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Social Business Documents: An Investigation of their Nature, Structure and Long-term Management
(2018)
Geschäftsdokumente beinhalten wertvolle Informationen. Sie müssen verwaltet werden, um gesetzlichen Anforderungen zu entsprechen, als organisatorisches Wissen zu dienen und Risiken zu vermeiden. Veränderungen der Technologien haben jedoch zu neuen Dokumententypen und neuen Interaktionsmöglichkeiten mit Dokumenten geführt. So hat das Web 2.0 zur Entwicklung von Enterprise Collaboration Systems (ECS) geführt, die Mitarbeitern die Nutzung von Wiki-, Blog- oder Forum-Anwendungen für ihre Geschäftstätigkeiten ermöglichen. Ein Teil der in ECS erstellten Inhalte können dabei als Social Business Documents (SBD) bezeichnet werden. Im Vergleich zu traditionellen digitalen Dokumenten haben SBD eine andere Beschaffenheit und Struktur. SBD sind beispielsweise unstrukturierter und folgen keinem strikten Lebenszyklus. Diese Charakteristika bringen neue Herausforderungen beim Verwalten von SBD mit sich. Jedoch fehlen in der wissenschaftlichen Literatur derzeit Untersuchungen zu den Charakteristika von SBD, ihren Besonderheiten und ihrem Management.
Als theoretische Linse nutzt diese Arbeit Dokumenten-Theorien und dokumentarische Praktiken mit dem Ziel, die neuen Herausforderungen des Langzeitmanagement von SBD in ECS zu untersuchen. Durch einen interpretativen, explorativen Mixed-Method-Ansatz vereint diese Arbeit zwei Forschungsteile. Erstens werden die Beschaffenheit und Struktur von SBD durch die Analyse von vier Systemen untersucht und SBD-Informationsmodelle entwickelt. Diese zeigen die zugrundeliegenden Komponenten von SBD, die Struktur, die Funktionen, die enthaltenen Metadaten, sowie die große Bandbreite von SBD-Charakteristika auf. Der zweite Teil wurde mit Unternehmensvertretern durchgeführt und besteht aus einer Fokusgruppe, einer Fallstudie mit Tiefeninterviews und einem Fragebogen. Die Fokusgruppe zeigt, dass die genutzte Art von SBD bezogen auf ihren Inhalt und Speicherort unternehmensabhängig ist und es derzeit fast keine SBD-Management-Praktiken gibt. Die Fallstudie ermöglichte tiefe Einblicke in allgemeine Dokumentenmanagement-Aktivitäten und untersuchte die Anforderungen, Herausforderungen und Prozesse des SBD-Managements. Der Fragebogen konsolidierte und vertiefte die vorherigen Erkenntnisse und gibt Einblicke in den Wert von SBD, aktuelle Management-Praktiken sowie Herausforderungen und Bedürfnisse bei deren Management. Auch zeigt er auf, dass zwar alle Unternehmen Informationen im ECS speichern, die verwaltet werden sollten, jedoch kaum SBD-Management-Aktivitäten durchführt werden und so noch viele Herausforderungen bestehen.
Zusammenfassend erlauben die Ergebnisse einen Beitrag zu Praxis und Theorie. Die Praxis ist mittels eines Frameworks adressiert, welches die Anforderungen, Herausforderungen und Aktivitäten des SBD-Managements, die Unternehmen beim Langzeitmanagement beachten müssen, aufzeigt. Des Weiteren erlauben die Erkenntnisse den theoretischen Fortschritt der dokumentenbezogenen Praktiken durch die Erweiterung der Dokumententypen um SBD. Auch werden die bestehenden Probleme der Definition von Records in Bezug auf SBD erläutert sowie die Charakteristika von Dokumenten um jene von Social Business Documents erweitert.
Das Internet der Dinge (IoT) ist ein Konzept, bestehend aus vernetzten physischen Objekten, welche in die virtuelle Welt integriert werden um aktive Teilnehmer von Geschäfts- und Alltagsprozessen zu werden (Uckelmann, Harrison and Michahelles, 2011; Shrouf, Ordieres and Miragliotta, 2014). Es wird erwartet, dass dieses Konzept einen großen Einfluss auf Unternehmen haben wird (Council, Nic and Intelligence, 2008). Geschäftsmodelle kleiner und mittelständischer Unternehmen (KMU) sind bedroht, sollten sie den sich abzeichnenden Trend nutzen (Sommer, 2015). Daher ist das Ziel dieser Arbeit, eine exemplarische Implementierung von vernetzten Geräten in einem kleinen Unternehmen um seine Vorteile darzustellen.
Diese Arbeit verwendet Design Science Research (DSR) um einen Prototyp zu entwickeln, der auf dem Anwendungsfall einer Holzwerkstatt aufbaut. Der Prototyp besteht aus einem physischen Sensor und einer Webapplikation, welche von dem kleinen Unternehmen zur Verbesserung seiner Prozesse genutzt werden kann. Die Arbeit dokumentiert den iterativen Entwicklungsprozess der Prototypen von Grund auf zu nutzbarer Hard- und Software.
Der Hauptbeitrag dieser Arbeit ist die beispielhafte Anwendung und Nutzung von IoT in einem kleinen Unternehmen.
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.
The physical-biological interactions that affect the temporal variability of benthic oxygen fluxes were investigated to gain improved understanding of the factors that control these processes. This study, for the first time is able to resolve benthic diffusive boundary layer (DBL) dynamics using the newly developed lifetime-based laser induced fluorescence (τLIF) oxygen imaging system, which enables study of the role of small-scale fluid mechanics generated by benthic organism activity, and hence a more detailed analysis of oxygen transport mechanisms across the sediment-water interface (SWI).
The net benthic oxygen flux across the sediment-water interface is controlled by sediment oxygen uptake and oxygen transport. While the oxygen transport is largely influenced by turbulence driven by large-scale flows, sediment oxygen uptake is mainly affected by oxygen production and biological- and chemical-oxygen degradation of organic matter. Both processes can be enhanced by the presence of fauna and are intimately coupled. The benthic oxygen flux can be influenced by fauna in two ways, i.e. by modulating the availability of oxygen, which enhances the sediment oxygen uptake, and by enhancing the transport of oxygen.
In-situ and a series of laboratory measurements were conducted to estimate the short- and seasonal variability of benthic fluxes including the effects of burrow ventilation activity by tube-dwelling animals using eddy correlation (EC) and τLIF oxygen imaging techniques, respectively.
The in-situ benthic oxygen fluxes showed high variability at hourly and seasonal timescales, where statistical analysis indicated that current velocity and water depth were the most significant predictors of benthic oxygen flux at the waterside, which co-varied with the discharge, temperature, and oxygen concentration. The range of variability of seasonal fluxes corresponded to the friction velocities which were driven by large-scale flows. Application of a simplified analytical model that couples the effect of hydrodynamic forcing of the diffusive boundary layer with a temperature-dependent oxygen consumption rate within the sediment showed that friction velocity and temperature cause similar variability of the steady-state benthic oxygen flux.
The application of τLIF oxygen imaging system in bioturbation experiments enabled the investigation and discovery of insights into oxygen transport mechanisms across the sediment-water interface. Distinct oxygen structures above burrow openings were revealed, these were associated with burrow ventilation. The DBL was degraded in the presence of burrow ventilation. Advective transport generated by the energetic plumes released at burrow outlets was the dominant transport driving mechanism. The contribution of diffusive flux to the total estimated decreased with increasing larval density. For a range of larvae densities, commonly observed in ponds and lakes, sediment oxygen uptake rates increased up to 2.5-fold in the presence of tube-dwelling animals, and the oxygen transport rate exceeded chironomid respiration by up to a factor of 4.
The coupled physical-biological factors affecting net benthic oxygen flux can be represented by temperature, which is a prominent factor that accounts for both oxygen transport and sediment oxygen uptake. Low oxygen transport by flow coincided with high summer temperatures, amplified by a reduction of benthic population density and pupation. It can also, however, be offset by increased ventilation activity. In contrast, low temperature coincided with high oxygen concentrations, an abundance of larvae, and higher flow is offset by less burrow ventilation activity. Investigation of the effect of hydrodynamics on oxygen transport alone suggested that the expected increase of benthic oxygen flux under global warming can be offset by a reduction in flow velocity, which could ultimately lead to increasing carbon burial rates, and in a growing importance of anaerobic mineralization pathways with increasing emission rates of methane.
This study suggests a significant contribution of biological induced benthic oxygen flux to physical transport driven by large-scale flow-fields contributing to bottom-boundary layer turbulence.
Fresh water resources like rivers and reservoirs are exposed to a drastically changing world. In order to safeguard these lentic ecosystems, they need stronger protection in times of global change and population growth. In the last years, the exploitation pressure on drinking water reservoirs has increased steadily worldwide. Besides securing the demands of safe drinking water supply, international laws especially in Europe (EU Water Framework Directive) stipulate to minimize the impact of dams on downstream rivers. In this study we investigate the potential of a smart withdrawal strategy at Grosse Dhuenn Reservoir to improve the temperature and discharge regime downstream without jeopardizing drinking water production. Our aim is to improve the existing withdrawal strategy for operating the reservoir in a sustainable way in terms of water quality and quantity. First, we set-up and calibrated a 1D numerical model for Grosse Dhuenn Reservoir with the open-source community model “General Lake Model” (GLM) together with its water quality module “Aquatic Ecodynamics” library (AED2). The reservoir model reproduced water temperatures and hypolimnetic dissolved oxygen concentrations accurately over a 5 year period. Second, we extended the model source code with a selective withdrawal functionality (adaptive offtake) and added operational rules for a realistic reservoir management. Now the model is able to autonomously determine the best withdrawal height according to the temperature and flow requirements of the downstream river and the raw water quality objectives. Criteria for the determination of the withdrawal regime are selective withdrawal, development of stratification and oxygen content in the deep hypolimnion. This functionality is not available in current reservoir models, where withdrawal heights are generally provided a priori to the model and kept fixed during the simulation. Third, we ran scenario simulations identifying an improved reservoir withdrawal strategy to balance the demands for downstream river and raw water supply. Therefore we aimed at finding an optimal parallel withdrawal ratio between cold hypolimnetic water and warm epilimnetic or metalimnetic water in order to provide a pre-defined temperature in the downstream river. The reservoir model and the proposed withdrawal strategy provide a simple and efficient tool to optimize reservoir management in a multi-objective view for mastering future reservoir management challenges.
Knowledge-based authentication methods are vulnerable to Shoulder surfing phenomenon.
The widespread usage of these methods and not addressing the limitations it has could result in the user’s information to be compromised. User authentication method ought to be effortless to use and efficient, nevertheless secure.
The problem that we face concerning the security of PIN (Personal Identification Number) or password entry is shoulder surfing, in which a direct or indirect malicious observer could identify the user sensitive information. To tackle this issue we present TouchGaze which combines gaze signals and touch capabilities, as an input method for entering user’s credentials. Gaze signals will be primarily used to enhance targeting and touch for selecting. In this work, we have designed three different PIN entry method which they all have similar interfaces. For the evaluation, these methods were compared based on efficiency, accuracy, and usability. The results uncovered that despite the fact that gaze-based methods require extra time for the user to get familiar with yet it is considered more secure. In regards to efficiency, it has the similar error margin to the traditional PIN entry methods.
This thesis addresses the automated identification and localization of a time-varying number of objects in a stream of sensor data. The problem is challenging due to its combinatorial nature: If the number of objects is unknown, the number of possible object trajectories grows exponentially with the number of observations. Random finite sets are a relatively new theory that has been developed to derive at principled and efficient approximations. It is based around set-valued random variables that contain an unknown number of elements which appear in arbitrary order and are themselves random. While extensively studied in theory, random finite sets have not yet become a leading paradigm in practical computer vision and robotics applications. This thesis explores random finite sets in visual tracking applications. The first method developed in this thesis combines set-valued recursive filtering with global optimization. The problem is approached in a min-cost flow network formulation, which has become a standard inference framework for multiple object tracking due to its efficiency and optimality. A main limitation of this formulation is a restriction to unary and pairwise cost terms. This circumstance makes integration of higher-order motion models challenging. The method developed in this thesis approaches this limitation by application of a Probability Hypothesis Density filter. The Probability Hypothesis Density filter was the first practically implemented state estimator based on random finite sets. It circumvents the combinatorial nature of data association itself by propagation of an object density measure that can be computed efficiently, without maintaining explicit trajectory hypotheses. In this work, the filter recursion is used to augment measurements with an additional hidden kinematic state to be used for construction of more informed flow network cost terms, e.g., based on linear motion models. The method is evaluated on public benchmarks where a considerate improvement is achieved compared to network flow formulations that are based on static features alone, such as distance between detections and appearance similarity. A second part of this thesis focuses on the related task of detecting and tracking a single robot operator in crowded environments. Different from the conventional multiple object tracking scenario, the tracked individual can leave the scene and later reappear after a longer period of absence. Therefore, a re-identification component is required that picks up the track on reentrance. Based on random finite sets, the Bernoulli filter is an optimal Bayes filter that provides a natural representation for this type of problem. In this work, it is shown how the Bernoulli filter can be combined with a Probability Hypothesis Density filter to track operator and non-operators simultaneously. The method is evaluated on a publicly available multiple object tracking dataset as well as on custom sequences that are specific to the targeted application. Experiments show reliable tracking in crowded scenes and robust re-identification after long term occlusion. Finally, a third part of this thesis focuses on appearance modeling as an essential aspect of any method that is applied to visual object tracking scenarios. Therefore, a feature representation that is robust to pose variations and changing lighting conditions is learned offline, before the actual tracking application. This thesis proposes a joint classification and metric learning objective where a deep convolutional neural network is trained to identify the individuals in the training set. At test time, the final classification layer can be stripped from the network and appearance similarity can be queried using cosine distance in representation space. This framework represents an alternative to direct metric learning objectives that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large scale person re-identification datasets where competitive results are achieved overall. In particular, the proposed method better generalizes to the test set compared to a network trained with the well-established triplet loss.
Die in den letzten Jahren fortschreitende Digitalisierung hat zur Ausbreitung und Popularisierung von Internet of Things (IoT) Technologie beigetragen (Mattern and Floerkemeier, 2010; Evans, 2013). Darüber hinaus wurde die Gesundheitsdomäne als eine der am stärksten aktiven IoT Bereiche identifiziert (Steele and Clarke, 2013). Die vorliegende Bachelorarbeit gibt einen Überblick über IoT gestützte Gamification und entwickelt ein Framework welches IoT und Gamification im Kontext einer Versicherung kombiniert. Beim Untersuchen von Gamification wurde ein konzeptuelles Modell entwickelt welches insbesondere die Rolle von IoT in einem solchen Ansatz verdeutlicht. Diesbezüglich wurde festgestellt, dass IoT bei der Aufgabenstellung Anwendung findet und diese zum einen in einem großen Rahmen ermöglicht sowie innovative und komplexere Aufgaben erlaubt. In diesem Zusammenhang wurden besonders die Vorteile und Notwendigkeit von tragbaren IoT Geräten erläutert. Eine Stakeholder Analyse beschäftigte sich mit den Vorteilen, welche durch IoT und Gamification erreicht werden können. Hierbei konnten zwei daraus erwachsende Paradigmenwechsel, für Versicherung und Versicherungsnehmer, identifiziert werden. Basierend auf den zuvor gewonnenen Erkenntnissen der Untersuchung der Gamification Ansätze und der Stakeholder Analyse wurde ein IoT gestütztes Gamification Framework entwickelt. Das Framework weißt einen Level-basierten Aufbau auf, welcher den Benutzer entlang des Entwurfsprozess leiten soll. Sowohl das erstellen, als auch das analysieren eines bestehenden Ansatzes ist mit dem Framework möglich. Darüber hinaus wurde das Framework anhand von Pokémon Go instanziiert um mögliche Mängel zu identifizieren und zu erklären. Die vorliegende Bachelorarbeit liefert eine Grundlage auf deren Basis umfassendere kontextbezogene Forschung betrieben werden kann