Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2018

Bassa Akim, Kröll Mark, Kern Roman

GerIE - An Open InformationExtraction System for the German Language

Journal of Universal Computer Science, 2018

Journal
Open Information Extraction (OIE) is the task of extracting relations fromtext without the need of domain speci c training data. Currently, most of the researchon OIE is devoted to the English language, but little or no research has been conductedon other languages including German. We tackled this problem and present GerIE, anOIE parser for the German language. Therefore we started by surveying the availableliterature on OIE with a focus on concepts, which may also apply to the Germanlanguage. Our system is built upon the output of a dependency parser, on which anumber of hand crafted rules are executed. For the evaluation we created two dedicateddatasets, one derived from news articles and one based on texts from an encyclopedia.Our system achieves F-measures of up to 0.89 for sentences that have been correctlypreprocessed.
2018

Duricic Tomislav, Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalenc

RecSys 2018, ACM, Vancouver, Canada, 2018

Konferenz
User-based Collaborative Filtering (CF) is one of the most popularapproaches to create recommender systems. Œis approach is basedon €nding the most relevant k users from whose rating history wecan extract items to recommend. CF, however, su‚ers from datasparsity and the cold-start problem since users o‰en rate only asmall fraction of available items. One solution is to incorporateadditional information into the recommendation process such asexplicit trust scores that are assigned by users to others or implicittrust relationships that result from social connections betweenusers. Such relationships typically form a very sparse trust network,which can be utilized to generate recommendations for users basedon people they trust. In our work, we explore the use of a measurefrom network science, i.e. regular equivalence, applied to a trustnetwork to generate a similarity matrix that is used to select thek-nearest neighbors for recommending items. We evaluate ourapproach on Epinions and we €nd that we can outperform relatedmethods for tackling cold-start users in terms of recommendationaccuracy
2018

Fruhwirth Michael, Breitfuß Gert, Pammer-Schindler Viktoria

Exploring challenges in data-driven business model innovation from Austrian enterprises

Proceedings in XXIX ISPIM Innovation Conference, Stockholm, 2018

Konferenz
The increasing amount of generated data and advances in technology and data analytics and are enablers and drivers for new business models with data as a key resource. Currently established organisations struggle with identifying the value and benefits of data and have a lack of know-how, how to develop new products and services based on data. There is very little research that is narrowly focused on data-driven business model innovation in established organisations. The aim of this research is to investigate existing activities within Austrians enterprises with regard to exploring data-driven business models and challenges encountered in this endeavour. The outcome of the research in progress paper are categories of challenges related to organisation, business and technology, established organisations in Austria face during data-driven business model innovation
2018

Ross-Hellauer Anthony, Kowald Dominik, Lex Elisabeth

Recommender Systems as Enabling Technology to Interlink Scholarly Information

Scholarly Communication Workshop co-located with WWW'2018, Lyon, 2018

Konferenz
2018

Kowald Dominik

Modeling Activation Processes in HumanMemory to Improve Tag Recommendation

SIGIR Newsletter, ACM, 2018

Journal
Social tagging systems enable users to collaboratively assign freely chosen keywords (i.e.,tags) to resources (e.g., Web links). In order to support users in nding descriptive tags, tagrecommendation algorithms have been proposed. One issue of current state-of-the-art tagrecommendation algorithms is that they are often designed in a purely data-driven way andthus, lack a thorough understanding of the cognitive processes that play a role when peopleassign tags to resources. A prominent example is the activation equation of the cognitivearchitecture ACT-R, which formalizes activation processes in human memory to determineif a speci c memory unit (e.g., a word or tag) will be needed in a speci c context. It is theaim of this thesis to investigate if a cognitive-inspired approach, which models activationprocesses in human memory, can improve tag recommendations.For this, the relation between activation processes in human memory and usage prac-tices of tags is studied, which reveals that (i) past usage frequency, (ii) recency, and (iii)semantic context cues are important factors when people reuse tags. Based on this, acognitive-inspired tag recommendation approach termed BLLAC+MPr is developed based onthe activation equation of ACT-R. An extensive evaluation using six real-world folksonomydatasets shows that BLLAC+MPr outperforms current state-of-the-art tag recommendationalgorithms with respect to various evaluation metrics. Finally, BLLAC+MPr is utilized forhashtag recommendations in Twitter to demonstrate its generalizability in related areas oftag-based recommender systems. The ndings of this thesis demonstrate that activationprocesses in human memory can be utilized to improve not only social tag recommendationsbut also hashtag recommendations. This opens up a number of possible research strands forfuture work, such as the design of cognitive-inspired resource recommender systems
2018

Kaiser René

Towards Applying the Virtual Director Concept to 360 Degree Video Content

Adjunct Publication of the 2018 ACM International Conference on Interactive Experiences for TV and Online Video, Figshare, Seoul, South Korea, 2018

Konferenz
This paper aims to contribute to the discussion on 360° video storytelling. It describes the 'Virtual Director' concept, an enabling technology that was developed to personalize video presentation in applications where multiple live streams are available at the same time. Users are supported in dynamically changing viewpoints, as the Virtual Director essentially automates the tasks of a human director. As research prototypes on a proof-of-concept maturity level, this approach has been evaluated for personalized live event broadcast, group video communication and distributed theatre performances. While on the capture side a 180° high-resolution panoramic video feed has been used in one of these application scenarios, so far, only traditional 2D video screen were investigated for playout. The research question this paper aims to contribute to is how technology in general, and an adaptation of the Virtual Director concept in particular, could assist users in their needs when consuming 360° content, both live and recorded. In contexts when users do not want to enjoy the freedom to look into any direction, or when content creators want them to look in a certain direction, how could the interaction with and intervention of a Virtual Director be applied from a storytelling point of view?
2018

Kaiser René

Opportunities and Challenges of Video Content and Video Technology in Smart Factories

Patrick Jost, Guido Kempter, Pabst Science Publishers, Dornbirn, AT, 2018

Konferenz
Production companies typically have not utilized video content and video technology in factory environ-ments to a significant extent in the past. However, the current Industry 4.0 movement inspires companies to reconsider production processes and job qualifications for their shop floor workforce. Infrastructure and machines get connected to central manufacturing execution systems in digitization and datafication efforts. In the realm of this fourth industrial revolution, companies are encouraged to revisit their strategy regarding video-based applications as well. This paper discusses the current situation and selected aspects of opportu-nities and challenges of video technology that might enable added value in such environments.
2018

Neuhold Robert, Gursch Heimo, Kern Roman, Cik Michael

Driver's Dashboard - Using Social Media Data as additional Information for Motorway Operators

Proceedings of the ITS World Congress 2018, Intelligent Transportation Society, Copenhagen, Denmark, 2018

Konferenz
Data collection on motorways for traffic management operations is traditionally based on local measurements points and camera monitoring systems. This work looks into social media as additional data source for the Austrian motorway operator ASFINAG. A system called Driver´s Dashboard was developed collecting incident descriptions from Facebook and RSS feeds, filtering relevant messages, and fusing them with traffic data. All collected texts were analysed for concepts describing road situations linking the texts from the web and social media with traffic messages and traffic data. Driver´s Dashboard was designed to examine the potential of social media for traffic monitoring with respect to Austrian characteristics in social media use and road transportation with only very few messages are available compared to other studies. Of 3,586 messages collected within a five-week period only 7.1% were automatically annotated as traffic relevant. Further, the traffic relevant messages for the motorway operator were analysed more in detail to identify correlations between message text and traffic data characteristics. A correlation of message text and traffic data was found in nine of eleven messages by comparing the speed profiles and traffic state data with the message text.
2018

Pammer-Schindler Viktoria, Thalmann Stefan, Fessl Angela, Füssel Julia

Virtualizing face-2-face trainings for training senior professionals: A Comparative Case Study on Financial Auditor

AC, London, 2018

Konferenz
Traditionally, professional learning for senior professionalsis organized around face-2-face trainings. Virtual trainingsseem to offer an opportunity to reduce costs related to traveland travel time. In this paper we present a comparative casestudy that investigates the differences between traditionalface-2-face trainings in physical reality, and virtualtrainings via WebEx. Our goal is to identify how the way ofcommunication impacts interaction between trainees,between trainees and trainers, and how it impactsinterruptions. We present qualitative results fromobservations and interviews of three cases in differentsetups (traditional classroom, web-based with allparticipants co-located, web-based with all participants atdifferent locations) and with overall 25 training participantsand three trainers. The study is set within one of the BigFour global auditing companies, with advanced seniorauditors as learning cohort
2018

di Sciascio Maria Cecilia, Brusilovsky Peter, Veas Eduardo Enrique

A Study on User-Controllable Social Exploratory Search

ACM Conference on Intelligent User Interfaces IUI, ACM, 2018

Konferenz
Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error and on-the-fly selections, gathers and organizes information (resources). A range of innovative interfaces with increased user control have been developed to support exploratory search process. In this work we present our attempt to increase the power of exploratory search interfaces by using ideas of social search, i.e., leveraging information left by past users of information systems. Social search technologies are highly popular nowadays, especially for improving ranking. However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking. This paper presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to (i) express information needs by selecting keywords and (ii) to express preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes search transparency through color-coded stacked bars and rich tooltips. In an online study investigating system accuracy and subjective aspects with a structural model we found that, when users actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only tool and users were more satisfied.
2018

Silva Nelson, Schreck Tobias, Veas Eduardo Enrique, Sabol Vedran, Eggeling Eva, Fellner Dieter W.

Leveraging Eye-gaze and Time-series Features to Predict User Interests and Build a Recommendation Model for Visual Analysis

ACM Symposium on Eye Tracking Research and Applications ETRA, ACM, 2018

Konferenz
We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.
2018

Cicchinelli Analia, Veas Eduardo Enrique, Pardo Abelardo, Pammer-Schindler Viktoria, Fessl Angela, Barreiros Carla, Lindstaedt Stefanie

Finding traces of self-regulated learning in activity streams

ACM Conference on Learning Analytics and Knowledge, LAK , ACM, ACM, 2018

Konferenz
This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.
2018

Dennerlein_Bildungskarenz Sebastian, Kowald Dominik, Lex Elisabeth, Ley Tobias, Pammer-Schindler Viktoria

Simulation-based Co-Creation of Algorithm

Workshop on Co-Creation in the Design, Development and Implementation of Technology-Enhanced Learning (CCTEL'2018, Springer, Leeds, England, 2018

Konferenz
Co-Creation methods for interactive computer systems design are by now widely accepted as part of the methodological repertoire in any software development process. As the communityis becoming more and more aware of the factthat software is driven by complex, artificially intelligent algorithms, the question arises what “co-creation of algorithms” in the sense of users ex-plicitly shaping the parameters of algorithms during co-creation, could mean, and how it would work. They are not tangible like featuresin a tool and desired effects are harder to be explained or understood. Therefore, we propose an it-erative simulation-based Co-Design approach that allows to Co-Create Algo-rithms together with the domain professionals by making their assumptions and effects observable. The proposal is a methodological idea for discussion within the EC-TEL community, yet to be applied in a research practice
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

Dennerlein_Bildungskarenz Sebastian, Mayr Melanie

med360 - Poster @ MEI 2018

2018

Konferenz
The number of scientific publications has rapidly increased over the last decades and still shows asteady growth. In addition, medical scientists and practitioners often have to deal with multiplesystems and databases for literature search. This results in information overload and professionalshardly being able to keep up to date with the latest scientific publications in their limited free time.We, therefore, founded a design team called ‘med360’ and developed a taylor-made web tool toprovide proactively literature in accordance to the interests of the user. Approximately five to eightpersons have been involved in the collaborative design of the med360-tool over a period of sixmonths from paper to software prototyping. For this purpose, workshops and interviews wereconducted with relevant stakeholders such as the domain professionals, developers and researchers.Instead of crawling through multiple systems like research gate, google scholar or publisher websites,our study indicates, that HC professionals require an easy-to-use tool. It must be in line with theirbusy work life and give access to literature in one place in reasonable extent. In consequence,med360 allows for a straight forward definition of the search scope by entering a few keywords andproviding a forecast of the expected number of papers per week. The identified literature ispresented in the well-adopted mailbox format on mobiles,tablets and personal computers frompredefined literature systems and databases.This way, med360 helps researcher to better cope with their workload: “With med360, I feel like Ican survive my work day”.
2018

Cuder Gerald, Baumgartner Christian

A data mining strategy for the search and classification of gene expression data in cancer

ÖGBMT - Jahrestagung 201, ÖGBMT - Österreichische Gesellschaft für Biomedizinische Techni, Hall in Tirol, 2018

Konferenz
Cancer is one of the most uprising diseases in our modern society and is defined by an uncontrolled growth of tissue. This growth is caused by mutation on the cellular level. In this thesis, a data-mining workflow was developed to find these responsible genes among thousands of irrelevant ones in three microarray datasets of different cancer types by applying machine learning methods such as classification and gene selection. In this work, four state-of-the-art selection algorithms are compared with a more sophisticated method, termed Stacked-Feature Ranking (SFR), further increasing the discriminatory ability in gene selection.
2018

Kowald Dominik, Lacic Emanuel, Theiler Dieter, Lex Elisabeth

AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments.

CIKM 2018 Workshop Proceedings, CEUR, Turin, Italy, 2018

Konferenz
In this paper, we present preliminary results of AFEL-REC, a rec-ommender system for social learning environments. AFEL-RECis build upon a scalable so‰ware architecture to provide recom-mendations of learning resources in near real-time. Furthermore,AFEL-REC can cope with any kind of data that is present in sociallearning environments such as resource metadata, user interactionsor social tags. We provide a preliminary evaluation of three rec-ommendation use cases implemented in AFEL-REC and we €ndthat utilizing social data in form of tags is helpful for not only im-proving recommendation accuracy but also coverage. ‘is papershould be valuable for both researchers and practitioners inter-ested in providing resource recommendations in social learningenvironments
2018

Lacic Emanuel, Kowald Dominik, Lex Elisabeth

Neighborhood Troubles: On the Value of User Pre-Filtering ToSpeed Up and Enhance Recommendation

CIKM 2018 Workshop Proceedings, Turin, Italy, 2018

Konferenz
In this paper, we present work-in-progress on applying user pre-filtering to speed up and enhance recommendations based on Collab-orative Filtering. We propose to pre-filter users in order to extracta smaller set of candidate neighbors, who exhibit a high numberof overlapping entities and to compute the final user similaritiesbased on this set. To realize this, we exploit features of the high-performance search engine Apache Solr and integrate them into ascalable recommender system. We have evaluated our approachon a dataset gathered from Foursquare and our evaluation resultssuggest that our proposed user pre-filtering step can help to achieveboth a better runtime performance as well as an increase in overallrecommendation accuracy
2018

Gursch Heimo, Silva Nelson, Reiterer Bernhard , Paletta Lucas , Bernauer Patrick, Fuchs Martin, Veas Eduardo Enrique, Kern Roman

Flexible Scheduling for Human Robot Collaboration in Intralogistics Teams

Mensch und Computer 2018, Gesellschaft für Informatik e.V., Gesellschaft für Informatik e.V., Bonn, Germany, 2018

Konferenz
The project Flexible Intralogistics for Future Factories (FlexIFF) investigates human-robot collaboration in intralogistics teams in the manufacturing industry, which form a cyber-physical system consisting of human workers, mobile manipulators, manufacturing machinery, and manufacturing information systems. The workers use Virtual Reality (VR) and Augmented Reality (AR) devices to interact with the robots and machinery. The right information at the right time is key for making this collaboration successful. Hence, task scheduling for mobile manipulators and human workers must be closely linked with the enterprise’s information systems, offering all actors on the shop floor a common view of the current manufacturing status. FlexIFF will provide useful, well-tested, and sophisticated solutions for cyberphysicals systems in intralogistics, with humans and robots making the most of their strengths, working collaboratively and helping each other.
2018

Lovric Mario, Banic Ivana, Cuder Gerald, Kern Roman, Turkalj Mirjana

Phenotype-driven prediction of treatment outcomes in pediatric asthma patient

Current opinion in Allergy and Clinical Immunolog, Elsevier, 2018

Journal
2018

Kowald Dominik, Lex Elisabeth

Studying Confirmation Bias in Hashtag Usage on Twitte

European Computation Social Sciences Symposium, Cologne, Germany, 2018

Konferenz
The micro-blogging platform Twitter allows its nearly 320 million monthly active users to build a network of follower connections to other Twitter users (i.e., followees) in order to subscribe to content posted by these users. With this feature, Twitter has become one of the most popular social networks on the Web and was also the first platform that offered the concept of hashtags. Hashtags are freely-chosen keywords, which start with the hash character, to annotate, categorize and contextualize Twitter posts (i.e., tweets).Although hashtags are widely accepted and used by the Twitter community, the heavy reuse of hashtags that are popular in the personal Twitter networks (i.e., own hashtags and hashtags used by followees) can lead to filter bubble effects and thus, to situations, in which only content associated with these hashtags are presented to the user. These filter bubble effects are also highly associated with the concept of confirmation bias, which is the tendency to favor and reuse information that confirms personal preferences. One example would be a Twitter user who is interested in political tweets of US president Donald Trump. Depending on the hashtags used, the user could either be stuck in a pro-Trump (e.g., #MAGA) or contra-Trump (e.g., #fakepresident) filter bubble. Therefore, the goal of this paper is to study confirmation bias and filter bubble effects in hashtag usage on Twitter by treating the reuse of hashtags as a phenomenon that fosters confirmation bias.
2018

Lex Elisabeth, Wagner Mario, Kowald Dominik

Mitigating Confirmation Bias on Twitter by Recommending Opposing View

European Computational Social Sciences Symposium, 2018

Konferenz
In this work, we propose a content-based recommendation approach to increase exposure to opposing beliefs and opinions. Our aim is to help provide users with more diverse viewpoints on issues, which are discussed in partisan groups from different perspectives. Since due to the backfire effect, people's original beliefs tend to strengthen when challenged with counter evidence, we need to expose them to opposing viewpoints at the right time. The preliminary work presented here describes our first step into this direction. As illustrative showcase, we take the political debate on Twitter around the presidency of Donald Trump.
2018

Luzhnica Granit, Veas Eduardo Enrique

Skin Reading Meets Speech Recognition and Object Recognition for Sensory Substitution

ACM Ubicomp, ACM, 2018

Konferenz
Sensory substitution has been a research subject for decades, and yet its applicability outside of the research is very limited. Thus creating scepticism among researchers that a full sensory substitution is not even possible [8]. In this paper, we do not substitute the entire perceptual channel. Instead, we follow a different approach which reduces the captured information drastically. We present concepts and implementation of two mobile applications which capture the user's environment, describe it in the form of text and then convey its textual description to the user through a vibrotactile wearable display. The applications target users with hearing and vision impairments.
2018

Luzhnica Granit, Veas Eduardo Enrique, Caitlyn Seim

Passive Haptic Learning for Vibrotactile Skin Reading

ACM International Symposium on Wearable Computing ISWC, ACM, 2018

Konferenz
This paper investigates the effects of using passive haptic learning to train the skill of comprehending text from vibrotactile patterns. The method of transmitting messages, skin-reading, is effective at conveying rich information but its active training method requires full user attention, is demanding, time-consuming, and tedious. Passive haptic learning offers the possibility to learn in the background while performing another primary task. We present a study investigating the use of passive haptic learning to train for skin-reading.
2018

Cuder Gerald, Breitfuß Gert, Kern Roman

E-Mobility and Big Data - Data Utilization of Charging Operations

Proceedings of XXIX ISPIM Conference, Stockholm, 2018

Konferenz
Electric vehicles have enjoyed a substantial growth in recent years. One essential part to ensure their success in the future is a well-developed and easy-to-use charging infrastructure. Since charging stations generate a lot of (big) data, gaining useful information out of this data can help to push the transition to E-Mobility. In a joint research project, the Know-Center, together with the has.to.be GmbH applied data analytics methods and visualization technologies on the provided data sets. One objective of the research project is, to provide a consumption forecast based on the historical consumption data. Based on this information, the operators of charging stations are able to optimize the energy supply. Additionally, the infrastructure data were analysed with regard to "predictive maintenance", aiming to optimize the availability of the charging stations. Furthermore, advanced prediction algorithms were applied to provide services to the end user regarding availability of charging stations.
2018

Lacic Emanuel, Traub Matthias, Duricic Tomislav, Haslauer Eva, Lex Elisabeth

Gone in 30 Days! Predictions for Car Import Planning

it - Information Technology, De Gruyter Oldenbourg, 2018

Journal
A challenge for importers in the automobile industry is adjusting to rapidly changing market demands. In this work, we describe a practical study of car import planning based on the monthly car registrations in Austria. We model the task as a data driven forecasting problem and we implement four different prediction approaches. One utilizes a seasonal ARIMA model, while the other is based on LSTM-RNN and both compared to a linear and seasonal baselines. In our experiments, we evaluate the 33 different brands by predicting the number of registrations for the next month and for the year to come.
2018

Bassa Kevin, Kern Roman, Kröll Mark

On-the-fly Data Set Generation for Single Fact Validation

SAC 2018, 2018

Konferenz
On the web, massive amounts of information are available, includingwrong (or conflicting) information. This spreading of erroneous or fake contentsmakes it hard for users to distinguish between what is true and what is not. Factfinding algorithms represent a means to validate information. Yet, these algorithmsrequire an already existing, structured data set to validate a single fact; anad-hoc validation is thus not supported making them impractical for usage in realworld applications. This work presents an approach to generate these data setson-the-fly. For three facts, we generate respective data sets and apply six state-ofthe-art fact finding algorithms for evaluation purposes. In addition, our approachcontributes to comparing fact finding algorithms in a more objective way.
2018

Santos Tiago, Walk Simon, Kern Roman, Strohmaier M., Helic Denis

Activity in Questions & Answers Websites

ACM Transactions on Social Computing, 2018

Journal
Millions of users on the Internet discuss a variety of topics on Question and Answer (Q&A) instances. However, not all instances and topics receive the same amount of attention, as some thrive and achieve self-sustaining levels of activity while others fail to attract users and either never grow beyond being a small niche community or become inactive. Hence, it is imperative to not only better understand but also to distill deciding factors and rules that define and govern sustainable Q&A instances. We aim to empower community managers with quantitative methods for them to better understand, control and foster their communities, and thus contribute to making the Web a more efficient place to exchange information. To that end, we extract, model and cluster user activity-based time series from 50 randomly selected Q&A instances from the StackExchange network to characterize user behavior. We find four distinct types of user activity temporal patterns, which vary primarily according to the users' activity frequency. Finally, by breaking down total activity in our 50 Q&A instances by the previously identified user activity profiles, we classify those 50 Q&A instances into three different activity profiles. Our categorization of Q&A instances aligns with the stage of development and maturity of the underlying communities, which can potentially help operators of such instances not only to quantitatively assess status and progress, but also allow them to optimize community building efforts
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

Arslanovic Jasmina, Lovric Mario, Kern Roman

EATING DISORDERS IN SYNCHRONIZED SWIMMING

2018

Journal
The aim of the present study was to identify eating disorders in synchronized swimming and the level of distortion of the body image of synchronized swimming athletes. Synchronized swimming is sport in which modality is considered of risk for development of eating disorder. It is a Olympic sport where synchronized swimmers are competing in the range of age 13-15 years old and that ages are critical for every young woman (puberty). Also, the beauty of movement is associated to low body mass and judges include thinness in their final score. Eating disorders include anorexia nervosa, bulimia nervosa, binge eating symptoms, and other specified (or non-specified) feeding or eating disorders which are presenting serious issue. Twenty synchronized swimmers age 13-16 years old was studied and the group of twenty female water polo players was used for comparison with athletes. To test the presence of some symptoms of the eating disorders was used The Eating Attitudes Test: “Eat26” (Garner and Garfunkel, 1979). Comparison of results have shown that statistically significant difference exist between synchronized swimmers and female water polo players in the image of dissatisfaction, with pathological control of body weight of synchronized swimmers. Almost every sport demands certain nutrition and most of them certain weight, but that should be achieved with the help of expert team. It is possible that synchronized swimmers are skinny and strong, but only with certain nutrition which is individual for every swimmer.
2018

Babić Sanja, Barišić Josip, Stipaničev Draženka, Repec Siniša, Lovric Mario, Malev Olga, Čož-Rakovac Rozalindra, Klobučar GIV

Assessment of river sediment toxicity: Combining empirical zebrafish embryotoxicity testing with in silico toxicity characterization

Science of the Total Environment, Elsevier, 2018

Journal
Quantitative chemical analyses of 428 organic contaminants (OCs) confirmed the presence of 313 OCs in the sediment extracts from river Sava, Croatia. Pharmaceuticals were present in higher concentration than pesticides thus confirming their increasing threat to freshwater ecosystems. Toxicity evaluation of the sediment extracts from four locations (Jesenice, Rugvica, Galdovo and Lukavec) using zebrafish embryotoxicity test (ZET) accompanied with semi-quantitative histopathological analyses exhibited good correlation with cumulative number and concentrations of OCs at investigated sites (10,048.6, 15,222.8, 1,247.6, and 9,130.5 ng/g respectively) and proved its role as a good indicator of toxic potential of complex contaminant mixtures. Toxicity prediction of sediment extracts and sediment was assessed using Toxic unit (TU) approach and PBT (persistence, bioaccumulation and toxicity) ranking. Also, prior-knowledge informed chemical-gene interaction models were generated and graph mining approaches used to identify OCs and genes most likely to be influential in these mixtures. Predicted toxicity of sediment extracts (TUext) for sampled locations was similar to the results obtained by ZET and associated histopathology resulting in Rugvica sediment as being the most toxic, followed by Jesenice, Lukavec and Galdovo. Sediment TU (TUsed) favoured OCs with low octanol-water partition coefficient like herbicide glyphosate and antibiotics ciprofloxacin and sulfamethazine thus indicating locations containing higher concentrations of these OCs (Galdovo and Rugvica) as most toxic. Results suggest that comprehensive in silico sediment toxicity predictions advocate providing equal attention to organic contaminants with either very low or very high log Kow
2018

Rexha Andi, Kröll Mark, Kern Roman

Multilingual Open Information Extraction using Parallel Corpora: The German Language Case

ACM Symposium on Applied Computing , Hisham M. Haddad, Roger L. Wainwright, ACM, 2018

Konferenz
In the past decade the research community has been continuously improving theextraction quality of Open Information Extraction systems. This was done mainlyfor the English language; other languages such as German or Spanish followedusing shallow or deep parsing information to derive language-specific patterns.More recent efforts focused on language agnostic approaches in an attempt tobecome less dependent on available tools and resources in that language. In linewith these efforts, we present a language agnostic approach which exploitsmanually aligned corpora as well as the solid performance of English OpenIEtools.
2018

Rexha Andi, Kröll Mark, Ziak Hermann, Kern Roman

Authorship Identification of Documents with High Content Similarity

Scientometrics, Wolfgang Glänzel, Springer Link, 2018

Journal
The goal of our work is inspired by the task of associating segments of text to their real authors. In this work, we focus on analyzing the way humans judge different writing styles. This analysis can help to better understand this process and to thus simulate/ mimic such behavior accordingly. Unlike the majority of the work done in this field (i.e., authorship attribution, plagiarism detection, etc.) which uses content features, we focus only on the stylometric, i.e. content-agnostic, characteristics of authors.Therefore, we conducted two pilot studies to determine, if humans can identify authorship among documents with high content similarity. The first was a quantitative experiment involving crowd-sourcing, while the second was a qualitative one executed by the authors of this paper.Both studies confirmed that this task is quite challenging.To gain a better understanding of how humans tackle such a problem, we conducted an exploratory data analysis on the results of the studies. In the first experiment, we compared the decisions against content features and stylometric features. While in the second, the evaluators described the process and the features on which their judgment was based. The findings of our detailed analysis could (i) help to improve algorithms such as automatic authorship attribution as well as plagiarism detection, (ii) assist forensic experts or linguists to create profiles of writers, (iii) support intelligence applications to analyze aggressive and threatening messages and (iv) help editor conformity by adhering to, for instance, journal specific writing style.
2018

Hojas Sebastian, Kröll Mark, Kern Roman

GerMeter - A Corpus for Measuring Text Reuse in the Austrian JournalisticDomain

Language Resources and Evaluation, Springer, 2018

Journal
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.
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

Lovric Mario

Molecular modeling of the quantitative structure activity relationship in Python – a tutorial (part I)

Journal of Chemists and Chemical Engineers, Croatian Society of Chemical Engineers, Zagreb, 2018

Journal
Today's data amount is significantly increasing. A strong buzzword in research nowadays is big data.Therefore the chemistry student has to be well prepared for the upcoming age where he does not only rule the laboratories but is a modeler and data scientist as well. This tutorial covers the very basics of molecular modeling and data handling with the use of Python and Jupyter Notebook. It is the first in a series aiming to cover the relevant topics in machine learning, QSAR and molecular modeling, as well as the basics of Python programming
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

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

Lovric Mario, Molero Perez Jose Manuel, Kern Roman

PySpark and RDKit: moving towards Big Data in QSAR

Molecular Informatics, Wiley, 2018

Journal
We present an implementation of the cheminformatics toolkit RDKit in a distributed computing environment, Apache Hadoop. Together with the Apache Spark analytics engine, wrapped in PySpark, resources from commodity scalable hardware can be used for cheminformatic calculations and query operations with basic knowledge in Python coding and understanding of the RDD abstraction. A comparison of the computing acceleration in the Hadoop cluster is presented in two computation tasks of querying substructures and calculating molecular descriptors, as well as the source code for the PySpark-RDKit implementation
2018

Hasani-Mavriqi Ilire, Kowald Dominik, Helic Denis, Lex Elisabeth

Consensus Dynamics in Online Collaboration Systems

Journal of Computational Social Networks , Ding-Zhu Du and My T. Thai, Springer Open, 2018

Journal
In this paper, we study the process of opinion dynamics and consensus building inonline collaboration systems, in which users interact with each other followingtheir common interests and their social pro les. Speci cally, we are interested inhow users similarity and their social status in the community, as well as theinterplay of those two factors inuence the process of consensus dynamics. Forour study, we simulate the di usion of opinions in collaboration systems using thewell-known Naming Game model, which we extend by incorporating aninteraction mechanism based on user similarity and user social status. Weconduct our experiments on collaborative datasets extracted from the Web. Our ndings reveal that when users are guided by their similarity to other users, theprocess of consensus building in online collaboration systems is delayed. Asuitable increase of inuence of user social status on their actions can in turnfacilitate this process. In summary, our results suggest that achieving an optimalconsensus building process in collaboration systems requires an appropriatebalance between those two factors.
2018

Lacic Emanuel, Kowald Dominik, Reiter-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

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

The International Water Association, 2018

Konferenz
2018

Santos Tiago, Walk Simon, Kern Roman, Helic Denis

Evolution of Collaborative Web Communities

ACM Hypertext 2018, 2018

Konferenz
Each day, millions of users visit collaborative Web communities, such as Wikipedia or StackExchange, either as large knowledge repositories or as up-to-date news sources.However, not all of Web communities are as successful as Wikipedia and, except for a few initial research results, our research community still knows only a little about what separates a successful from an unsuccessful community.Thus, we still need to (i) gain a better understanding of the underlying community evolution dynamics, and (ii) based on this understanding support activity and growth on such platforms.To that end, we distill temporal dynamics of community activity and thereby identify key factors leading to success or failure of communities.In particular, we study the differences between growing and declining communities by leveraging multivariate Hawkes processes. Furthermore, we compare communities hosted on different platforms such as StackExchange and Reddit, as well as topically diverse communities such as STEM and humanities.We find that all growing communities exhibit (i) an active core of power users reacting to the community as a whole, and (ii) numerous casual users strongly interacting with other casual users suggesting community openness towards less active users.Moreover, our results suggest that communities in the humanities are centered around power users, whereas in STEM communities activity is more evenly distributed among power and casual users.These results are of practical importance for community managers to quantitatively assess the status of their communities and guide them towards thriving community structures
2018

Lovric Mario

Chemical outlier dataset

Zenodo, 2018

The objects are numbered. The Y-variable are boiling points. Other features are structural features of molecules. In the outlier column the outliers are assigned with a value of 1.The data is derived from a published chemical dataset on boiling point measurements [1] and from public data [2]. Features were generated by means of the RDKit Python library [3]. The dataset was infused with known outliers (~5%) based on significant structural differences, i.e. polar and non-polar molecules. Cherqaoui D., Villemin D. Use of a Neural Network to determine the Boiling Point of Alkanes. J CHEM SOC FARADAY TRANS. 1994;90(1):97–102. https://pubchem.ncbi.nlm.nih.gov/ RDKit: Open-source cheminformatics; http://www.rdkit.org
2018

Koncar Philipp

Synthetic Dataset for Outlier Detection

Zenodo, 2018

This synthetically generated dataset can be used to evaluate outlier detection algorithms. It has 10 attributes and 1000 observations, of which 100 are labeled as outliers. Two-dimensional combinations of attributes form differently shaped clusters. Attribute 0 & Attribute 1: Two circular clusters Attribute 2 & Attribute 3: Two banana shaped clusters Attribute 4 & Attribute 5: Three point clouds Attribute 6 & Attribute 7: Two point clouds with variances Attribute 8 & Attribute 9: Three anisotropic shaped clusters. The "outlier" column states whether an observation is an outlier or not. Additionally, the .zip file contains 10 stratified randomized train test splits (70% train, 30% test).
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

d'Aquin Mathieu , 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

Pammer-Schindler Viktoria, Wertner Alfred, Stern Hermann, Weghofer Franz

Talk2Me – Sprachgesteuerte Kommissionierung mit off-the-shelf Hardware

Beiträge zum Usability Day XVI: Assistenztechnologien in der Arbeitswelt , Patrick Jost, Guido Kempter, PABST SCIENCE PUBLISHERS, 2018

Konferenz
Sprachsteuerung stellt ein potentiell sehr mächtiges Werkzeug dar und sollte rein von der Theorie (grundlegende Spracheingabe) her schon seit 20 Jahren einsetzbar sein. Sie ist in der Vergangenheit im industriellen Umfeld jedoch primär an nicht ausgereifter Hardware oder gar der Notwendigkeit einer firmenexternen aktiven Datenverbindung gescheitert. Bei Magna Steyr am Standort Graz wird die Kommissionierung bisher mit Hilfe von Scan-nern erledigt. Dieser Prozess ließe sich sehr effektiv durch eine durchgängige Sprachsteue-rung unterstützen, wenn diese einfach, zuverlässig sowie Compliance-konform umsetzbar wäre und weiterhin den Menschen als zentralen Mittelpunkt und Akteur (Stichwort Hu-man in the Loop) verstehen würde. Daher wurden bestehende Spracherkennungssysteme für mobile Plattformen sowie passende „off the shelf“ Hardware (Smartphones und Headsets) ausgewählt und prototypisch als Android Applikation („Talk2Me“) umgesetzt. Ziel war es, eine Aussage über die Einsetzbarkeit von sprachgesteuerten mobilen Anwen-dungen im industriellen Umfeld liefern zu können.Mit dem Open Source Speech Recognition Kit CMU Sphinx in Kombination mit speziell auf das Vokabular der abgebildeten Prozesse angepassten Wörterbüchern konnten wir eine sehr gute Erkennungsrate erreichen ohne das Sprachmodell individuell auf einzelne Mitar-beiterInnen trainieren zu müssen. Talk2Me zeigt innovativ, wie erprobte, kostengünstige und verfügbare Technologie (Smartphones und Spracherkennung als Eingabe sowie Sprachsynthese als Ausgabe) Ein-zug in unseren Arbeitsalltag haben kann.
2018

Lassnig Markus, Stabauer Petra, Breitfuß Gert, Mauthner Katrin

Geschäftsmodellinnovationen im Zeitalter von Digitalisierung und Industrie 4.0

HMD Praxis der Wirtschaftsinformatik Wirtschaftsinformatik, Stefan Meinhard, Karl-Michael Popp, Springer Fachmedien Wiesbaden, Wiesbaden, 2018

Journal
Zahlreiche Forschungsergebnisse im Bereich Geschäftsmodellinnovationenhaben gezeigt, dass über 90% aller Geschäftsmodelle der letzten50 Jahre aus einer Rekombination von bestehenden Konzepten entstanden sind.Grundsätzlich gilt das auch für digitale Geschäftsmodellinnovationen. Angesichtsder Breite potenzieller digitaler Geschäftsmodellinnovationen wollten die Autorenwissen, welche Modellmuster in der wirtschaftlichen Praxis welche Bedeutung haben.Deshalb wurde die digitale Transformation mit neuen Geschäftsmodellen ineiner empirischen Studie basierend auf qualitativen Interviews mit 68 Unternehmenuntersucht. Dabei wurden sieben geeignete Geschäftsmodellmuster identifiziert, bezüglichihres Disruptionspotenzials von evolutionär bis revolutionär klassifiziert undder Realisierungsgrad in den Unternehmen analysiert.Die stark komprimierte Conclusio lautet, dass das Thema Geschäftsmodellinnovationendurch Industrie 4.0 und digitale Transformation bei den Unternehmenangekommen ist. Es gibt jedoch sehr unterschiedliche Geschwindigkeiten in der Umsetzungund im Neuheitsgrad der Geschäftsmodellideen. Die schrittweise Weiterentwicklungvon Geschäftsmodellen (evolutionär) wird von den meisten Unternehmenbevorzugt, da hier die grundsätzliche Art und Weise des Leistungsangebots bestehenbleibt. Im Gegensatz dazu gibt es aber auch Unternehmen, die bereits radikale Änderungenvornehmen, die die gesamte Geschäftslogik betreffen. Entsprechend wird imvorliegenden Artikel ein Clustering von Geschäftsmodellinnovatoren vorgenommen – von Hesitator über Follower über Optimizer bis zu Leader in Geschäftsmodellinnovationen
2018

Rexha Andi, Dragoni Mauro , Federici Marco

An Unsupervised Aspect Extraction Strategy For Monitoring Real-Time Reviews Stream

Elsevier, 2018

Journal
One of the most important opinion mining research directions falls in the extraction ofpolarities referring to specific entities (aspects) contained in the analyzed texts. Thedetection of such aspects may be very critical especially when documents come fromunknown domains. Indeed, while in some contexts it is possible to train domainspecificmodels for improving the effectiveness of aspects extraction algorithms, inothers the most suitable solution is to apply unsupervised techniques by making suchalgorithms domain-independent and more efficient in a real-time environment. Moreover,an emerging need is to exploit the results of aspect-based analysis for triggeringactions based on these data. This led to the necessity of providing solutions supportingboth an effective analysis of user-generated content and an efficient and intuitive wayof visualizing collected data. In this work, we implemented an opinion monitoringservice implementing (i) a set of unsupervised strategies for aspect-based opinion miningtogether with (ii) a monitoring tool supporting users in visualizing analyzed data.The aspect extraction strategies are based on the use of an open information extractionstrategy. The effectiveness of the platform has been tested on benchmarks provided by the SemEval campaign and have been compared with the results obtained by domainad aptedtechniques.
2018

Barreiros Carla, Veas Eduardo Enrique, Pammer-Schindler_TU Viktoria

Bringing Nature into Our Lives

Human-Computer Interaction, Lecture Notes in Computer Science, Springer International Publishing, 2018

Konferenz
In the context of the Internet of Things (IoT), every device have sensing and computing capabilities to enhance many aspects of human life. There are more and more IoT devices in our homes and at our workplaces, and they still depend on human expertise and intervention for tasks as maintenance and (re)configuration.
Kontakt Karriere

Hiermit erkläre ich ausdrücklich meine Einwilligung zum Einsatz und zur Speicherung von Cookies. Weiter Informationen finden sich unter Datenschutzerklärung

The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.

Close