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Folksonomies are Web 2.0 platforms where users share resources with each other. Furthermore, they can assign keywords (called tags) to the resources for categorizing and organizing the resources. Numerous types of resources like websites (Delicious), images (Flickr), and videos (YouTube) are supported by different folksonomies. The folksonomies are easy to use and thus attract the attention of millions of users. Together with the ease they offer, there are also some problems. This thesis addresses different problems of folksonomies and proposes solutions for these problems. The first problem occurs when users search for relevant resources in folksonomies. Often, the users are not able to find all relevant resources because they don't know which tags are relevant. The second problem is assigning tags to resources. Although many folksonomies (like Delicious) recommend tags for the resources, other folksonomies (like Flickr) do not recommend any tags. Tag recommendation helps the users to easily tag their resources. The third problem is that tags and resources are lacking semantics. This leads for example to ambiguous tags. The tags are lacking semantics because they are freely chosen keywords. The automatic identification of the semantics of tags and resources helps in reducing problems that arise from this freedom of the users in choosing the tags. This thesis proposes methods which exploit semantics to address the problems of search, tag recommendation, and the identification of tag semantics. The semantics are discovered from a variety of sources. In this thesis, we exploit web search engines, online social communities and the co-occurrences of tags as sources of semantics. Using different sources for discovering semantics reduces the efforts to build systems which solve the problems mentioned earlier. This thesis evaluates the proposed methods on a large scale data set. The evaluation results suggest that it is possible to exploit the semantics for improving search, recommendation of tags, and automatic identification of the semantics of tags and resources.
A fundamental understanding of attachment of engineered nanoparticles to environmentalrnsurfaces is essential for the prediction of nanoparticle fate and transport in the environment.
The present work investigates the attachment of non-coated silver nanoparticles and citraterncoated silver nanoparticles to different model surfaces and environmental surfaces in thernpresence and absence of humic acid. Batch sorption experiments were used for this investigation.
The objective of this thesis was to investigate how silver nanoparticles interactrnwith surfaces having different chemical functional groups. The effect of presence of HA, on the particle-surface interactions was also investigated. In the absence of humic acid, nanoparticle-surface interactions or attachment was influencedrnby the chemical nature of the interacting surfaces. On the other hand, in the presence ofrnhumic acid, nanoparticle-surface attachment was influenced by the specific surface area of the sorbent surfaces. The sorption of non-coated silver nanoparticles and citrate coatedrnnanoparticles to all the surfaces was nonlinear and best described by Langmuir isotherm, indicating monolayer sorption of nanoparticles on to the surfaces. This can be explained as due to the blocking effect generated by the particle-particle repulsion. In the presence of humic acid, sorption of nanoparticles to the surfaces was linear. When the humic acid was present in the interacting medium, both the nanoparticles and surfaces were getting coated with humic acid and this masks the chemical functionalities of the surfaces. This leads to the change in particle-surface interactions, in the presence of humic acid. For the silver nanoparticle sorption from an unstable suspension, the sorption isotherms did not follow any classical sorption models, suggesting interplay between aggregation and sorption. Citrate coated silver nanoparticles and humic acid coated silver nanoparticles showed arndepression in sorption compared to the sorption of non-coated silver nanoparticles. In therncase of citrate coated silver nanoparticles the decrease in sorption can be explained by thernmore negative zeta potential of citrate coated nanoparticles compared to non-coated ones. For humic acid coated nanoparticles the sorption depression can be due to the steric hindrance caused by the free humic acid molecules which may coat the sorbent surface or due to the competition for sorption sites between the nanoparticle and free humic acid molecules present in the suspension. Thus nanoparticle surface chemistry is an important factor that determines the attachment of nanoparticles towards surfaces and it makes the characterization of nanoparticle surface an essential step in the study of their fate in the environment.
Another aim of this study was to introduce the potential of chemical force microscopy for nanoparticle surface characterization. With the use of this technique, it was possible to distinguish between bare silver nanoparticles, citrate coated silver nanoparticles, and humic acid coated silver nanoparticles. This was possible by measuring the adhesion forces between the nanoparticles and five different AFM probes having different chemical functionalization.
Vertebrate biodiversity is rapidly decreasing worldwide with amphibians being the most endangered vertebrate group. In the EU, 21 of 89 amphibian species are recognized as being endangered. The intensively used European agricultural landscape is one of the major causes for these declines. As agriculture represents an essential habitat for amphibians, exposure to pesticides can have adverse effects on amphibian populations. Currently, the European risk assessment of pesticides for vertebrates requires specific approaches for fish regarding aquatic vertebrate toxicity and birds as well as mammals for terrestrial vertebrate toxicity but does not address the unique characteristics of amphibians. Therefore, the overall goal of this thesis was to investigate the ecotoxicological effects of pesticides on Central European anuran amphibians. For this, effects on aquatic and terrestrial amphibian life stages as well as on reproduction were investigated. Then, in anticipation of a risk assessment of pesticides for amphibians, this thesis discussed potential regulatory risk assessment approaches.
For the investigated pesticides and amphibian species, it was observed that the acute aquatic toxicity of pesticides can be addressed using the existing aquatic risk assessment approach based on fish toxicity data. However, lethal as well as sublethal effects were observed in terrestrial juveniles after dermal exposure to environmentally realistic pesticide concentrations, which cannot be covered using an existing risk assessment approach. Therefore, pesticides should also be evaluated for potential terrestrial toxicity using risk assessment tools before approval. Additionally, effects of co-formulants and adjuvants of pesticides need specific consideration in a future risk assessment as they can increase toxicity of pesticides to aquatic and terrestrial amphibian stages. The chronic duration of combined aquatic and terrestrial exposure was shown to affect amphibian reproduction. Currently, such effects cannot be captured by the existing risk assessment as data involving field scenarios analysing effects of multiple pesticides on amphibian reproduction are too rare to allow comparison to data of other terrestrial vertebrates such as birds and mammals. In the light of these findings, future research should not only address acute and lethal effects, but also chronic and sublethal effects on a population level. As pesticide exposure can adversely affect amphibian populations, their application should be considered even more carefully to avoid further amphibian declines. Overall, this thesis emphasizes the urgent need for a protective pesticide risk assessment for amphibians to preserve and promote stable amphibian populations in agricultural landscapes.
Nowadays, almost any IT system involves personal data processing. In
such systems, many privacy risks arise when privacy concerns are not
properly addressed from the early phases of the system design. The
General Data Protection Regulation (GDPR) prescribes the Privacy by
Design (PbD) principle. As its core, PbD obliges protecting personal
data from the onset of the system development, by effectively
integrating appropriate privacy controls into the design. To
operationalize the concept of PbD, a set of challenges emerges: First, we need a basis to define privacy concerns. Without such a basis, we are not able to verify whether personal data processing is authorized. Second, we need to identify where precisely in a system, the controls have to be applied. This calls for system analysis concerning privacy concerns. Third, with a view to selecting and integrating appropriate controls, based on the results of system analysis, a mechanism to identify the privacy risks is required. Mitigating privacy risks is at the core of the PbD principle. Fourth, choosing and integrating appropriate controls into a system are complex tasks that besides risks, have to consider potential interrelations among privacy controls and the costs of the controls.
This thesis introduces a model-based privacy by design methodology to handle the above challenges. Our methodology relies on a precise definition of privacy concerns and comprises three sub-methodologies: model-based privacy analysis, modelbased privacy impact assessment and privacy-enhanced system design modeling. First, we introduce a definition of privacy preferences, which provides a basis to specify privacy concerns and to verify whether personal data processing is authorized. Second, we present a model-based methodology to analyze a system model. The results of this analysis denote a set of privacy design violations. Third, taking into account the results of privacy analysis, we introduce a model-based privacy impact assessment methodology to identify concrete privacy risks in a system model. Fourth, concerning the risks, and taking into account the interrelations and the costs of the controls, we propose a methodology to select appropriate controls and integrate them into a system design. Using various practical case studies, we evaluate our concepts, showing a promising outlook on the applicability of our methodology in real-world settings.
Im Zentrum der Untersuchung steht die Entwicklung des künstlerischen Siebdrucks in Deutschland seit dem Zweiten Weltkrieg. Nach der thematischen Einführung und einer ersten kritischen Bestandsaufnahme der Forschung zum Siebdruck wird im Folgenden der Verlauf und die methodische Vorgehensweise der Arbeit verdeutlicht. Im zweiten Kapitel „Etablierung einer künstlerischen Technik“ wird zuerst die Geschichte des Siebdrucks dem Rahmen dieser Arbeit gemäß nachgezeichnet. Nachfolgend wird in einem Unterkapitel dargestellt, wie sich die Serigrafie als künstlerisches Verfahren parallel zum industriellen Siebdruck etablierte. In der Folge wendet sich die Untersuchung der Entstehung und Entwicklung des künstlerischen Siebdrucks in Deutschland zu. Dabei wird Willi Baumeister als Beispiel für die erste Generation von Künstlern in Deutschland herangezogen, die sich mit der Serigrafie beschäftigte. Im nächsten Schritt wird ein Überblick zu den Willi Baumeister nachfolgenden deutschen bzw. deutschsprachigen Serigrafie–Künstlern präsentiert. In dem sich anschließenden Exkurs-Kapitel liegt der Schwerpunkt auf der Zusammenarbeit zwischen Künstler und Drucker. Dabei werden an Beispielen namhafter Drucker wie Luitpold Domberger und Hans-Peter Haas unter anderem Einflüsse der Drucker auf die Entstehung und den Ausdruck der Kunstwerke aufgezeigt.Nach einer Einführung in die Entstehung und Geschichte der Serigrafie in Deutschland und einem ersten Überblick über deutsche Serigrafie-Künstler wird im vierten Kapitel der Einsatz der Serigrafie-Technik bei verschiedenen Künstlern aus unterschiedlichen Zeiträumen eingehend untersucht. Besonderes Augenmerk gilt dabei dem Einfluss der Technik auf die Ausdrucksmöglichkeiten und Inhalte der Werke, die Anwendung innerhalb einer und verschiedener Kunstrichtungen sowie die Experimente, die mit der Siebdrucktechnik gemacht wurden. Dazu werden Werke ausgewählter Künstler analysiert. Im Fokus stehen Künstler der Pop Art, des Neuen und des Kritischen Realismus, der Abstrakten und Konkreten Kunst. Ferner werden Künstler, deren Werk durch einen expressiven, bisweilen primitiven Charakter geprägt ist, in die Betrachtung mit einbezogen. Eine weitere Gruppe bilden Künstler, die die Serigrafie in einen Zusammenhang zur Architektur und zum öffentlichen Raum stellen. Die aus der Untersuchung hervorgehenden Ergebnisse dienen insbesondere dazu, die Charakteristika der Technik in ihrer künstlerischen Verwendung herauszustellen und Aspekte einer künstlerisch motivierten Weiterentwicklung aufzuzeigen. Die Entwicklung des druckgraphischen Werkes und der Technik von Gerd Winner werden in Kapitel 5 eingehend untersucht. Auf diese Weise lässt sich eine systematische, nicht rein formale, sondern ästhetische und inhaltliche Darstellung der Serigrafie in Deutschland erstellen. Ein besonderes Augenmerk liegt dabei auf der Frage, wie sich die Technik und deren Neuerungen auf den Inhalt auswirkten.
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.
Efficient Cochlear Implant (CI) surgery requires prior knowledge of the cochlea’s size and its characteristics. This information helps to select suitable implants for different patients. Registered and fused images helps doctors by providing more informative image that takes advantages of different modalities. The cochlea’s small size and complex structure, in addition to the different resolutions and head positions during imaging, reveals a big challenge for the automated registration of the different image modalities. To obtain an automatic measurement of the cochlea length and the volume size, a segmentation method of cochlea medical images is needed. The goal of this dissertation is to introduce new practical and automatic algorithms for the human cochlea multi-modal 3D image registration, fusion, segmentation and analysis. Two novel methods for automatic cochlea image registration (ACIR) and automatic cochlea analysis (ACA) are introduced. The proposed methods crop the input images to the cochlea part and then align the cropped images to obtain the optimal transformation. After that, this transformation is used to align the original images. ACIR and ACA use Mattes mutual information as similarity metric, the adaptive stochastic gradient descent (ASGD) or the stochastic limited memory Broyden–Fletcher–Goldfarb–Shanno (s-LBFGS) optimizer to estimate the parameters of 3D rigid transform. The second stage of nonrigid registration estimates B-spline coefficients that are used in an atlas-model-based segmentation to extract cochlea scalae and the relative measurements of the input image. The image which has segmentation is aligned to the input image to obtain the non-rigid transformation. After that the segmentation of the first image, in addition to point-models are transformed to the input image. The detailed transformed segmentation provides the scala volume size. Using the transformed point-models, the A-value, the central scala lengths, the lateral and the organ of corti scala tympani lengths are computed. The methods have been tested using clinical 3D images of total 67 patients: from Germany (41 patients) and Egypt (26 patients). The atients are of different ages and gender. The number of images used in the experiments is 217, which are multi-modal 3D clinical images from CT, CBCT, and MRI scanners. The proposed methods are compared to the state of the arts ptimizers related medical image registration methods e.g. fast adaptive stochastic gradient descent (FASGD) and efficient preconditioned tochastic gradient descent (EPSGD). The comparison used the root mean squared distance (RMSE) between the ground truth landmarks and the resulted landmarks. The landmarks are located manually by two experts to represent the round window and the top of the cochlea. After obtaining the transformation using ACIR, the landmarks of the moving image are transformed using the resulted transformation and RMSE of the transformed landmarks, and at the same time the fixed image landmarks are computed. I also used the active length of the cochlea implant electrodes to compute the error aroused by the image artifact, and I found out an error ranged from 0.5 mm to 1.12 mm. ACIR method’s RMSE average was 0.36 mm with a standard deviation (SD) of 0.17 mm. The total time average required for registration of an image pair using ACIR was 4.62 seconds with SD of 1.19 seconds. All experiments are repeated 3 times for justifications. Comparing the RMSE of ACIR2017 and ACIR2020 using paired T-test shows no significant difference (p-value = 0.17). The total RMSE average of ACA method was 0.61 mm with a SD of 0.22 mm. The total time average required for analysing an image was 5.21 seconds with SD of 0.93 seconds. The statistical tests show that there is no difference between the results from automatic A-value method and the manual A-value method (p-value = 0.42). There is no difference also between length’s measurements of the left and the right ear sides (p-value > 0.16). Comparing the results from German and Egypt dataset shows there is no difference when using manual or automatic A-value methods (p-value > 0.20). However, there is a significant difference when using ACA2000 method between the German and the Egyptian results (p-value < 0.001). The average time to obtain the segmentation and all measurements was 5.21 second per image. The cochlea scala tympani volume size ranged from 38.98 mm3 to 57.67 mm3 . The combined scala media and scala vestibuli volume size ranged from 34.98 mm 3 to 49.3 mm 3 . The overall volume size of the cochlea should range from 73.96 mm 3 to 106.97 mm 3 . The lateral wall length of scala tympani ranged from 42.93 mm to 47.19 mm. The organ-of-Corti length of scala tympani ranged from 31.11 mm to 34.08 mm. Using the A-value method, the lateral length of scala tympani ranged from 36.69 mm to 45.91 mm. The organ-of-Corti length of scala tympani ranged from 29.12 mm to 39.05 mm. The length from ACA2020 method can be visualised and has a well-defined endpoints. The ACA2020 method works on different modalities and different images despite the noise level or the resolution. In the other hand, the A-value method works neither on MRI nor noisy images. Hence, ACA2020 method may provide more reliable and accurate measurement than the A-value method. The source-code and the datasets are made publicly available to help reproduction and validation of my result.
Social media provides a powerful way for people to share opinions and sentiments about a specific topic, allowing others to benefit from these thoughts and feelings. This procedure generates a huge amount of unstructured data, such as texts, images, and references that are constantly increasing through daily comments to related discussions. However, the vast amount of unstructured data presents risks to the information-extraction process, and so decision making becomes highly challenging. This is because data overload may cause the loss of useful data due to its inappropriate presentation and its accumulation. To this extent, this thesis contributed to the field of analyzing and detecting feelings in images and texts. And that by extracting the feelings and opinions hidden in a huge collection of image data and texts on social networks After that, these feelings are classified into positive, negative, or neutral, according to the features of the classified data. The process of extracting these feelings greatly helps in decision-making processes on various topics as will be explained in the first chapter of the thesis. A system has been built that can classify the feelings inherent in the images and texts on social media sites, such as people’s opinions about products and companies, personal posts, and general messages. This thesis begins by introducing a new method of reducing the dimension of text data based on data-mining approaches and then examines the sentiment based on neural and deep neural network classification algorithms. Subsequently, in contrast to sentiment analysis research in text datasets, we examine sentiment expression and polarity classification within and across image datasets by building deep neural networks based on the attention mechanism.
The bio-insecticide Bacillus thuringiensis israelensis (Bti) has worldwide become the most commonly used agentin mosquito control programs that pursue two main objectives: the control of vector-borne diseases and the reduction of nuisance, mainly coming frommosquitoes that emerge in large quantities from seasonal wetlands. The Upper Rhine Valley, a biodiversity hotspot in Germany, has been treated withBti for decades to reduce mosquito-borne nuisance and increase human well-being.Although Btiis presumed to be an environmentally safe agent,adverse effects on wetland ecosystems are still a matter of debate especially when it comes to long-term and indirect effects on non-target organisms. In light of the above, this thesis aims at investigating direct and indirect effects of Bti-based mosquito control on non-target organisms within wetland food chains.Effects were examinedin studies with increasingeco(toxico)logical complexity, ranging from laboratory over mesocosm to field approaches with a focus on the non-biting Chironomidae and amphibian larvae (Rana temporaria, Lissotriton sp.).In addition, public acceptance of environmentally less invasive alternative mosquito control methods was evaluated within surveys among the local population.
Chironomids were the most severely affected non-target aquatic invertebrates. Bti substantially reduced larval and adult chironomid abundances and modified their species composition. Repeated exposures to commonly used Bti formulations induced sublethal alterations of enzymatic biomarkers activityin frog tadpoles. Bti-induced reductions of chironomid prey availability indirectly decreased body size of newts at metamorphosis and increased predation on newt larvae in mesocosm experiments. Indirect effects of severe reductions in midge biomassmight equally be passed through aquatic but also terrestrial food chains influencing predators of higher trophic levels. The majority ofaffectedpeople in the Upper Rhine Valley expressed a high willingness to contributefinancially to environmentally less harmful mosquito control.Alternative approaches could still include Bti applications excepting treatment of ecologically valuable areas. Potentially rising mosquito levels could be counteracted with local acting mosquito traps in domestic and urban areas because mosquito presence was experienced as most annoying in the home environment.
As Bti-based mosquito control can adversely affect wetland ecosystems, its large-scale applications, including nature conservation areas, should be considered more carefully to avoid harmful consequences for the environmentat the Upper Rhine Valley.This thesis emphasizesthe importance to reconsiderthe current practice of mosquito control and encourage research on alternative mosquito control concepts that are endorsed by the local population. In the context ofthe ongoing amphibian and insect declinesfurther human-induced effects onwetlands should be avoided to preserve biodiversity in functioning ecosystems.