004 Datenverarbeitung; Informatik
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This paper describes the robot Lisa used by team
homer@UniKoblenz of the University of Koblenz Landau, Germany, for the participation at the RoboCup@Home 2016 in Leipzig, Germany. A special focus is put on novel system components and the open source contributions of our team. We have released packages for object recognition, a robot face including speech synthesis, mapping and navigation, speech recognition interface via android and a GUI. The packages are available (and new packages will be released) on http://wiki.ros.org/agas-ros-pkg.
Der Fachbereich 4 (Informatik) besteht aus fünfundzwanzig Arbeitsgruppen unter der Leitung von Professorinnen und Professoren, die für die Forschung und Lehre in sechs Instituten zusammenarbeiten.
In jedem Jahresbericht stellen sich die Arbeitsgruppen nach einem einheitlichen Muster dar, welche personelle Zusammensetzung sie haben, welche Projekte in den Berichtszeitraum fallen und welche wissenschaftlichen Leistungen erbracht wurden. In den folgenden Kapiteln werden einzelne Parameter aufgeführt, die den Fachbereich in quantitativer Hinsicht, was Drittmitteleinwerbungen, Abdeckung der Lehre, Absolventen oder Veröffentlichungen angeht, beschreiben.
Der Fachbereich 4 (Informatik) besteht aus fünfundzwanzig Arbeitsgruppen unter der Leitung von Professorinnen und Professoren, die für die Forschung und Lehre in sechs Instituten zusammenarbeiten.
In jedem Jahresbericht stellen sich die Arbeitsgruppen nach einem einheitlichen Muster dar, welche personelle Zusammensetzung sie haben, welche Projekte in den Berichtszeitraum fallen und welche wissenschaftlichen Leistungen erbracht wurden. In den folgenden Kapiteln werden einzelne Parameter aufgeführt, die den Fachbereich in quantitativer Hinsicht, was Drittmitteleinwerbungen, Abdeckung der Lehre, Absolventen oder Veröffentlichungen angeht, beschreiben.
Information systems research has started to use crowdsourcing platforms such as Amazon Mechanical Turks (MTurk) for scientific research, recently. In particular, MTurk provides a scalable, cheap work-force that can also be used as a pool of potential respondents for online survey research. In light of the increasing use of crowdsourcing platforms for survey research, the authors aim to contribute to the understanding of its appropriate usage. Therefore, they assess if samples drawn from MTurk deviate from those drawn via conventional online surveys (COS) in terms of answers in relation to relevant e-commerce variables and test the data in a nomological network for assessing differences in effects.
The authors compare responses from 138 MTurk workers with those of 150 German shoppers recruited via COS. The findings indicate, inter alia, that MTurk workers tend to exhibit more positive word-of mouth, perceived risk, customer orientation and commitment to the focal company. The authors discuss the study- results, point to limitations, and provide avenues for further research.
Der Fachbereich 4 (Informatik) besteht aus fünfundzwanzig Arbeitsgruppen unter der Leitung von Professorinnen und Professoren, die für die Forschung und Lehre in sechs Instituten zusammenarbeiten.
In jedem Jahresbericht stellen sich die Arbeitsgruppen nach einem einheitlichen Muster dar, welche personelle Zusammensetzung sie haben, welche Projekte in den Berichtszeitraum fallen und welche wissenschaftlichen Leistungen erbracht wurden. In den folgenden Kapiteln werden einzelne Parameter aufgeführt, die den Fachbereich in quantitativer Hinsicht, was Drittmitteleinwerbungen, Abdeckung der Lehre, Absolventen oder Veröffentlichungen angeht, beschreiben.
The aim of this paper is to identify and understand the risks and issues companies are experiencing from the business use of social media and to develop a framework for describing and categorising those social media risks. The goal is to contribute to the evolving theorisation of social media risk and to provide a foundation for the further development of social media risk management strategies and processes. The study findings identify thirty risk types organised into five categories (technical, human, content, compliance and reputational). A risk-chain is used to illustrate the complex interrelated, multi-stakeholder nature of these risks and directions for future work are identified.
The way information is presented to users in online community platforms has an influence on the way the users create new information. This is the case, for instance, in question-answering fora, crowdsourcing platforms or other social computation settings. To better understand the effects of presentation policies on user activity, we introduce a generative model of user behaviour in this paper. Running simulations based on this user behaviour we demonstrate the ability of the model to evoke macro phenomena comparable to the ones observed on real world data.
Modeling and publishing Linked Open Data (LOD) involves the choice of which vocabulary to use. This choice is far from trivial and poses a challenge to a Linked Data engineer. It covers the search for appropriate vocabulary terms, making decisions regarding the number of vocabularies to consider in the design process, as well as the way of selecting and combining vocabularies. Until today, there is no study that investigates the different strategies of reusing vocabularies for LOD modeling and publishing. In this paper, we present the results of a survey with 79 participants that examines the most preferred vocabulary reuse strategies of LOD modeling. Participants of our survey are LOD publishers and practitioners. Their task was to assess different vocabulary reuse strategies and explain their ranking decision. We found significant differences between the modeling strategies that range from reusing popular vocabularies, minimizing the number of vocabularies, and staying within one domain vocabulary. A very interesting insight is that the popularity in the meaning of how frequent a vocabulary is used in a data source is more important than how often individual classes and properties arernused in the LOD cloud. Overall, the results of this survey help in understanding the strategies how data engineers reuse vocabularies, and theyrnmay also be used to develop future vocabulary engineering tools.
This paper presents a method for the evolution of SHI ABoxes which is based on a compilation technique of the knowledge base. For this the ABox is regarded as an interpretation of the TBox which is close to a model. It is shown, that the ABox can be used for a semantically guided transformation resulting in an equisatisfiable knowledge base. We use the result of this transformation to effciently delete assertions from the ABox. Furthermore, insertion of assertions as well as repair of inconsistent ABoxes is addressed. For the computation of the necessary actions for deletion, insertion and repair, the E-KRHyper theorem prover is used.