Deep Learning for Reversible Steganography: Principles and Insights

Deep-learning\textendash{centric} reversible steganography has emerged as a promising research paradigm. A direct way of applying deep learning to reversible steganography is to construct a pair of encoder and decoder, whose parameters are trained jointly, thereby learning the steganographic system as a whole. This end-to-end framework, however, falls short of the reversibility requirement because it is difficult for this kind of monolithic system, as a black box, to create or duplicate intricate reversible mechanisms. In response to this issue, a recent approach is to carve up the steganographic system and work on modules independently. In particular, neural networks are deployed in an analytics module to learn the data distribution, while an established mechanism is called upon to handle the remaining tasks. In this paper, we investigate the modular framework and deploy deep neural networks in a reversible steganographic scheme referred to as prediction-error modulation, in which an analytics module serves the purpose of pixel intensity prediction. The primary focus of this study is on deep-learning\textendash{based} context-aware pixel intensity prediction. We address the unsolved issues reported in related literature, including the impact of pixel initialisation on prediction accuracy and the influence of uncertainty propagation in dual-layer embedding. Furthermore, we establish a connection between context-aware pixel intensity prediction and low-level computer vision and analyse the performance of several advanced neural networks

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Why less can be more: A Bayesian framework for heuristics

When making decisions under uncertainty, one common view is that people rely on simple heuristics that deliberately ignore information. One of the greatest puzzles in cognitive science concerns why heuristics can sometimes outperform full-information models, such as linear regression, which make full use of the available information. In this thesis, I will contribute the novel idea that heuristics can be thought of as embodying extreme Bayesian priors. Thereby, an explanation for less-is-more is that the heuristics’ relative simplicity and inflexibility amounts to a strong inductive bias, that is suitable for some learning and decision problems. I will formalize this idea by introducing Bayesian models within which heuristics are an extreme case along a continuum of model flexibility defined by the strength and nature of the prior. Crucially, the Bayesian models include heuristics at one of the Bayesian prior strength and classic full-information models at the other end of the Bayesian prior. This allows for a comparative test between the intermediate models along the continuum and the extremes of heuristics and full regression model. Indeed, I will show that intermediate models perform best across simulations, suggesting that down-weighting information is preferable to entirely ignoring it. These results refute an absolute version of less-is-more, demonstrating that heuristics will usually be outperformed by a model that takes into account the full information but weighs it appropriately. Thereby, the thesis provides a novel explanation for less-is-more: Heuristics work well because they embody a Bayesian prior that approximates the optimal prior. While the main contribution is formal, the final Chapter will explore whether less is more at the psychological level, and finds that people do not use heuristics, but rely on the full information instead. A consistent perspective will emerge throughout the whole thesis, which is that less is not more

Application of modern statistical methods in worldwide health insurance

With the increasing availability of internal and external data in the (health) insurance industry, the demand for new data insights from analytical methods is growing. This dissertation presents four examples of the application of advanced regression-based prediction techniques for claims and network management in health insurance: patient segmentation for and economic evaluation of disease management programs, fraud and abuse detection and medical quality assessment. Based on different health insurance datasets, it is shown that tailored models and newly developed algorithms, like Bayesian latent variable models, can optimize the business steering of health insurance companies. By incorporating and structuring medical and insurance knowledge these tailored regression approaches can at least compete with machine learning and artificial intelligence methods while being more transparent and interpretable for the business users. In all four examples, methodology and outcomes of the applied approaches are discussed extensively from an academic perspective. Various comparisons to analytical and market best practice methods allow to also judge the added value of the applied approaches from an economic perspective.Mit der wachsenden Verfügbarkeit von internen und externen Daten in der (Kranken-) Versicherungsindustrie steigt die Nachfrage nach neuen Erkenntnissen gewonnen aus analytischen Verfahren. In dieser Dissertation werden vier Anwendungsbeispiele für komplexe regressionsbasierte Vorhersagetechniken im Schaden- und Netzwerkmanagement von Krankenversicherungen präsentiert: Patientensegmentierung für und ökonomische Auswertung von Gesundheitsprogrammen, Betrugs- und Missbrauchserkennung und Messung medizinischer Behandlungsqualität. Basierend auf verschiedenen Krankenversicherungsdatensätzen wird gezeigt, dass maßgeschneiderte Modelle und neu entwickelte Algorithmen, wie bayesianische latente Variablenmodelle, die Geschäftsteuerung von Krankenversicherern optimieren können. Durch das Einbringen und Strukturieren von medizinischem und versicherungstechnischem Wissen können diese maßgeschneiderten Regressionsansätze mit Methoden aus dem maschinellen Lernen und der künstlichen Intelligenz zumindest mithalten. Gleichzeitig bieten diese Ansätze dem Businessanwender ein höheres Maß an Transparenz und Interpretierbarkeit. In allen vier Beispielen werden Methodik und Ergebnisse der angewandten Verfahren ausführlich aus einer akademischen Perspektive diskutiert. Verschiedene Vergleiche mit analytischen und marktüblichen Best-Practice-Methoden erlauben es, den Mehrwert der angewendeten Ansätze auch aus einer ökonomischen Perspektive zu bewerten

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Model Averaging and its Use in Economics

The method of model averaging has become an important tool to deal with model uncertainty, for example in situations where a large amount of different theories exist, as are common in economics. Model averaging is a natural and formal response to model uncertainty in a Bayesian framework, and most of the paper deals with Bayesian model averaging. The important role of the prior assumptions in these Bayesian procedures is highlighted. In addition, frequentist model averaging methods are also discussed. Numerical methods to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on uncertainty regarding the choice of covariates in normal linear regression models, but the paper also covers other, more challenging, settings, with particular emphasis on sampling models commonly used in economics. Applications of model averaging in economics are reviewed and discussed in a wide range of areas, among which growth economics, production modelling, finance and forecasting macroeconomic quantities.Comment: forthcoming; accepted versio