The Stereotype Content Modell (SCM; Fiske et al., 2002) proposes two fundamental dimensions of social evaluation: Warmth, or the intentions of the target, and Competence, or the ability to enact these intentions. The practical applications of the SCM are very broad and have led to an assumption of universality of warmth and competence as fundamental dimensions of social evaluation. This thesis has identified five mainly methodological shortcomings of the current SCM research and literature: (I) An insufficient initial scale development; (II) the usage of varying warmth and competence scales without sufficient scale property assessment in later research; (III) the dominant application of first-generation analytical approaches; (IV) the insufficient definition and empirical proof for the SCM’s assumption of universality; and (V) the limited application of the SCM for some social targets. These shortcomings were addressed in four article manuscripts strictly following open science recommendations. Manuscript # 1 re-analysed published research using English SCM measures to investigate the measurement properties of the used warmth and competence scales. It reported the scales’ reliability, dimensionality and comparability across targets as well as the indicator-based parameter performance in a (multiple group) confirmatory factor analysis framework. The findings indicate that about two thirds of all re-analysed scales do not show the theoretically expected warmth and competence dimensionality. Moreover, only about eleven per cent allowed meaningful mean value comparisons between targets. Manuscript # 2 presents a replication of Manuscript # 1 in the national and language of German(y) generating virtually identical results as Manuscript # 1 did. Manuscript # 3 investigated the stereotype content of refugee subgroups in Germany. We showed that refugees was generally perceived unfavourably in terms of warmth and competence, but that the stereotype content varied based on the refugees’ geographic origin, religious affiliation, and flight motive. These results were generated using a reliability-corrected approach to compare mean values named alignment optimisation procedure. Manuscript # 4 developed and tested a high-performing SCM scale assessing occupational stereotypes a number of exploratory and confirmatory factor analyses.

The Web is an essential component of moving our society to the digital age. We use it for communication, shopping, and doing our work. Most user interaction in the Web happens with Web page interfaces. Thus, the usability and accessibility of Web page interfaces are relevant areas of research to make the Web more useful. Eye tracking is a tool that can be helpful in both areas, performing usability testing and improving accessibility. It can be used to understand users' attention on Web pages and to support usability experts in their decision-making process. Moreover, eye tracking can be used as an input method to control an interface. This is especially useful for people with motor impairment, who cannot use traditional input devices like mouse and keyboard. However, interfaces on Web pages become more and more complex due to dynamics, i.e., changing contents like animated menus and photo carousels. We need general approaches to comprehend dynamics on Web pages, allowing for efficient usability analysis and enjoyable interaction with eye tracking. In the first part of this thesis, we report our work on improving gaze-based analysis of dynamic Web pages. Eye tracking can be used to collect the gaze signals of users, who browse a Web site and its pages. The gaze signals show a usability expert what parts in the Web page interface have been read, glanced at, or skipped. The aggregation of gaze signals allows a usability expert insight into the users' attention on a high-level, before looking into individual behavior. For this, all gaze signals must be aligned to the interface as experienced by the users. However, the user experience is heavily influenced by changing contents, as these may cover a substantial portion of the screen. We delineate unique states in Web page interfaces including changing contents, such that gaze signals from multiple users can be aggregated correctly. In the second part of this thesis, we report our work on improving the gaze-based interaction with dynamic Web pages. Eye tracking can be used to retrieve gaze signals while a user operates a computer. The gaze signals may be interpreted as input controlling an interface. Nowadays, eye tracking as an input method is mostly used to emulate mouse and keyboard functionality, hindering an enjoyable user experience. There exist a few Web browser prototypes that directly interpret gaze signals for control, but they do not work on dynamic Web pages. We have developed a method to extract interaction elements like hyperlinks and text inputs efficiently on Web pages, including changing contents. We adapt the interaction with those elements for eye tracking as the input method, such that a user can conveniently browse the Web hands-free. Both parts of this thesis conclude with user-centered evaluations of our methods, assessing the improvements in the user experience for usability experts and people with motor impairment, respectively.

Human action recognition from a video has received growing attention in computer vision and has made significant progress in recent years. Action recognition is described as a requirement to decide which human actions appear in videos. The difficulties involved in distinguishing human actions are due to the high complexity of human behaviors as well as appearance variation, motion pattern variation, occlusions, etc. Many applications use human action recognition on captured video from cameras, resulting in video surveillance systems, health monitoring, human-computer interaction, and robotics. Action recognition based on RGB-D data has increasingly drawn more attention to it in recent years. RGB-D data contain color (Red, Green, and Blue (RGB)) and depth data that represent the distance from the sensor to every pixel in the object (object point). The main problem that this thesis deals with is how to automate the classification of specific human activities/actions through RGB-D data. The classification process of these activities utilizes a spatial and temporal structure of actions. Therefore, the goal of this work is to develop algorithms that can distinguish these activities by recognizing low-level and high-level activities of interest from one another. These algorithms are developed by introducing new features and methods using RGB-D data to enhance the detection and recognition of human activities. In this thesis, the most popular state-of-the-art techniques are reviewed, presented, and evaluated. From the literature review, these techniques are categorized into hand-crafted features and deep learning-based approaches. The proposed new action recognition framework is based on these two categories that are approved in this work by embedding novel methods for human action recognition. These methods are based on features extracted from RGB-D data that are evaluated using machine learning techniques. The presented work of this thesis improves human action recognition in two distinct parts. The first part focuses on improving current successful hand-crafted approaches. It contributes into two significant areas of state-of-the-art: Execute the existing feature detectors, and classify the human action in the 3D spatio-temporal domains by testing a new combination of different feature representations. The contributions of this part are tested based on machine learning techniques that include unsupervised and supervised learning to evaluate this suitability for the task of human action recognition. A k-means clustering represents the unsupervised learning technique, while the supervised learning technique is represented by: Support Vector Machine, Random Forest, K-Nearest Neighbor, Naive Bayes, and Artificial Neural Networks classifiers. The second part focuses on studying the current deep-learning-based approach and how to use it with RGB-D data for the human action recognition task. As the first step of each contribution, an input video is analyzed as a sequence of frames. Then, pre-processing steps are applied to the video frames, like filtering and smoothing methods to remove the noisy data from each frame. Afterward, different motion detection and feature representation methods are used to extract features presented in each frame. The extracted features are represented by local features, global features, and feature combination besides deep learning methods, e.g., Convolutional Neural Networks. The feature combination achieves an excellent accuracy performance that outperforms other methods on the same RGB-D datasets. All the results from the proposed methods in this thesis are evaluated based on publicly available datasets, which illustrate that using spatiotemporal features can improve the recognition accuracy. The competitive experimental results are achieved overall. In particular, the proposed methods can be better applied to the test set compared to the state-of-the-art methods using the RGB-D datasets.

As a multilingual system,Wikipedia provides many challenges for academics and engineers alike. One such challenge is cultural contextualisation of Wikipedia content, and the lack of approaches to effectively quantify it. Additionally, what seems to lack is the intent of establishing sound computational practices and frameworks for measuring cultural variations in the data. Current approaches seem to mostly be dictated by the data availability, which makes it difficult to apply them in other contexts. Another common drawback is that they rarely scale due to a significant qualitative or translation effort. To address these limitations, this thesis develops and tests two modular quantitative approaches. They are aimed at quantifying culture-related phenomena in systems which rely on multilingual user-generated content. In particular, they allow to: (1) operationalise a custom concept of culture in a system; (2) quantify and compare culture-specific content- or coverage biases in such a system; and (3) map a large scale landscape of shared cultural interests and focal points. Empirical validation of these approaches is split into two parts. First, an approach to mapping Wikipedia communities of shared co-editing interests is validated on two large Wikipedia datasets comprising multilateral geopolitical and linguistic editor communities. Both datasets reveal measurable clusters of consistent co-editing interest, and computationally confirm that these clusters correspond to existing colonial, religious, socio economic, and geographical ties. Second, an approach to quantifying content differences is validated on a multilingual Wikipedia dataset, and a multi-platform (Wikipedia and Encyclopedia Britannica) dataset. Both are limited to a selected knowledge domain of national history. This analysis allows, for the first time on the large scale, to quantify and visualise the distribution of historical focal points in the articles on national histories. All results are cross-validated either by domain experts, or external datasets. Main thesis contributions. This thesis: (1) presents an effort to formalise the process of measuring cultural variations in user-generated data; (2) introduces and tests two novel approaches to quantifying cultural contextualisation in multilingual data; (3) synthesises a valuable overview of literature on defining and quantifying culture; (4) provides important empirical insights on the effect of culture on Wikipedia content and coverage; demonstrates that Wikipedia is not contextfree, and these differences should not be treated as noise, but rather, as an important feature of the data. (5) makes practical service contributions through sharing data and visualisations.

Feuerbetone werden als Auskleidung in industriellen Hochtemperaturaggregaten, wie beispielsweise in der Eisen- und Stahlindustrie, eingesetzt. Nach dem Mischen und dem Gießen eines Feuerbetons in Formen bzw. Schalungen, muss dieser abbinden und eine ausreichende Festigkeit ausbilden. Die Kinetik der Abbindevorgänge und somit auch das Erhärtungsverhalten variiert dabei stark in Abhängigkeit der Zusammensetzung, vor allem hinsichtlich des Bindemittels und der Additive, des Feuerbetons. In der Praxis der Herstellung von Feuerbetonen kommt es häufig zu Beschädigungen der hergestellten Bauteile oder Auskleidungen durch das Fließen noch nicht ausreichend abgebundener Feuerbetone bzw. die Beschädigung von Ecken und Kanten während des Ausschalens oder Bauteilen reißen durch mechanische Belastung beim Umsetzen oder beim Transport. Diese Beschädigungen basieren auf Fehleinschätzungen zum Abbindefortschritt und der korrespondierenden Festigkeitsausprägung der Feuerbetone. Diese wiederum sind auf Lücken im Stand der Technik zurück zu führen. Für PCE-verflüssigte und CA-Zement-gebundene Korund-Feuerbetonen mit einer Al₂O₃- und Al₂O₃-SiO₂-Matrix, werden die Defizite des Stands der Technik für diese Feuerbetonklasse identifiziert: Diese liegen im Bereich der Verflüssigungswirkung und des ersten Ansteifens der Feuerbetone, der Hydratation des CA-Zements in den Feuerbetonen und der Festigkeitsausprägung der Feuerbetone. Hieraus leitet sich ein entsprechender Forschungsbedarf ab. Am Beispiel von zwei PCE-verflüssigten (PCE mit kurzer Hauptkette und langen Seitenketten sowie PCE mit langer Hauptkette und kurzen Seitenketten) und CA-Zement-gebundenen (70 % Al₂O₃) Feuerbetonen mit einer reaktivtonerde-basierten und einer reaktivtonerde-mikrosilika-basierten Matrix werden abbindekinetische Untersuchungen durchgeführt. Anhand verschiedener abbindekinetischer Messmethoden, wie Schallgeschwindigkeit oder elektrischer Leitfähigkeit, und einigen ergänzenden Messung, wie beispielsweise das ζ-Potential, wird der Abbindeverlauf der Feuerbetone untersucht und die Defizite aus dem Stand der Technik aufgeklärt. Im Detail wurde der Stand der Technik um folgende Erkenntnisse ergänzt: • Verflüssigung von Feuerbetonen mit PCE-Molekülen: Es wurde festgestellt, dass die Verflüssigungswirkung und das erste Ansteifen maßgeblich durch die Struktur der Verflüssigermoleküle hervorgerufen werden. PCE-Moleküle mit langen Seitenketten verflüssigen eher sterisch. Durch die Vermittlung von Ca²⁺ aus dem CA-Zement wird die Adsorption der PCE-Moleküle verstärkt. Freie PCE-Moleküle können auf CAH-Phasen (Hydratationsprodukte) adsorbieren und somit die Fließfähigkeit des Feuerbetons für eine gewisse Zeit aufrechterhalten. PCE-Moleküle mit kurzen Seitenketten verflüssigen elektrosterisch. Bei Lösung von Ca²⁺ aus dem CA-Zement kommt es zu einer Ca-PCE Gel-Bildung und einer korrespondierenden Koagulation der Feuerbetonmatrix und der Feuerbeton steift zeitnah nach dem Mischen an. • Hydratation von CA-Zement: Die Hydratation von CA-Zement in den Feuerbetonen wird wesentlich durch die Länge der Seitenketten der PCE-Moleküle bzw. der Anwesenheit von Zitronensäure beeinflusst. In Feuerbetonen die mit PCE-Molekülen mit langen Seitenketten verflüssigt wurden, kann der CA-Zement weitestgehend frei hydratisieren. Der CAH-Phasenanteil steigt in zwei Abschnitten, getrennt von einer dormanten Phase. Die dormante Phase der CA Zementhydratation wurde auf eine Lösungshemmung der Al-O-H-Passivierungsschicht auf dem CA-Zement bei mäßigem pH-Wert (pH = 12,3) zurückgeführt. In Feuerbetonen die mit PCE-Molekülen mit kurze Seitenketten und Zitronensäure verflüssigt werden, wird die Hydratationsreaktion durch eine Ca-Citrat-PCE-Al(OH)₄-Gelbildung stark gehemmt. Es besteht die Vermutung, dass die Bindung von Ca²⁺ in dem Gel zum einen eine sehr ausgeprägte dormante Phase (pH < 12,3), mit einer schlechten Löslichkeit der Passivierungsschicht, bewirkt und zum anderen auch die Fällung von CAH-Phasen unterdrückt. • Festigkeitsausprägung von CA-Zement-gebundenen Feuerbetonen: Eine Koagulation bewirkt die erste Festigkeitssteigerung der Feuerbetone auf σB < 1 MPa. Im Anschluss findet die Hauptfestigkeitssteigerung auf Grund von Austrocknung durch Hydratation und Verdunstung statt. Der weitere Teil der Festigkeitssteigerung wird durch die Reduzierung der Porosität durch expansive CA-Zementhydratation und die hohe spezifische Oberfläche sowie deren weitere Erhöhung durch die CA-Zementhydratation bewirkt. Aus den einzelnen abbindekinetischen Effekten können, zusammen mit dem Stand der Technik und den neuen Erkenntnissen, mikrostrukturelle Abbindemodelle und Modelle zur Festigkeitsentwicklung der Feuerbetone abgeleitet werden. In Folge kann für die zwei Feuerbetone zu jedem Zeitpunkt des Abbindens eine Aussage zum Abbindefortschritt und zur Festigkeitsausprägung getätigt werden. Einige der Abbindemechanismen und festigkeitsbildenden Mechanismen können auf andere Feuerbetonzusammensetzungen übertragen werden.