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The Internet of Things is still one of the most relevant topics in the field of economics and research powered by the increasing demand of innovative services. Cost reductions in manufacturing of IoT hardware and the development of completely new communication ways has led to the point of bil-lions of devices connected to the internet. But in order to rule this new IoT landscape a standardized solution to conquer these challenges must be developed, the IoT Architecture.
This thesis examines the structure, purpose and requirements of IoT Architecture Models in the global IoT landscape and proposes an overview across the selected ones. For that purpose, a struc-tured literature analysis on this topic is conducted within this thesis, including an analysis on three existing research approaches trying to frame this topic and a tool supported evaluation of IoT Archi-tecture literature with over 200 accessed documents.
Furthermore, a coding of literature with the help of the specialised coding tool ATLAS.ti 8 is conduct-ed on 30 different IoT Architecture Models. In a final step these Architecture Models are categorized and compared to each other showing that the environment of IoT and its Architectures gets even more complex the further the research goes.
Die automatische Detektion der Lage und Ausrichtung von Unterwasser-Kabeln oder -Pipelines in Kamerabildern ermöglicht es, Unterwasserfahrzeuge autonome Kontrollfahrten durchführen zu lassen. Durch Pflanzenwuchs auf und in der Nähe von Kabeln bzw. Pipelines wird deren visuelle Erfassung jedoch erschwert: Die Bestimmug der Lage über die Detektion von Kanten mit anschließender Linien-Extraktion schlägt oft fehl. Probabilistische Ansätze sind hier den deterministischen überlegen. Durch die Modellierung von Wahrscheinlichkeiten kann trotz geringer Anzahl von extrahierten Merkmalen eine Aussage über den Zustand des Systems getroffen werden. Diese Arbeit stellt ein neues auf Partikelfiltern basierendes Tracking-System für die Verfolgung von Kabeln und Pipelines in Bildsequenzen vor. Umfangreiche Experimente auf realistischen Unterwasser-Videos zeigen die Robustheit und Performanz des gewählten Ansatzes sowie Vorteile gegenüber vorangegangenen Arbeiten.
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.
Der Klimawandel stellt eine existenzielle Bedrohung für das menschliche Überleben, die soziale Organisation der Gesellschaft und die Stabilität der Ökosysteme dar. Er ist daher zutiefst beängstigend. Im Angesicht von Bedrohungen wollen sich Menschen häufig schützen, anstatt sich proaktiv zu verhalten. Wenn psychologische Ressourcen zur Bewältigung nicht ausreichen, reagieren Menschen oft mit verschiedenen Formen der Leugnung. Diese Dissertation leistet einen Beitrag zum Verständnis des vielschichtigen Phänomens der Klimawandelleugnung aus psychologischer Sicht.
Es gibt vier Forschungslücken in der Literatur zur Klimawandelleugnung: Erstens hat das Spektrum der Klimawandelleugnung als Selbst-schützende Reaktion auf die Klimakrise innerhalb der Psychologie bisher keine Beachtung gefunden. Zweitens wurde psychologische Grundbedürfnisbefriedigung, ein fundamentaler Indikator für menschliches Funktionieren und die Fähigkeit, mit Bedrohungen umzugehen, bisher nicht als Prädiktor für Klimawandelleugnung untersucht. Drittens sind Beziehungen des Spektrums der Klimawandelleugnung zu klimarelevanten Emotionen, insbesondere der Klimaangst, bisher nicht empirisch untersucht worden. Viertens wurde bisher nicht untersucht, wie sich das Spektrum der Klimawandelleugnung zu etablierten Prädiktoren der Klimawandelleugnung, d.h. rechtsideologischen Überzeugungen und männlichem Geschlecht, verhält. Um diese Lücken zu schließen, untersuche ich, wie sich das Spektrum der Klimawandelleugnung im deutschen Kontext manifestiert und wie es mit psychologischer Grundbedürfnisbefriedigung und -frustration, umweltfreundlichem Verhalten, Klimaangst, ideologischer Uberzeugung und Geschlecht zusammenhängt.
Fünf Manuskripte zeigen, dass Klimawandelleugnung im deutschen Kontext auf einem Spektrum existiert, das von der Verzerrung von Fakten (interpretative Leugnung, insbesondere Leugnung der persönlichen und globalen Folgenschwere) bis zur Leugnung von Implikationen reicht (implikatorische Leugnung, insbesondere Vermeidung, Leugnung von Schuld und Rationalisierung der eigenen Beteiligung). Über alle Analysen hinweg war niedrige psychologische Grundbedürfnisbefriedigung Prädiktor für das Spektrum der Klimawandelleugnung, das wiederum mit umweltfreundlichem Verhalten assoziiert war. Klimawandelleugnung stand generell in einem negativen Zusammenhang mit Klimaangst, mit Ausnahme einer positiven Assoziation von Vermeidung und Klimaangst. Rechtsideologische Überzeugung war der stärkste Prädiktor für Klimawandelleugnung über das gesamte Spektrum hinweg. Niedrige Bedürfnisbefriedigung und männliches Geschlecht waren weitere, aber schwächere Prädiktoren für implikatorische Leugnung.
Diese Ergebnisse legen nahe, dass das Spektrum der Klimawandelleugnung viele psychologische Funktionen erfüllt. Klimawandelleugnung ist möglicherweise sowohl eine Selbst-schützende Strategie, um Emotionen herunter zu regulieren, als auch um sich vor dem Verlust von Privilegien zu schützen. Kurz gesagt stellt Klimawandelleugnung eine Barriere für Klimaschutzmaßnahmen dar, die möglicherweise erst dann überwunden wird, wenn Menschen über ausreichende psychologische Ressourcen verfügen, um sich der Bedrohung durch den Klimawandel zu stellen und mit zugrundeliegenden Selbst-schützenden, emotionalen Reaktionen umzugehen.
In a world where language defines the boundaries of one's understanding, the words of Austrian philosopher Ludwig Wittgenstein resonate profoundly. Wittgenstein's assertion that "Die Grenzen meine Sprache bedeuten die Grenzen meiner Welt" (Wittgenstein 2016: v. 5.6) underscores the vital role of language in shaping our perceptions. Today, in a globalized and interconnected society, fluency in foreign languages is indispensable for individual success. Education must break down these linguistic barriers, and one promising approach is the integration of foreign languages into content subjects.
Teaching content subjects in a foreign language, a practice known as Content Language Integrated Learning (CLIL), not only enhances language skills but also cultivates cognitive abilities and intercultural competence. This approach expands horizons and aligns with the core principles of European education (Leaton Gray, Scott & Mehisto 2018: 50). The Kultusministerkonferenz (KMK) recognizes the benefits of CLIL and encourages its implementation in German schools (cf. KMK 2013a).
With the rising popularity of CLIL, textbooks in foreign languages have become widely available, simplifying teaching. However, the appropriateness of the language used in these materials remains an unanswered question. If textbooks impose excessive linguistic demands, they may inadvertently limit students' development and contradict the goal of CLIL.
This thesis focuses on addressing this issue by systematically analyzing language requirements in CLIL teaching materials, emphasizing receptive and productive skills in various subjects based on the Common European Framework of Reference. The aim is to identify a sequence of subjects that facilitates students' language skill development throughout their school years. Such a sequence would enable teachers to harness the full potential of CLIL, fostering a bidirectional approach where content subjects facilitate language learning.
While research on CLIL is extensive, studies on language requirements for bilingual students are limited. This thesis seeks to bridge this gap by presenting findings for History, Geography, Biology, and Mathematics, allowing for a comprehensive understanding of language demands. This research endeavors to enrich the field of bilingual education and CLIL, ultimately benefiting the academic success of students in an interconnected world.
The purpose of this thesis is to explore the sentiment distributions of Wikipedia concepts.
We analyse the sentiment of the entire English Wikipedia corpus, which includes 5,669,867 articles and 1,906,375 talks, by using a lexicon-based method with four different lexicons.
Also, we explore the sentiment distributions from a time perspective using the sentiment scores obtained from our selected corpus. The results obtained have been compared not only between articles and talks but also among four lexicons: OL, MPQA, LIWC, and ANEW.
Our findings show that among the four lexicons, MPQA has the highest sensitivity and ANEW has the lowest sensitivity to emotional expressions. Wikipedia articles show more sentiments than talks according to OL, MPQA, and LIWC, whereas Wikipedia talks show more sentiments than articles according to ANEW. Besides, the sentiment has a trend regarding time series, and each lexicon has its own bias regarding text describing different things.
Moreover, our research provides three interactive widgets for visualising sentiment distributions for Wikipedia concepts regarding the time and geolocation attributes of concepts.
Foliicolous lichens are one of the most abundant epiphytes in tropical rainforests and one of the few groups of organisms that characterize these forests. Tropical rainforests are increasingly affected by anthropogenic disturbance resulting in forest destruction and degradation. However, not much is known on the effects of anthropogenic disturbance on the diversity of foliicolous lichens. Understanding such effects is crucial for the development of appropriate measures for the conservation of such organisms. In this study, foliicolous lichens diversity was investigated in three tropical rainforests in East Africa. Godere Forest in Southwest Ethiopia is a transitional rainforest with a mixture of Afromontane and Guineo-Congolian species. The forest is secondary and has been affected by shifting cultivation, semi-forest coffee management and commercial coffee plantation. Budongo Forest in West Uganda is a Guineo-Congolian rainforest consisting of primary and secondary forests. Kakamega Forest in western Kenya is a transitional rainforest with a mixture of Guineo-Congolian and Afromontane species. The forest is a mosaic of near-primary forest, secondary forests of different seral stages, grasslands, plantations, and natural glades.
While the 1960s and 1970s still knew permanent education (Council of Europe), recurrent education (OECD) and lifelong education (UNESCO), over the past 20 years, lifelong learning has become the single emblem for reforms in (pre-) primary, higher and adult education systems and international debates on education. Both highly industrialized and less industrialized countries embrace the concept as a response to the most diverse economic, social and demographic challenges - in many cases motivated by international organizations (IOs).
Yet, literature on the nature of this influence, the diffusion of the concept among IOs and their understanding of it is scant and usually focuses on a small set of actors. Based on longitudinal data and a large set of education documents, the work identifies rapid diffusion of the concept across a heterogeneous, expansive and dynamic international field of 88 IOs in the period 1990-2013, which is difficult to explain with functionalist accounts.
Based on the premises of world polity theory, this paper argues that what diffuses resembles less the bundle of systemic reforms usually associated with the concept in the literature and more a surprisingly detailed model of a new actor " the lifelong learner.
The estimation of various social objects is necessary in different fields of social life, science, education, etc. This estimation is usually used for forecasting, for evaluating of different properties and for other goals in complex man-machine systems. At present this estimation is possible by means of computer and mathematical simulation methods which is connected with significant difficulties, such as: - time-distributed process of receiving information about the object; - determination of a corresponding mathematical device and structure identification of the mathematical model; - approximation of the mathematical model to real data, generalization and parametric identification of the mathematical model; - identification of the structure of the links of the real social object. The solution of these problems is impossible without a special intellectual information system which combines different processes and allows predicting the behaviour of such an object. However, most existing information systems lead to the solution of only one special problem. From this point of view the development of a more general technology of designing such systems is very important. The technology of intellectual information system development for estimation and forecasting the professional ability of respondents in the sphere of education can be a concrete example of such a technology. Job orientation is necessary and topical in present economic conditions. It helps tornsolve the problem of expediency of investments to a certain sphere of education. Scientifically validated combined diagnostic methods of job orientation are necessary to carry out professional selection in higher education establishments. The requirements of a modern society are growing, with the earlier developed techniques being unable to correspond to them sufficiently. All these techniques lack an opportunity to account all necessary professional and personal characteristics. Therefore, it is necessary to use a system of various tests. Thus, the development of new methods of job orientation for entrants is necessary. The information model of the process of job orientation is necessary for this purpose. Therefore, it would be desirable to have an information system capable of giving recommendations concerning the choice of a trade on the basis of complex personal characteristics of entrants.