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This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems—as required for calibration—is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 cm was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring.
Social networks are ubiquitous structures that we generate and enrich every-day while connecting with people through social media platforms, emails, and any other type of interaction. While these structures are intangible to us, they carry important information. For instance, the political leaning of our friends can be a proxy to identify our own political preferences. Similarly, the credit score of our friends can be decisive in the approval or rejection of our own loans. This explanatory power is being leveraged in public policy, business decision-making and scientific research because it helps machine learning techniques to make accurate predictions. However, these generalizations often benefit the majority of people who shape the general structure of the network, and put in disadvantage under-represented groups by limiting their resources and opportunities. Therefore it is crucial to first understand how social networks form to then verify to what extent their mechanisms of edge formation contribute to reinforce social inequalities in machine learning algorithms.
To this end, in the first part of this thesis, I propose HopRank and Janus two methods to characterize the mechanisms of edge formation in real-world undirected social networks. HopRank is a model of information foraging on networks. Its key component is a biased random walker based on transition probabilities between k-hop neighborhoods. Janus is a Bayesian framework that allows to identify and rank plausible hypotheses of edge formation in cases where nodes possess additional information. In the second part of this thesis, I investigate the implications of these mechanisms - that explain edge formation in social networks - on machine learning. Specifically, I study the influence of homophily, preferential attachment, edge density, fraction of inorities, and the directionality of links on both performance and bias of collective classification, and on the visibility of minorities in top-k ranks. My findings demonstrate a strong correlation between network structure and machine learning outcomes. This suggests that systematic discrimination against certain people can be: (i) anticipated by the type of network, and (ii) mitigated by connecting strategically in the network.
In this thesis the possibilities for real-time visualization of OpenVDB
files are investigated. The basics of OpenVDB, its possibilities, as well
as NanoVDB and its GPU port, were studied. A system was developed
using PNanoVDB, the graphics API port of OpenVDB. Techniques were
explored to improve and accelerate a single ray approach of ray tracing.
To prove real-time capability, two single scattering approaches were
also implemented. One of these was selected, further investigated and
optimized to achieve interactive real-time rendering.
It is important to give artists immediate feedback on their adjustments, as
well as the possibility to change all parameters to ensure a user friendly
creation process.
In addition to the optical rendering, corresponding benchmarks were
collected to compare different improvement approaches and to prove
their relevance. Attention was paid to the rendering times and memory
consumption on the GPU to ensure optimal use. A special focus, when
rendering OpenVDB files, was put on the integrability and extensibility of
the program to allow easy integration into an existing real-time renderer
like U-Render.
Semantic Web technologies have been recognized to be key for the integration of distributed and heterogeneous data sources on the Web, as they provide means to define typed links between resources in a dynamic manner and following the principles of dataspaces. The widespread adoption of these technologies in the last years led to a large volume and variety of data sets published as machine-readable RDF data, that once linked constitute the so-called Web of Data. Given the large scale of the data, these links are typically generated by computational methods that given a set of RDF data sets, analyze their content and identify the entities and schema elements that should be connected via the links. Analogously to any other kind of data, in order to be truly useful and ready to be consumed, links need to comply with the criteria of high quality data (e.g., syntactically and semantically accurate, consistent, up-to-date). Despite the progress in the field of machine learning, human intelligence is still essential in the quest for high quality links: humans can train algorithms by labeling reference examples, validate the output of algorithms to verify their performance on a data set basis, as well as augment the resulting set of links. Humans —especially expert humans, however, have limited availability. Hence, extending data quality management processes from data owners/publishers to a broader audience can significantly improve the data quality management life cycle.
Recent advances in human computation and peer-production technologies opened new avenues for human-machine data management techniques, allowing to involve non-experts in certain tasks and providing methods for cooperative approaches. The research work presented in this thesis takes advantage of such technologies and investigates human-machine methods that aim at facilitating link quality management in the Semantic Web. Firstly, and focusing on the dimension of link accuracy, a method for crowdsourcing ontology alignment is presented. This method, also applicable to entities, is implemented as a complement to automatic ontology alignment algorithms. Secondly, novel measures for the dimension of information gain facilitated by the links are introduced. These entropy-centric measures provide data managers with information about the extent the entities in the linked data set gain information in terms of entity description, connectivity and schema heterogeneity. Thirdly, taking Wikidata —the most successful case of a linked data set curated, linked and maintained by a community of humans and bots— as a case study, we apply descriptive and predictive data mining techniques to study participation inequality and user attrition. Our findings and method can help community managers make decisions on when/how to intervene with user retention plans. Lastly, an ontology to model the history of crowd contributions across marketplaces is presented. While the field of human-machine data management poses complex social and technical challenges, the work in this thesis aims to contribute to the development of this still emerging field.
Despite widespread plans of big companies like Amazon and Google to develop unmanned delivery drones, scholarly research in this field is scarce, especially in the information systems field. From technical and legal perspectives, drone delivery in last-mile scenarios is in a quite mature state. However, estimates of user acceptance are varying between high skepticism and exaggerated optimism. This research follows a mixed method approach consisting both qualitative and quantitative research, to identify and test determinants of consumer delivery drone service adoption. The qualitative part rests on ten interviews among average consumers, who use delivery services on a regular basis. Insights gained from the qualitative part were used to develop an online survey and to assess the influence of associated risks on adoption intentions. The quantitative results show that especially financial and physical risks impede drone delivery service adoption. Delivery companies who are currently thinking about providing a delivery drone service may find these results useful when evaluating usage behaviors in the future market for delivery drones.
On-screen interactive presentations have got immense popularity in the domain of attentive interfaces recently. These attentive screens adapt their behavior according to the user's visual attention. This thesis aims to introduce an application that would enable these attentive interfaces to change their behavior not just according to the gaze data but also facial features and expressions. The modern era requires new ways of communications and publications for advertisement. These ads need to be more specific according to people's interests, age, and gender. When advertising, it's important to get a reaction from the user but not every user is interested in providing feedback. In such a context more, advance techniques are required that would collect user's feedback effortlessly. The main problem this thesis intends to resolve is, to apply advanced techniques of gaze and face recognition to collect data about user's reactions towards different ads being played on interactive screens. We aim to create an application that enables attentive screens to detect a person's facial features, expressions, and eye gaze. With eye gaze data we can determine the interests and with facial features, age and gender can be specified. All this information will help in optimizing the advertisements.
This paper describes the robots TIAGo and Lisa used by
team homer@UniKoblenz of the University of Koblenz-Landau, Germany,
for the participation at the RoboCup@Home 2019 in Sydney,
Australia. We ended up first at RoboCup@Home 2019 in the Open Platform
League and won the competition in our league now three times
in a row (four times in total) which makes our team the most successful
in RoboCup@Home. We demonstrated approaches for learning from
demonstration, touch enforcing manipulation and autonomous semantic
exploration in the finals. 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, gesture recognition
and imitation learning. The packages are available (and new packages
will be released) on http://homer.uni-koblenz.de.
This paper describes the robots TIAGo and Lisa used by team homer@UniKoblenz of the University of Koblenz-Landau, Germany, for the participation at the RoboCup@Home 2018 in Montreal, Canada. Further this paper serves as qualification material for the RoboCup-@Home participation in 2018. A special focus is put on novel system components and the open source contributions of our team. This year the team from Koblenz won the biggest annual scientianc robot competition in Montreal in the RoboCup@Home Open Platform track for the third time and also won the RoboCup@Home German Open for the second time. As a research highlight a novel symbolic imitation learning approach was demonstrated during the annals. The TIAGo robotic research platform was used for the first time by the 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. Further information can be found on our project page http://homer.uni-koblenz.de.
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 2017 in Nagoya, Japan. 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.
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.
In order to enhance the company’s appeal for potential employees and improve the satisfaction of already salaried workers, it is necessary to offer a variety of work-life balance measures. But as their implementation causes time and financial costs, a prioritization of measures is needed. To express a recommendation for companies, this study is led by the questions if there are work-life balance measures which have more impact on employee satisfaction than others, how big the relative impact of work-life balance measures on job satisfaction in comparison to other work and private life variables is, if there is a relation between the effectiveness of measures and their use and if there is a difference between the measures which are most important from the employees’ perspective and the companies’ offers.
These questions are formulated in eight research hypotheses which are examined in a quantitative research design with online survey data from 289 employees of fifteen different German companies. The formation of a hierarchy of the effectiveness of measures towards job satisfaction as well as the investigation of the relative impact in comparison to other variables is performed using a multiple regression analysis, whilst the differences between employees’ expectations and the availability of offers are examined with t-tests.
Support in childcare, support in voluntary activities and teambuilding events have a significantly higher impact on job satisfaction than other work-life balance measures, and their potential use is higher than the actual use which leads to the conclusion that there is yet potential for companies to improve their employees’ satisfaction by implementing these measures. In addition, flexible work hours, flexible work locations and free time and overtime accounts are the most important measures from the employees’ point of view and already widely offered by the surveyed companies. In general, the overall use of the available measures and the quantity of offered measures are more important with regard to job satisfaction than the specific kind of measure. In addition, work-life balance measures are more important towards job satisfaction for younger people.
Six and Gimmler have identified concrete capabilities that enable users to use the Internet in a competent way. Their media competence model can be used for the didactical design of media usage in secondary schools. However, the special challenge of security awareness is not addressed by the model. In this paper, the important dimension of risk and risk assessment will be introduced into the model. This is especially relevant for the risk of the protection of personal data and privacy. This paper will apply the method of IT risk analysis in order to select those dimensions of the Six/Gimmler media competence model that are appropriate to describe privacy aware Internet usage. Privacy risk aware decisions for or against the Internet usage is made visible by the trust model of Mayer et al.. The privacy extension of the competence model will lead to a measurement of the existing privacy awareness in secondary schools, which, in turn, can serve as a didactically well-reasoned design of Informatics modules in secondary schools. This paper will provide the privacy-extended competence model, while empirical measurement and module design is planned for further research activities.
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.
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.
Large amounts of qualitative data make the utilization of computer-assisted methods for their analysis inevitable. In this thesis Text Mining as an interdisciplinary approach, as well as the methods established in the empirical social sciences for analyzing written utterances are introduced. On this basis a process of extracting concept networks from texts is outlined and the possibilities of utilitzing natural language processing methods within are highlighted. The core of this process is text processing, to whose execution software solutions supporting manual as well as automated work are necessary. The requirements to be met by these solutions, against the background of the initiating project GLODERS, which is devoted to investigating extortion racket systems as part of the global fiσnancial system, are presented, and their fulσlment by the two most preeminent candidates reviewed. The gap between theory and pratical application is closed by a prototypical application of the method to a data set of the research project utilizing the two given software solutions.
This thesis describes the implementation of a Path-planning algorithm for multi-axle vehicles using machine learning algorithms. For that purpose, a general overview over Genetic Algorithms is given and alternative machine learning algorithms are briefly explained. The software developed for this purpose is based on the EZSystem Simulation Software developed by the AG Echtzeitysteme at the University Koblenz-Landau and a path correction algorithm developed by Christian Schwarz, which is also detailed in this paper. This also includes a description of the vehicle used in these simulations. Genetic Algorithms as a solution for path-planning in complex scenarios are then evaluated based on the results of the developed simulation software and compared to alternative, non-machine learning solutions, which are also shortly presented.
We present the conceptual and technological foundations of a distributed natural language interface employing a graph-based parsing approach. The parsing model developed in this thesis generates a semantic representation of a natural language query in a 3-staged, transition-based process using probabilistic patterns. The semantic representation of a natural language query is modeled in terms of a graph, which represents entities as nodes connected by edges representing relations between entities. The presented system architecture provides the concept of a natural language interface that is both independent in terms of the included vocabularies for parsing the syntax and semantics of the input query, as well as the knowledge sources that are consulted for retrieving search results. This functionality is achieved by modularizing the system's components, addressing external data sources by flexible modules which can be modified at runtime. We evaluate the system's performance by testing the accuracy of the syntactic parser, the precision of the retrieved search results as well as the speed of the prototype.