer heftier food, that women take care of everydaycooking, and that men cook to impress. In this paper, weshow that these differences can also quantitatively be observed in alarge dataset of almost 200 thousand members of an online recipecommunity. Further, we show that, using a set of 88 features, thegender of the cooks can be predicted with fairly good accuracy of75%, with preference for particular dishes, the use of spices andthe use of kitchen utensils being the strongest predictors. Finally,we show the positive impact of our results on online food reciperecommender systems that take gender information into account.
2016
Kowald Dominik, Lex Elisabeth, Kopeinik Simone
Which Algorithms Suit Which Learning Environments? A Comparative Study of Recommender Systems in TEL
European Conference on Technology Enhanced Learning, EC-TEL'2016, Springer, Toledo, Spain, 2016
Konferenz
In recent years, a number of recommendation algorithmshave been proposed to help learners find suitable learning resources online.Next to user-centered evaluations, offline-datasets have been usedto investigate new recommendation algorithms or variations of collaborativefiltering approaches. However, a more extensive study comparinga variety of recommendation strategies on multiple TEL datasets ismissing. In this work, we contribute with a data-driven study of recommendationstrategies in TEL to shed light on their suitability forTEL datasets. To that end, we evaluate six state-of-the-art recommendationalgorithms for tag and resource recommendations on six empiricaldatasets: a dataset from European Schoolnets TravelWell, a dataset fromthe MACE portal, which features access to meta-data-enriched learningresources from the field of architecture, two datasets from the socialbookmarking systems BibSonomy and CiteULike, a MOOC dataset fromthe KDD challenge 2015, and Aposdle, a small-scale workplace learningdataset. We highlight strengths and shortcomings of the discussed recommendationalgorithms and their applicability to the TEL datasets.Our results demonstrate that the performance of the algorithms stronglydepends on the properties and characteristics of the particular dataset.However, we also find a strong correlation between the average numberof users per resource and the algorithm performance. A tag recommenderevaluation experiment reveals that a hybrid combination of a cognitiveinspiredand a popularity-based approach consistently performs best onall TEL datasets we utilized in our study.
2016
Kern Roman, Klampfl Stefan, Rexha Andi
Identifying Referenced Text in ScientificPublications by Summarisation andClassification Techniques
BIRNDL 2016 Joint Workshop on Bibliometric-enhanced Information Retrieval and NLP for Digital Libraries, G. Cabanac, Muthu Kumar Chandrasekaran, Ingo Frommholz , Kokil Jaidka, Min-Yen Kan, Philipp Mayr, Dietmar Wolfram, ACM, New Jersey, USA, 2016
Konferenz
This report describes our contribution to the 2nd ComputationalLinguistics Scientific Document Summarization Shared Task (CLSciSumm2016), which asked to identify the relevant text span in a referencepaper that corresponds to a citation in another document that citesthis paper. We developed three different approaches based on summarisationand classification techniques. First, we applied a modified versionof an unsupervised summarisation technique, TextSentenceRank, to thereference document, which incorporates the similarity of sentences tothe citation on a textual level. Second, we employed classification to selectfrom candidates previously extracted through the original TextSentenceRankalgorithm. Third, we used unsupervised summarisation of therelevant sub-part of the document that was previously selected in a supervisedmanner.
2016
Gursch Heimo, Ziak Hermann, Kröll Mark, Kern Roman
Context-Driven Federated Recommendations for Knowledge Workers
Proceedings of the 17th European Conference on Knowledge Management (ECKM), Dr. Sandra Moffett and Dr. Brendan Galbraith, Academic Conferences and Publishing International Limited, Belfast, Northern Ireland, UK, 2016
Konferenz
Modern knowledge workers need to interact with a large number of different knowledge sources with restricted or public access. Knowledge workers are thus burdened with the need to familiarise and query each source separately. The EEXCESS (Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources) project aims at developing a recommender system providing relevant and novel content to its users. Based on the user’s work context, the EEXCESS system can either automatically recommend useful content, or support users by providing a single user interface for a variety of knowledge sources. In the design process of the EEXCESS system, recommendation quality, scalability and security where the three most important criteria. This paper investigates the scalability aspect achieved by federated design of the EEXCESS recommender system. This means that, content in different sources is not replicated but its management is done in each source individually. Recommendations are generated based on the context describing the knowledge worker’s information need. Each source offers result candidates which are merged and re-ranked into a single result list. This merging is done in a vector representation space to achieve high recommendation quality. To ensure security, user credentials can be set individually by each user for each source. Hence, access to the sources can be granted and revoked for each user and source individually. The scalable architecture of the EEXCESS system handles up to 100 requests querying up to 10 sources in parallel without notable performance deterioration. The re-ranking and merging of results have a smaller influence on the system's responsiveness than the average source response rates. The EEXCESS recommender system offers a common entry point for knowledge workers to a variety of different sources with only marginally lower response times as the individual sources on their own. Hence, familiarisation with individual sources and their query language is not necessary.
2016
Rexha Andi, Klampfl Stefan, Kröll Mark, Kern Roman
Towards a more fine grained analysis of scientific authorship: Predicting the number of authors using stylometric features
BIR 2016 Workshop on Bibliometric-enhanced Information Retrieval, Atanassova, I.; Bertin, M.; Mayr, P., Springer, Padova, Italy, 2016
Konferenz
To bring bibliometrics and information retrieval closer together, we propose to add the concept of author attribution into the pre-processing of scientific publications. Presently, common bibliographic metrics often attribute the entire article to all the authors affecting author-specific retrieval processes. We envision a more finegrained analysis of scientific authorship by attributing particular segments to authors. To realize this vision, we propose a new feature representation of scientific publications that captures the distribution of tylometric features. In a classification setting, we then seek to predict the number of authors of a scientific article. We evaluate our approach on a data set of ~ 6100 PubMed articles and achieve best results by applying random forests, i.e., 0.76 precision and 0.76 recall averaged over all classes.
2016
Gursch Heimo, Kern Roman
Internet of Things meets Big Data: An Infrastructure to Collect, Connect, and Analyse Sensor Data
VDE Kongress 2016: Internet der Dinge (VDE Kongress 2016), DE Verlag GmbH, Berlin - Offenbach, Congress Center Rosengarten, Mannheim, Germany, 2016
Konferenz
Many different sensing, recording and transmitting platforms are offered on today’s market for Internet of Things (IoT) applications. But taking and transmitting measurements is just one part of a complete system. Also long time storage and processing of recorded sensor values are vital for IoT applications. Big Data technologies provide a rich variety of processing capabilities to analyse the recorded measurements. In this paper an architecture for recording, searching, and analysing sensor measurements is proposed. This architecture combines existing IoT and Big Data technologies to bridge the gap between recording, transmission, and persistency of raw sensor data on one side, and the analysis of data on Hadoop clusters on the other side. The proposed framework emphasises scalability and persistence of measurements as well as easy access to the data from a variety of different data analytics tools. To achieve this, a distributed architecture is designed offering three different views on the recorded sensor readouts. The proposed architecture is not targeted at one specific use-case, but is able to provide a platform for a large number of different services.
2016
Dennerlein Sebastian, Gutounig Robert, Goldgruber Eva , Schweiger Stefan
Web 2.0 Messaging Tools for Knowledge Management? Exploring the Potentials of Slack
Proceedings of the 17th European Conference on Knowledge Management, Academic Conferences International Limited, Ulster University, Northern Ireland, 2016
Konferenz
There are many web-based tools like social networks, collaborative writing, or messaging tools that connectorganizations in accordance with web 2.0 principles. Slack is such a web 2.0 instant messaging tool. As per developer, itintegrates the entire communication, file-sharing, real-time messaging, digital archiving and search at one place. Usage inline with these functionalities would reflect expected appropriation, while other usage would account for unexpectedappropriation. We explored which factors of web 2.0 tools determine actual usage and how they affect knowledgemanagement (KM). Therefore, we investigated the relation between the three influencing factors, proposed tool utility fromdeveloper side, intended usage of key implementers, and context of application, to the actual usage in terms of knowledgeactivities (generate, acquire, organize, transfer and save knowledge). We conducted episodic interviews with keyimplementers in five different organizational contexts to understand how messaging tools affect KM by analyzing theappropriation of features. Slack was implemented with the intention to enable exchange between project teams, connectingdistributed project members, initiate a community of learners and establish a communication platform. Independent of thecontext, all key implementers agreed on knowledge transfer, organization and saving in accordance with Slack’s proposedutility. Moreover, results revealed that a usage intention of internal management does not lead to acquisition of externalknowledge, and usage intention of networking not to generation of new knowledge. These results suggest that it is not thecontext of application, but the intended usage that mainly affects the tool's efficacy with respect to KM: I.e. intention seemsto affect tool selection, first, explaining commonalities with respect to knowledge activities (expected appropriation) and,subsequently, intention also affects unexpected appropriation beyond the developers’ tool utility. A messaging tool is, hence,not only a messaging tool, but it is ‘what you make of it!’
2016
Gutounig Robert, Goldgruber Eva, Dennerlein Sebastian, Schweiger Stefan
Mehr als ein Kommunikationstool. Wissensmanagement-Potenziale von Social Software am Beispiel von Slack
Kremser Wissensmanagement-Tagen 2016, Edition Donau-Universität Krems , Krems, 2016
Konferenz
2016
Goldgruber Eva, Gutounig Robert, Schweiger Stefan, Dennerlein Sebastian
Potential von "Slack" im E-Learning
E-Learning Tag 2016, 2016
Konferenz
2016
Ziak Hermann, Rexha Andi, Kern Roman
KNOW At The Social Book Search Lab 2016 Mining Track
CLEF 2016 Social Book Search Lab, Krisztian Balog, Linda Cappellato, Nicola Ferro,Craig Macdonald, Springer, Évora, Portugal, 2016
Konferenz
This paper describes our system for the mining task of theSocial Book Search Lab in 2016. The track consisted of two task, theclassification of book request postings and the task of linking book identifierswith references mentioned within the text. For the classificationtask we used text mining features like n-grams and vocabulary size, butalso included advanced features like average spelling errors found withinthe text. Here two datasets were provided by the organizers for this taskwhich were evaluated separately. The second task, the linking of booktitles to a work identifier, was addressed by an approach based on lookuptables. For the dataset of the first task our approach was ranked third,following two baseline approaches of the organizers with an accuracy of91 percent. For the second dataset we achieved second place with anaccuracy of 82 percent. Our approach secured the first place with anF-score of 33.50 for the second task.
2016
Lacic Emanuel
Real-Time Recommendations in a Multi-Domain Environment
ACM Hypertext Doctoral Consortium, ACM, 2016
Konferenz
Recommender systems are acknowledged as an essential instru-
ment to support users in finding relevant information. However,
adapting to different domain specific data models is a challenge,
which many recommender frameworks neglect. Moreover, the ad-
vent of the big data era has posed the need for high scalability and
real-time processing of frequent data updates, and thus, has brought
new challenges for the recommender systems’ research community.
In this work, we show how different item, social and location data
features can be utilized and supported to provide real-time recom-
mendations. We further show how to process data updates online
and capture user’s real-time interest without recalculating recom-
mendations. The presented recommendation framework provides
a scalable and customizable architecture suited for providing real-
time recommendations to multiple domains. We further investigate
the impact of an increasing request load and show how the runtime
can be decreased by scaling the framework.
2016
Gursch Heimo, Körner Stefan, Krasser Hannes, Kern Roman
Parameter Forecasting for Vehicle Paint Quality Optimisation
Mensch und Computer 2016 – Workshopband, Benjamin Weyers, Anke Dittmar, Gesellschaft für Informatik e.V., Aachen, 2016
Konferenz
Painting a modern car involves applying many coats during a highly complex and automated process. The individual coats not only serve a decoration purpose but are also curial for protection from damage due to environmental influences, such as rust. For an optimal paint job, many parameters have to be optimised simultaneously. A forecasting model was created, which predicts the paint flaw probability for a given set of process parameters, to help the production managers modify the process parameters to achieve an optimal result. The mathematical model was based on historical process and quality observations. Production managers who are not familiar with the mathematical concept of the model can use it via an intuitive Web-based Graphical User Interface (Web-GUI). The Web-GUI offers production managers the ability to test process parameters and forecast the expected quality. The model can be used for optimising the process parameters in terms of quality and costs.
2016
Berndt Rene, Silva Nelson, Edtmayr Thomas, Sunk Alexander, Krispel Ulrich, Caldera Christian, Eggeling Eva, Fellner Dieter W., Sihn Wilfried
VASCO - Mastering the Shoals of Value Stream Mapping
CONTENT 2016, The Eighth International conference on Creative Content Technologies, Hans-Werner Sehring, René Berndt, IARIA, Rome, Italy, 2016
Konferenz
Value stream mapping is a lean management method for analyzing and optimizing a series of events for production or services. Even today the first step in value stream analysis - the acquisition of the current state - is still created using pen & paper by physically visiting the production place. We capture a digital representation of how manufacturing processes look like in reality. The manufacturing processes can be represented and efficiently analyzed for future production planning by using a meta description together with a dependency graph. With our Value Stream Creator and explOrer (VASCO) we present a tool, which contributes to all parts of value stream analysis - from data acquisition, over planning, comparison with previous realities, up to simulation of future possible states.
2016
Silva Nelson, Shao Lin, Schreck Tobias, Eggeling Eva, Fellner Dieter W.
Visual Exploration of Hierarchical Data Using Degree-of-Interest Controlled by Eye-Tracking
FMT 2016 : 9th Forum Media Technology 2016, Wolfgang Aigner , Grischa Schmiedl , Kerstin Blumenstein , Matthias Zeppelzauer , Michael Iber, St. Pölten, 2016
Konferenz
Effective visual exploration of large data sets is an important problem. A standard tech- nique for mapping large data sets is to use hierarchical data representations (trees, or dendrograms) that users may navigate. If the data sets get large, so do the hierar- chies, and effective methods for the naviga- tion are required. Traditionally, users navi- gate visual representations using desktop in- teraction modalities, including mouse interac- tion. Motivated by recent availability of low- cost eye-tracker systems, we investigate ap- plication possibilities to use eye-tracking for controlling the visual-interactive data explo- ration process. We implemented a proof-of- concept system for visual exploration of hier- archic data, exemplified by scatter plot dia- grams which are to be explored for grouping and similarity relationships. The exploration includes usage of degree-of-interest based dis- tortion controlled by user attention read from eye-movement behavior. We present the basic elements of our system, and give an illustra- tive use case discussion, outlining the applica- tion possibilities. We also identify interesting future developments based on the given data views and captured eye-tracking information. (13) Visual Exploration of Hierarchical Data Using Degree-of-Interest Controlled by Eye-Tracking. Available from: https://www.researchgate.net/publication/309479681_Visual_Exploration_of_Hierarchical_Data_Using_Degree-of-Interest_Controlled_by_Eye-Tracking [accessed Oct 3, 2017].
2016
Silva Nelson, Shao Lin, Schreck Tobias, Eggeling Eva, Fellner Dieter W.
Sense.me - Open Source Framework for the Exploration and Visualization of Eye Tracking Data
IEEEVis - Proc. IEEE Conference on Information Visualization, Baltimore, Maryland, USA, 2016
Konferenz
We present a new open-source prototype framework to exploreand visualize eye-tracking experiments data. Firstly, standard eyetrackersare used to record raw eye gaze data-points on user experiments.Secondly, the analyst can configure gaze analysis parameters,such as, the definition of areas of interest, multiple thresholdsor the labeling of special areas, and we upload the data to a searchserver. Thirdly, a faceted web interface for exploring and visualizingthe users’ eye gaze on a large number of areas of interest isavailable. Our framework integrates several common visualizationsand it also includes new combined representations like an eye analysisoverview and a clustered matrix that shows the attention timestrength between multiple areas of interest. The framework can bereadily used for the exploration of eye tracking experiments data.We make available the source code of our prototype framework foreye-tracking data analysis.
2016
Luzhnica Granit, Öjeling Christoffer, Veas Eduardo Enrique, Pammer-Schindler Viktoria
Technical Concept and Technology Choices for Implementing a Tangible Version of the Sokoban Game
Mixed and Augmented Reality (ISMAR-Adjunct), 2016 IEEE International Symposium on, IEEE, Merida, Mexico, 2016
Konferenz
This paper presents and discusses the technical concept of a virtualreality version of the Sokoban game with a tangible interface. Theunderlying rationale is to provide spinal-cord injury patients whoare learning to use a neuroprosthesis to restore their capability ofgrasping with a game environment for training. We describe as rel-evant elements to be considered in such a gaming concept: input,output, virtual objects, physical objects, activity tracking and per-sonalised level recommender. Finally, we also describe our experi-ences with instantiating the overall concept with hand-held mobilephones, smart glasses and a head mounted cardboard setup
2016
Ziak Hermann, Kern Roman
KNOW At The Social Book Search Lab 2016 Suggestion Track
CLEF 2016 Social Book Search Lab, Krisztian Balog, Linda Cappellato, Nicola Ferro, Craig Macdonal, CEUR Workshop Proceeding, Évora, Portugal, 2016
Konferenz
Within this work represents the documentation of our ap-proach on the Social Book Search Lab 2016 where we took part in thesuggestion track. The main goal of the track was to create book recom-mendation for readers only based on their stated request within a forum.The forum entry contained further contextual information, like the user’scatalogue of already read books and the list of example books mentionedin the user’s request. The presented approach is mainly based on themetadata included in the book catalogue provided by the organizers ofthe task. With the help of a dedicated search index we extracted severalpotential book recommendations which were re-ranked by the use of anSVD based approach. Although our results did not meet our expectationwe consider it as first iteration towards a competitive solution.
2016
Cik Michael, Hebenstreit Cornelia, Horn Christopher, Schulze Gunnar, Traub Matthias, Schweighofer Erich, Hötzendorf Walter, Fellendorf Martin
Using cell phone and social media data to enhance safety at mega events
Transportation Research Board (TRB) 96th Annual Meeting, Washington DC, 2016
Konferenz
Guaranteeing safety during mega events has always played a role for organizers, their security guards and the action force. This work was realized to enhance safety at mega events and demonstrations without the necessity of fixed installations. Therefore a low cost monitoring system supporting the organization and safety personnel was developed using cell phone data and social media data in combination with safety concepts to monitor safety during the event in real time. To provide the achieved results in real time to the event and safety personnel an application for a Tablet-PC was established. Two representative events were applied as case studies to test and evaluate the results and to check response and executability of the app on site. Because data privacy is increasingly important, legal experts were closely involved and provided legal support.
2016
Thalmann Stefan, Ilvonen Ilona, Manhart Markus , Sillaber Christian
Knowledge Protection for Digital Innovations: Integrating Six Perspectives
Proceedings of the 11th Pre-ICIS Workshop on Information Security and Privacy, AIS Electronic Library (AISeL), Dublin, Ireland, 2016
Konferenz
New ways of combining digital and physical innovations, as well as intensified inter-organizational collaborations, create new challenges to the protection of organizational knowledge. Existing research on knowledge protection is at an early stage and scattered among various research domains. This research-in-progress paper presents a plan for a structured literature review on knowledge protection, integrating the perspectives of the six base domains of knowledge, strategic, risk, intellectual property rights, innovation, and information technology security management. We define knowledge protection as a set of capabilities comprising and enforcing technical, organizational, and legal mechanisms to protect tacit and explicit knowledge necessary to generate or adopt innovations.
2016
Thalmann Stefan, Manhart Markus
Balancing Knowledge Protection and Sharing in Networks of SME
Proceedings of IKNOW 2016, 2016
Konferenz
Organizations join networks to acquire external knowledge. This is especially important for SMEs since they often lack resources and are dependent on external knowledge to achieve and sustain competitive advantage. However, finding the right balance between measures facilitating knowledge sharing and measures protecting knowledge is a challenge. Whilst sharing is the raison d’être of networks, neglecting knowledge protection can be also detrimental to network, e.g., lead to one-sided skimming of knowledge. We identified four practices SMEs currently apply to balance protection of competitive knowledge and knowledge sharing in the network: (a) share in subgroups with high trust, (b) share partial aspects of the knowledge base, (c) share with people with low proximities, and (d) share common knowledge and protect the crucial. We further found that the application of the practices depends on the maturity of the knowledge. Further, we discuss how the practices relate to organizational protection capabilities and how the network can provide IT to support the development of these capabilities.
2016
Rexha Andi, Kern Roman, Dragoni Mauro , Kröll Mark
Exploiting Propositions for Opinion Mining
ESWC-16 Challenge on Semantic Sentiment Analysis, Springer Link, Springer-Verlag, Crete, Greece, 2016
Konferenz
With different social media and commercial platforms, users express their opinion about products in a textual form. Automatically extracting the polarity (i.e. whether the opinion is positive or negative) of a user can be useful for both actors: the online platform incorporating the feedback to improve their product as well as the client who might get recommendations according to his or her preferences. Different approaches for tackling the problem, have been suggested mainly using syntactic features. The “Challenge on Semantic Sentiment Analysis” aims to go beyond the word-level analysis by using semantic information. In this paper we propose a novel approach by employing the semantic information of grammatical unit called preposition. We try to drive the target of the review from the summary information, which serves as an input to identify the proposition in it. Our implementation relies on the hypothesis that the proposition expressing the target of the summary, usually containing the main polarity information.
2016
Traub Matthias, Lacic Emanuel, Kowald Dominik, Kahr Martin, Lex Elisabeth
Need Help? Recommending Social Care Institutions
Workshop on Recommender Systems and Big Data Analytics co-located with i-know 2016 conference, RSBDA'16, ACM, Graz, 2016
Konferenz
In this paper, we present work-in-progress on a recommender system designed to help people in need find the best suited social care institution for their personal issues. A key requirement in such a domain is to assure and to guarantee the person's privacy and anonymity in order to reduce inhibitions and to establish trust. We present how we aim to tackle this barely studied domain using a hybrid content-based recommendation approach. Our approach leverages three data sources containing textual content, namely (i) metadata from social care institutions, (ii) institution specific FAQs, and (iii) questions that a specific institution has already resolved. Additionally, our approach considers the time context of user questions as well as negative user feedback to previously provided recommendations. Finally, we demonstrate an application scenario of our recommender system in the form of a real-world Web system deployed in Austria.
2016
di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
Rank As You Go: User-Driven Exploration of Search Results
ACM IUI 2016, ACM New York, NY, USA ©201, New York, 2016
Konferenz
Whenever users engage in gathering and organizing new information, searching and browsing activities emerge at the core of the exploration process. As the process unfolds and new knowledge is acquired, interest drifts occur inevitably and need to be accounted for. Despite the advances in retrieval and recommender algorithms, real-world interfaces have remained largely unchanged: results are delivered in a relevance-ranked list. However, it quickly becomes cumbersome to reorganize resources along new interests, as any new search brings new results. We introduce uRank and investigate interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. uRank includes views summarizing the contents of a recommendation set and interactive methods conveying the role of users' interests through a recommendation ranking. A formal evaluation showed that gathering items relevant to a particular topic of interest with uRank incurs in lower cognitive load compared to a traditional ranked list. A second study consisting in an ecological validation reports on usage patterns and usability of the various interaction techniques within a free, more natural setting.
2016
Stanisavljevic Darko, Hasani-Mavriqi Ilire, Lex Elisabeth, Strohmaier Markus, Helic Denis
Semantic Stability in Wikipedia
Complex Networks and their Applications, Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A., Springer International Publishing AG, Cham, Switzerland, 2016
Konferenz
In this paper we assess the semantic stability of Wikipedia by investigat-ing the dynamics of Wikipedia articles’ revisions over time. In a semantically stablesystem, articles are infrequently edited, whereas in unstable systems, article contentchanges more frequently. In other words, in a stable system, the Wikipedia com-munity has reached consensus on the majority of articles. In our work, we measuresemantic stability using the Rank Biased Overlap method. To that end, we prepro-cess Wikipedia dumps to obtain a sequence of plain-text article revisions, whereaseach revision is represented as a TF-IDF vector. To measure the similarity betweenconsequent article revisions, we calculate Rank Biased Overlap on subsequent termvectors. We evaluate our approach on 10 Wikipedia language editions includingthe five largest language editions as well as five randomly selected small languageeditions. Our experimental results reveal that even in policy driven collaborationnetworks such as Wikipedia, semantic stability can be achieved. However, there aredifferences on the velocity of the semantic stability process between small and largeWikipedia editions. Small editions exhibit faster and higher semantic stability than large ones. In particular, in large Wikipedia editions, a higher number of successiverevisions is needed in order to reach a certain semantic stability level, whereas, insmall Wikipedia editions, the number of needed successive revisions is much lowerfor the same level of semantic stability.
2016
Luzhnica Granit, Simon Jörg Peter, Lex Elisabeth, Pammer-Schindler Viktoria
A Sliding Window Approach to Natural Hand Gesture Recognition using a Custom Data Glove
Proceedings of the IEEE 3DUI 2016 Symposium on 3D User Interfaces, IEEE, Greenville, SC, USA, 2016
Konferenz
This paper explores the recognition of hand gestures based on a data
glove equipped with motion, bending and pressure sensors. We se-
lected 31 natural and interaction-oriented hand gestures that can
be adopted for general-purpose control of and communication with
computing systems. The data glove is custom-built, and contains
13 bend sensors, 7 motion sensors, 5 pressure sensors and a magne-
tometer. We present the data collection experiment, as well as the
design, selection and evaluation of a classification algorithm. As we
use a sliding window approach to data processing, our algorithm is
suitable for stream data processing. Algorithm selection and feature
engineering resulted in a combination of linear discriminant anal-
ysis and logistic regression with which we achieve an accuracy of
over 98. 5% on a continuous data stream scenario. When removing
the computationally expensive FFT-based features, we still achieve
an accuracy of 98. 2%.
2016
Barreiros Carla, Veas Eduardo Enrique, Pammer-Schindler Viktoria
Pre-attentive Features in Natural Augmented Reality Visualizations
2016 IEEE International Symposium on Mixed and Augmented Reality Adjunct Proceedings ISMAR, 2016 IEEE International Symposium on Mixed and Augmented Reality Adjunct Proceedings, Merida, Yucatan, Mexico, 2016
Konferenz
The movement towards cyberphysical systems and Industry 4.0promises to imbue each and every stage of production with a myr-iad of sensors. The open question is how people are to comprehendand interact with data originating from industrial machinery. Wepropose a metaphor that compares machines with natural beingsthat appeal to people by representing machine states with patternsoccurring in nature. Our approach uses augmented reality (AR)to represent machine states as trees of different shapes and col-ors (BioAR). We performed a study on pre-attentive processing ofvisual features in AR to determine if our BioAR metaphor con-veys fast changes unambiguously and accurately. Our results indi-cate that the visual features in our BioAR metaphor are processedpre-attentively. In contrast to previous research, for the BioARmetaphor, variations in form induced less errors than variations inhue in the target detection task.
2016
Luzhnica Granit, Veas Eduardo Enrique, Pammer-Schindler Viktoria
Skin Reading: Encoding Text in a 6-Channel Haptic Display
ISWC '16, ACM, Heidelberg, 2016
Konferenz
This paper investigates the communication of natural lan-guage messages using a wearable haptic display. Our re-search spans both the design of the haptic display, as wellas the methods for communication that use it. First, threewearable configurations are proposed basing on haptic per-ception fundamentals. To encode symbols, we devise an over-lapping spatiotemporal stimulation (OST) method, that dis-tributes stimuli spatially and temporally with a minima gap.An empirical study shows that, compared with spatial stimu-lation, OST is preferred in terms of recall. Second, we pro-pose an encoding for the entire English alphabet and a train-ing method for letters, words and phrases. A second study in-vestigates communication accuracy. It puts four participantsthrough five sessions, for an overall training time of approx-imately 5 hours per participant. Results reveal that after onehour of training, participants were able to discern 16 letters,and identify two- and three-letter words. They could discernthe full English alphabet (26letters,92%accuracy) after ap-proximately three hours of training, and after five hours par-ticipants were able to interpret words transmitted at an aver-age duration of0.6s per word
2016
Luzhnica Granit, Pammer-Schindler Viktoria, Fessl Angela, Mutlu Belgin, Veas Eduardo Enrique
Designing Generic Visualisations for Activity Log Data
Workshop on Awareness and Reflection in Technology Enhanced Learning (ARTEL16), CEUR-WS, Lyon, 2016
Konferenz
Especially in lifelong or professional learning, the picture of
a continuous learning analytics process emerges. In this proces
s, het-
erogeneous and changing data source applications provide data
relevant
to learning, at the same time as questions of learners to data cha
nge.
This reality challenges designers of analytics tools, as it req
uires ana-
lytics tools to deal with data and analytics tasks that are unk
nown at
application design time. In this paper, we describe a generic vi
sualiza-
tion tool that addresses these challenges by enabling the vis
ualization
of any activity log data. Furthermore, we evaluate how well parti
cipants
can answer questions about underlying data given such generic
versus
custom visualizations. Study participants performed better in
5 out of
10 tasks with the generic visualization tool, worse in 1 out of 1
0 tasks,
and without significant difference when compared to the visuali
zations
within the data-source applications in the remaining 4 of 10 ta
sks. The
experiment clearly showcases that overall, generic, standalon
e visualiza-
tion tools have the potential to support analytical tasks suffi
ciently well
2016
Eberhard Lukas, Trattner Christoph
Recommending Sellers to Buyers in Virtual Marketplaces Leveraging Social Information
WWW '16 Companion Proceedings of the 25th International Conference Companion on World Wide Web, WWW '16, Canton of Geneva, 2016
Konferenz
Social information such as stated interests or geographic check-insin social networks has shown to be useful in many recommendertasks recently. Although many successful examples exist, not muchattention has been put on exploring the extent to which social im-pact is useful for the task of recommending sellers to buyers in vir-tual marketplaces. To contribute to this sparse field of research wecollected data of a marketplace and a social network in the virtualworld of Second Life and introduced several social features andsimilarity metrics that we used as input for a user-basedk-nearestneighbor collaborative filtering method. As our results reveal, mostof the types of social information and features which we used areuseful to tackle the problem we defined. Social information suchas joined groups or stated interests are more useful, while otherssuch as places users have been checking in, do not help much forrecommending sellers to buyers. Furthermore, we find that some ofthe features significantly vary in their predictive power over time,while others show more stable behaviors. This research is rele-vant for researchers interested in recommender systems and onlinemarketplace research as well as for engineers interested in featureengineering.
2016
Trattner Christoph, Oberegger Alexander, Eberhard Lukas, Parra Denis, Marinho Leandro
Understanding the Impact of Weather for POI recomennder systems
RecTour’16,, ACM, Boston, 2016
Konferenz
POI (point of interest) recommender systems for location-
based social network services, such as Foursquare or Yelp,
have gained tremendous popularity in the past few years.
Much work has been dedicated into improving recommenda-
tion services in such systems by integrating different features
that are assumed to have an impact on people’s preferences
for POIs, such as time and geolocation. Yet, little atten-
tion has been paid to the impact of weather on the users’
final decision to visit a recommended POI. In this paper we
contribute to this area of research by presenting the first
results of a study that aims to predict the POIs that users
will visit based on weather data. To this end, we extend the
state-of-the-art Rank-GeoFM POI recommender algorithm
with additional weather-related features, such as tempera-
ture, cloud cover, humidity and precipitation intensity. We
show that using weather data not only significantly increases
the recommendation accuracy in comparison to the origi-
nal algorithm, but also outperforms its time-based variant.
Furthermore, we present the magnitude of impact of each
feature on the recommendation quality, showing the need to
study the weather context in more detail in the light of POI
recommendation systems.
2016
Kusmierczyk Tomasz, Trattner Christoph, Norvag K.
Understanding and Predicting Online Food Recipe Production Patterns
HT '16 Proceedings of the 27th ACM Conference on Hypertext and Social Media, ACM, Halifax, NS, Canada, 2016
Konferenz
Studying online food patterns has recently become an active field
of research. While there are a growing body of studies that investi-
gate how online food in consumed, little effort has been devoted yet
to understand how online food recipes are being created. To con-
tribute to this lack of knowledge in the area, we present in this paper
the results of a large-scale study that aims at understanding how
historical, social and temporal factors impact on the online food
creation process. Several experiments reveal the extent to which
various factors are useful in predicting future recipe production.
2016
Fessl Angela, Wesiak Gudrun, Pammer-Schindler Viktoria
A Reflective Quiz in a Professional Qualification Program for Stroke Nurses: A Field Trial
EC-TEL 2016 Proceedings, Springer Link, Springer-Verlag, Cham, 2016
Konferenz
Reflective learning is an important strategy to keep the vast body of theoretical knowledge fresh, stay up-to-date with new knowledge, and to relate theoretical knowledge to practical experience. In this work, we present a study situated in a qualification program for stroke nurses in Germany. In the seven-week study, $21$ stroke nurses used a quiz on medical knowledge as additional learning instrument. The quiz contained typical quiz questions (``content questions'') as well as reflective questions that aimed at stimulating nurses to reflect on the practical relevance of the learned knowledge.We particularly looked at how reflective questions can support the transfer of theoretical knowledge to practice.The results show that by playful learning and presenting reflective questions at the right time, participants were motivated to reflect, deepened their knowledge and related theoretical knowledge to practical experience. Subsequently, they were able to better understand patient treatments and increased their self-confidence.
2016
Dennerlein Sebastian, Lex Elisabeth, Ruiz-Calleja Adolfo, Ley Elisabeth
Visualizing workplace learning data with the SSS Dashboard
Learning Analytics for Workplace and Professional Learning (LA for Work) workshop at LAK 2016, CEUR Workshop Proceedings, Edinburgh, 2016
Konferenz
This paper reports the design and development of a visual Dashboard, called the SSS Dashboard, which visualizes data from informal workplace learning processes from different viewpoints. The SSS Dashboard retrieves its data from the Social Semantic Server (SSS), an infrastructure that integrates data from several workplace learning applications into a semantically-enriched Artifact-Actor Network. A first evaluation with end users in a course for professional teachers gave promising results. Both a trainer and a learner could understand the learning process from different perspectives using the SSS Dashboard. The results obtained will pave the way for the development of future Learning Analytics applications that exploit the data collected by the SSS.
2016
Malarkodi C. S., Lex Elisabeth, Sobha Lalitha Devi
Named Entity Recognition for the Agricultural Domain
17th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING 2016); Research in Computing Science, CICLING 2016, Springer Lecture Notes in Computer Science, Konya, Turkey, 2016
Konferenz
Agricultural data have a major role in the planning and success of
rural development activi
ties. Agriculturalists, planners, policy makers, gover
n-
ment officials, farmers and researchers require relevant information to trigger
decision making processes. This paper presents our approach towards extracting
named entities from real
-
world agricultura
l data from different areas of agricu
l-
ture using Conditional Random Fields (CRFs). Specifically, we have created a
Named Entity tagset consisting of 19 fine grained tags. To the best of our
knowledge, there is no specific tag set and annotated corpus avail
able for the
agricultural domain. We have performed several experiments using different
combination of features and obtained encouraging results.
Most of the issues
observed in an error analysis have been addressed by post
-
processing heuristic
rules, which
resulted in a significant improvement of our system’s accuracy
2016
Lacic Emanuel, Kowald Dominik, Lex Elisabeth
High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Reviews.
27th ACM Conference on Hypertext and Hypermedia, Hypertext'2016, ACM, Halifax, 2016
Konferenz
Air travel is one of the most frequently used means of transportation in our every-day life. Thus, it is not surprising that an increasing number of travelers share their experiences with airlines and airports in form of online reviews on the Web. In this work, we thrive to explain and uncover the features of airline reviews that contribute most to traveler satisfaction. To that end, we examine reviews crawled from the Skytrax air travel review portal. Skytrax provides four review categories to review airports, lounges, airlines and seats. Each review category consists of several five-star ratings as well as free-text review content. In this paper, we conducted a comprehensive feature study and we find that not only five-star rating information such as airport queuing time and lounge comfort highly correlate with traveler satisfaction but also textual features in the form of the inferred review text sentiment. Based on our findings, we created classifiers to predict traveler satisfaction using the best performing rating features. Our results reveal that given our methodology, traveler satisfaction can be predicted with high accuracy. Additionally, we find that training a model on the sentiment of the review text provides a competitive alternative when no five star rating information is available. We believe that our work is of interest for researchers in the area of modeling and predicting user satisfaction based on available review data on the Web.
2016
Tschinkel Gerwald, Hasitschka Peter, Sabol Vedran, Hafner R
Using Micro-Visualisations to Support Faceted Filtering of Recommender Results
Information Visualisation (IV), 2016 20th International Conference, IEEE, Lisbon, Portugal, 2016
Konferenz
Faceted search is a well known and broadly imple-
mented paradigm for filtering information with various
types of structured information. In this paper we introduce
a multiple-view faceted interface, consisting of one main
visualisation for exploring the data and multiple minia-
turised visualisations showing the filters. The Recommen-
dation Dashboard tool provides several interactive visual-
isations for analysing recommender results along various
faceted dimensions specific to cultural heritage and scien-
tific content. As our aim is to reduce the user load and opti-
mise the use of screen area, we permit only one main visu-
alisation to be visible at a time, and introduce the concept
of micro-visualisations – small, simplified views conveying
only the necessary information – to provide natural, easy
to understand representation of the the active filter set.
2016
Dennerlein Sebastian, Ley Tobias, , Lex Elisabeth, Seitlinger Paul
Take up my Tags: Exploring Benefits of Collaborative Learning in a Social Tagging Field Study at the Workplace
European Conference on Technology Enhanced Learning (EC-TEL 2016), EC-TEL 2016, Springer-Verlag, Cham, 2016
Konferenz
In the digital realm, meaning making is reflected in the reciprocal manipulation of mediating artefacts. We understand uptake, i.e. interaction with and understanding of others’ artefact interpretations, as central mechanism and investigate its impact on individual and social learning at work. Results of our social tagging field study indicate that increased uptake of others’ tags is related to a higher shared understanding of collaborators as well as narrower and more elaborative exploration in individual information search. We attribute the social and individual impact to accommodative processes in the high uptake condition.
2016
Kowald Dominik, Lex Elisabeth
The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems
27th ACM Conference on Hypertext and Hypermedia, Hypertext'2016, ACM, Halifax, 2016
Konferenz
In this paper, we study factors that in
uence tag reuse behavior
in social tagging systems. Our work is guided by the
activation equation of the cognitive model ACT-R, which
states that the usefulness of information in human memory
depends on the three factors usage frequency, recency and
semantic context. It is our aim to shed light on the in
uence
of these factors on tag reuse. In our experiments, we
utilize six datasets from the social tagging systems Flickr,
CiteULike, BibSonomy, Delicious, LastFM and MovieLens,
covering a range of various tagging settings. Our results
con rm that frequency, recency and semantic context positively
in
uence the reuse probability of tags. However, the
extent to which each factor individually in
uences tag reuse
strongly depends on the type of folksonomy present in a social
tagging system. Our work can serve as guideline for researchers
and developers of tag-based recommender systems
when designing algorithms for social tagging environments.
2016
Klampfl Stefan, Kern Roman
Reconstructing the Logical Structure of a Scientific Publication using Machine Learning
Semantic Web Challenges, Communications in Computer and Information Science, Springer Link, Springer-Verlag, 2016
Konferenz
Semantic enrichment of scientific publications has an increasing impact on scholarly communication. This document describes our contribution to Semantic Publishing Challenge 2016, which aims at investigating novel approaches for improving scholarly publishing through semantic technologies. We participated in Task 2 of this challenge, which requires the extraction of information from the content of a paper given as PDF. The extracted information allows answering queries about the paper’s internal organisation and the context in which it was written. We build upon our contribution to the previous edition of the challenge, where we categorised meta-data, such as authors and affiliations, and extracted funding information. Here we use unsupervised machine learning techniques in order to extend the analysis of the logical structure of the document as to identify section titles and captions of figures and tables. Furthermore, we employ clustering techniques to create the hierarchical table of contents of the article. Our system is modular in nature and allows a separate training of different stages on different training sets.
2016
Urak Günter, Ziak Hermann, Kern Roman
Do Ambiguous Words Improve Probing For Federated Search?
International Conference on Theory and Practice of Digital Libraries, TPDL 2016, Springer-Verlag, 2016
Konferenz
The core approach to distributed knowledge bases is federated search. Two of the main challenges for federated search are the source representation and source selection. Different solutions to these problems were proposed in the literature. Within this work we present our novel approach for query-based sampling by relying on knowledge bases. We show the basic correctness of our approach and we came to the insight that the ambiguity of the probing terms has just a minor impact on the representation of the collection. Finally, we show that our method can be used to distinguish between niche and encyclopedic knowledge bases.
2016
Horn Christopher, Gursch Heimo, Kern Roman, Cik Michael
QZTool – Automatically generated Origin-Destination Matrices from Cell Phone Trajectories
Advances in The Human Side of Service Engineering: Proceedings of the AHFE 2016 International Conference on Human Factors and Sustainable Infrastructure, July 27-31, 2016, Walt Disney World®, Florida, USA, Jerzy Charytonowicz (series Editor), Neville A. Stanton and Steven Landry and Giuseppe Di Bucchianico and Andrea Vallicelli, Springer International Publishing, Cham, Switzerland, 2016
Konferenz
Models describing human travel patterns are indispensable to plan and operate road, rail and public transportation networks. For most kind of analyses in the field of transportation planning, there is a need for origin-destination (OD) matrices, which specify the travel demands between the origin and destination zones in the network. The preparation of OD matrices is traditionally a time consuming and cumbersome task. The presented system, QZTool, reduces the necessary effort as it is capable of generating OD matrices automatically. These matrices are produced starting from floating phone data (FPD) as raw input. This raw input is processed by a Hadoop-based big data system. A graphical user interface allows for an easy usage and hides the complexity from the operator. For evaluation, we compare a FDP-based OD matrix to an OD matrix created by a traffic demand model. Results show that both matrices agree to a high degree, indicating that FPD-based OD matrices can be used to create new, or to validate or amend existing OD matrices.
2016
Falk Stefan, Rexha Andi, Kern Roman
Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction
Conference: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), SemEval 2016, ACL Anthology, San Diego, USA, 2016
Konferenz
This paper describes our participation in SemEval-2016 Task 5 for Subtask 1, Slot 2.The challenge demands to find domain specific target expressions on sentence level thatrefer to reviewed entities. The detection of target words is achieved by using word vectorsand their grammatical dependency relationships to classify each word in a sentence into target or non-target. A heuristic based function then expands the classified target words tothe whole target phrase. Our system achievedan F1 score of 56.816% for this task.
2016
Dragoni Mauro, Rexha Andi, Kröll Mark, Kern Roman
Polarity Classification for Target Phrases in Tweets: A Word2Vec approach
The Semantic Web, ESWC 2016 Satellite Events, ESWC 2016, Springer-Verlag, Crete, Greece, 2016
Konferenz
Twitter is one of the most popular micro-blogging serviceson the web. The service allows sharing, interaction and collaboration viashort, informal and often unstructured messages called tweets. Polarityclassification of tweets refers to the task of assigning a positive or a nega-tive sentiment to an entire tweet. Quite similar is predicting the polarityof a specific target phrase, for instance@Microsoftor#Linux,whichiscontained in the tweet.In this paper we present a Word2Vec approach to automatically pre-dict the polarity of a target phrase in a tweet. In our classification setting,we thus do not have any polarity information but use only semantic infor-mation provided by a Word2Vec model trained on Twitter messages. Toevaluate our feature representation approach, we apply well-establishedclassification algorithms such as the Support Vector Machine and NaiveBayes. For the evaluation we used theSemeval 2016 Task #4dataset.Our approach achieves F1-measures of up to∼90 % for the positive classand∼54 % for the negative class without using polarity informationabout single words.
2016
Pimas Oliver, Klampfl Stefan, Kohl Thomas, Kern Roman, Kröll Mark
Generating Tailored Classification Schemas for German Patents
21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Springer-Verlag, Salford, UK, 2016
Konferenz
Patents and patent applications are important parts of acompany’s intellectual property. Thus, companies put a lot of effort indesigning and maintaining an internal structure for organizing their ownpatent portfolios, but also in keeping track of competitor’s patent port-folios. Yet, official classification schemas offered by patent offices (i) areoften too coarse and (ii) are not mappable, for instance, to a company’sfunctions, applications, or divisions. In this work, we present a first steptowards generating tailored classification. To automate the generationprocess, we apply key term extraction and topic modelling algorithmsto 2.131 publications of German patent applications. To infer categories,we apply topic modelling to the patent collection. We evaluate the map-ping of the topics found via the Latent Dirichlet Allocation method tothe classes present in the patent collection as assigned by the domainexpert.
2016
Steinbauer Florian, Kröll Mark
Sentiment Analysis for German Facebook Pages
21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Springer-Verlag, Salford, UK, 2016
Konferenz
Social media monitoring has become an important means forbusiness analytics and trend detection, for instance, analyzing the senti-ment towards a certain product or decision. While a lot of work has beendedicated to analyze sentiment for English texts, much less effort hasbeen put into providing accurate sentiment classification for the Germanlanguage. In this paper, we analyze three established classifiers for theGerman language with respect to Facebook posts. We then present ourown hierarchical approach to classify sentiment and evaluate it using adata set of∼640 Facebook posts from corporate as well as governmentalFacebook pages. We compare our approach to three sentiment classifiersfor German, i.e. AlchemyAPI, Semantria and SentiStrength. With anaccuracy of 70 %, our approach performs better than the other classi-fiers. In an application scenario, we demonstrate our classifier’s abilityto monitor changes in sentiment with respect to the refugee crisis.
2016
Mutlu Belgin, Sabol Vedran, Gursch Heimo, Kern Roman
From Data to Visualisations and Back: Selecting Visualisations Based on Data and System Design Considerations
arXiv, 2016
Konferenz
Graphical interfaces and interactive visualisations are typical mediators between human users and data analytics systems. HCI researchers and developers have to be able to understand both human needs and back-end data analytics. Participants of our tutorial will learn how visualisation and interface design can be combined with data analytics to provide better visualisations. In the first of three parts, the participants will learn about visualisations and how to appropriately select them. In the second part, restrictions and opportunities associated with different data analytics systems will be discussed. In the final part, the participants will have the opportunity to develop visualisations and interface designs under given scenarios of data and system settings.
2016
Luzhnica Granit, Veas Eduardo Enrique, Pammer-Schindler Viktoria
Technical Concept and Technology Choices for Implementing a Tangible Version of the Sokoban Gamehoices for Implementing a Tangible Version of the Sokoban Game
2016 IEEE International Symposium on Mixed and Augmented Reality Adjunct Proceedings ISMAR 2016, 2016
Konferenz
This paper presents and discusses the technical concept of a virtualreality version of the Sokoban game with a tangible interface. Theunderlying rationale is to provide spinal-cord injury patients whoare learning to use a neuroprosthesis to restore their capability ofgrasping with a game environment for training. We describe as rel-evant elements to be considered in such a gaming concept: input,output, virtual objects, physical objects, activity tracking and per-sonalised level recommender. Finally, we also describe our experi-ences with instantiating the overall concept with hand-held mobilephones, smart glasses and a head mounted cardboard setup.Index Terms: H.5.2 [HCI]: User Interfaces—Input devicesand strategies; H.5.1 [HCI]: Multimedia Information Systems—Artificial, augmented, and virtual realities
2016
Gursch Heimo, Wuttei Andreas, Gangloff Theresa
Learning Systems for Manufacturing Management Support
Proceedings of the 1st International Workshop on Science, Application and Methods in Industry 4.0, Roman Kern, Gerald Reiner, Olivia Bluder, Graz, Austria, 2016
Konferenz
Highly optimised assembly lines are commonly used in various manufacturing domains, such as electronics, microchips, vehicles, electric appliances, etc. In the last decades manufacturers have installed software systems to control and optimise their shop foor processes. Machine Learning can enhance those systems by providing new insights derived from the previously captured data. This paper provides an overview of Machine Learning felds and an introduction to manufacturing management systems. These are followed by a discussion of research projects in the feld of applying Machine Learning solutions for condition monitoring, process control, scheduling, and predictive maintenance.
2016
Santos Tiago, Kern Roman
A Literature Survey of Early Time Series Classification and Deep Learning
SamI40 workshop at i-KNOW'16, 2016
Konferenz
This paper provides an overview of current literature on timeseries classification approaches, in particular of early timeseries classification.A very common and effective time series classification ap-proach is the 1-Nearest Neighbor classifier, with differentdistance measures such as the Euclidean or dynamic timewarping distances. This paper starts by reviewing thesebaseline methods.More recently, with the gain in popularity in the applica-tion of deep neural networks to the field of computer vision,research has focused on developing deep learning architec-tures for time series classification as well. The literature inthe field of deep learning for time series classification hasshown promising results.Early time series classification aims to classify a time se-ries with as few temporal observations as possible, whilekeeping the loss of classification accuracy at a minimum.Prominent early classification frameworks reviewed by thispaper include, but are not limited to, ECTS, RelClass andECDIRE. These works have shown that early time seriesclassification may be feasible and performant, but they alsoshow room for improvement
2016
Kern Roman, Ziak Hermann
Query Splitting For Context-Driven Federated Recommendations
Database and Expert Systems Applications (DEXA), 2016 27th International Workshop on, IEEEE, Porto, Portugal, 2016
Konferenz
Context-driven query extraction for content-basedrecommender systems faces the challenge of dealing with queriesof multiple topics. In contrast to manually entered queries, forautomatically generated queries this is a more frequent problem. For instances if the information need is inferred indirectly viathe user's current context. Especially for federated search systemswere connected knowledge sources might react vastly differentlyon such queries, an algorithmic way how to deal with suchqueries is of high importance. One such method is to split mixedqueries into their individual subtopics. To gain insight how amulti topic query can be split into its subtopics we conductedan evaluation where we compared a naive approach against amore complex approaches based on word embedding techniques:One created using Word2Vec and one created using GloVe. Toevaluate these two approaches we used the Webis-QSeC-10 queryset, consisting of about 5,000 multi term queries. Queries of thisset were concatenated and passed through the algorithms withthe goal to split those queries again. Hence the naive approach issplitting the queries into several groups, according to the amountof joined queries, assuming the topics are of equal query termcount. In the case of the Word2Vec and GloVe based approacheswe relied on the already pre-trained datasets. The Google Newsmodel and a model trained with a Wikipedia dump and theEnglish Gigaword newswire text archive. The out of this datasetsresulting query term vectors were grouped into subtopics usinga k-Means clustering. We show that a clustering approach basedon word vectors achieves better results in particular when thequery is not in topical order. Furthermore we could demonstratethe importance of the underlying dataset.
2016
Trattner Christoph, Elsweiler David, Howard Simon
Estimating the Healthiness of Internet recipes: Implications for Recommender Systems and Meal Planning
British Medical Journal (BMJ) , Frontiers in Public Health, 2016
Konferenz
One government response to increasing incidence of lifestyle related illnesses, such as obesity, has been to encourage people to cook for themselves. The healthiness of home cooking will, nevertheless, depend on what people cook and how they cook it. In this article one common source of cooking inspiration - Internet-sourced recipes - is investigated in depth. The energy and macronutrient content of 5237 main meal recipes from the food website Allrecipes.com are compared with those of 100 main meal recipes from five bestselling cookery books from popular celebrity chefs and 100 ready meals from the three leading UK supermarkets. The comparison is made using nutritional guidelines published by the World Health Organisation and the UK Food Standards Agency. The main conclusions drawn from our analyses are that Internet recipes sourced from Allrecipes.com are less healthy than TV-chef recipes and ready meals from leading UK supermarkets. Only 6 out of 5237 Internet recipes fully complied with the WHO recommendations. Internet recipes were more likely to meet the WHO guidelines for protein than both other classes of meal (10.88% v 7% (TV), p<0.01; 10.86% v 9% (ready), p<0.01). However, the Internet recipes were less likely to meet the criteria for fat (14.28% v 24% (TV) v 37% (ready); p<0.01), saturated fat (25.05% v 33% (TV) v 34% (ready); p<0.01) and fibre (compared to ready meals 16.50% v 56%; p<0.01). More Internet recipes met the criteria for sodium density than ready meals (19.63% v 4%; p<0.01), but fewer than the TV-chef meals (19.32% v 36%; p<0.01). For sugar, no differences between Internet recipes and TV-chef recipes were observed (81.1% v 81% (TV); p=0.86), although Internet recipes were less likely to meet the sugar criteria than ready meals (81.1% v 83 % (ready); p<0.01). Repeating the analyses for each year of available data shows that the results are very stable over time.
2015
Trattner Christoph, Balby Marinho Leandro, Parra Denis
Are Real-World Place Recommender Algorithms Useful in Virtual World Environments?
Proceedings of the 9th {ACM} Conference on Recommender Systems, ACM, 2015
Konferenz
Large scale virtual worlds such as massive multiplayer online gamesor 3D worlds gained tremendous popularity over the past few years.With the large and ever increasing amount of content available, virtualworld users face the information overload problem. To tacklethis issue, game-designers usually deploy recommendation serviceswith the aim of making the virtual world a more joyful environmentto be connected at. In this context, we present in this paper the resultsof a project that aims at understanding the mobility patternsof virtual world users in order to derive place recommenders forhelping them to explore content more efficiently. Our study focuson the virtual world SecondLife, one of the largest and mostprominent in recent years. Since SecondLife is comparable to realworldLocation-based Social Networks (LBSNs), i.e., users canboth check-in and share visited virtual places, a natural approach isto assume that place recommenders that are known to work well onreal-world LBSNs will also work well on SecondLife. We have putthis assumption to the test and found out that (i) while collaborativefiltering algorithms have compatible performances in both environments,(ii) existing place recommenders based on geographicmetadata are not useful in SecondLife.
2015
Lacic Emanuel, Traub Matthias, Kowald Dominik, Lex Elisabeth
ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture
In the Large-Scale Recommender Systems Workshop (LSRS'15) at the 9th International Conference on Recommender Systems, RecSys'2015, ACM, Vienna, Austria, 2015
Konferenz
In this paper, we present our approach towards an effective scalable recommender framework termed ScaR. Our framework is based on the microservices architecture and exploits search technology to provide real-time recommendations. Since it is our aim to create a system that can be used in a broad range of scenarios, we designed it to be capable of handling various data streams and sources. We show its efficacy and scalability with an initial experiment on how the framework can be used in a large-scale setting.
2015
Pimas Oliver, Kröll Mark, Kern Roman
Know-Center at PAN 2015 author identification
Lecture Notes in Computer Science, Working Notes Papers of the CLEF 2015 Evaluation Labs, Springer Link, Toulouse, France, 2015
Konferenz
Our system for the PAN 2015 authorship verification challenge is basedupon a two step pre-processing pipeline. In the first step we extract different fea-tures that observe stylometric properties, grammatical characteristics and purestatistical features. In the second step of our pre-processing we merge all thosefeatures into a single meta feature space. We train an SVM classifier on the gener-ated meta features to verify the authorship of an unseen text document. We reportthe results from the final evaluation as well as on the training datasets
2015
di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
uRank: Visual analytics approach for search result exploration
Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on, IEEE, 2015
Konferenz
uRankis a Web-based tool combining lightweight text analyticsand visual methods for topic-wise exploration of document sets.It includes a view summarizing the content of the document setin meaningful terms, a dynamic document ranking view and a de-tailed view for further inspection of individual documents. Its ma-jor strength lies in how it supports users in reorganizing documentson-the-fly as their information interests change. We present a pre-liminary evaluation showing that uRank helps to reduce cognitiveload compared to a traditional list-based representation.
2015
Ziak Hermann, Kern Roman
Evaluation of Pseudo Relevance Feedback Techniques for Cross Vertical Aggregated Search
6th International Conference of the CLEF Association, CLEF'15, Toulouse, France, September 8-11, 2015, Proceedings, Springer, 2015
Konferenz
Cross vertical aggregated search is a special form of meta search, were multiple search engines from different domains and varying behaviour are combined to produce a single search result for each query. Such a setting poses a number of challenges, among them the question of how to best evaluate the quality of the aggregated search results. We devised an evaluation strategy together with an evaluation platform in order to conduct a series of experiments. In particular, we are interested whether pseudo relevance feedback helps in such a scenario. Therefore we implemented a number of pseudo relevance feedback techniques based on knowledge bases, where the knowledge base is either Wikipedia or a combination of the underlying search engines themselves. While conducting the evaluations we gathered a number of qualitative and quantitative results and gained insights on how different users compare the quality of search result lists. In regard to the pseudo relevance feedback we found that using Wikipedia as knowledge base generally provides a benefit, unless for entity centric queries, which are targeting single persons or organisations. Our results will enable to help steering the development of cross vertical aggregated search engines and will also help to guide large scale evaluation strategies, for example using crowd sourcing techniques.
2015
Dennerlein Sebastian, Theiler Dieter, Marton Peter, Lindstaedt Stefanie , Lex Elisabeth, Santos Rodriguez Patricia, Cook John
KnowBrain: An Online Social Knowledge Repository for Informal Workplace Learning
In Proceedings of the European Conference on Technology Enhanced Learning, Springer International Publishing (in press)., Springer, Toledo, Spain, 2015
Konferenz
We present KnowBrain (KB), an open source Dropbox-like knowledge repository with social features for informal workplace learning. KB enables users (i) to share and collaboratively structure knowledge, (ii) to access knowledge via sophisticated content- and metadatabased search and recommendation, and (iii) to discuss artefacts by means of multimedia-enriched Q&A. As such, KB can support, integrate and foster various collaborative learning processes related to daily work-tasks.
2015
Fessl Angela, Wesiak Gudrun, Feyertag Sandra, Rivera-Pelayo V.
In-App Reflection Guidance for Workplace Learning
Proceedings of the 10th European Conference on Technoloy Enhanced Learning (ECTEL 2015), Springer, NULL, 2015
Konferenz
In-app reflection guidance for workplace learning means motivating and guiding users to reflect on their working and learning, based on users' activities captured by the app. In this paper, we present ageneric concept for such in-app reflection guidance for workplace learning, its implementation in three di erent applications, and its evaluation in three di erent settings (one setting per app). From this experience, we draw the following lessons learned: First, the implemented in-appreflection guidance components are perceived as useful tools for reflective learning and their usefulness increases with higher usage rates. Second, smart technological support is su fficient to trigger reflection, however with di fferent implemented components also reflective learning takesplace on di erent stages. A sophisticated, unobtrusive integration in the working environment is not trivial at all. Automatically created prompts need a sensible timing in order to be perceived as useful and must not disrupt the current working processes.
2015
Schulze Gunnar, Horn Christopher, Kern Roman
Map-Matching Cell Phone Trajectories of Low Spatial and Temporal Accuracy
2015 IEEE 18th International Conference on Intelligent Transportation Systems, IEEE, IEEE, 2015
Konferenz
This paper presents an approach for matching cell phone trajectories of low spatial and temporal accuracy to the underlying road network. In this setting, only the position of the base station involved in a signaling event and the timestamp are known, resulting in a possible error of several kilometers. No additional information, such as signal strength, is available. The proposed solution restricts the set of admissible routes to a corridor by estimating the area within which a user is allowed to travel. The size and shape of this corridor can be controlled by various parameters to suit different requirements. The computed area is then used to select road segments from an underlying road network, for instance OpenStreetMap. These segments are assembled into a search graph, which additionally takes the chronological order of observations into account. A modified Dijkstra algorithm is applied for finding admissible candidate routes, from which the best one is chosen. We performed a detailed evaluation of 2249 trajectories with an average sampling time of 260 seconds. Our results show that, in urban areas, on average more than 44% of each trajectory are matched correctly. In rural and mixed areas, this value increases to more than 55%. Moreover, an in-depth evaluation was carried out to determine the optimal values for the tunable parameters and their effects on the accuracy, matching ratio and execution time. The proposed matching algorithm facilitates the use of large volumes of cell phone data in Intelligent Transportation Systems, in which accurate trajectories are desirable.
2015
Kowald Dominik, Lex Elisabeth
Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study
Proceedings of 9th International Conference on Recommender Systems, RecSys'2015, ACM, Vienna, Austria, 2015
Konferenz
To date, the evaluation of tag recommender algorithms has mostly been conducted in limited ways, including $p$-core pruned datasets, a small set of compared algorithms and solely based on recommender accuracy. In this study, we use an open-source evaluation framework to compare a rich set of state-of-the-art algorithms in six unfiltered, open datasets via various metrics, measuring not only accuracy but also the diversity, novelty and computational costs of the approaches. We therefore provide a transparent and reproducible tag recommender evaluation in real-world folksonomies. Our results suggest that the efficacy of an algorithm highly depends on the given needs and thus, they should be of interest to both researchers and developers in the field of tag-based recommender systems.
2015
Lacic Emanuel, Luzhnica Granit, Simon Jörg Peter, Traub Matthias, Lex Elisabeth, Kowald Dominik
Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems
Proceedings of 9th International Conference on Recommender Systems, RecSys'2015, ACM, Vienna, Austria, 2015
Konferenz
In this paper, we present work-in-progress on a recommender system based on Collaborative Filtering that exploits location information gathered by indoor positioning systems. This approach allows us to provide recommendations for "extreme" cold-start users with absolutely no item interaction data available, where methods based on Matrix Factorization would not work. We simulate and evaluate our proposed system using data from the location-based FourSquare system and show that we can provide substantially better recommender accuracy results than a simple MostPopular baseline that is typically used when no interaction data is available.
2015
Wozelka Ralph, Kröll Mark, Sabol Vedran
Exploring Time Relations in Semantic Graphs
Proceedings of SIGRAD, SIGRAD, Linköping University Electronic Press, Stockholm, Sweden, 2015
Konferenz
The analysis of temporal relationships in large amounts of graph data has gained significance in recent years. In-formation providers such as journalists seek to bring order into their daily work when dealing with temporally dis-tributed events and the network of entities, such as persons, organisations or locations, which are related to these events. In this paper we introduce a time-oriented graph visualisation approach which maps temporal information to visual properties such as size, transparency and position and, combined with advanced graph navigation features, facilitates the identification and exploration of temporal relationships. To evaluate our visualisation, we compiled a dataset of ~120.000 news articles from international press agencies including Reuters, CNN, Spiegel and Aljazeera. Results from an early pilot study show the potentials of our visualisation approach and its usefulness for analysing temporal relationships in large data sets.
2015
Dennerlein Sebastian, Rella Matthias, Tomberg Vladimir, Theiler Dieter, Treasure-Jones Tamsin, Kerr Micky, Ley Tobias, Al-Smadi Mohammad, Trattner Christoph
Making Sense of Bits and Pieces: A Sensemaking Tool for Informal Workplace Learning
European Conference on Technology Enhanced Learning, Springer International Publishing, 2015
Konferenz
Sensemaking at the workplace and in educational contexts has beenextensively studied for decades. Interestingly, making sense out of the own wealthof learning experiences at the workplace has been widely ignored. To tackle thisissue, we have implemented a novel sensemaking interface for healthcare professionalsto support learning at the workplace. The proposed prototype supportsremembering of informal experiences from episodic memory followed by sensemakingin semantic memory. Results from an initial study conducted as part ofan iterative co-design process reveal the prototype is being perceived as usefuland supportive for informal sensemaking by study participants from the healthcaredomain. Furthermore, we find first evidence that re-evaluation of collectedinformation is a potentially necessary process that needs further exploration tofully understand and support sensemaking of informal learning experiences.
2015
Larrain Santiago, Parra Denis, Graells-Garrido Eduardo, Norvag Kjetil, Trattner Christoph
Good Times Bad Times: A Study on Recency Effects in Collaborative Filtering for Social Tagging
Proceedings of the 9th {ACM} Conference on Recommender Systems, ACM, 2015
Konferenz
In this paper, we present work-in-progress of a recently startedproject that aims at studying the effect of time in recommendersystems in the context of social tagging. Despite the existence ofprevious work in this area, no research has yet made an extensiveevaluation and comparison of time-aware recommendation methods.With this motivation, this paper presents results of a studywhere we focused on understanding (i) “when” to use the temporalinformation into traditional collaborative filtering (CF) algorithms,and (ii) “how” to weight the similarity between users and itemsby exploring the effect of different time-decay functions. As theresults of our extensive evaluation conducted over five social taggingsystems (Delicious, BibSonomy, CiteULike, MovieLens, andLast.fm) suggest, the step (when) in which time is incorporated inthe CF algorithm has substantial effect on accuracy, and the typeof time-decay function (how) plays a role on accuracy and coveragemostly under pre-filtering on user-based CF, while item-basedshows stronger stability over the experimental conditions.
2015
di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
uRank: Exploring Document Recommendations through an Interactive User-Driven Approach
RecSys Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'15), CEUR-WS, 2015
Konferenz
Whenever we gather or organize knowledge, the task of searching inevitably takes precedence. As exploration unfolds, it becomes cumbersome to reorganize resources along new interests, as any new search brings new results. Despite huge advances in retrieval and recommender systems from the algorithmic point of view, many real-world interfaces have remained largely unchanged: results appear in an infinite list ordered by relevance with respect to the current query. We introduce uRank, a user-driven visual tool for exploration and discovery of textual document recommendations. It includes a view summarizing the content of the recommendation set, combined with interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. We provide a formal experiment showing that uRank users can browse the document collection and efficiently gather items relevant to particular topics of interest with significantly lower cognitive load compared to traditional list-based representations.
2015
Scherer Reinhold, Schwarz Andreas , Müller Putz Gernot , Pammer-Schindler Viktoria, Lloria Garcia Mariano
Game-based BCI training: Interactive design for individuals with cerebral palsy
IEEE International Conference on Systems, Man, Cybernetics 2015, 2015
Konferenz
Mutual brain-machine co-adaptation is the mostcommon approach used to gain control over spontaneouselectroencephalogram (EEG) based brain-computer interfaces(BCIs). Co-adaptation means the concurrent or alternating useof machine learning and the brain’s reinforcement learningmechanisms. Results from the literature, however, suggest thatcurrent implementations of this approach does not lead todesired results (“BCI inefficiency”). In this paper, we proposean alternative strategy that implements some recommendationsfrom educational psychology and instructional design. We presenta jigsaw puzzle game for Android devices developed to train theBCI skill in individuals with cerebral palsy (CP). Preliminaryresults of a supporting study in four CP users suggest high useracceptance. Three out of the four users achieved better thanchance accuracy in arranging pieces to form the puzzle.Index Terms—Brain-Computer Interface, Electroencephalo-gram, Human-Computer Interaction, Game-based learning,Cerebral palsy.
2015
Simon Jörg Peter, Pammer-Schindler Viktoria, Schmidt, P.
An Energy Efficient Implementation of Differential Synchronization on Mobile Devices
Proceedings of 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) , London, 2015
Konferenz
Synchronisation algorithms are central components of collab- orative editing software. The energy efficiency for such algo- rithms becomes of interest to a wide community of mobile application developers. In this paper we explore the differen- tial synchronisation (diffsync) algorithm with respect to en- ergy consumption on mobile devices.We identify three areas for optimisation: a.) Empty cycles where diffsync is executed although no changes need to be processed b.) tail energy by adapting cycle intervals and c.) computational complexity. We propose a push-based diffsync strategy in which synchronisation cycles are triggered when a device connects to the network or when a device is notified of changes. Discussions within this paper are based on real usage data of PDF annotations via the Mendeley iOS app.
2015
Dennerlein Sebastian, Kowald Dominik, Lex Elisabeth, Lacic Emanuel, Theiler Dieter, Ley Tobias
The Social Semantic Server: A Flexible Framework to Support Informal Learning at the Workplace
In Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business, i-know 2015, ACM, Graz, Austria, 2015
Konferenz
Informal learning at the workplace includes a multitude of processes. Respective activities can be categorized into multiple perspectives on informal learning, such as reflection, sensemaking, help seeking and maturing of collective knowledge. Each perspective raises requirements with respect to the technical support, this is why an integrated solution relying on social, adaptive and semantic technologies is needed. In this paper, we present the Social Semantic Server, an extensible, open-source application server that equips clientside tools with services to support and scale informal learning at the workplace. More specifically, the Social Semantic Server semantically enriches social data that is created at the workplace in the context of user-to-user or user-artifact interactions. This enriched data can then in turn be exploited in informal learning scenarios to, e.g., foster help seeking by recommending collaborators, resources, or experts. Following the design-based research paradigm, the Social Semantic Server has been implemented based on design principles, which were derived from theories such as Distributed Cognition and Meaning Making. We illustrate the applicability and efficacy of the Social Semantic Server in the light of three real-world applications that have been developed using its social semantic services. Furthermore, we report preliminary results of two user studies that have been carried out recently.
2015
Pujari Subhash Chandra, Hadgu Asmelah Teka, Lex Elisabeth, Jäschke Robert
Social Activity versus Academic Activity: A Case Study of Computer Scientists on Twitter
In Proceedings of the 15th International Conference on Knowledge Technologies and Data-Driven Business (i-KNOW 2015), ACM, Graz, Austria, 2015
Konferenz
In this work, we study social and academic network activities
of researchers from Computer Science. Using a recently
proposed framework, we map the researchers to their Twitter
accounts and link them to their publications. This enables
us to create two types of networks: first, networks that
reflect social activities on Twitter, namely the researchers’
follow, retweet and mention networks and second, networks
that reflect academic activities, that is the co-authorship and
citation networks. Based on these datasets, we (i) compare
the social activities of researchers with their academic activities,
(ii) investigate the consistency and similarity of communities
within the social and academic activity networks,
and (iii) investigate the information flow between different
areas of Computer Science in and between both types of
networks. Our findings show that if co-authors interact on
Twitter, their relationship is reciprocal, increasing with the
numbers of papers they co-authored. In general, the social
and the academic activities are not correlated. In terms of
community analysis, we found that the three social activity
networks are most consistent with each other, with the
highest consistency between the retweet and mention network.
A study of information flow revealed that in the follow
network, researchers from Data Management, HumanComputer
Interaction, and Artificial Intelligence act as a
source of information for other areas in Computer Science.
2015
Traub Matthias, Kowald Dominik, Lacic Emanuel, Lex Elisabeth, Schoen Pepjin, Supp Gernot
Smart booking without looking: providing hotel recommendations in the TripRebel portal
Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business, i-know 2015, ACM, Graz, Austria, 2015
Konferenz
In this paper, we present a scalable hotel recommender system for TripRebel, a new online booking portal. On the basis of the open-source enterprise search platform Apache Solr, we developed a system architecture with Web-based services to interact with indexed data at large scale as well as to provide hotel recommendations using various state-of-the-art recommender algorithms. We demonstrate the efficiency of our system directly using the live TripRebel portal where, in its current state, hotel alternatives for a given hotel are calculated based on data gathered from the Expedia AffiliateNetwork (EAN).
2015
Fessl Angela, Feyertag Sandra, Pammer-Schindler Viktoria
Designing Innovative Digital Technologies for Knowledge Management and Data-driven Business: A Case Study
In Proceeding of 15th International Conference on Knowledge Technologies and Data-driven Business, 2015
Konferenz
This paper presents a case study on co-designing digitaltechnologies for knowledge management and data-driven businessfor an SME. The goal of the case study was to analysethe status quo of technology usage and to develop designsuggestions in form of mock-ups tailored to the company’sneeds. We used both requirements engineering and interactivesystem design methods such as interviews, workshops,and mock-ups for work analysis and system design. The casestudy illustrates step-by-step the processes of knowledge extractionand combination (analysis) and innovation creation(design). These processes resulted in non-functional mockups,which are planned to be implemented within the SME.
2015
Cook John, Ley Tobias, Maier Ronald, Mor Yishay, Santos Patricia, Lex Elisabeth, Dennerlein Sebastian, Trattner Christoph, Holley Debbie
Using the Hybrid Social Learning Network to Explore Concepts, Practices, Designs and Smart Services for Networked Professional Learning
In Proceedings of the International Conference on Smart Learning Environments 2015 (ICSLE 2015), Springer, Sinaia, Romania, 2015
Konferenz
In this paper we define the notion of the Hybrid Social Learning Network. We propose mechanisms for interlinking and enhancing both the practice of professional learning and theories on informal learning. Our approach shows how we employ empirical and design work and a participatory pattern workshop to move from (kernel) theories via Design Principles and prototypes to social machines articulating the notion of a HSLN. We illustrate this approach with the example of Help Seeking for healthcare professionals.
2015
Ruiz-Calleja Adolfo, Dennerlein Sebastian, Tomberg Vladimir , Ley Tobias , Theiler Dieter, Lex Elisabeth
Integrating data across workplace learning applications with a social semantic infrastructure
Proceedings of the International Conference on Web-based Learning, Springer International Publishing, Hong Kong, China, 2015
Konferenz
This paper presents our experiences using a social semantic infrastructure that implements a semantically-enriched Actor Artifact Network (AAN) to support informal learning at the workplace. Our previous research led us to define the Model of Scaling Informal Learning, to identify several common practices when learning happens at the workplace, and to propose a social semantic infrastructure able to support them. This paper shows this support by means of two illustrative examples where practitioners employed several applications integrated into the infrastructure. Thus, this paper clarifies how workplace learning processes can be supported with such infrastructure according to the aforementioned model. The initial analysis of these experiences gives promising results since it shows how the infrastructure mediates in the sharing of contextualized learning artifacts and how it builds up an AAN that makes explicit the relationships between actors and artifacts when learning at the workplace.
2015
Ruiz-Calleja Adolfo, Dennerlein Sebastian, Tomberg Vladimir , Pata Kai, Ley Tobias, Theiler Dieter, Lex Elisabeth
Supporting learning analytics for informal workplace learning with a social semantic infrastructure
In Proceedings of the European Conference on Technology Enhanced Learning, Springer International Publishing (in press)., Springer, Toledo, Spain, 2015
Konferenz
This paper presents the potential of a social semantic infrastructure that implements an Actor Artifact Network (AAN) with the final goal of supporting learning analytics at the workplace. Two applications were built on top of such infrastructure and make use of the emerging relations of such a AAN. A preliminary evaluation shows that an AAN can be created out of the usage of both applications, thus opening the possibility to implement learning analytics at the workplace.
2015
Trattner Christoph, Parra Denis, Brusilovsky Peter, Marinho Leandro B.
Report on the SIGIR 2015 Workshop on Social Personalization and Search
SIGIR FORUM, ACM, 2015
Konferenz
The use of contexts –side information associated to information tasks– has been one ofthe most important dimensions for the improvement of Information Retrieval tasks, helpingto clarify the information needs of the users which usually start from a few keywords in atext box. Particularly, the social context has been leveraged in search and personalizationsince the inception of the Social Web, but even today we find new scenarios of informationfiltering, search, recommendation and personalization where the use of social signals canproduce a steep improvement. In addition, the action of searching has become a social processon the Web, making traditional assumptions of relevance obsolete and requiring newparadigms for matching the most useful resources that solve information needs. This escenariohas motivated us for organizing the Social Personalization and Search (SPS) workshop,a forum aimed at sharing and discussing research that leverage social data for improvingclassic personalization models for information access and to revisiting search from individualphenomena to a collaborative process.
2015
Kern Roman, Frey Matthias
Efficient Table Annotation for Digital Articles
4th International Workshop on Mining Scientific Publications, D-Lib, 2015
Konferenz
Table recognition and table extraction are important tasks in information extraction, especially in the domain of schol- arly communication. In this domain tables are commonplace and contain valuable information. Many different automatic approaches for table recognition and extraction exist. Com- mon to many of these approaches is the need for ground truth datasets, to train algorithms or to evaluate the results. In this paper we present the PDF Table Annotator, a web
based tool for annotating elements and regions in PDF doc- uments, in particular tables. The annotated data is intended to serve as a ground truth useful to machine learning algo- rithms for detecting table regions and table structure. To make the task of manual table annotation as convenient as possible, the tool is designed to allow an efficient annotation process that may spawn multiple session by multiple users. An evaluation is conducted where we compare our tool to three alternative ways of creating ground truth of tables in documents. Here we found that our tool overall provides an efficient and convenient way to annotate tables. In addition, our tool is particularly suitable for complex table structures, where it provided the lowest annotation time and the highest accuracy. Furthermore, our tool allows to annotate tables following a logical or a functional model. Given that by the use of our tool ground truth datasets for table recognition and extraction are easier to produce, the quality of auto- matic tables extraction should greatly benefit.
General
2015
Pammer-Schindler Viktoria, Bratic Marina, Feyertag Sandra, Faltin Nils
The Value of Self-tracking and the Added Value of Coaching in the Case of Improving Time Management
Proceedings of the10th European Conference on Technology Enhanced Learning (ECTEL 2015), 2015
Konferenz
We report two 6-week studies, each with 10 participants, on improving time management. In each study a different interventions was administered, in parallel to otherwise regular work: In the self-tracking setting, participants used only an activity logging tool to track their time use and a reflective practice, namely daily review of time use, to improve time management. In the coaching setting, participants did the same, but additionally received weekly bilateral coaching. In both settings, participants reported learning about time management. This is encouraging, as such self-directed learning is clearly cheaper than coaching. Only participants in the coaching setting however improved their self-assessment with respect to predefined time management best practices.
The Value of Self-tracking and the Added Value of Coaching in the Case of Improving Time Management. Available from: https://www.researchgate.net/publication/300259607_The_Value_of_Self-tracking_and_the_Added_Value_of_Coaching_in_the_Case_of_Improving_Time_Management [accessed Oct 24 2017].
2015
Seitlinger Paul, Kowald Dominik, Kopeinik Simone, Hasani-Mavriqi Ilire, Ley Tobias, Lex Elisabeth
Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics
In 24rd International World Wide Web Conference, Web-Science Track, Aldo Gangemi, Stefano Leonardi and Alessandro Panconesi, ACM, Florence, 2015
Konferenz
Classic resource recommenders like Collaborative Filtering(CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and inter-pretation. In this paper, we propose a novel hybrid rec-ommendation strategy that re nes CF by capturing thesedynamics. The evaluation results reveal that our approachsubstantially improves CF and, depending on the dataset,successfully competes with a computationally much moreexpensive Matrix Factorization variant.
2015
Wertner Alfred, Czech Paul, Pammer-Schindler Viktoria
An Open Labelled Dataset for Mobile Phone Sensing Based Fall Detection
12th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2015
Konferenz
Fall detection is a classical use case for mobile phone sensing.Nonetheless, no open dataset exists that could be used totrain, test and compare fall detection algorithms.We present a dataset for mobile phone sensing-based fall detection.The dataset contains both accelerometer and gyroscopedata. Data were labelled with four types of falls(e.g., “stumblingâ€) and ten types of non-fall activities (e.g.,“sit downâ€). The dataset was collected with martial artistswho simulated falls. We used five different state-of-the-artAndroid smartphone models worn on the hip in a small bag.Due to the datasets properties of using multiple devices andbeing labelled with multiple fall- and non-fall categories, weargue that it is suitable to serve as benchmark dataset.
2015
Kröll Mark, Strohmaier Markus
Associating Intent with Sentiment in Weblogs
International Conference on Applications of Natural Language to Information Systems, NLDB'15, Springer-Verlag, Passau, Germany, 2015
Konferenz
People willingly provide more and more information about themselves on social media platforms. This personal information about users’ emotions (sentiment) or goals (intent) is particularly valuable, for instance, for monitoring tools. So far, sentiment and intent analysis were conducted separately. Yet, both aspects can complement each other thereby informing processes such as explanation and reasoning. In this paper, we investigate the relation between intent and sentiment in weblogs. We therefore extract ~90,000 human goal instances from the ICWSM 2009 Spinn3r dataset and assign respective sentiments. Our results indicate that associating intent with sentiment represents a valuable addition to research areas such as text analytics and text understanding.
2015
Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran
Towards a Recommender Engine for Personalized Visualizations
UMAP, 2015
Konferenz
isualizations have a distinctive advantage when dealing with the information overload problem: being grounded in basic visual cognition, many people understand visualizations. However, when it comes to creating them, it requires specific expertise of the domain and underlying data to determine the right representation. Although there are rules that help generate them, the results are too broad as these methods hardly account for varying user preferences. To tackle this issue, we propose a novel recommender system that suggests visualizations based on (i) a set of visual cognition rules and (ii) user preferences collected in Amazon-Mechanical Turk. The main contribution of this paper is the introduction and the evaluation of a novel approach called VizRec that is able suggest an optimal list of top-n visualizations for heterogeneous data sources in a personalized manner.
2015
Peters, I, Kraker Peter, Lex Elisabeth, Gumpenberger C., Gorraiz, J.
Research Data Explored: Citations versus Altmetrics
15th International Conference on Scientometrics and Informetrics, Online, 2015
Konferenz
The study explores the citedness of research data, its distribution over time and how it is related to the availability of a DOI (Digital Object Identifier) in Thomson Reuters' DCI (Data Citation Index). We investigate if cited research data "impact" the (social) web, reflected by altmetrics scores, and if there is any relationship between the number of citations and the sum of altmetrics scores from various social media-platforms. Three tools are used to collect and compare altmetrics scores, i.e. PlumX, ImpactStory, and Altmetric.com. In terms of coverage, PlumX is the most helpful altmetrics tool. While research data remain mostly uncited (about 85%), there has been a growing trend in citing data sets published since 2007. Surprisingly, the percentage of the number of cited research data with a DOI in DCI has decreased in the last years. Only nine repositories account for research data with DOIs and two or more citations. The number of cited research data with altmetrics scores is even lower (4 to 9%) but shows a higher coverage of research data from the last decade. However, no correlation between the number of citations and the total number of altmetrics scores is observable. Certain data types (i.e. survey, aggregate data, and sequence data) are more often cited and receive higher altmetrics scores.
2015
Rexha Andi, Klampfl Stefan, Kröll Mark, Kern Roman
Towards Authorship Attribution for Bibliometrics using Stylometric Features
Proc. of the Workshop Mining Scientific Papers: Computational Linguistics and Bibliometrics, Atanassova, I.; Bertin, M.; Mayr, P., ACL Anthology, Istanbul, Turkey, 2015
Konferenz
The overwhelming majority of scientific publications are authored by multiple persons; yet, bibliographic metrics are only assigned to individual articles as single entities. In this paper, we aim at a more fine-grained analysis of scientific authorship. We therefore adapt a text segmentation algorithm to identify potential author changes within the main text of a scientific article, which we obtain by using existing PDF extraction techniques. To capture stylistic changes in the text, we employ a number of stylometric features. We evaluate our approach on a small subset of PubMed articles consisting of an approximately equal number of research articles written by a varying number of authors. Our results indicate that the more authors an article has the more potential author changes are identified. These results can be considered as an initial step towards a more detailed analysis of scientific authorship, thereby extending the repertoire of bibliometrics.
2015
Veas Eduardo Enrique, Sabol Vedran, Singh Santokh, Ulbrich Eva Pauline
Reading through Graphics: Interactive Landscapes to Explore Dynamic Topic Spaces
Proceedings Part I of the 17th HCI International Conference, HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, 2015
Konferenz
An information landscape is commonly used to represent relatedness in large, high-dimensional datasets, such as text document collections. In this paper we present interactive metaphors, inspired in map reading and visual transitions, that enhance the landscape representation for the analysis of topical changes in dynamic text repositories. The goal of interactive visualizations is to elicit insight, to allow users to visually formulate hypotheses about the underlying data and to prove them. We present a user study that investigates how users can elicit information about topics in a large document set. Our study concentrated on building and testing hypotheses using the map reading metaphors. The results show that people indeed relate topics in the document set from spatial relationships shown in the landscape, and capture the changes to topics aided by map reading metaphors.
2015
Tschinkel Gerwald, di Sciascio Maria Cecilia, Mutlu Belgin, Sabol Vedran
The Recommendation Dashboard: A System to Visualise and Organise Recommendations
Proceedings of the 19th International Conference on Information Visualisation (IV2015), 2015
Konferenz
Recommender systems are becoming common tools supportingautomatic, context-based retrieval of resources.When the number of retrieved resources grows large visualtools are required that leverage the capacity of humanvision to analyse large amounts of information. Weintroduce a Web-based visual tool for exploring and organisingrecommendations retrieved from multiple sourcesalong dimensions relevant to cultural heritage and educationalcontext. Our tool provides several views supportingfiltering in the result set and integrates a bookmarkingsystem for organising relevant resources into topic collections.Building upon these features we envision a systemwhich derives user’s interests from performed actions anduses this information to support the recommendation process.We also report on results of the performed usabilityevaluation and derive directions for further development.
2015
Rauch Manuela, Klieber Hans-Werner, Wozelka Ralph, Singh Santokh, Sabol Vedran
Knowminer Search - a Multi-Visualisation Collaborative Approach to Search Result Analysis
Proceedings of the 19th International Conference on Information Visualisation (IV2015), 2015
Konferenz
The amount of information available on the internet and within enterprises has reached an incredible dimension.Efficiently finding and understanding information and thereby saving resources remains one of the major challenges in our daily work. Powerful text analysis methods, a scalable faceted retrieval engine and a well-designed interactive user interface are required to address the problem. Besides providing means for drilling-down to the relevant piece of information, a part of the challenge arises from the need of analysing and visualising data to discover relationships and correlations, gain an overview of data distributions and unveil trends. Visual interfaces leverage the enormous bandwidth of the human visual system to support pattern discovery in large amounts of data. Our Knowminer search builds upon the well-known faceted search approach which is extended with interactive visualisations allowing users to analyse different aspects of the result set. Additionally, our system provides functionality for organising interesting search results into portfolios, and also supports social features for rating and boosting search results and for sharing and annotating portfolios.
2015
Veas Eduardo Enrique, di Sciascio Maria Cecilia
Interactive topic analysis with visual analytics and recommender systems.
IJCAI 2015 Workshop on Cognitive Knowledge Acquisition and Applications (Cognitum 2015), 2015
Konferenz
The ability to analyze and organize large collections,to draw relations between pieces of evidence, to buildknowledge, are all part of an information discovery process.This paper describes an approach to interactivetopic analysis, as an information discovery conversationwith a recommender system. We describe a modelthat motivates our approach, and an evaluation comparinginteractive topic analysis with state-of-the-art topicanalysis methods.
2015
Horn Christopher, Kern Roman
Deriving Public Transportation Timetables with Large-Scale Cell Phone Data
Procedia Computer Science, 2015
Konferenz
In this paper, we propose an approach to deriving public transportation timetables of a region (i.e. country) based on (i) large- scale, non-GPS cell phone data and (ii) a dataset containing geographic information of public transportation stations. The presented algorithm is designed to work with movements data, which are scarce and have a low spatial accuracy but exists in vast amounts (large-scale). Since only aggregated statistics are used, our algorithm copes well with anonymized data. Our evaluation shows that 89% of the departure times of popular train connections are correctly recalled with an allowed deviation of 5 minutes. The timetable can be used as feature for transportation mode detection to separate public from private transport when no public timetable is available.
2015
Veas Eduardo Enrique, di Sciascio Maria Cecilia
Interactive Preference Elicitation for Scientific and Cultural Recommendations
IJCAI 2015 Workshop on INTELLIGENT PERSONALIZATION (IP'2015), CEUR-WS, 2015
Konferenz
This paper presents a visual interface developed on the basis of control and transparency to elicit preferences in the scientific and cultural domain. Preference elicitation is a recognized challenge in user modeling for personalized recommender systems. The amount of feedback the user is willing to provide depends on how trustworthy the system seems to be and how invasive the elicitation process is. Our approach ranks a collection of items with a controllable text analytics model. It integrates control with the ranking and uses it as implicit preference for content based recommendations.
2015
Trattner Christoph, Parra Denis , Brusilovsky Peter, , Leandro Marinho
SPS'15: 2015 International Workshop on Social Personalization & Search
Proceedings of the 38th International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval, ACM, 2015
Konferenz
2015
Hasani-Mavriqi Ilire, Geigl Florian, Pujari Subhash Chandra, Lex Elisabeth, Helic Denis
Influence of Status Social on Consensus Building in Collaboration Networks
In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2015), Jian Pei, Fabrizio Silvestri and Jie Tang, ACM/IEEE, Paris, France, 2015
Konferenz
In this paper, we analyze the influence of social
status on opinion dynamics and consensus building in collaboration
networks. To that end, we simulate the diffusion of opinions
in empirical collaboration networks by taking into account both
the network structure and the individual differences of people
reflected through their social status. For our simulations, we
adapt a well-known Naming Game model and extend it with
the Probabilistic Meeting Rule to account for the social status
of individuals participating in a meeting. This mechanism is
sufficiently flexible and allows us to model various situations in
collaboration networks, such as the emergence or disappearance
of social classes. In this work, we concentrate on studying
three well-known forms of class society: egalitarian, ranked and
stratified. In particular, we are interested in the way these society
forms facilitate opinion diffusion. Our experimental findings
reveal that (i) opinion dynamics in collaboration networks is
indeed affected by the individuals’ social status and (ii) this
effect is intricate and non-obvious. In particular, although the
social status favors consensus building, relying on it too strongly
can slow down the opinion diffusion, indicating that there is a
specific setting for each collaboration network in which social
status optimally benefits the consensus building process.
2015
Rubien Raoul, Ziak Hermann, Kern Roman
Efficient Search Result Diversification via Query Expansion Using Knowledge Bases
Proceedings of 12th International Workshop on Text-based Information Retrieval (TIR), 2015
Konferenz
Underspecified search queries can be performed via
result list diversification approaches, which are often compu- tationally complex and require longer response times. In this paper, we explore an alternative, and more efficient way to diversify the result list based on query expansion. To that end, we used a knowledge base pseudo-relevance feedback algorithm. We compared our algorithm to IA-Select, a state-of-the-art diversification method, using its intent-aware version of the NDCG (Normalized Discounted Cumulative Gain) metric. The results indicate that our approach can guarantee a similar extent of diversification as IA-Select. In addition, we showed that the supported query language of the underlying search engines plays an important role in the query expansion based on diversification. Therefore, query expansion may be an alternative when result diversification is not feasible, for example in federated search systems where latency and the quantity of handled search results are critical issues.
2015
Dennerlein Sebastian, Kaiser René, Barreiros Carla, Gutounig Robert , Rauter Romana
Knowledge Strategies in Organisations – a Case for the Barcamp Format
Proceedings of the 16th European Conference on Knowledge Management, ACPI, Udine, Italy, 2015
Konferenz
Barcamps are events for open knowledge exchange. They are generally open to everyone, irrespective of background or discipline, and request no attendance fee. Barcamps are structured by only a small set of common rules and invite participants to an interactive and interdisciplinary discourse on an equal footing. In contrast to scientific conferences, the program is decided by the participants themselves on-site. Barcamps are often called un-conferences or ad-hoc conferences. Since barcamps are typically attended by people in their spare time, their motivation to actively engage and benefit from participating is very high. This paper presents a case study conducted at the annual Barcamp Graz in Austria. Within the case study, two field studies (quantitative and qualitative) and a parallel participant observation were carried out between 2010 and 2014. In these investigations we elaborated on the differences of the barcamp to scientific conferences, inferred characteristics of barcamps for knowledge generation, sharing and transfer in organizations and propose three usages of barcamps in organizations: further education of employees, internal knowledge transfer and getting outside knowledge in. Barcamps can be used as further education for employees enabling not only knowledge sharing, generation and transfer via the participating employees, but also for informally promoting a company’s competences. With respect to internal knowledge transfer, hierarchical boundaries can be temporarily broken by allowing informal and interactive discussion. This can lead to the elicitation of ‘hidden’ knowledge, knowledge transfer resulting in more efficient teamwork and interdepartmental cooperation. Finally, external stakeholders such as customers and partners can be included in this process to get outside knowledge in and identify customer needs, sketch first solutions and to start concrete projects. As a result of the case study, we hypothesise as a step towards further research that organisations can benefit from utilising this format as knowledge strategy.
2015
Dennerlein Sebastian, Treasure Jones T, Tomberg V, Theiler Dieter, Lex Elisabeth, Ley Tobias
Making Sense of Informal Learning at the Workplace
AMEE - Conference (The Association for Medical Education in Europe), Glasgow, UK, 2015
Konferenz
Sensemaking at the workplace and in educational contexts has been extensively studied for decades. Interestingly, making sense out of the own wealth of learning experiences at the workplace has been widely ignored. To tackle this issue, we have implemented a novel sensemaking interface for healthcare professionals to support learning at the workplace. The proposed prototype supports remembering of informal experiences from episodic memory followed by sensemaking in semantic memory. Results from an initial study conducted as part of an iterative co-design process reveal the prototype is being perceived as useful and supportive for informal sensemaking by study participants from the healthcare domain. Furthermore, we find first evidence that re-evaluation of collected information is a potentially necessary process that needs further exploration to fully understand and support sensemaking of informal learning experiences.
2015
Veas Eduardo Enrique, Mutlu Belgin, di Sciascio Maria Cecilia, Tschinkel Gerwald, Sabol Vedran
Visual Recommendations for Scientific and Cultural Content
IVAPP 2015, Berlin, 2015
Konferenz
Supporting individuals who lack experience or competence to evaluate an overwhelming amout of informationsuch as from cultural, scientific and educational content makes recommender system invaluable to cope withthe information overload problem. However, even recommended information scales up and users still needto consider large number of items. Visualization takes a foreground role, letting the user explore possiblyinteresting results. It leverages the high bandwidth of the human visual system to convey massive amounts ofinformation. This paper argues the need to automate the creation of visualizations for unstructured data adaptingit to the user’s preferences. We describe a prototype solution, taking a radical approach considering bothgrounded visual perception guidelines and personalized recommendations to suggest the proper visualization.
2015
Gursch Heimo, Ziak Hermann, Kern Roman
Unified Information Access for Knowledge Workers via a Federated Recommender System
Mensch und Computer 2015 – Workshopband, Anette Weisbecker, Michael Burmester, Albrecht Schmidt, De Gruyter Oldenbourg, Berlin, 2015
Konferenz
The objective of the EEXCESS (Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources) project is to develop a system that can automatically recommend helpful and novel content to knowledge workers. The EEXCESS system can be integrated into existing software user interfaces as plugins which will extract topics and suggest the relevant material automatically. This recommendation process simplifies the information gathering of knowledge workers. Recommendations can also be triggered manually via web frontends. EEXCESS hides the potentially large number of knowledge sources by semi or fully automatically providing content suggestions. Hence, users only have to be able to in use the EEXCESS system and not all sources individually. For each user, relevant sources can be set or auto-selected individually. EEXCESS offers open interfaces, making it easy to connect additional sources and user program plugins.
2015
Klampfl Stefan, Kern Roman
Machine Learning Techniques for Automatically Extracting Contextual Information from Scientific Publications
Semantic Web Evaluation Challenges. SemWebEval 2015 at ESWC 2015, Portorož, Slovenia, May 31 – June 4, 2015, Revised Selected Papers, Gandon, F.; Cabrio, E.; Stankovic, M.; Zimmermann, A. , Springer International Publishing, 2015
Konferenz
Scholarly publishing increasingly requires automated systems that semantically enrich documents in order to support management and quality assessment of scientific output.However, contextual information, such as the authors' affiliations, references, and funding agencies, is typically hidden within PDF files.To access this information we have developed a processing pipeline that analyses the structure of a PDF document incorporating a diverse set of machine learning techniques.First, unsupervised learning is used to extract contiguous text blocks from the raw character stream as the basic logical units of the article.Next, supervised learning is employed to classify blocks into different meta-data categories, including authors and affiliations.Then, a set of heuristics are applied to detect the reference section at the end of the paper and segment it into individual reference strings.Sequence classification is then utilised to categorise the tokens of individual references to obtain information such as the journal and the year of the reference.Finally, we make use of named entity recognition techniques to extract references to research grants, funding agencies, and EU projects.Our system is modular in nature.Some parts rely on models learnt on training data, and the overall performance scales with the quality of these data sets.
2015
Kraker Peter, Enkhbayar Asuraa, Lex Elisabeth
Exploring Coverage and Distribution of Identifiers on the Scholarly Web
In Proceedings of the 14th International Symposium of Information Science – ISI 2015, Zadar, Croatia, 2015
Konferenz
In a scientific publishing environment that is increasingly moving online,
identifiers of scholarly work are gaining in importance. In this paper, we
analysed identifier distribution and coverage of articles from the discipline of
quantitative biology using arXiv, Mendeley and CrossRef as data sources.
The results show that when retrieving arXiv articles from Mendeley, we were
able to find more papers using the DOI than the arXiv ID. This indicates that
DOI may be a better identifier with respect to findability. We also find that
coverage of articles on Mendeley decreases in the most recent years, whereas
the coverage of DOIs does not decrease in the same order of magnitude. This
hints at the fact that there is a certain time lag involved, before articles are
covered in crowd-sourced services on the scholarly web.
2015
Mutlu Belgin, Veas Eduardo Enrique, Trattner Christoph, Sabol Vedran
VizRec: A Two-Stage Recommender System for Personalized Visualizations
ACM IUI, ACM, Atlanta, Georgia, USA, 2015
Konferenz
Identifying and using the information from distributed and heterogeneous information sources is a challenging task in many application fields. Even with services that offer welldefined structured content, such as digital libraries, it becomes increasingly difficult for a user to find the desired information. To cope with an overloaded information space, we propose a novel approach – VizRec– combining recommender systems (RS) and visualizations. VizRec suggests personalized visual representations for recommended data. One important aspect of our contribution and a prerequisite for VizRec are user preferences that build a personalization model. We present a crowd based evaluation and show how such a model of preferences can be elicited.
2015
Vignoli Michaela, Kraker Peter, Sevault A.
Paving the way for Science 2.0: top-down and bottom-up approaches
International Conference for E-Democracy and Open Government (CEDEM'15), Krems, Austria, 2015
Konferenz
Science 2.0 is the current trend towards using Web 2.0 tools in research and practising a more open science. We are currently at the beginning of a transition phase in which traditional structures, processes, value systems, and means of science communication are being put to the proof. New strategies and models under the label of “open†are being explored and partly implemented. This situation implies a number of insecurities for scientists as well as for policy makers and demands a rethinking and overcoming of some habits and conventions persisting since an era before the internet. This paper lists current barriers to practising Open Science from the point of view of researchers and reflects which measures could help overcoming them. The central question is which initiatives should be taken on institutional or political level and which ones on level of the community or the individual scientist to support the transition to Science 2.0.
2015
Kraker Peter, Lex Elisabeth, Gorraiz, J., Gumpenberger, C., Peters, I.
Research Data Explored II: the Anatomy and Reception of figshare
n Proceedings of the 20th International Conference on Science and Technology Indicators (STI 2015), Lugano, Schweiz, 2015
Konferenz
2015
Renner Bettina, Wesiak Gudrun, Cress, U.
Quantified self app usage tested in the workplace.
17th congress of the European Association of Work and Organizational Psychology (EAWOP), 2015
Konferenz
Purpose: This contribution relates the Quantified Self approach to computer supported
workplace learning. It shows results of a large field study where 12 different apps where used in
several work contexts.
Design/Methodology: Participants used the apps during their work and during training sessions
to track their behaviour and mood at work and capture problematic experiences. Data capturing
was either automatically, e.g. tracking program usage on a computer, or by participants
manually documenting their experiences. Users then reflected individually or collaboratively
about their experiences.
Results: Results show that participants liked the apps and used the opportunity to learn
something from their work experiences. Users evaluated apps as useful for professional training
and having long-term benefits when used in the work life. Computer supported reflection about
own data and experiences seems to have especially potential where new processes happen, e.g.
with unexperienced workers or in training settings.
Limitations: Apps were used in the wild so control about potential external influencing factors is
limited.
Research/Practical Implications: Results show a successful application of apps supporting
individual learning in the work life. This shows that the concept of Quantified Self is not
limited to private life but also has chances to foster vocational development.
Originality/Value: This contribution combines the pragmatic Quantified Self approach with the
theoretical background of reflective learning. It presents data from a broad-based study of using
such apps in real work life. The results of the study give insights about its potential in this area
and about possible influencing factors and barriers.
2015
Kowald Dominik
Modeling Cognitive Processes in Social Tagging to Improve Tag Recommendations
Proceedings of the 24th International Conference on World Wide Web Companion, WWW'2015, ACM, Florence, Italy, 2015
Konferenz
With the emergence of Web 2.0, tag recommenders have becomeimportant tools, which aim to support users in ndingdescriptive tags for their bookmarked resources. Althoughcurrent algorithms provide good results in terms of tag predictionaccuracy, they are often designed in a data-drivenway and thus, lack a thorough understanding of the cognitiveprocesses that play a role when people assign tags toresources. This thesis aims at modeling these cognitive dynamicsin social tagging in order to improve tag recommendationsand to better understand the underlying processes.As a rst attempt in this direction, we have implementedan interplay between individual micro-level (e.g., categorizingresources or temporal dynamics) and collective macrolevel(e.g., imitating other users' tags) processes in the formof a novel tag recommender algorithm. The preliminaryresults for datasets gathered from BibSonomy, CiteULikeand Delicious show that our proposed approach can outperformcurrent state-of-the-art algorithms, such as CollaborativeFiltering, FolkRank or Pairwise Interaction TensorFactorization. We conclude that recommender systems canbe improved by incorporating related principles of humancognition.
2015
Mutlu Belgin, Sabol Vedran
Visual Analysis of Scientific Content
STCSN E-LETTER ON SCIENCE 2.0, 2015
Konferenz
The steadily increasing amount of scientific publications demands for more powerful, user-oriented technologiessupporting querying and analyzing scientific facts therein. Current digital libraries that provide services to accessscientific content are rather closed in a way that they deploy their own meta-models and technologies to query and analysethe knowledge contained in scientific publications. The goal of the research project CODE is to realize a framework basedon Linked Data principles which aims to provide methods for federated querying within scientific data, and interfacesenabling user to easily perform exploration and analysis tasks on received content. The main focus in this paper lieson the one hand on extraction and organization of scientific facts embedded in publications and on the other hand on anintelligent framework facilitating search and visual analysis of scientific facts through suggesting visualizations appropriatefor the underlying data.
2015
Wesiak Gudrun, Al-Smadi, M, Gütl Christian, Höfler Margit
CSCL in non-technological environments: Evaluation of a Wiki system with integrated self- and peer assessment.
Proceedings of InPACT 2015 (International Psychological Applications Conference and Trends 2015), Ljubljana, Slovenia Cite this publication Gudrun Wesiak, 2015
Konferenz
Computer-supported collaborative learning (CSCL) is already a central element of online learning
environments, but is also gaining increasing importance in traditional classroom settings where course
work is carried out in groups. For these situations social interaction, sharing and construction of
knowledge among the group members are important elements of the learning process. The use of
computers and the internet facilitates such group work by allowing asynchronous as well as synchronous
contributions to
to foster CSCL is the employment of Wiki systems, e.g. for collaboratively working on a writing
assignment. We developed an enhanced Wiki system with self- and peer assessment, visualizations, and
-
science students showed its usefulness for collaborative course work. However, results from studies with
tech-savvy participants, who are typically familiar with the benefits as well as drawbacks of such tools,
are often limited regarding the generalizability to other populations. Thus, we introduced the Wiki in a
non-technological environment and evaluated it with respect to usability, usefulness, and motivational
components. Thirty psychology students used the co-writing Wiki to work collaboratively on a short
paper. Besides providing an interface for generating and changing a document, the co-writing Wiki offers
tools for formative assessment activities (integrated self-, peer-, and group assessment activities) as well
-data
(activity tracking) as well as questionnaire data gathered at before and after working with the Wiki.
Additionally, the instructor evaluated the co-writing Wiki concerning its usefulness for CSCL activities in
academic settings. Despite technical problems and consequently low system usability scores, participants
perceived the offered functionalities as helpful to keep a good overview on the current status of their
paper and the contributions of their group members. The integrated self-assessment tool helped them to
get aware of their strengths and weaknesses. In addition, students showed a high intrinsic motivation
while working with the co-Writing Wiki, which did not change over the course of the study. From the
-writing Wiki allowed to effectively monitor the progress of the groups and
enabled formative feedback by the instructor. Summarizing, the results indicate that using Wikis for
CSCL is a promising way to also support students with no technological background.
environments, but is also gaining increasing importance in traditional classroom settings where course
work is carried out in groups. For these situations social interaction, sharing and construction of
knowledge among the group members are important elements of the learning process. The use of
computers and the internet facilitates such group work by allowing asynchronous as well as synchronous contributions to a common learning object independent of student’s working time and location. One way to foster CSCL is the employment of Wiki systems, e.g. for collaboratively working on a writing assignment. We developed an enhanced Wiki system with self- and peer assessment, visualizations, and functionalities for continuous teacher feedback. First evaluations of this ‘co-writing Wiki’ with computer science students showed its usefulness for collaborative course work. However, results from studies with tech-savvy participants, who are typically familiar with the benefits as well as drawbacks of such tools, are often limited regarding the generalizability to other populations. Thus, we introduced the Wiki in a non-technological environment and evaluated it with respect to usability, usefulness, and motivational components. Thirty psychology students used the co-writing Wiki to work collaboratively on a short paper. Besides providing an interface for generating and changing a document, the co-writing Wiki offers tools for formative assessment activities (integrated self-, peer-, and group assessment activities) as well as monitoring the progress of the group’s collaboration. The evaluation of the tool is based on log-data (activity tracking) as well as questionnaire data gathered at before and after working with the Wiki. Additionally, the instructor evaluated the co-writing Wiki concerning its usefulness for CSCL activities in academic settings. Despite technical problems and consequently low system usability scores, participants perceived the offered functionalities as helpful to keep a good overview on the current status of their paper and the contributions of their group members. The integrated self-assessment tool helped them to get aware of their strengths and weaknesses. In addition, students showed a high intrinsic motivation while working with the co-Writing Wiki, which did not change over the course of the study. From the instructor’s perspective, the co-writing Wiki allowed to effectively monitor the progress of the groups and enabled formative feedback by the instructor. Summarizing, the results indicate that using Wikis for CSCL is a promising way to also support students with no technological background.
2014
Silva Nelson, Settgast Volker, Eggeling Eva, Grill Florian, Zeh Theodor, Fellner Dieter W.
Sixth Sense - Air Traffic Control Prediction Scenario Augmented by Sensors
I-Know 2014, 2014
Konferenz
2014
Silva Nelson
The Sixth Sense in Air Traffic Control - Automated error detection through sensor augmentation, while keeping the humans in the main decision loop of ATC
I-Know 2014, 2014
Konferenz
2014
Silva Nelson
The Sixth Sense of an Air Traffic Controller - Increasing Fault Tolerance of Human Machine Interfaces
SID 2014, 2014
Konferenz
2014
Mutlu Belgin, Tschinkel Gerwald, Veas Eduardo Enrique, Sabol Vedran, Stegmaier Florian, Granitzer Michael
Suggesting visualisations for published data
Information Visualization Theory and Applications (IVAPP), 2014 International Conference on, IEEE, 2014
Konferenz
Research papers are published in various digital libraries, which deploy their own meta-models and tech-nologies to manage, query, and analyze scientific facts therein. Commonly they only consider the meta-dataprovided with each article, but not the contents. Hence, reaching into the contents of publications is inherentlya tedious task. On top of that, scientific data within publications are hardcoded in a fixed format (e.g. tables).So, even if one manages to get a glimpse of the data published in digital libraries, it is close to impossibleto carry out any analysis on them other than what was intended by the authors. More effective querying andanalysis methods are required to better understand scientific facts. In this paper, we present the web-basedCODE Visualisation Wizard, which provides visual analysis of scientific facts with emphasis on automatingthe visualisation process, and present an experiment of its application. We also present the entire analyticalprocess and the corresponding tool chain, including components for extraction of scientific data from publica-tions, an easy to use user interface for querying RDF knowledge bases, and a tool for semantic annotation ofscientific data set
2014
Granitzer MIchael, Veas Eduardo Enrique, Seifert Christin
Linked Data Query Wizard: A Novel Interface for Accessing SPARQL Endpoints.
LDOW, 2014
Konferenz
In an interconnected world, Linked Data is more importantthan ever before. However, it is still quite di cult to accessthis new wealth of semantic data directly without havingin-depth knowledge about SPARQL and related semantictechnologies. Also, most people are currently used to consumingdata as 2-dimensional tables. Linked Data is by de -nition always a graph, and not that many people are used tohandle data in graph structures. Therefore we present theLinked Data Query Wizard, a web-based tool for displaying,accessing, ltering, exploring, and navigating Linked Datastored in SPARQL endpoints. The main innovation of theinterface is that it turns the graph structure of Linked Datainto a tabular interface and provides easy-to-use interactionpossibilities by using metaphors and techniques from currentsearch engines and spreadsheet applications that regular webusers are already familiar with.
2014
Sabol Vedran, Tschinkel, Veas Eduardo Enrique, Mutlu Belgin, Granitzer Michael
Discovery and visual analysis of linked data for humans
International Semantic Web Conference, Springer, Cham, 2014
Konferenz
Linked Data has grown to become one of the largest availableknowledge bases. Unfortunately, this wealth of data remains inaccessi-ble to those without in-depth knowledge of semantic technologies. Wedescribe a toolchain enabling users without semantic technology back-ground to explore and visually analyse Linked Data. We demonstrateits applicability in scenarios involving data from the Linked Open DataCloud, and research data extracted from scientific publications. Our fo-cus is on the Web-based front-end consisting of querying and visuali-sation tools. The performed usability evaluations unveil mainly positiveresults confirming that the Query Wizard simplifies searching, refiningand transforming Linked Data and, in particular, that people using theVisualisation Wizard quickly learn to perform interactive analysis taskson the resulting Linked Data sets. In making Linked Data analysis ef-fectively accessible to the general public, our tool has been integratedin a number of live services where people use it to analyse, discover anddiscuss facts with Linked Data.
2014
Tschinkel Gerwald, Veas Eduardo Enrique, Mutlu Belgin, Sabol Vedran
Using semantics for interactive visual analysis of linked open data
Proceedings of the 2014 International Conference on Posters & Demonstrations Track-Volume 1272, CEUR-WS. org, 2014
Konferenz
Providing easy to use methods for visual analysis of LinkedData is often hindered by the complexity of semantic technologies. Onthe other hand, semantic information inherent to Linked Data providesopportunities to support the user in interactively analysing the data. Thispaper provides a demonstration of an interactive, Web-based visualisa-tion tool, the “Vis Wizard”, which makes use of semantics to simplify theprocess of setting up visualisations, transforming the data and, most im-portantly, interactively analysing multiple datasets using brushing andlinking method
2014
Rauch Manuela, Wozelka Ralph, Veas Eduardo Enrique, Sabol Vedran
Semantic Blossom Graph: A new Approach for Visual Graph Exploration
18th International Conference on Information Visualisation, IEEE Computer Society CPS, 2014
Konferenz
Graphs are widely used to represent relationshipsbetween entities. Indeed, their simplicity in depicting connect-edness backed by a mathematical formalism, make graphs anideal metaphor to convey relatedness between entities irrespec-tive of the domain. However, graphs pose several challenges forvisual analysis. A large number of entities or a densely con-nected set quickly render the graph unreadable due to clutter.Typed relationships leading to multigraphs cannot clearly berepresented in hierarchical layout or edge bundling, commonclutter reduction techniques. We propose a novel approach tovisual analysis of complex graphs based on two metaphors:semantic blossom and selective expansion. Instead of showingthe whole graph, we display only a small representative subsetof nodes, each with a compressed summary of relations in asemantic blossom. Users apply selective expansion to traversethe graph and discover the subset of interest. A preliminaryevaluation showed that our approach is intuitive and usefulfor graph exploration and provided insightful ideas for futureimprovements.
2014
Pammer-Schindler Viktoria, Simon Jörg Peter, Wilding Karin, Keller Stephan, Scherer Reinhold
Designing for Engaging BCI Training: A Jigsaw Puzzle
Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play (CHI Play 2015), 2014
Konferenz
Brain-computer interface (BCI) technology translatesbrain activity to machine-intelligible patterns, thusserving as input “device” to computers. BCI traininggames make the process of acquiring training data forthe machine learning more engaging for the users. Inthis work, we discuss the design space for BCI traininggames based on existing literature, and a traininggame in form of a Jigsaw Puzzle. The game wastrialled with four cerebral palsy patients. All patientswere very acceptant of the involved technology, which,we argue, relates back to the concept of BCI traininggames plus the adaptations we made. On the otherhand, the data quality was unsatisfactory. Hence, infuture work both concept and implementation need tobe finetuned to achieve a balance between useracceptance and data quality.
2013
Silva Nelson
The 6th Sense of an Air Traffic Controller Increasing Fault Tolerance of Human Machine Interfaces
SID 2013, 2013
Konferenz
2013
Tatzgern Markus, Grasset Raphael, Veas Eduardo Enrique, Kalkofen Denis, Schmalstieg Dieter
Exploring Distant Objects with Augmented Reality.
EGVE/EuroVR, 2013
Konferenz
Augmented reality (AR) enables users to retrieve additional information about the real world objects and locations.Exploring such location-based information in AR requires physical movement to different viewpoints, which maybe tiring and even infeasible when viewpoints are out of reach. In this paper, we present object-centric explorationtechniques for handheld AR that allow users to access information freely using a virtual copy metaphor to explorelarge real world objects. We evaluated our interfaces in controlled conditions and collected first experiences in areal world pilot study. Based on our findings, we put forward design recommendations that should be consideredby future generations of location-based AR browsers, 3D tourist guides, or in situated urban plannin
2013
Kalkofen Denis, Veas Eduardo Enrique, Zollmann Stefanie, Steinberger Markus, Schmalstieg Dieter
Adaptive ghosted views for augmented reality
Mixed and Augmented Reality (ISMAR), 2013 IEEE International Symposium on, IEEE, 2013
Konferenz
In Augmented Reality (AR), ghosted views allow a viewer to ex-plore hidden structure within the real-world environment. A bodyof previous work has explored which features are suitable to sup-port the structural interplay between occluding and occluded ele-ments. However, the dynamics of AR environments pose seriouschallenges to the presentation of ghosted views. While a modelof the real world may help determine distinctive structural features,changes in appearance or illumination detriment the composition ofoccluding and occluded structure. In this paper, we present an ap-proach that considers the information value of the scene before andafter generating the ghosted view. Hereby, a contrast adjustment ofpreserved occluding features is calculated, which adaptively variestheir visual saliency within the ghosted view visualization. This al-lows us to not only preserve important features, but to also supporttheir prominence after revealing occluded structure, thus achieving a positive effect on the perception of ghosted views.
2004
Wissen richtig managen – Methoden, Technologien und Erfahrungen
Zeitschrift TELEMATIK 04/2004, Schwerpunktheft, Graz, Austria, 2004
Konferenz
2004
Wie Wissen wirklich wirkt
Austria Innovativ, Ausgabe 1/2004, 2004
Konferenz
2004
Lindstaedt Stefanie , Koller S., Krämer T.
Eine Wissensinfrastruktur für Projektrisikomanagement - Identifikation und Management von Wissensrisiken
Tagungsband zur KnowTech 2004, 6. Konferenz zum Einsatz von Knowledge Management in Wirtschaft und Verwaltung, Gronau, N., Petkoff, B., Schildhauer, T., München,Germany, 2004
Konferenz
2004
Trendanalyse Wissensmanagement: Orientierungshilfen für Praxisprojekte von morgen
wissensmanagement - Das Magazin für Führungskräfte, 2004
Konferenz
2004
Special Issue 'Beyond State-of-the Art Knowledge Management'
Journal of Universal Computer Science, Bd. 10, Nr. 6, 2004
Konferenz
2004
Proceedings of the I-KNOW '04, 4th International Conference on Knowledge Management
J.UCS, Springer, Graz, Austria, 2004
Konferenz
2004
Farmer J., Lindstaedt Stefanie , Droschl G., Luttenberger P.
AD-HOC - Work-integrated Technology-supported Teaching and Learning
Proceedings of the 5th International Conference on Organizational Knowledge, Learning, and Capabilities, Innsbruck, 2004
Konferenz
2004
Das Know-Center und die neue Fakultät für Informatik an der TU Graz
Zeitschrift TELEMATIK 04/2004, Schwerpunktheft, Graz, Austria, 2004
Konferenz
2004
Timbrell G., Koller S., Lindstaedt Stefanie
A Knowledge Infrastructure Hierarchy Model for Call Centre Processes
Proceedings of the I-KNOW `04, 4th International Conference on Knowledge Management, Graz, Austria, 2004
Konferenz
2003
Kompetenzmanagement als formalisierbare Abbildung von Wissen und Handeln für das Personalwesen
Wissensmanagement – psychologische Perspektiven und Redefinitionen. Wirtschaftspsychologie Themenheft, 5, 3, Wehner, T., Dick, M., Pabst Science Publishers, 2003
Konferenz
2003
Themenkarten als Ergänzung zu hierarchiebasierter Navigation und Suche in Wissensmanagementsystemen
4. Oldenburger Forum Wissensmanagement, Oldenburg, Germany, 2003
Konferenz
2003
Special Issue "Hot Spots in Knowledge Management"
Journal of Universal Computer Science, Bd. 6, Nr. 6, 2003
Konferenz
2003
Skills Management - An 'all-purpose' tool?
In Proceedings of I-Know 03 - Third International Conference on Knowledge Management, Springer, Graz, Austria, 2003
Konferenz
2003
Tochtermann K., Zirm K., Lindstaedt Stefanie
Einführung von Wissensmanagement im Umweltbundesamt Wien
Proceedings des 17. Symposiums Umweltinformatik, Cottbus, Germany, 2003
Konferenz
2003
Proceedings of the I-KNOW '03, 3rd International Conference on Knowledge Management
Springer Verlag, Graz, Austria, 2003
Konferenz
2003
Command Style and Team Performance in Dynamic Decision-Making Tasks
In: S. L. Schneider and J. Shanteau (Eds.). Emerging Perspectives on Judgment and Decision Research, S. L. Schneider & J. Shanteau, Cambridge University Press, 2003
Konferenz
2003
Identifying Employee Competencies in Dynamic Work Domains: Methodological Considerations and a Case Study
Journal of Universal Computer Science, Special Issue, Springer Verlag, 2003
Konferenz
2003
Lindstaedt Stefanie , Farmer J., Hrastnik J., Rollett H., Strohmaier M.
Integration von Prozess- und Wissensmanagement-orientierten Designstrategien zur Erstellung benutzerfreundlicher Unternehmensportale
Wissensmanagement 2003 - 2. Konferenz Professionelles Wissensmanagement, Luzern/Schweiz, 2.-4.4.2003, Reimer, U., Abecker, A., Staab, S., Stumme, G., Köllen Druck Verlag GmbH, Luzern, Schweiz, 2003
Konferenz
2002
Intelligente Multimedia Bibliothek
to be Published in TELEMATIK 03/2002, Telematik Ingenieur Verband, Graz, Austria, 2002
Konferenz
2002
Technology-Oriented Knowledge Management
Special Issue Journal of Universal Computer Science, Springer Verlag 2002, 2002
Konferenz
2002
XML and MPEG-7 for Interactive Annotation and Retrieval Using Semantic Meta-data
IICM, JUCS online, Graz, Austria, 2002
Konferenz
2002
Wissensretrieval und Wissensvisualisierung
to be Published in TELEMATIK 03/2002, Telematik Ingenieur Verband, Graz, Austria, 2002
Konferenz
2002
Lindstaedt Stefanie
Wissensmanagement und Unternehmensgedächtnisse
TELEMATIK 03/2002, Telematik Ingenieur Verband, Graz, Austria, 2002
Konferenz
2002
Wissensbilanzen als ein Instrument zum Management Intellektuellen Kapitals
TELEMATIK 03/2002, Telematik Ingenieur Verband, Graz, Austria, 2002
Konferenz
2002
Environmental Informatics
Encyclopedia of Environmetrics, El-Shaarawi, A., Piegorsch, W., John Wiley & Sons, 2002
Konferenz
2002
Achieving benefits through integrating eLearning and Strategic Knowledge Management
International Workshop on Interactive Computer Aided Learning, Interactive Computer Aided Learning (ICL), Villach, Austria, 2002
Konferenz
2002
WebRat
to be Published in TELEMATIK 03/2002, Telematik Ingenieur Verband, Graz, Austria, 2002
Konferenz
2002
Adaptive Competence Testing in eLearning
European Journal of Open and Distance Learning, 2002
Konferenz
2002
Environmental Information Systems
Encyclopedia of Environmetrics, El-Shaarawi, A., Piegorsch, W., John Wiley & Sons, 2002
Konferenz
2002
The InfoSky Visual Explorer: Exploiting Hierarchical Structure and Document Similarities
Information Visulization, Palgrave Journals, London, England, 2002
Konferenz
2002
XML and MPEG-7 for Interactive Annotation and Retrieval Using Semantic Metadata
Journal of Computer Science (www.jucs.org), Springer-Verlag, 2002
Konferenz
2002
Special Issue: Hypermedia - State-of-the-Art 2002
Journal of Universal Computer Science (J.UCS), Springer, Graz Austria, 2002
Konferenz
2002
Lindstaedt Stefanie , Strohmaier M.
Introduction of Knowledge Management (KMIntro)
TELEMATIK 03/2002, Telematik Ingenieur Verband, Graz, Austria, 2002
Konferenz
2002
Lindstaedt Stefanie
Aufgaben-Orientiertes Kooperatives Lernen
Informatik bewegt: Informatik 2002 - 32. Jahrestagung der Gesellschaft für Informatik e.v. (GI), Gesellschaft für Informatik e.v. (GI), Dortmund, Germany, 2002
Konferenz
2002
Implikationen von Praxiserfahrungen für die IT-Unterstützung von Wissensmanagement
In Manfred Bornemann and Martin Sammer, editors, Anwendungsorientiertes Wissensmanagement: Ansätze und Fallstudien aus der betrieblichen und der universitären Praxis, Gabler Edition Wissenschaft, Deutscher Universitäts-Verlag, Wiesbaden, Germany, 2002
Konferenz
2002
InfoSky: Eine neue Technologie zur Erforschung großer, hierarchischer Wissensräume
KnowTech 2002, 4. Konferenz zum Einsatz von Knowledge Management in Wirtschaft und Verwaltung (www.knowtech2002.de), München,Germany, 2002
Konferenz
2002
Proceedings of I-KNOW 02, 2nd International Conference on Knowledge Management
Springer Verlag, Graz, Austria, 2002
Konferenz
2002
People-Oriented Knowledge Management
Special Issue Journal of Universal Computer Science, Springer Verlag, Vol. 8 , No. 5, 2002, 2002
Konferenz
2002
Lindstaedt Stefanie , Fischer M.
People Locator und Recommender Systeme (recommend)
TELEMATIK 03/2002, Telematik Ingenieur Verband, Graz, Austria, 2002
Konferenz
2002
Westbomke J., Kussmaul A., Raiber A., Haase M., Hicks D., Lindstaedt Stefanie
Knowledge Management within Digital Libraries by means of Personalization
Journal of Universal Computer Science (J.UCS), Proceedings of I-KNOW02, 2nd International Conference on Knowledge Management, July 11-12, 2002, Graz, Austria, 2002
Konferenz
2002
Knowledge Discovery Space
to be Published in TELEMATIK 03/2002, Telematik Ingenieur Verband, Graz, Austria, 2002
Konferenz
2002
Lindstaedt Stefanie , Strohmaier M.
KMap: Providing Orientation for Practitioners when Introducing Knowledge Management
Proceedings of the 4th International Conference on Practical Aspects of Knowledge Management (PAKM 2002), Springer, Vienna Austria, 2002
Konferenz
2001
Requirements Analysis and Evaluation of Streaming Technologies with Respect to Interaction in Multimedia E-Learning Courses
WebNet 2001, 2001
Konferenz
2001
Wissensmanagement, Management intellektuellen Kapitals und eLearning: Alleinstellungsmerkmale und Zusammenhänge
In Kurt Bauknecht, Wilfried Brauer, and Thomas Mück, editors, Informatik 2001, Tagungsband der GI/OCG-Jahrestagung, Österreichische Computer Gessellschaft, Wien, Austria, 2001
Konferenz
2000
Lindstaedt Stefanie
Globales Netz - Eine Revolution fuer alle Geschaeftsprozesse
Wirtschaftsforum, 2000
Konferenz
