Abstract
The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker's cognitive profile. There are two common methods for assessing a crowd worker's cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model's performance.

Abstract
At the University of Applied Sciences Technikum Wien, the intended learning outcomes (ILO) for individual study programs are well defined. These ILO are derived from qualification profiles and should ensure well-educated graduates for professional success. However, at the university level across several study programs, a lack of coordination in ILO development exists. A comparison across study programs and individual courses can show synergies of curricula. It can identify course similarities across programs, allowing collaborative development and standardization with the aim of cost-effective quality improvement. Thus, this paper proposes a solution to this challenge by harmonizing ILO and employing taxonomies for clear outcome classification. Therefore, text analysis, text enrichment with additional information, taxonomy mapping, and the annotation of the intended learning outcomes are the main steps of the prototype.

Abstract
Metaplasia detection in upper gastrointestinal endoscopy is crucial to identify patients at higher risk of gastric cancer. Deep learning algorithms can be useful for detecting and localising these lesions during an endoscopy exam. However, to train these types of models, a lot of annotated data is needed, which can be a problem in the medical field. To overcome this, data augmentation techniques are commonly applied to increase the dataset's variability but need to be adapted to the specificities of the application scenario. In this study, we discuss the potential benefits and identify four key research challenges of a promising data augmentation approach, namely image combination methodologies, such as CutMix, for metaplasia detection and localisation in gastric endoscopy imaging modalities.

Abstract
Location Privacy-Preserving Mechanisms (LPPMs) have been proposed to mitigate the risks of privacy disclosure yielded from location sharing. However, due to the nature of this type of data, spatio-temporal correlations can be leveraged by an adversary to extenuate the protections. Moreover, the application of LPPMs at collection time has been limited due to the difficulty in configuring the parameters and in understanding their impact on the privacy level by the end-user. In this work we adopt the velocity of the user and the frequency of reports as a metric for the correlation between location reports. Based on such metric we propose a generalization of Geo-Indistinguishability denoted Velocity-Aware Geo-Indistinguishability (VA-GI). We define a VA-GI LPPM that provides an automatic and dynamic trade-off between privacy and utility according to the velocity of the user and the frequency of reports. This adaptability can be tuned for general use, by using city or country-wide data, or for specific user profiles, thus warranting fine-grained tuning for users or environments. Our results using vehicular trajectory data show that VA-GI achieves a dynamic trade-off between privacy and utility that outperforms previous works. Additionally, by using a Gaussian distribution as estimation for the distribution of the velocities, we provide a methodology for configuring our proposed LPPM without the need for mobility data. This approach provides the required privacy-utility adaptability while also simplifying its configuration and general application in different contexts.

Abstract
Missing data can be described by the absence of values in a dataset, which can be a critical issue in domains such as healthcare. A common solution for this problem is imputation, where the missing values are replaced by estimations. Most imputation methods are suitable for the Missing Completely At Random (MCAR) and Missing At Random (MAR) mechanisms but produce biased results for Missing Not At Random (MNAR) values. An effective approach to mitigate this bias effect is to use the delta-adjustment method. This method assumes the imputation is performed for the MAR mechanism and adjusts the imputed values to become valid under MNAR assumptions by applying a correction factor. Such adjustment is usually defined manually by a domain expert, which often makes this method unfeasible. In this work, we propose an automatic procedure to find an approximate delta adjustment value for every feature of the dataset, which we call Automatic Delta-Adjustment Method. The proposed procedure is validated in an experimental setup comprising 10 datasets of the healthcare domain injected with MNAR values. The results from seven state-of-the-art imputation methods are compared with and without the adjustment, and applying the correction provides a significantly lower imputation error for all methods.

Abstract
Systems for wireless energy transmission (WET) are gaining prominence nowadays. This work presents a WET system based on the photo-thermoelectric effect. With an incident laser beam at lambda = 1450 nm, a temperature gradient is generated in the radial flexible thermoelectric (TE) device, with a carbon-based light collector in its center to enhance the photoheating. The three-part prototype presents a unique approach by using a radial TE device with one simple manufacturing process - screen-printing. A TE ink with a polymeric matrix of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate and doped-Poly(vinyl alcohol) with Sb-Bi-Te microparticles is developed (S similar to 33 mu VK-1 and s similar to 10.31 Sm-1), presenting mechanical and electrical stability. Regarding the device, a full electrical analysis is performed, and the influence of the light collector is investigated using thermal tests, spectrophotometry, and numerical simulations. A maximum output voltage (Vout) of similar to 16 mV and maximum power density of similar to 25 mu Wm(-2) are achieved with Plaser = 2 W. Moreover, the device's viability under extreme conditions is explored. At T similar to 180 K, a 25% increase in Vout compared to room-temperature conditions is achieved, and at low pressures (similar to 10(-6) Torr), an increase of 230% is obtained. Overall, this prototype allows the supply of energy at long distances and remote places, especially for space exploration.