Improving latent variable descriptiveness by modelling rather than ad-hoc factors

Powerful generative models, particularly in natural language modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variable. We discuss an alternative and general approach to latent variable modelling, based on an objective that encourages a perfect reconstruction by tying a stochastic autoencoder with a variational autoencoder (VAE). This ensures by design that the latent variable captures information about the observations, whilst retaining the ability to generate well. Interestingly, although our model is fundamentally different to a VAE, the lower bound attained is identical to the standard VAE bound but with the addition of a simple pre-factor; thus, providing a formal interpretation of the commonly used, ad-hoc pre-factors in training VAEs

Predictors of Cognitive Reactivity in Depression

Cognitive theories of depression posit that, when activated by an external stressor, negative self-schemas negatively bias information processing. The congruency hypothesis suggests that higher overlap between schema and stressor content results in greater degrees of schema activation. To evaluate these theoretical premises, the current study evaluated whether: 1) cognitive organization is predictive of negative information processing biases following a negative mood prime; and, 2) content domain of cognitive organization interacts with content of mood prime to predict information processing biases. Undergraduate students (N = 157) completed a measure of cognitive organization, underwent a negative mood prime, and completed a measure of interpretation biases. Consistent with hypotheses, cognitive organization in the negative achievement and interpersonal positive domains was predictive of information processing biases. Contrary to predictions, no interaction effects were found. Findings support the notion that cognitive organization is an important vulnerability factor in depression. Limitations and future directions are discussed

Representation Learning and Applications in Local Differential Privacy

Latent variable models (LVMs) provide an elegant, efficient, and interpretable approach to learning the generation process of observed data. Latent variables can capture salient features within often highly-correlated data, forming powerful tools in machine learning. For high-dimensional data, LVMs are typically parameterised by deep neural networks, and trained by maximising a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variable. In this work, we first introduce a novel approach to latent variable modelling, based on an objective that encourages both data reconstruction and generation. This ensures by design that the latent representations capture information about the data. Second, we consider a novel approach to inducing local differential privacy (LDP) in high dimensions with a specifically-designed LVM. LDP offers a rigorous approach to preserving one’s privacy against both adversaries and the database administrator. Existing LDP mechanisms struggle to retain data utility in high dimensions owing to prohibitive noise requirements. We circumvent this by inducing LDP on the low- dimensional manifold underlying the data. Further, we introduce a novel approach for downstream model learning using LDP training data, enabling the training of performant machine learning models. We achieve significant performance gains over current state-of-the-art LDP mechanisms, demonstrating far-reaching implications for the widespread practice of data collection and sharing. Finally, we scale up this approach, adapting current state-of-the-art representation learning models to induce LDP in even higher-dimensions, further widening the scope of LDP mechanisms for high-dimensional data collection

Patch-based semantic labelling of images.

PhDThe work presented in this thesis is focused at associating a semantics to the content of an image, linking the content to high level semantic categories. The process can take place at two levels: either at image level, towards image categorisation, or at pixel level, in se- mantic segmentation or semantic labelling. To this end, an analysis framework is proposed, and the different steps of part (or patch) extraction, description and probabilistic modelling are detailed. Parts of different nature are used, and one of the contributions is a method to complement information associated to them. Context for parts has to be considered at different scales. Short range pixel dependences are accounted by associating pixels to larger patches. A Conditional Random Field, that is, a probabilistic discriminative graphical model, is used to model medium range dependences between neighbouring patches. Another contribution is an efficient method to consider rich neighbourhoods without having loops in the inference graph. To this end, weak neighbours are introduced, that is, neighbours whose label probability distribution is pre-estimated rather than mutable during the inference. Longer range dependences, that tend to make the inference problem intractable, are addressed as well. A novel descriptor based on local histograms of visual words has been proposed, meant to both complement the feature descriptor of the patches and augment the context awareness in the patch labelling process. Finally, an alternative approach to consider multiple scales in a hierarchical framework based on image pyramids is proposed. An image pyramid is a compositional representation of the image based on hierarchical clustering. All the presented contributions are extensively detailed throughout the thesis, and experimental results performed on publicly available datasets are reported to assess their validity. A critical comparison with the state of the art in this research area is also presented, and the advantage in adopting the proposed improvements are clearly highlighted

Acceptability of speed limits and other policy measures in German cities

An increasing number of German cities currently demand the Federal Government to empower cities to implement 30 kph speed limits at their own discretion. Setting area-wide 30 kph as the maximum speed, as already installed in many other European cities, could therefore soon become a viable policy option in Germany. This thesis conducts a stated choice (SC) experiment to determine the acceptability of such area-wide standard 30 kph speed limits compared to the acceptability of the expansion of shared space zones, costs for inner-city on-street car parking and public transport ticket fares. Combining the policies as attributes in an unlabeled experiment allows to juxtapose the policies in terms of their relative importance for the respondents’ choice decision. 129 adults from German cities with more than 100,000 inhabitants participated in an online survey during September 2022. The results show that respondents evaluate the introduction of standard 30 kph speed limit in the city center as utility increasing compared to the prevalent status quo with standard 50 kph. Setting a standard 30 kph speed limit in the whole city also has a positive parameter in the base model, but does not significantly influence the respondents’ utility. The expansion of shared space seems to have no relevant effect on the choice decision of respondents. Higher ticket fares for public transport show to be utility decreasing for respondents of this study, whereas an increase in car parking costs is assessed positively. Clear differences in the policy assessment of different subgroups of respondents can be observed. In line with literature, city-wide implementation of a standard 30 kph speed limit shows low acceptability among the group of frequent car users. In turn, voters of mayoral candidates for the Green Party (Bündnis 90/Die Grünen) or Left Party (Die Linke) expect a positive effect on their personal utility when a standard 30 kph speed limit is established in the whole city or in the city center only. Respondents’ gender does not seem to affect the assessment of 30 kph speed limit policy

Success factors of social influencers – multiple dimensions and contingencies of a prosperous campaign

Social Influencer haben sich zu einem mächtigen Mittel der Marketing-Kommunikation entwickelt. Gegenwärtig übersteigt die Höhe der Ausgaben für Social Influencer Marketing die der traditionellen Werbung (wie Fernsehspots, Print- oder Plakatwerbung). Angesichts des großen Einflusses, den Social Influencer auf Konsumenten haben können, stellt sich die Frage, wie man eine Influencer-Kampagne erfolgreich durchführt. Erste Ansätze haben Engagement-Variablen berücksichtigt - z. B. die Anzahl der Follower eines Influencers. Allerdings haben sich diese Ansätze oft genug als zu schlicht und eindimensional erwiesen. Tatsächlich beruht der Erfolg eines Influencer-Endorsements auf einem komplexen System von Erfolgsfaktoren, deren Bedeutung variieren kann. Dazu gehören unter anderem Faktoren, die in der Person des Influencers liegen, das Zusammenspiel zwischen Influencer und Zielgruppe, das Setup von Influencer und Marke/Produkt, der Kommunikationsstil des Influencers und die Vermeidung von Influencer-Fehlverhalten. Diese Elemente können miteinander verbunden sein und auch in gegenseitigem Konflikt stehen. Die vorliegende Dissertation widmet sich der Erforschung dieses komplexen Systems und der Schließung von Forschungslücken. Das erste Modul (1 Beitrag) legt ein Fundament, indem die drei Faktoren Attraktivität, Expertise und Vertrauenswürdigkeit untersucht werden. Im zweiten Modul, das zwei Forschungsarbeiten umfasst, wird das Zusammenspiel zwischen Influencer, Konsument und Marke/Produkt behandelt. Das erste Paper fokussiert die Persönlichkeit und untersucht die Übereinstimmung der Influencer-Persönlichkeit mit dem tatsächlichen und gewünschten Selbstkonzept des Konsumenten sowie mit der Markenpersönlichkeit. Dabei wird auch die moderierende Rolle des Produktinvolvements berücksichtigt. Im zweiten Beitrag wird das Zusammenspiel von Influencer- und Konsumentenattraktivität sowie Geschlecht untersucht. Das dritte Modul (4 Beiträge) konzentriert sich auf die Erfolgsfaktoren für verschiedene Produktarten bzw. Endorsement-Anlässe; dabei wird ein starker Bezug zur Kommunikation des Influencers hergestellt. Paper 1 und 2 ziehen eine grundsätzliche Grenze zwischen hedonischen und utilitaristischen Produkten und untersuchen die Bedeutung von Kommunikationsstil, Faktizität, Expertise und demographischer Ähnlichkeit. Der dritte Beitrag untersucht die Rolle der Attraktivität und Expertise von Influencern für attraktivitätsbezogene und nicht-attraktivitätsbezogene Produkte. Der vierte Beitrag schließlich diskutiert die Besonderheiten eines Influencer-Endorsements im Non-Profit-Kontext. Im letzten Modul werden die Schattenseiten des Influencer-Marketings, nämlich die schädliche Wirkung von Skandalen, in einem Beitrag beleuchtet. Diese Arbeit verdeutlicht die Vielfalt und Kontingenz der Faktoren, die ein erfolgreiches Influencer Endorsement ausmachen. Alle Faktoren müssen gegeneinander abgewogen und diskutiert werden; dabei spielen Unterschiede wie die angesprochene Zielgruppe oder das beworbene Produkt bzw. Anliegen eine große Rolle. Die Ergebnisse liefern wertvolle Implikationen für Praktiker vieler Branchen, um ihre Influencer-Kampagnen erfolgreich zu gestalten und umzusetzen. Ebenso eröffnen die Ergebnisse viele Perspektiven für zukünftige Forschung. Ein großes Forschungspotenzial kann in einer qualitativen Ergänzung der durchgeführten quantitativen Studien liegen. Auf diese Weise könnten die Gedanken, Gefühle und Handlungsabsichten von Influencern, Konsumenten und Praktikern, die die Grundlage der vorliegenden Ergebnisse bilden, aufgedeckt werden

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR